دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Spatio-temporal monitoring of piping and assessment of erosion and sedimentation using Multi-temporal UAV data پایش زمانی – مکانی پایپینگ و ارزیابی فرسایش و رسوب با استفاده از داده‌های پهپادی چندزمانه 1 20 4308 10.22098/mmws.2025.18901.1735 FA هانیه پورجواد دانشجوی دکتری مدیریت و کنترل بیابان، گروه بیابانزدایی، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران 0009-0007-3911-2375 محمدرضا یزدانی دانشیار، گروه آموزشی بیابان‌زدایی، دانشکده کویرشناسی، دانشگاه سمنان، سمنان، ایران محسن حسینعلی زاده دانشیار، گروه مدیریت مناطق بیابانی، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران نرگس کریمی نژاد استادیار، گروه مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران Journal Article 2025 11 24 Extended Abstract Introduction Soil erosion is one of the most critical environmental challenges in semi-arid regions worldwide, particularly in landscapes dominated by loessic deposits, where weak physical and mechanical characteristics significantly increase susceptibility to subsurface erosion and the development of piping features. Piping often initiates and progresses covertly during its early stages, eventually leading to sudden surface collapse, topographic instability, accelerated sediment delivery, reduced land productivity, and disruption of hydrological and ecological systems. Despite extensive research on soil erosion, most existing studies have adopted static or single-temporal approaches, and substantial scientific gaps still remain regarding multi-temporal and spatial monitoring of piping frequency, distribution, density, and evolutionary trends. Furthermore, the combined and interactive influences of topography, vegetation cover, and land use patterns on piping development in loess-derived terrains are not yet adequately understood, posing challenges for soil and water conservation planning, bioengineering practices, and watershed management decisions. Accordingly, the present study aims to monitor four-year temporal changes in the number, location, and evolution of piping features, and to evaluate the controlling roles of slope, elevation, vegetation cover, and land use using ultra-high-resolution UAV data combined with multi-temporal Digital Elevation Model of Difference (DoD) analysis. This approach enables the assessment of spatial patterns of erosion and deposition in areas with and without piping and the estimation of annual erosion-sedimentation rates, thereby improving the identification of high-risk zones and supporting evidence-based management and mitigation strategies in loess environments. Materials and Methods Initially, two sub-watersheds with different proportions of rangeland and agricultural land use were selected. To ensure accurate detection and temporal monitoring of piping development, the spatial location of all piping features within both sub-watersheds was recorded using GPS during the 2019 and 2023 survey campaigns. Multi-temporal UAV surveys were conducted under comparable illumination and meteorological conditions using a Phantom 4Pro UAV, and the acquired high-resolution imagery was processed using a photogrammetric workflow in ContextCapture to generate three-dimensional point clouds and high-precision Digital Elevation Models (DEMs) with a spatial accuracy of approximately 5 cm. To quantify volumetric topographic changes, the DoD approach was applied within ArcGIS, resulting in spatially explicit erosion deposition maps as well as annual mean volumetric change estimates (expressed as tons per hectare per year) for each sub-watershed. Land use classification was carried out through visual interpretation of UAV imagery combined with extensive field verification. Slope and elevation layers were extracted from the DEM using ArcGIS to examine topographic control on piping distribution. Density plots generated in R software were used to statistically explore the relationships between piping occurrence, slope gradient, and elevation range. Finally, temporal variations in piping frequency, spatial displacement, initiation, expansion, or disappearance were compared between the two sub-watersheds to identify dominant geomorphic and land-management drivers of piping dynamics. Results and Discussion The findings indicated that in Sub-watershed 1, with 85% agricultural land, the piping density was only 10%, of which 2 occurred in croplands and 18 in rangelands. In Sub-watershed 2, with 70% rangeland, the density was considerably higher at 55%, with 198 cases occurring in rangelands. Piping mainly occurred at lower elevations (370–410 m and 300–340 m in Sub-watersheds 1 and 2), on steep slopes (25–35°), and weak vegetation. DoD analysis over the period 2019–2023 revealed that in agricultural lands, deposition was the dominant process, whereas in rangelands, erosion were more pronounced; in Sub-watershed 1, 72% of the area experienced deposition and 28% erosion, while in Sub-watershed 2, 77% erosion and 23% deposition were recorded. Annual rates were ±5 t/ha/yr in Sub-watershed 1 and 15–25 t/ha/yr in Sub-watershed 2. Over four years, two agricultural piping features were lost, but two new features formed in Sub-watershed 1 and ten in Sub-watershed 2. The main advantage of this study lies in the integration of real UAV data, precise DoD analysis, pixel-based monitoring of erosion, and piping relocation, enabling identification of high-risk areas and prioritization for management interventions. Conclusion Based on the findings of this research, Steeper slopes, lower elevations, reduced vegetation density and land-use type were identified as the primary environmental factors controlling the initiation and development of piping in semi-arid loess landscapes. Moreover, the integration of multi-temporal UAV data with the DoD technique enabled accurate detection of morphological evolution and delineation of susceptible areas over time. According to the results, Sub-watershed 1, dominated by agricultural land use, was mainly characterized by depositional processes, and the total number of piping features remained constant during the four-year monitoring period. In contrast, Sub-watershed 2, where rangelands are dominant, experienced severe erosion, resulting in the formation of eight new piping. This discrepancy can be attributed to contrasting land management practices: agricultural operations such as tillage and crop cultivation may lead to the infilling or concealment of existing pipes, whereas terrain forms, overgrazing and vegetation degradation in rangelands facilitate accelerated piping expansion. Field observations also revealed a dual functional role of piping features. While they intensify subsurface discharge, soil erosion, and desertification, their internal cavities may also serve as favorable microhabitats for the establishment of drought-resistant plant species such as wild pomegranate and Amygdalus scoparia. The outcomes of this research can directly support soil and water conservation planning, particularly for prioritizing preventive measures in fragile dryland environments. Enhancing deep-rooted vegetation, regulating grazing patterns, and applying bio-engineering strategies around piping zones are recommended for controlling further degradation. Future studies are advised to integrate UAV observations with seasonal satellite datasets and high-resolution DEM modeling while assessing climate-change-driven rainfall scenarios to better predict long-term piping dynamics. نهشته‌های لسی شرق استان گلستان به‌دلیل حساسیت ساختاری و هیدرولوژیکی، مستعد فرسایش متمرکز و شکل‌گیری پایپینگ هستند. پایپینگ‌ها و فرسایش تونلی، با ایجاد مجاری زیرسطحی موجب تخلیه شدید خاک و ناپایداری دامنه‌ها می‌شود. هدف این پژوهش، بررسی نقش هم‌زمان شیب، ارتفاع، پوشش گیاهی و کاربری اراضی بر شکل‌گیری پایپینگ‌ها و همچنین پایش تغییرات مکانی و زمانی تعداد و موقعیت آن‌ها و نرخ میانگین فرسایش و رسوب از سال 1398 تا 1402 در دو زیرحوضه همجوار از حوزه آبخیز آق‌چاتال (نهشته‌های لسی شرق استان گلستان) است. بدین منظور، موقعیت پایپینگ‌ها با GPS و تصاویر پهپادی دو دوره (1398 و 1402) ثبت و با روش فتوگرامتری پردازش شد. سپس مدل‌های رقومی ارتفاع با دقت 5 سانتی‌متر تهیه و با استفاده از روش تفاضل مدل ارتفاعی (DoD)، نقشه‌های تغییرات فرسایش و رسوب تولید شد. همچنین نقشه‌های شیب و ارتفاع از DEM و نقشه کاربری اراضی با مشاهدات میدانی و تصاویر پهپاد استخراج شدند و با نحوه پراکنش پایپینگ‌ها بررسی شدند. نتایج نشان داد که تراکم پایپینگ‌ها در زیرحوضه (2) با غالبیت کاربری مرتع به مراتب بیشتر از زیرحوضه (1) با غالبیت کشاورزی (به‌ترتیب 55 و 10%) است. بیشترین فراوانی و احتمال رخداد پایپینگ‌ها در ارتفاع‌های پایین‌تر (400–300 متر) و شیب‌های تندتر (35–25 درجه) مشاهده شد. همچنین، بخش اعظم زیرحوضه (1) دارای نرخ متعادل فرسایش و رسوب در محدوده ±5 تن بر هکتار در سال بود؛ در حالی که زیرحوضه (2) با فرسایش شدید 15 تا 25 تن بر هکتار در سال مواجه بود. ارزیابی تغییرات زمانی نیز نشان داد که در هر دو زیرحوضه طی 4 سال، دو پایپینگ در کاربری کشاورزی حذف شدند؛ در حالی که در زیرحوضه (1) دو پایپینگ جدید و در زیرحوضه (2) ده پایپینگ جدید در کاربری مرتع ظاهر شدند. این نتایج بیانگر نقش تعیین‌کننده‌ی شرایط توپوگرافی و کاربری اراضی در کنترل پایداری دامنه‌ها بوده و بر ضرورت مدیریت پوشش گیاهی برای کاهش و گسترش پایپینگ در مناطق لسی تأکید دارد.

https://mmws.uma.ac.ir/article_4308_ca1253fb71eafe84eaaaf3fbab003ae8.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Evaluation of the biodegradation of black liquor derived from soda process effluent using Phanerochaete chrysosporium in free and immobilized cell systems ارزیابی تخریب زیستی مایع سیاه حاصل از پساب فرایند سودا با استفاده از قارچ Phanerochaete chrysosporium به‌صورت سلول آزاد و تثبیت‌شده 21 34 4310 10.22098/mmws.2025.18853.1731 FA معراج شرری دانشیار، گروه علوم و صنایع چوب و کاغذ، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران المیرا فعال شیرکده دانش‌آموخته کارشناسی ارشد مهندسی صنایع چوب و فراورده‌های سلولزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران محمد احمدی دانشیار، گروه علوم و صنایع چوب و کاغذ، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران 0000-0002-9238-0631 بیتا معزی پور استادیار، گروه علوم و صنایع چوب و کاغذ، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران فرج اله حاجی علیزاده دانشجوی کارشناسی ارشد مهندسی صنایع چوب و فرآورده‌های سلولزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران اکبر رستم پور هفتخوانی دانشیار، گروه علوم و صنایع چوب و کاغذ، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران Journal Article 2025 11 18 Extended Abstract Introduction Large quantities of wastewater containing complex organic compounds, lignin, phenolics, suspended solids, and various toxic substances are generated by the pulp and paper industry. Effluents with elevated BOD, COD, TSS, and TDS pose serious environmental and public-health risks if discharged without adequate treatment. Black liquor produced from soda pulping particularly when using non-wood raw materials such as wheat straw also contains high levels of silica, making conventional treatment methods expensive, inefficient, and in many cases impractical. Biological treatment using microorganisms has therefore gained attention as a more eco-friendly and cost-effective option, capable of degrading complex organic pollutants while reducing the need for chemicals and energy. However, free microbial cells often experience problems such as washout, gradual loss of activity, and sensitivity to toxic components. Immobilizing biomass on porous supports, especially polyurethane foam, helps retain the cells, stabilize enzyme activity, increase tolerance to environmental fluctuations, and enable repeated use. In this study, the performance of the white-rot fungus P. chrysosporium in both free and immobilized forms was evaluated for the treatment of soda black liquor. The focus was on reducing major pollution indicators, including COD, BOD, TDS, and TSS, to provide a sustainable approach for managing industrial wastewater. Materials and Methods Black liquor was obtained from laboratory-scale soda pulping of wheat straw. A 50-g oven-dry sample of wheat straw was cooked in a batch digester at 160 °C for 30 minutes using an active alkali charge of 16% NaOH based on oven-dry straw. After washing the pulp, the resulting black liquor was collected, filtered, and stored at 4 °C until needed. Before biological treatment, the liquor was diluted tenfold with distilled water. Fungal treatment was carried out at 30 °C using free and immobilized P. chrysosporium cells, with polyurethane foam (PUF) serving as the immobilization matrix. Experiments were conducted under near-optimal pH conditions (6.5–7) over treatment periods of 0, 1, 3, 7, 11, and 14 days. Pollution parameters COD, BOD, TDS, and TSS were measured at each interval. All experiments were performed in triplicate. Statistical analyses were conducted using SPSS software. Independent-samples t-tests were used to determine significant differences between treatment groups, F-tests were applied for variance analysis, and Duncan’s multiple range test was employed for comparing mean values. Results and Discussion In soda black liquor, both free and immobilized cells of P. chrysosporium substantially reduced the organic and dissolved solids load, but the immobilized fungus consistently showed superior performance for all monitored parameters. By day 14, the immobilized biomass achieved reductions of 78.03% in COD, 87.54% in BOD and 74.89% in TDS, whereas the free-cell system resulted in 58.05%, 71.54% and 56.22% reduction, respectively. The highest degradation rates for both systems occurred during the early stages of treatment, particularly up to day 7, after which the removal efficiency increased more slowly. This decline in the rate of pollutant removal can be attributed to the depletion of readily biodegradable organic matter, gradual limitation of nutrients and oxygen within the biomass, and partial autolysis or aging of fungal cells. The consistently higher performance of the immobilized fungus indicates that attachment on polyurethane foam improves contact between the biomass and soluble substrates, enhances local retention of enzymes and metabolites, and protects the cells against hydraulic wash-out and fluctuations in wastewater composition. The three-dimensional structure and high porosity of the carrier likely facilitate better mass transfer and provide additional active sites for adsorption and subsequent enzymatic degradation. Overall, the results demonstrate that immobilization not only increases the extent of COD, BOD and TDS removal, but also stabilizes fungal activity over time, thereby improving the robustness and overall efficiency of biological treatment for soda black liquor. Conclusion This study demonstrates that P. chrysosporium particularly in immobilized form on polyurethane foam—is an efficient, stable, and environmentally sound option for the primary biotreatment of soda black liquor. Both free and immobilized systems reduced COD, BOD, TDS, and TSS under controlled laboratory conditions, but the immobilized fungus consistently outperformed free cells. By day 14, the immobilized system achieved reductions of 78.03% (COD), 87.54% (BOD), and 74.89% (TDS), compared to 58.05%, 71.54%, and 56.22% for free cells, highlighting the improved treatment efficiency. The highest removal rates occurred during the first week, after which the process slowed, likely due to nutrient depletion and partial saturation of the immobilization matrix. These findings confirm the strong potential of P. chrysosporium to degrade complex organic pollutants in black liquor through its active ligninolytic enzymes. Immobilization enhances biomass stability, increases resilience to environmental changes, and improves enzymatic performance by ensuring sustained substrate access. Accordingly, immobilization on carriers such as polyurethane foam offers a cost-effective, environmentally friendly, and operationally stable strategy for primary treatment of effluents from the pulp and paper industry. Moreover, this approach can serve as an effective pretreatment before secondary processes such as activated sludge or advanced treatment, thereby supporting the development of semi-industrial and industrial-scale biotreatment systems based on immobilized microorganisms. Overall, the study highlights immobilized white-rot fungi as a scalable and sustainable alternative to conventional chemical or thermal treatment methods for industrial wastewater management. مایع سیاه حاصل از فرآیند پخت سودا کاه گندم در صنایع خمیر و کاغذ، حاوی مقادیر قابل‌توجهی لیگنین، ترکیبات فنولی، مواد گوگرددار، سیلیس و جامدات محلول و معلق است. این ترکیبات به دلیل ساختار پیچیده و پایداری بالا، به‌سختی تجزیه می‌شوند و باعث افزایش شاخص‌های آلایندگی مانند COD و BOD می‌گردند. در این مطالعه، مایع سیاه حاصل از پخت آزمایشگاهی کاه گندم با استفاده از سود سوزآور در دمای ۱۶۰ درجه سلسیوس و با قلیاییت فعال ۱۶ درصد تهیه شد. پس از شست‌وشوی خمیر، مایع سیاه جمع‌آوری و برای آزمایش‌های بعدی در دمای زیر ۴ درجه سانتی‌گراد نگهداری شد. به‌منظور ارزیابی قابلیت تیمار زیستی، نمونه‌ها با آب مقطر ده برابر رقیق‌سازی شدند. با توجه به هزینه بالای روش‌های مرسوم فیزیکی و شیمیایی، استفاده از روش‌های زیستی به‌عنوان رویکردی اقتصادی و سازگار با محیط‌زیست ضروری است. عملکرد قارچ Phanerochaete chrysosporium در دو حالت سلول آزاد و تثبیت‌شده بر فوم پلی‌یورتان (PUF) برای کاهش آلاینده‌های COD، BOD، TDS و TSS در مایع سیاه سودا بررسی شد. تیمارها تحت شرایط pH=7، دمای ۳۰ درجه سانتی‌گراد و دوره ۱۴ روزه انجام گرفت و شاخص‌های کیفی پساب طبق استاندارد APHA اندازه‌گیری شدند. تحلیل آماری داده‌ها با استفاده از آزمون t مستقل و آزمون دانکن صورت پذیرفت. نتایج نشان داد هر دو تیمار زیستی کاهش قابل‌توجهی در آلاینده‌ها داشتند اما قارچ تثبیت‌شده عملکرد بهتری نشان داد. در پایان روز چهاردهم، میزان کاهش COD، BOD و TDS در تیمار تثبیت‌شده به‌ترتیب 03/78، 54/87 و 89/74 درصد بود؛ در حالی‌ که در تیمار سلول آزاد این مقادیر به‌ترتیب 05/58، 54/71 و 22/56 درصد ثبت شد. بیشترین کاهش در هفته نخست مشاهده گردید و پس از آن به دلیل کاهش مواد آلی قابل‌تجزیه و افت فعالیت متابولیکی، روند کاهش شاخص‌ها کندتر شد. یافته‌ها نشان می‌دهند که قارچ P. chrysosporium، در حالت تثبیت‌شده، گزینه‌ای موثر و پایدار برای تصفیه اولیه مایع سیاه سودا است و می‌تواند نقش مؤثری در کاهش بار آلودگی و بهبود کارایی مراحل بعدی تصفیه مانند لجن فعال یا سامانه‌های تولید انرژی زیستی ایفا کند. این سامانه زیستی می‌تواند جایگزینی کم‌هزینه و سازگار با محیط‌زیست برای روش‌های شیمیایی و حرارتی پرهزینه در صنعت خمیر و کاغذ باشد.

https://mmws.uma.ac.ir/article_4310_2972505a6db44340f60909d2ea33afde.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Performance evaluation of different potential evapotranspiration models and application of optimal models for drought monitoring using the RDI index across different climate regimes of Iran ارزیابی عملکرد مدل‌های تبخیر و تعرق پتانسیل و کاربرد مدل‌های بهینه در پایش خشکسالی با شاخص RDI در اقلیم‌های مختلف ایران 35 55 4328 10.22098/mmws.2025.18956.1741 FA خدیجه جوان دانشیار، گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه ارومیه، ارومیه، ایران آمنه یحیوی دیزج پژوهشگر پسادکتری، گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه ارومیه، ارومیه، ایران بهناز شاهنی کارشناسی جغرافیا، گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه ارومیه، ارومیه، ایران Journal Article 2025 12 02 Introduction Drought is a climatic anomaly that results from long-term disruptions in components of the water balance (Wang et al., 2021; Portner et al., 2022; Zhang et al., 2023; Kartal, 2024; Tareke, 2025). This phenomenon has both direct and indirect adverse impacts, with water resources being the most significantly affected (Balooei et al., 2024). Water scarcity and its associated challenges are recognized as among the most critical and urgent global crises (Zarei et al., 2019a). Although drought cannot be prevented, understanding its nature and characteristics enhances the potential for partial prediction and, through preparedness and planning, helps reduce—and, where possible, control—its detrimental effects (Rezaei et al., 2024). Therefore, greater attention must be paid to drought and to identifying the key factors influencing it across different regions, particularly in vulnerable countries such as Iran, which face growing water scarcity. The increasing need to understand drought and its consequences has motivated extensive global research aimed at developing various drought indices. Among these, the Reconnaissance Drought Index (RDI), introduced by Tsakiris et al. (2007), is one of the most notable. Materials and Methods In this study, daily observational data from nine synoptic stations covering a 30-year period (1991–2020) were obtained from the Iran Meteorological Organization to estimate potential evapotranspiration and the RDI index. These stations were selected so that each represents one of Iran’s major climatic groups. In this research, the performance of six temperature-based models and three radiation-based models for estimating potential evapotranspiration was evaluated. The primary goal of the analysis is to identify which of these simplified approaches provides results most consistent with the FAO Penman–Monteith (FAO-56 PM) model, which is widely recognized as the standard reference method (Allen et al., 1998). Results and Discussion Evaluation of Daily Potential Evapotranspiration Models The results indicated that the performance of evapotranspiration models is strongly influenced by the climatic conditions of each region. For example, the Blaney–Criddle model performed best in certain climates, while the same model showed lower accuracy in others. This finding is consistent with previous studies, including Eghtedarnezhad et al. (2016), which emphasized the role of climatic factors in drought monitoring. Moreover, the variability in model performance across different regions further underscores the need to evaluate and select models that are appropriate for the specific climatic conditions of each area. This observation also aligns with the findings of Lehner et al. (2020) and Beobide-Arsuaga et al. (2021), who stressed the importance of model adaptability to local conditions. Overall, it can be concluded that choosing the appropriate model for estimating evapotranspiration is a crucial step in drought studies and water resource management, and must be carried out with careful consideration of each region’s climatic characteristics. Evaluation of Monthly Potential Evapotranspiration Models Overall, the results demonstrate that, similar to the daily scale, temperature-based models do not exhibit uniform behavior across different climates at the monthly scale. A model may perform exceptionally well in one climate while ranking among the weakest in another. These differences highlight the necessity of considering climate type, geographical characteristics, the study period, and careful model selection in climatological research (Latrech et al., 2024). Assessment of Drought Using the 6- and 12-Month RDI Indices for Optimal Models at Selected Stations The overall findings of this study indicate that at the 6-month timescale, the frequency of drought and wet periods is higher, whereas at the 12-month timescale, their frequency decreases but their persistence increases. This result is in agreement with the study by Ahrari and Raja (2025), who examined meteorological, agricultural, and hydrological drought indices in the Mahabad plain. Furthermore, based on the results of the present study, the 12-month RDI was identified as a more suitable timescale for monitoring drought and wet periods, which is consistent with the findings of Nouri and Homaee (2020), Torabinejad et al. (2023), and Rezaei et al. (2024). Conclusion Frequent drought events and the significant damages they cause in various sectors, including agriculture, the environment, socio-economic, and other areas, have made this phenomenon one of the fundamental challenges in different regions of the world. The present study aimed to evaluate the performance of different potential evapotranspiration models and their influence on the RDI drought index across diverse climatic zones, using 30 years (1991–2020) of data from nine synoptic stations representing nine distinct climate types in Iran (BSh, BSk, BWh, BWk, Cfa, Csa, Csb, Dsa, Dsb). The evaluation of nine evapotranspiration models showed that their performance is strongly affected by the climatic characteristics of each region. For example, the Droogers–Allen model performed best in cold semi-arid (BSk), Mediterranean temperate with warm summers (Csb), and cold climates with dry, warm summers (Dsb), whereas the same model exhibited poor performance in hot semi-arid climates (BSh) and several other regions. In the drought analysis section, comparison of the 6- and 12-month RDI indices revealed that although both timescales confirm the occurrence of frequent droughts during most of the study period, the 12-month RDI was identified as the more suitable scale for drought monitoring and analysis in Iran. This is due to its ability to filter out short-term fluctuations and better reflect the persistence of drought periods. خشکسالی یکی از مهم‌ترین پدیده‌های اقلیمی است که هر ساله خسارات قابل‌توجهی در مناطق مختلف جهان ایجاد می‌کند. بررسی دقیق خشکسالی و شاخص‌های مرتبط با آن می‌تواند اطلاعات ارزشمندی برای برنامه‌ریزی و مدیریت منابع آب فراهم سازد. در این پژوهش، عملکرد 9 مدل تبخیر و تعرق پتانسیل (PET) و تأثیر آن‌ها بر شاخص خشکسالی RDI در ایران طی دوره 2020–1991 مورد ارزیابی قرار گرفت. برای این منظور، داده‌های 12 ایستگاه همدید نماینده 9 گروه اقلیمی ایران شامل اقلیم‌ نیمه‌بیابانی گرم (BSh)، نیمه‌بیابانی سرد (BSk)، بیابانی گرم (BWh)، بیابانی سرد (BWk)، معتدل پر باران با تابستان‌های گرم (Cfa)، معتدل پر باران با تابستان‌های بسیار گرم (Csa)، معتدل با تابستان‌های گرم (Csb)، برفی با تابستان‌های بسیار گرم (Dsa) و برفی با تابستان‌های گرم (Dsb) استفاده شد. دقت مدل‌ها با معیارهای RMSE، MAE، R² و d در مقیاس روزانه و ماهانه ارزیابی و مدل بهینه هر اقلیم برای محاسبه شاخص‌های RDI-6 و RDI-12 به‌کار رفت. نتایج نشان داد که عملکرد مدل‌های PET در اقلیم‌های مختلف متفاوت است؛ برای مثال، مدل دروگرز-آلن در مقیاس روزانه در اقلیم‌های BSk (تبریز و مشهد) وDsb (آبعلی) به‌ترتیب با RMSE برابر 12/0، 03/1 و 81/0 میلی‌متر در روز و ضریب توافق (d) بالای 95/0 بهترین عملکرد را داشت، اما در اقلیم‌هایBSh و Csa به‌ترتیب با RMSE برابر 51/1 و 75/1 کمترین دقت را نشان داد. در بین مدل‌های مبتنی بر دما، مدل بلانی-کریدل در 5 ایستگاه (با RMSE بین 27/0 تا 57/1) دقیق‌ترین برآورد را ارائه کرد. در میان مدل‌های تابشی، مدل ایرماک در هفت اقلیم از ۹ اقلیم مورد مطالعه بهترین نتایج را داشت، اما در اقلیم Csb (با RMSE برابر 54/9) عملکرد آن ضعیف بود. همچنین، شاخص RDI–12 به‌علت کاهش نوسانات کوتاه‌مدت و اثرپذیری کمتر از تغییرات فصلی، مناسب‌ترین مقیاس زمانی برای پایش خشکسالی و ترسالی در اقلیم‌های متنوع ایران است.

https://mmws.uma.ac.ir/article_4328_32115854f32fde2bbeb7cc399c7d4b4e.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Source identification and multi-index evaluation of heavy metal contamination in surface soils of the Pakal sub-watershed, Shazand شناسایی منشأ و ارزیابی چندشاخصی آلودگی فلزات سنگین در خاک‌های سطحی حوزه آبخیز پاکل- شازند 56 77 4380 10.22098/mmws.2026.18959.1744 FA فروزان آذرینوند دانشجوی دکتری علوم و مهندسی آبخیز، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران ابراهیم امیدوار دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران حمید غلامی استاد گروه مهندسی منابع طبیعی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران Journal Article 2025 12 03 Extended Abstract Introduction Heavy metal contamination in terrestrial ecosystems has become a major environmental and public health concern worldwide, particularly in regions exposed to intensive industrial operations, agricultural inputs, and land-use alterations. Soil acts as both a reservoir and a medium for the transport of potentially toxic elements (PTEs), and therefore the evaluation of its contamination status is essential for sustainable land management and ecosystem protection. The Shazand region in Markazi Province, central Iran, hosts several heavy industries—including the Imam Khomeini Oil Refinery, a petrochemical complex, a large thermal power plant, and numerous mining activities, which have previously been reported as major pollution sources. Nevertheless, earlier investigations have predominantly focused on downstream plains and industrial zones, with limited knowledge regarding the contamination status of upstream sub-watersheds that are assumed to be less affected by anthropogenic pressures. The present study aims to fill this critical gap by providing a multi-index assessment of heavy metal contamination in the upstream Pakal sub-watershed in Shazand County, with an emphasis on how different land-use types (rangeland, cultivation, and orchards) influence the distribution, enrichment, and ecological risks of seven key heavy metals: Pb, Cd, Cu, Zn, Ni, Mn, and Fe. Materials and Methods A total of 32 composite soil samples were collected from surface layers (0–30 cm), following a systematic sampling design based on land units that integrated slope, land use, and lithology. The fine fraction (<0.063 mm) of the soils was analyzed using a near-total four-acid digestion method, and metal concentrations were determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), ensuring high analytical accuracy for trace and ultra-trace elements. To comprehensively evaluate contamination status, multiple geochemical and ecological indices, including the Contamination Factor (CF), Degree of Contamination (Cd), Modified Degree of Contamination (mCd), Pollution Load Index (PLI), Geo-accumulation Index (Igeo), Enrichment Factor (EF), and Potential Ecological Risk (Eri and RI)—were employed. Moreover, multivariate statistical analyses, including Principal Component Analysis (PCA) with Varimax rotation and Hierarchical Cluster Analysis (HCA), were conducted to distinguish between natural (geogenic) and anthropogenic sources. Results and Discussion The results of one-way ANOVA indicated that none of the measured heavy metals exhibited statistically significant differences among the three land-use categories (P > 0.05), suggesting that spatial variation is primarily governed by natural geochemical controls rather than recent anthropogenic inputs. This finding was strongly supported by PCA and HCA. PCA extracted three principal components that together explained 69.59% of the total variance. The second component, dominated by Fe and Mn with extremely high loadings, clearly represented geogenic contributions associated with parent material and mineralogical composition. In contrast, the first component (characterized by Cd, Cu, and Pb) and the third component (Zn, Ni, and Fe) reflected mixed or anthropogenic influences, likely originating from agricultural activities such as phosphate fertilizers, pesticides, and machinery emissions. HCA further confirmed these groupings by separating the elements into two distinct clusters: a geogenic cluster (Fe and Mn) and an anthropogenic/mixed cluster (Pb, Cd, Cu, Zn, and Ni). Despite the statistical homogeneity implied by ANOVA, contamination indices revealed noteworthy evidence of cumulative anthropogenic enrichment. CF values showed that Pb, Cu, Zn, Ni, Mn, and Fe had moderate contamination levels (CF > 1) across most land uses. Pb exhibited the highest CF value, particularly in agricultural soils (2.31), highlighting its elevated sensitivity to anthropogenic activities. Cadmium, although present in low absolute concentrations, demonstrated substantial ecological importance due to its high toxic response factor. The Degree of Contamination (Cd) ranged from 7.93 to 9.77, classifying all land uses as moderately contaminated. Notably, the Pollution Load Index exceeded unity for rangeland (1.05) and cultivated land (1.15), indicating cumulative pollution, while orchards (0.94) remained below the contamination threshold. This discrepancy between ANOVA and PLI highlights that while spatial variation is not statistically significant, long-term pollutant accumulation has occurred. Geochemical indices further corroborated the dominance of natural sources. EF values for most metals fell within the “no enrichment” to “minor enrichment” categories (EF ≤ 3), confirming minimal anthropogenic addition. Only Pb showed consistent minor enrichment across land uses, particularly in orchards and agricultural soils, aligning with patterns typically associated with the historical deposition of lead-containing particulates and agrochemical inputs. Similarly, Igeo values for all metals, except Pb, were negative, indicating unpolluted conditions. Pb in cultivation and orchard soils was classified as “unpolluted to moderately polluted” (0 < Igeo ≤ 1), marking it as the only element with a detectable anthropogenic signal. Ecological risk assessment showed that individual ecological risk values (Eri) for all metals fell within the low-risk category. However, Cd accounted for the highest proportion of ecological risk due to its high toxicity, despite its relatively low concentration. The integrated ecological risk index (RI) ranged from 32.30 to 44.08 for the three land uses—well below the threshold of 150—indicating a low overall ecological threat in the upstream Pakal sub-watershed. Conclusions In conclusion, although geogenic factors remain the primary determinant of heavy metal distribution in the upstream Pakal sub-watershed, pollution indices reveal subtle yet meaningful anthropogenic contributions, particularly from agricultural activities. The slight enrichment of Pb and Cu, the moderate contamination levels indicated by CF and Cd, and the PLI values exceeding unity in cultivated and rangeland soils collectively demonstrate the early stages of cumulative pollution in areas traditionally considered pristine. These findings underscore the necessity of continuous monitoring, stricter management of agricultural inputs, and preventive measures to mitigate further contamination and potential ecological risks. Given the proximity to major industrial sources and expanding agricultural practices, upstream sub-watersheds like Pakal serve as critical zones for early detection of contamination trends that may escalate if left unaddressed. آلودگی فلزات سنگین در خاک حوزه‌های آبخیز تحت تأثیر فعالیت‌های انسانی، یک تهدید جدی برای محیط زیست و سلامت است. این پژوهش با هدف ارزیابی غلظت و منشأ فلزات سنگین (Pb، Cd، Cu، Zn، Ni، Mn و Fe) در خاک‌های سطحی زیرحوزه بالادست پاکل (شازند) و بررسی نقش کاربری‌های اراضی (مرتع، زراعت، باغ) در توزیع آن‌ها انجام شد. به‌طوری‌که پژوهش با رویکرد توصیفی- تحلیلی و مبتنی بر مطالعات میدانی و آزمایشگاهی انجام شد و داده‌ها با استفاده از شاخص‌های آلودگی ژئوشیمیایی و تحلیل‌های آماری چندمتغیره مورد ارزیابی قرار گرفتند. پس از تفکیک واحدهای کاری منطقه بر اساس معیارهای شیب، کاربری اراضی و سنگ‌شناسی، نمونه‌برداری خاک سطحی به روش تصادفی-ترکیبی انجام گرفت. نمونه‌ها پس از خشک‌کردن، یکنواخت‌سازی و آماده‌سازی آزمایشگاهی، تحت هضم اسیدی قرار داده شدند و غلظت فلزات سنگین با استفاده از روش طیف‌سنجی جرمی پلاسمای جفت‌شده القایی (ICP-MS) اندازه‌گیری شد. نتایج تحلیل واریانس (ANOVA) نشان داد که تفاوت غلظت هیچ‌یک از عناصر بین کاربری‌های زمین از نظر آماری معنی‌دار نیست (< P 05/0) که دلالت بر غلبه الگوی غلظت زمینه دارد. با این حال، مقادیر به‌دست ‌آمده برای برخی عناصر بالقوه آلاینده، نشان‌دهنده تأثیر عوامل انسان‌زاد در مقیاس محلی است. این یافته با تحلیل مؤلفه‌های اصلی (PCA) تأیید شد، به‌گونه‌ای که عناصر Fe و Mn در خوشه‌ای مجزا با ماهیت زمین‌زاد دسته‌بندی شدند. با این وجود، ارزیابی دقیق شاخص‌های آلودگی، افزایش ناشی از فعالیت‌های انسان‌زاد را نشان داد. شاخص‌های CF، Igeo و EF، عناصر خوشه آنتروپوژنیک (Pb ، Cd ، Cu ، Zn و Ni) را در سطح آلودگی/غنی‌سازی متوسط طبقه‌بندی کردند. بیش‌‎ترین میزان غنی‌سازی مربوط به عنصر Pb بود که نقش فعالیت‌های کشاورزی و انسانی در افزایش غلظت آن را برجسته می‌سازد. همچنین دیگر نتایج نشان داد که شاخص بار آلودگی (PLI) برای مرتع (05/1) و زراعت (15/1) را آلوده گزارش کرد، در حالی که کاربری باغ در محدوده غیرآلوده قرار گرفت. در مجموع، اگرچه الگوی ژئوشیمیایی منطقه همچنان تحت کنترل سنگ مادر است، اما شواهد حاصل از شاخص‌های تجمعی و تحلیل‌های چندمتغیره نشان‌دهنده افزایش تدریجی بار آلودگی فلزات انسان‌زاد در زیرحوضه بالادست است که ضرورت پایش مداوم و مدیریت ورودی‌های کشاورزی را برای حفظ کیفیت محیط زیست منطقه برجسته می‌سازد.

https://mmws.uma.ac.ir/article_4380_4c01c51992f930f353c794b9a2865613.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Assessment of groundwater resources vulnerability, reliability and resilience under drought condition (Case study: Dehgolan plain, Kurdistan province) بررسی آسیب‌پذیری، قابلیت اطمینان و تاب‌آوری منابع آب زیرزمینی در مواجهه با خشک‌سالی (مطالعه موردی: دشت دهگلان استان کردستان) 78 92 4381 10.22098/mmws.2026.18955.1742 FA مهدیه مرادنیا دانشجوی کارشناسی ارشد مهندسی و مدیریت منابع آب، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران 0009-0000-4745-8818 محسن ایثاری استادیار، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران داود مشیرپناهی دانش‌آموخته مقطع دکتری، گروه آب و محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران حامد فاروقی استادیار، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران سید دانا پریزادی دانشجوی دکتری سازه، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران Journal Article 2025 12 02 Extended Abstract Introduction Groundwater resources play a vital role in sustaining socio-economic development, particularly in arid and semi-arid regions where surface water is scarce. In recent decades, these resources have come under increasing stress due to climate change, prolonged droughts, and unsustainable abstraction. The vulnerability of aquifers to drought has therefore become a central issue in water resources management and policy-making. The resilience, reliability, and vulnerability (RRV) framework provides a robust means to assess the performance and sustainability of groundwater systems under hydrological stress. This study focuses on evaluating the groundwater resources of the Dehgolan plain, located in Kurdistan Province, Iran, to determine their vulnerability, reliability, and resilience in the face of recurrent droughts. The Dehgolan plain is one of the most important agricultural zones in the region, yet it has experienced significant groundwater decline over the past few decades. Continuous extraction for irrigation, coupled with reduced precipitation and recharge, has led to critical drops in groundwater levels and deterioration in aquifer storage. Using long-term hydrological and meteorological data, this research aims to (1) quantify the temporal and spatial variations of drought intensity and duration, (2) assess groundwater system behavior during drought events, and (3) evaluate overall aquifer performance through the RRV indices. The results provide a scientific foundation for improving groundwater management strategies in drought-prone regions. Materials and Methods The study area encompasses the Qorveh–Dehgolan plain in eastern Kurdistan Province, bounded by latitudes 34°56′ to 35°02′ N and longitudes 47°07′ to 47°24′ E. The region is characterized by a cold semi-arid climate with an average annual precipitation of approximately 350 mm and average temperatures ranging from –23°C to 41°C. The study utilized data from seven meteorological stations and 54 observation wells covering the 1977–2022 period. Drought assessment was performed using two major indicators: The Standardized Precipitation Index (SPI) for meteorological drought and the Groundwater Resource Index (GRI) for hydrogeological drought. SPI values were calculated for 6- and 12-month timescales using gamma probability distribution fitting. GRI was derived by standardizing deviations of monthly groundwater levels from their long-term means, thereby reflecting groundwater storage anomalies. Reliability (Rel) represents the probability of the system being in a satisfactory state, Resiliency (Res) measures the speed of recovery after a drought, and Vulnerability (Vul) quantifies the magnitude of failure when the system departs from acceptable conditions. Results and Discussion The SPI analysis revealed alternating wet and dry cycles, with severe droughts during 1977–1981, 1987–1992, and 1997–2001. SPI values ranged from –1.8 to +1.8, indicating alternating meteorological extremes and a general decline in precipitation after 1990. The GRI results showed continuous groundwater depletion, particularly in the central and eastern zones of the plain, where water-level declines exceeded 20 m. Average drought duration varied between 1.5 and 3.5 months, while 16–32% of the observation wells experienced recurrent droughts. RRV indicators quantified system performance under drought stress. Reliability values ranged from 0.91 to 0.94 (mean 0.92), showing that the aquifer remained in a satisfactory state most of the time. Resilience ranged between 0.72 and 0.95 (mean 0.82), reflecting moderate recovery capacity after drought. Vulnerability values varied from 0.23 to 0.39 (mean 0.31), signifying moderate failure magnitude. The composite RRV index averaged 0.62 based on SPI data and 0.68 using GRI data, representing a moderately stable yet declining system. Spatially, higher RRV values occurred in northern sectors, while central and southern zones exhibited reduced resilience and higher vulnerability. Overall, the findings indicate that although the Dehgolan aquifer retains moderate reliability, its recovery capacity has weakened due to prolonged overexploitation and limited recharge. These results align with previous research in western Iran, confirming that unsustainable groundwater abstraction combined with persistent droughts is reducing aquifer stability and resilience. Conclusion The integrated analysis of meteorological and groundwater droughts in the Dehgolan plain demonstrates that the aquifer system is under increasing pressure, primarily driven by climatic variability and excessive abstraction. The combined use of SPI, GRI, and RRV indices proved effective in identifying spatial and temporal patterns of groundwater vulnerability and in quantifying the system’s performance under drought stress. The mean reliability value (≈0.92) indicates that the groundwater system generally maintains acceptable performance, but the moderate resilience (≈0.82) and vulnerability (≈0.31) highlight limitations in recovery capacity and the potential for further degradation if current extraction rates persist. The RRV index values (0.62–0.68) collectively suggest a moderately stable yet increasingly fragile system. To enhance groundwater sustainability, it is essential to implement adaptive management strategies such as controlled abstraction, artificial recharge, improved irrigation efficiency, and continuous monitoring of groundwater levels. The results of this study provide a quantitative foundation for policymakers and regional planners to develop drought mitigation frameworks and long-term groundwater management plans aimed at preserving aquifer resilience and reliability under future climatic uncertainties. در سال‌های اخیر، پدیده خشکسالی باعث کاهش شدید آب های سطحی در دشت قروه-دهگلان شده است. این امر موجب افزایش بهره برداری از منابع آب زیرزمینی و تشدید افت تراز سفره‌ای این دشت شده است. هدف از این پژوهش بررسی تاب‌آوری منابع آب زیرزمینی در مواجهه با خشک‌سالی است. برای برآورد تاب‌آوری منابع آب در این منطقه، در این پژوهش از دو مفهوم دیگر یعنی قابلیت اطمینان و آسیب‌پذیری نیز استفاده شد، که به چارچوب RRV معروف هستند. بر اساس داده‌های دوره آماری ۱۳۷۷ تا ۱۴۰۲ با استفاده از شاخص‌های SPI و GRI و اطلاعات ایستگاه‌های هواشناسی و چاه‌های پیزومتری دشت قروه-دهگلان مورد ارزیابی قرار گرفتند. از منظر منابع آب زیرزمینی و شاخص GRI، میانگین قابلیت اطمینان، تاب‌آوری و آسیب‌پذیری به‌ترتیب 82/0 ، 71/0 و 6/0 به دست آمد. این نتایج نشان می‌دهد شاخص‌های قابلیت اطمینان و تاب‌آوری بر اساس داده‌های هیدروژئولوژیکی نسبت به‌شاخص‌های هواشناسی کاهش و شاخص آسیب‌پذیری افزایش داشته است، که روند نزولی و افت منابع آب زیرزمینی را تأیید می‌کند، با این حال، شاخص RRV بر اساس SPI با میانگین 62/0 و بر اساس GRI با میانگین 68/0 نشان می‌دهد که امکان بازگشت منابع آب زیرزمینی به شرایط پایدار هم‌چنان وجود دارد. نتایج کلی پژوهش بیانگر این است که با وجود کاهش برخی شاخص‌ها، مدیریت بهینه منابع آب زیرزمینی و توجه به تاب‌آوری می‌تواند اثرات خشکسالی بر منابع آب دشت قروه-دهگلان را کاهش دهد و پایداری آن‌ها را تضمین کند.

https://mmws.uma.ac.ir/article_4381_7187b9a6f6656352927d5952a976056a.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Sensitivity and Uncertainty Analysis of Hydrological Parameters of Rafsanjan Plain Using the SWAT Model تحلیل حساسیت و عدم‌قطعیت پارامترهای هیدرولوژیکی دشت رفسنجان با استفاده از مدل SWAT 93 110 4388 10.22098/mmws.2026.19072.1753 FA فرزانه دهقانی قناتغستانی دانشجوی کارشناسی ارشد، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران سعید اکبری فرد استادیار گروه مهندسی آب، دانشکده مهندسی عمران و نقشه‌برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران عاطفه جعفرپور استادیار، بخش منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان، کرمان، ایران محمد ذونعمت کرمانی استاد گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان، ایران Journal Article 2025 12 19 Extended Abstract Introduction Rafsanjan Plain, an arid region of Iran, has experienced groundwater depletion and significant hydrological changes in recent decades. This study applied the semi-distributed, process-based SWAT model to simulate hydrological processes and estimate the water balance. Spatial inputs (DEM, land use, soil type, slope) and daily climate data (precipitation, temperature, humidity, wind, solar radiation) for 2002–2024 were used. The watershed was divided into sub-basins and HRUs, and the model was calibrated and validated using SUFI-2 in SWAT-CUP, with sensitivity and uncertainty analyses conducted. Results indicated that curve number, saturated hydraulic conductivity, and baseflow parameters most influenced streamflow simulation. Model performance was acceptable (P-factor 0.42, R-factor 2.62 in calibration; 0.42 and 0.39 in validation). Over 60% of annual precipitation was lost via evapotranspiration, with surface runoff contributing less than 0.1%. These findings demonstrate SWAT’s effectiveness for water resources management and climate impact assessment in arid regions. Materials and Methods The Rafsanjan Plain, encompassing 12,513 km² within the Kavir-e Dranjir-Saghand basin, was delineated into 12 sub-basins using a 30-meter Digital Elevation Model. The model configuration incorporated land use/land cover maps (derived from Landsat imagery), soil classification data from the Iranian Soil and Water Research Institute, and slope categories to generate Hydrological Response Units (HRUs) through an overlay process. Daily climatic inputs spanning 2002-2024 included precipitation, maximum/minimum temperature, and relative humidity from the Rafsanjan synoptic station; wind speed and solar radiation were obtained from NASA's POWER database to fill data gaps. Monthly discharge observations from two hydrometric stations facilitated model calibration (2002-2017) and independent validation (2018-2024). The SUFI-2 algorithm in SWAT-CUP performed automated calibration through iterative Latin Hypercube sampling, accounting for parameter uncertainty by bracketing observations within 95% prediction uncertainty bounds. Sensitivity analysis employed the t-test method to rank 27 parameters related to runoff generation, soil water movement, and groundwater flow. Performance evaluation utilized the P-factor (percentage of observations within uncertainty band) and R-factor (average width of uncertainty band normalized by standard deviation), supplemented by coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE). The water balance equation in SWAT quantified precipitation partitioning into evapotranspiration, surface runoff, lateral flow, baseflow, and deep aquifer recharge components. Results and Discussion Global sensitivity analysis identified 27 parameters significantly influencing streamflow simulation, with the curve number (CN2), saturated hydraulic conductivity (SOL_K), and baseflow recession constant exhibiting the highest sensitivity based on t-statistics and p-values (<0.05). Calibration achieved P-factor=0.42 and R-factor=2.62, while validation yielded P-factor=0.42 and R-factor=0.39, indicating acceptable model performance according to ASABE guidelines. Statistical metrics demonstrated strong agreement (R²≈0.91, NSE=0.84) between simulated and observed monthly discharge, though the model overestimated peak flows in extreme years (2006, 2014) due to limited availability of sub-daily precipitation data and simplified representation of runoff generation during high-intensity events. Water balance analysis revealed that 63.2% of mean annual precipitation (142 mm) was lost through actual evapotranspiration, 28.4% contributed to deep aquifer recharge, 7.8% generated surface runoff, and return flow constituted merely 0.08%, characterizing typical hyper-arid hydrology. Baseflow dominated river discharge during dry months (June-September), comprising 85% of total flow, while snowmelt contributed significantly to spring peaks. Uncertainty analysis demonstrated that parameters controlling runoff partitioning and soil water retention (CN2, SOL_K, soil available water capacity) contributed 68% of total prediction uncertainty. Seasonal patterns showed that precipitation and runoff peaked in March-April, while potential evapotranspiration reached maximum values during June-August. The model's performance in simulating baseflow recession was superior to its representation of quickflow response, reflecting its conceptual structure and parameterization limitations in capturing rapid runoff processes. Conclusion This study successfully calibrated and validated the SWAT model for the Rafsanjan Plain, demonstrating its capability to simulate hydrological processes in data-scarce arid environments with acceptable uncertainty levels. The identification of 27 sensitive parameters, particularly CN2 and SOL_K, highlights that accurate characterization of soil hydraulic properties is critical for reducing simulation uncertainty and improving prediction reliability. Quantification of water balance components revealed severe water loss through evapotranspiration (>60%) and minimal groundwater recharge, emphasizing the unsustainable nature of current water use practices and the urgent need for demand management strategies. While the model effectively reproduced seasonal flow patterns and baseflow dynamics (R²≈0.91), overestimation of peak flows indicates limitations in representing extreme rainfall-runoff events, attributable to coarse temporal resolution of precipitation data and simplified infiltration processes. These findings provide a robust scientific foundation for evaluating climate change scenarios, land use change impacts, and water management interventions such as deficit irrigation and artificial recharge. Future research should integrate SWAT with groundwater quality modules to address salinization, incorporate higher-resolution meteorological forcing data, and couple with optimization algorithms to support multi-objective water allocation decisions. The established parameter ranges and methodological framework offer transferable guidance for hydrological modeling in similar arid watersheds, ultimately supporting evidence-based policies for sustainable water resource management. دشت رفسنجان به ‌عنوان یکی از مناطق مهم خشک ایران، طی دهه‌های اخیر با کاهش منابع آب تجدیدپذیر، افت سطح آب زیرزمینی و تغییرات قابل‌توجه در رژیم هیدرولوژیکی مواجه بوده است. در این پژوهش، به‌منظور شبیه‌سازی فرآیندهای هیدرولوژیکی و برآورد مؤلفه‌های بیلان آب این دشت، از مدل نیمه‌توزیعی و فرآیندمحور SWAT استفاده شد. به منظور پیاده سازی این مدل از داده‌های مکانی شامل مدل رقومی ارتفاع، کاربری اراضی، نوع خاک و شیب، به‌همراه داده‌های اقلیمی روزانه شامل بارش، دما، رطوبت نسبی، سرعت باد و تابش خورشیدی برای دوره ۲۰۰۲ تا ۲۰۲۴ استفاده شد. حوضه پس از تفکیک به زیرحوضه‌ها و واحدهای پاسخ هیدرولوژیکی، با استفاده از الگوریتم SUFI-2 در نرم‌افزار SWAT-CUP واسنجی، اعتبارسنجی و تحلیل حساسیت و عدم‌قطعیت شد. از جمله مهم‌ترین محدودیت‌های این مدل می‌توان به کاهش دقت نتایج در بازه‌های زمانی طولانی مدت نسبت به دوره‌های کوتاه، عدم پیوستگی مکانی واحدهای پاسخ هیدرولوژیک و وابستگی شدید به کیفیت داده‌های ورودی اشاره کرد. نتایج تحلیل حساسیت نشان داد که پارامترهای شماره منحنی، هدایت هیدرولیکی اشباع خاک و پارامترهای مرتبط با جریان پایه بیشترین تأثیر را بر شبیه‌سازی جریان دارند. ارزیابی عملکرد مدل بیانگر تطابق قابل‌قبول بین مقادیر شبیه‌سازی‌شده و مشاهداتی بود، به‌گونه‌ای که مقادیر شاخص‌های P-factor و R-factor در مراحل واسنجی به ترتیب 42/0 و 62/2 و در مرحله اعتبارسنجی به ترتیب 42/0 و 39/0 بدست آمد که در محدوده قابل‌قبول قرار گرفت. نتایج بیلان آب نشان داد که بیش از ۶۰ درصد بارش سالانه به‌صورت تبخیر و تعرق از حوضه خارج شده و سهم برگاب کمتر از ۰٫۱ درصد است. به‌طور کلی، نتایج این پژوهش بیانگر توانایی مناسب مدل SWAT در بازنمایی رفتار هیدرولوژیکی دشت رفسنجان و کاربرد آن در مدیریت منابع آب و تحلیل اثرات تغییر اقلیم در مناطق خشک و نیمه‌خشک است.

https://mmws.uma.ac.ir/article_4388_4eff1016b5482bdccbdc05de94a22007.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Analysis of meteorological drought characteristics in Iran using high-resolution TerraClimate data and the runs theory تحلیل ویژگی‌های خشکسالی هواشناسی ایران با داده‌های قدرت تفکیک بالای TerraClimate و نظریه دنباله‌ها 111 130 4446 10.22098/mmws.2026.19172.1766 FA ایوب میرزائی حسنلو گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران مهدی عرفانیان گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه سیما کاظم پور چورسی گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران Journal Article 2025 12 31 Extended Abstract Introduction Drought is one of the most significant natural hazards in arid and semi-arid regions, affecting water resources, agriculture, vegetation, and human health. Iran, located in the arid belt of the world, frequently experiences severe and prolonged droughts, which have intensified in recent decades due to climate change and precipitation variability. Assessing drought characteristics and monitoring is essential for effective water resource management and risk reduction. Drought can be classified as meteorological, agricultural, or hydrological, depending on the component of the hydrological cycle affected. Traditional drought monitoring relies on sparse ground-based station data, which often has limited coverage and spatial resolution. High-resolution gridded climate datasets, such as TerraClimate, provide long-term monthly data on precipitation, temperature, evapotranspiration, and other hydrological variables, overcoming the limitations of sparse station networks. The Standardized Precipitation Evapotranspiration Index (SPEI), a widely used meteorological drought index, integrates precipitation and potential evapotranspiration to quantify drought intensity and duration more realistically, particularly under changing climatic conditions. Event-based approaches, such as the Runs Theory, enable the identification and characterization of drought episodes, including their duration, intensity, magnitude (severity), and interevent intervals. This study applies SPEI and the runs theory to high-resolution TerraClimate data (1985–2024) to assess drought characteristics across Iran. At the national scale, this framework enables detailed spatiotemporal analysis of short-, medium-, and long-term droughts, providing valuable information for water management, agricultural planning, and climate adaptation strategies. Materials and Methods TerraClimate gridded data (1985–2024), comprising monthly precipitation and potential evapotranspiration at a spatial resolution of 1/24° (approximately 4 km), were employed. The SPEI at pixel level was computed at 3-, 9-, and 12-month timescales to evaluate short-, medium-, and long-term drought events. Calculation involved: (1) derivation of the monthly climatic water balance (precipitation minus potential evapotranspiration); and (2) standardisation using a three-parameter log-logistic probability distribution and transferring the probability value to a normal distribution. Drought events were delineated using the run theory, with monthly percentile thresholds applied to account for seasonal variability and consecutive drought periods. Principal drought characteristics included duration, magnitude or severity, intensity, inter-event intervals, and event frequency over the period 1985–2024. Trend analysis used the modified Mann-Kendall test to identify significant spatiotemporal changes in SPEI, with serial correlation adjusted for. All analyses were performed in Python, using a raster-based dataset to ensure comprehensive spatial coverage and to detect localized patterns that are often undetected by station networks. This integrated approach—combining multi-timescale drought assessment, event-based characterisation, and trend detection—provides a thorough evaluation of drought risk and dynamics across Iran's arid, semi-arid, and relatively humid regions. Results and Discussion Drought conditions in Iran intensified in duration, severity, and spatial extent from 1985 to 2024, exhibiting considerable regional heterogeneity. Seasonal or short-term (3-month) droughts occurred frequently in northern Iran, whereas the central, eastern, and southeastern arid regions experienced longer and more intense droughts. At the 9-month timescale, droughts extended regionally, revealing persistent water deficits in the central and eastern areas. Annual or long-term (12-month) droughts affected nearly the entire country, sparing only narrow northern coastal zones, and underscoring widespread hydrological stress. Analyses of cumulative severity and intensity indicated disproportionate impacts in central and southeastern Iran, aligning with prior reports of elevated drought risk in these zones. Event frequency revealed that arid regions experienced fewer but more severe and persistent droughts, suggesting delayed recovery and accumulated hydrological deficits. The modified Mann-Kendall test detected significant negative trends in SPEI across more than 95% of the country at the 3-month timescale, over 99% at the 9-month timescale, and nationwide at the 12-month scale. These trends reflect a progression from localized seasonal droughts to pervasive national-scale phenomena, extending even to historically wetter northern areas. High-resolution gridded datasets demonstrated clear advantages over traditional station-based methods in resolving fine-scale and regional drought patterns. The combination of SPEI and run theory provides a robust framework for characterising drought properties, temporal evolution, and spatial variability, offering essential insights for water resource management and climate adaptation. Conclusion This analysis provides a comprehensive evaluation of drought characteristics and trends in Iran from 1985 to 2024, based on high-resolution TerraClimate data, SPEI, and run theory. Results show increased drought duration, intensity, and spatial extent, particularly in central, eastern, and southeastern regions, with a shift from seasonal events to persistent nationwide hydrological stress. Run theory enabled precise quantification of duration, severity, intensity, and inter-event intervals, highlighting limitations of station-based monitoring in resolving fine-scale dynamics. Nationwide significant negative SPEI trends underscore escalating hydrological drought and the need for multi-timescale, data-informed management approaches. The framework serves as an operational tool for early warning, climate adaptation, agricultural planning, and water allocation, with potential application to other arid and semi-arid regions worldwide. Integration of high-resolution gridded data, multi-timescale indices, and event-based analysis enhances resilience to climate variability and supports evidence-based policy for sustainable water and agricultural management. This transferable methodology facilitates broader national and regional drought risk assessment. خشکسالی از مهم‌ترین مخاطرات اقلیمی در مناطق خشک و نیمه‌خشک است. در دهه‌های اخیر، تداوم، شدت، فراوانی خشکسالی‌ها تحت تأثیر تغییرات به طور چشمگیری افزایش یافته است. پهنه ایران به‌دلیل موقعیت جغرافیایی و نوسانات بارش، به‌طور گسترده‌ در معرض خشکسالی قرار دارد. تحلیل خشکسالی در دو مقیاس مکانی و زمانی به منظور مدیریت منابع آب، اهمیت اساسی دارد. در این پژوهش برای اولین بار در ایران، شاخص خشکسالی هواشناسی SPEI یا شاخص استاندارد شده تفاضل بارش و تبخیر-تعرق پتانسیل در مقیاس‌های زمانی ۳، ۹ و ۱۲ ماهه با استفاده از داده‌های ماهانه پایگاه جهانی TerraClimate (قدرت تفکیک مکانی ۴ کیلومتر یا یک چهارم درجه) به‌صورت شبکه رستری 0.04 درجه در دوره آماری 2024-1985 استخراج شد. در هر پیکسل با، ویژگی‌های خشکسالی‌ها شامل ‌تداوم، شدت، بزرگی، فاصله و فراوانی یا تعداد رخدادها با استفاده از نظریه دنباله‌ها (Runs Theory) و آستانه‌های صدکی استخراج شد. بعلاوه، از آزمون من–کندال اصلاح‌شده (MMK) برای تحلیل روند شاخص SPEI در هر پیکسل از ایران (4*4 کیلومتر) استفاده شد. نتایج نشان داد با افزایش مقیاس زمانی(3 تا 12ماه)، تداوم، بزرگی و فاصله رخدادها، به‌طور چشمگیری افزایش داشته اما شدت خشکسالی، کاهش می‌یابد. مناطق مرکزی، شرقی و جنوب‌شرقی ایران، طولانی‌ترین تداوم، بیشترین شدت خشکسالی هواشناسی را تجربه کردند. در مقیاس ۱۲ ماهه، ویژگی‌های خشکسالی تقریباً در سراسر ایران به طور یکنواخت می‌باشد. تحلیل خشکسالی در حوضه‌های آبریز اصلی نشان داد، حوضه دریاچه خزر، کمترین و حوضه‌های مرکزی، شرقی-هامون و سرخس-قره‌قوم، بیشترین آسیب‌پذیری از نظر خشکسالی را دارند. آزمون MMK روند کاهشی معنادار شاخص SPEI را در پهنه ایران، بیش از ۹۵ درصد در مقیاس ۳ ماهه، بیش از ۹۹ درصد در مقیاس ۹ ماهه و ۱۰۰ درصد در مقیاس ۱۲ ماهه نشان می‌دهد. نتایج پژوهش نشان می‌دهد استفاده از داده‌های شبکه‎ای اقلیمی با قدرت تفکیک بالای پایگاه TerraClimate و نظریه دنباله‌ها، امکان پایش سریع خشکسالی هواشناسی را در تمام مناطق ایران فراهم ساخته و به‌عنوان ابزار مؤثر در برنامه‌ریزی منابع آب توصیه می‌شود.

https://mmws.uma.ac.ir/article_4446_51a161500735a8221fad760316386cdd.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Effects of surface roughness on sediment heterogeneity under different slopes and rainfall intensities using rainfall simulator تاثیر زبری سطحی بر میزان ناهمگنی رسوب در شیب و شدت‌های بارندگی مختلف با استفاده از شبیه‌ساز باران 131 147 4447 10.22098/mmws.2026.18835.1736 FA اکبر نوروزی کارشناس برنامه‌ریزی و تخصیص آب، شرکت آب منطقه‌ای اردبیل، اردبیل، ایران مهدی پژوهش دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران خدایار عبدالهی دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران Journal Article 2025 11 24 Extended Abstract Introduction The efficient management of vital soil and water resources requires a deep and precise understanding of the complex mechanisms of sedimentation and runoff formation in various ecosystems. This understanding must encompass the variable conditions of topography, land slope, and surface cover, as effective erosion control, especially on steep slopes prone to degradation, is considered the cornerstone of sustainable development in environmental and agricultural sectors. The intensity of soil erosion is a function of the complex interaction of numerous factors such as regional climate, inherent soil characteristics, topographic features, land use type; These factors collectively determine the final fate of eroded sediments, which may lead to their drainage from the system or storage in lower points of the watershed. Meanwhile, surface runoff acts as the primary driver for soil particle detachment. Key hydrological processes, including runoff generation, water infiltration, and ultimately sediment transport, are strongly influenced by the physical characteristics of the soil surface. Specifically, surface roughness, or microtopography—which involves small elevation changes (on the scale of 2 to 25 cm)—plays a pivotal role. This roughness, influenced by agricultural activities and vegetation type, directly affects the intensity of erosion process and the overall sediment transport rate by creating resistance to or guiding the flow. Materials and Methods This study investigated the effect of surface roughness on sediment heterogeneity using an artificial rainfall simulator. This system, operating at a height of 2.5 meters with a droplet spray mechanism, was installed over a plot measuring approximately 0.9 m×0.5 m to replicate natural rainfall conditions at a small scale. The experimental variables included several key components for system operation and control: an electric motor to supply the necessary power, a computerized control unit for precise nozzle management, a water reservoir, a pump, and a pressure gauge for regulating water flow and pressure. Rainfall intensities (45,60, and 70 mm/h and slopes10%, 20%, and 30%), selected based on the erosivity limits of the study area. Experiments were conducted on both bare and vegetated soil conditions. Each treatment was replicated three times, with each test run lasted for 60 minutes and was divided into six 10‑minute intervals. to allow for the collection and measurement of runoff and sediment yield. The influence of surface roughness on sediment heterogeneity was assessed using indices for intra-cluster and inter-cluster heterogeneity. Total heterogeneity was defined as the algebraic sum of these two components: intra-cluster heterogeneity indicating internal variation within blocks of a cluster, and inter-cluster heterogeneity representing the differences between neighboring clusters (based on rainfall intensity). Furthermore, a two-way Analysis of Variance (ANOVA) was employed to evaluate the main and interactive effects of rainfall intensity and slope on the resulting sediment yield. Results and Discussion The highest sedimentation without roughness at an intensity of 45 mm/h was related to a 30% slope, which increased with increasing slope due to increased shear energy and runoff. At an intensity of 60 mm/h, the highest sedimentation without roughness was related to a 20% slope, which indicates the complex effect of slope angle on surface layer protection and the strong role of runoff volume in this rainfall intensity. At an intensity of 70 mm/h, the highest sedimentation without roughness was observed at a 20% slope and the lowest at a 30% slope. In all intensities, the presence of surface roughness generally affected the sedimentation rate. The results also showed that surface roughness is an effective and acceptable factor for adjusting the volume of surface runoff and weakens the effect of runoff washing on different slopes. In general, surface roughness plays an effective role in reducing sedimentation and increases water and soil protection. Vegetation acts as a barrier to runoff, increases infiltration time, and also reduces the kinetic energy of raindrops before they directly hit the soil surface. According to two-way analysis of variance, rainfall intensity and slope are factors affecting sediment production. Increasing flow intensity and kinetic energy of rain make soil particles more likely to be transported. The results also showed that rainfall intensity of 60 mm/h and steep slopes create the greatest heterogeneity (variability) in sedimentation, which is due to the complex interaction of dynamic and physical environmental factors. Conclusion This study investigated the effect of surface roughness heterogeneity on sediment yield under different slope gradients and rainfall intensities using a rainfall simulator. The results indicated that at a rainfall intensity of 45 mm h⁻¹, surface roughness significantly reduced sediment yield across all slopes. On smooth surfaces, the highest sediment yield occurred at a 30% slope, while on rough surfaces it appeared at 20%. Roughness reduced flow velocity and enhanced infiltration, thereby limiting sediment transport and controlling erosion. At 60 mm h⁻¹, the maximum sediment yield was recorded on a 20% slope (smooth surface) and the minimum on a 10% slope, which may be attributed to thinner surface soil layers and changes in infiltration capacity. Under a rainfall intensity of 70 mm h⁻¹, the lowest sediment yield occurred on the steepest slope (30%), likely due to increased infiltration resulting from exposure of subsurface pores after the surface layer was eroded by raindrop impact. Two-way ANOVA confirmed that both rainfall intensity and slope gradient had significant effects on sediment yield, with the highest variability observed at 60 mm h⁻¹ and on steep slopes. Overall, findings highlight the critical role of surface roughness in reducing runoff and mitigating soil erosion. Despite experimental limitations such as calibration precision of the rainfall simulator, wind effects, and difficulties in instrument setup on different slopes, the study provides valuable insight into the interactions among rainfall intensity, slope gradient, surface roughness, and sediment generation processes. زبری سطحی خاک یا میکروتوپوگرافی، یکی از عوامل کلیدی مؤثر بر فرسایش و رواناب است که با تغییرات کوچک ارتفاع سطح زمین مشخص می‌شود و تحت تأثیر ویژگی‌های خاک، پوشش گیاهی و فعالیت‌های کشاورزی قرار دارد. این ویژگی با کنترل مسیر و همگرایی جریان آب، در شدت فرسایش و میزان رسوب نقش مؤثری ایفا می‌کند. هدف این پژوهش، بررسی تأثیر زبری سطح خاک بر میزان ناهمگنی تولید رسوب در شرایط بارندگی‌های با شدت 45، 60 و 70 میلی‌متر بر ساعت و در شیب‌های مختلف 10، 20 و 30 درصد با بهره‌گیری از شبیه‌ساز باران است. در این روش برای اندازه‌گیری رسوب معلق، نمونه آب و رسوب به مدت ۴۸ ساعت در بشر به حالت سکون نگه داشته شد. سپس آب روی رسوبات به‌ آرامی تخلیه و حجم رواناب اندازه‌گیری گردید. رسوبات باقی‌مانده شسته شده و در فویل‌های آلومینیومی وزن‌شده قرار گرفتند و در دمای ۱۰۵ درجه سانتی‌گراد به مدت ۲۴ ساعت خشک و مقدار رسوب معلق محاسبه شد. به منظور ارزیابی میزان تاثیر زبری سطحی بر ناهمگنی رسوب از عددناهمگنی بین خوشه ای و درون خوشه ای بهره گرفته شد. نتایج حاصل از این تحقیق نشان داد که زبری سطحی به طور کلی موجب کاهش محسوس رسوب در تمام شیب‌ها بالاخص در شدت 45 میلی‌متر بر ساعت (83/14 گرم بر مترمربع) شد. در شدت‌های بالاتر، نتایج پیچیده‌تر بود و مشاهده شد که شیب 20 درصد در برخی موارد بیشترین رسوب را داشت (07/87 گرم بر متر مربع)، که این امر به تأثیر عواملی مانند توزیع انرژی برخورد قطرات و ویژگی‌های نفوذ خاک نسبت داده شد. در نهایت، نتایج آنالیز واریانس دوطرفه نشان داد که شدت بارندگی و شیب هر دو از عوامل اصلی تولید رسوب هستند. همچنین با توجه به ارتباط مستقیم ضریب تغییرات با میزان تغییرپذیری، نتایج حاکی از آن است که ضریب تغییرات در شدت بارندگی 60 و 45 میلی‌متر بر ساعت در منطقه مورد مطالعه به ترتیب بیشترین و کمترین میزان رسوب را به خود اختصاص داده است. پیشنهاد می‌شود در تحقیقات آتی، پارامتر زبری سطح به عنوان یک متغیر مستقل حیاتی در مدل‌سازی درصد پوشش گیاهی، زمان اوج و زمان تأخیر رواناب در نظر گرفته شود.

https://mmws.uma.ac.ir/article_4447_c602a7572cec87fb03744f200c40a514.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Estimation parameters of potato root water uptake model under salinity stress in greenhouse conditions برآورد پارامترهای مدل جذب آب ریشه گیاه سیب‌زمینی تحت تنش شوری در شرایط گلخانه‌ای‌ 148 161 4695 10.22098/mmws.2026.19799.1814 FA مجید رئوف استاد گروه مهندسی آب و پژوهشکده مدیریت آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران Haruyuki Fujimaki استاد، مرکز تحقیقات سرزمین‌های خشک، دانشگاه ملی توتوری، هماساکا 1390، توتوری، ژاپن Journal Article 2026 05 15 Introduction Accurate prediction of root water uptake under salinity stress contributes to efficient water management in arid and semi-arid regions. Salinity caused by irrigation is a major limiting factor for crop production in these dry environments. Despite water scarcity, the amount of irrigation water must exceed evapotranspiration to leach excess salts and prevent further soil salinization. To minimize water consumption while avoiding yield reduction, precise prediction of root water uptake under salinity stress is essential. Macroscopic root water uptake models, which employ stress response functions describing the dependence of reduction coefficients on matric or osmotic potential at each soil depth, are widely used in soil water and solute transport simulation models such as HYDRUS and SWAP. Materials and Methods In this study, an optimization approach was used to determine the root water uptake parameters within a macroscopic model, and the corresponding root water uptake was quantified. The experimental work was conducted in a greenhouse using potato plants. Six pots were prepared for cultivation, three of which were subjected to salinity stress, while the remaining three were used to measure potential transpiration. Each pot was equipped with two moisture sensors installed at different depths to monitor soil moisture and electrical conductivity. Two potato seed tubers were planted in each pot on September 1, 2018, and ten days later (after germination), the number of plants was reduced to one per pot. To prevent soil evaporation, the pot surfaces were covered during the daytime. The pots were weighed manually on a daily basis to determine the actual transpiration rate. The drought stress period began on December 13, 2018, after the volumetric water content had been reduced to 0.35. Salinity stress was applied to the relevant treatments immediately after the onset of drought stress in two stages. In the first stage, irrigation was performed using a 3000‑ppm NaCl solution starting on December 13, 2018. Since no reduction in plant transpiration was observed by December 23, 2018, a 5000‑ppm NaCl solution was applied on December 23, marking the second stage of salinity stress. The experiments were continued until the relative transpiration (the ratio of actual to potential transpiration) dropped below 0.5. After completing the experiments and following full plant development, all pots were emptied. At the end of the experiment and after the salinity stress period, the root density distribution was determined by harvesting the plants. The root uptake parameters were estimated inversely by minimizing the sum of squared differences between the observed and simulated daily transpiration rates. Finally, root water uptake at each depth and time was calculated by substituting the linearly interpolated osmotic potential into the stress response function. Results and Discussion The results showed that the optimized daily transpiration agreed well with the observed values. In addition, the deviations in the three optimized stress response functions were small under low to moderate stress levels, indicating the reliability of the method. In the non-stress treatments, most of the root volume was located in the first and fourth soil quartiles. The high root volume in the fourth quartile (the lower part of the soil profile) was due to the limitation of root penetration by the impermeable bottom of the pots. In the salinity-stress treatments, no specific trend was observed, which indicates that the plant was showed no consistent pattern in root development and water uptake due to the presence of osmotic stress. A comparison between the calculated and measured transpiration values around the 1:1 line shows that, in both the non-stress and salinity-stress treatments, most points were located below the 1:1 line and tended toward the calculated transpiration values. This indicates that the transpiration equation is overestimated and can be corrected by a coefficient for potato. In the salinity-stress treatments, transpiration values were much lower than those in the non-stress treatments. The average value of parameter P2 (the exponent of the water uptake reduction coefficient equation) was obtained as 4.98. The average value of parameter ho50 (the osmotic potential at which root water uptake reaches 50% of its potential uptake) was obtained as 4244 cm of water. A comparison of the ho50 values shows that potato is less tolerant to salinity than canola and more tolerant than bean. In the non-stress treatments, since irrigation was not carried out with saline water, no significant reduction in the uptake reduction coefficient was observed; therefore, the uptake reduction coefficient (α) can be considered equal to unity in this case. In all three salinity-stress treatments, as the salt concentration in the soil solution increased (due to irrigation with saline water and the reduction in soil moisture caused by root water uptake), the uptake reduction coefficient decreased. In the salinity-stress treatments, as the osmotic potential increased from 100 to 10,000 cm, the root uptake coefficient decreased from 1 to zero. Conclusion As time progressed, and particularly during the mid-growth stage of the potato plants, the transpiration values in the stressed treatments became closer to the mean transpiration of the non-stressed treatments. In the late growth stage, the application of salinity stress in the salinity-stressed treatments led to a decrease in both transpiration and relative transpiration values. This indicates a reduction in the root water uptake capacity in the pots subjected to salinity stress, resulting from an increase in osmotic potential. In the non-stressed treatments, since irrigation was not carried out with saline water, no considerable reduction in the uptake reduction coefficient was observed; therefore, the uptake reduction coefficient (α) may be considered equal to unity in this case. In all three salinity-stressed treatments, as the salt concentration in the soil solution increased due to irrigation with saline water and the reduction in soil moisture caused by root water uptake, the uptake reduction coefficient decreased. In the salinity-stressed treatments, as the osmotic potential increased from 100 to 10,000 cm, the root uptake coefficient decreased from 1 to zero. پیش‌بینی دقیق جذب آب ریشه تحت تنش شوری برای مدیریت کارآمد آب در اراضی خشک و نیمه‌خشک ضروری است. در این تحقیق، با استفاده از روش بهینه‌سازی معکوس، پارامترهای مدل ماکروسکوپی جذب آب ریشه برای گیاه سیب‌زمینی در شرایط شوری برآورد شد. آزمایش‌های گلخانه‌ای در شش گلدان شامل سه گلدان تحت تنش شوری و سه گلدان شاهد برای تعیین تعرق بالقوه انجام گرفت. در هر گلدان، تعداد 2 سنسور رطوبت و هدایت الکتریکی در اعماق مختلف نصب شد و برای جلوگیری از تبخیر، سطح خاک پوشانده شد. وزن گلدان‌ها نیز روزانه اندازه‌گیری شد تا تعرق روزانه به‌دست آید. نتایج نشان داد که در تیمارهای بدون تنش، ضریب کاهش جذب آب ریشه در طول دوره آزمایش تقریباً ثابت و برابر یک بود. در مقابل، در تیمارهای دارای تنش شوری، با افزایش غلظت نمک و افزایش پتانسیل اسمزی از 100 به 10000 سانتی‌متر، ضریب کاهش جذب آب ریشه از 1 به صفر کاهش یافت که نشان‌دهنده افت شدید توان جذب آب توسط ریشه در شرایط شوری است. مقایسه پارامترهای برآوردشده نشان داد که میانگین P2 برابر 98/4 و میانگین ho50 برابر 4244 سانتی‌متر آب به دست آمد. همچنین تحلیل حساسیت نشان داد که مدل نسبت به P2 حساس‌تر از ho50 است. تعرق روزانه بهینه‌شده نیز تطابق خوبی با مشاهدات داشت. این نتایج نشان می‌دهد که پارامترهای برآوردشده می‌توانند برای بهبود شبیه‌سازی جذب آب ریشه در مدل‌های HYDRUS و SWAP و نیز برای مدیریت آبیاری در شرایط استفاده از آب شور مورد استفاده قرار گیرند.

https://mmws.uma.ac.ir/article_4695_5a726a7d5f8c0402b0906edddb40f954.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 The effect of design discharge and the performance of culverts on the natural flood crisis in selected watershed of Yazd province تاثیر دبی طراحی و عملکرد زیرآبگذرها در بحران طبیعی سیل در تعدادی از حوزه‌های آبخیز منتخب استان یزد 162 180 4462 10.22098/mmws.2026.19278.1778 FA سیدمحسن آتشی دانشجوی دکتری علوم و مهندسی آبخیزداری، گروه مرتع و آبخیزداری، دانشگاه یزد، یزد، ایران حسین ملکی نژاد دانشیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران محمدرضا هادیان استادیار، گروه مهندسی عمران، دانشکده مهندسی عمران، دانشگاه یزد، یزد، ایران اصغر زارع چاهوکی استادیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران Journal Article 2026 01 28 Extended Abstract Introduction Culverts are among the most common and important transitional structures used to convey water, materials, or buried facilities beneath the ground. Therefore, the structures in question should be designed and constructed to maintain their safety, durability, and efficiency in a variety of environmental conditions. Several factors, including the flow pattern at the structure site, maximum instantaneous flood discharge, backflow due to blockage, morphological stability of the river, and erosion and scour effects, influence the hydraulic performance of crossing structures. Therefore, considering various influencing factors in the design and location of these structures to improve their performance is essential. On the other hand, hydrologic models are effective tools for simulating surface and subsurface hydrologic processes in watersheds and are widely used to enhance water resource management. Prediction of flood events and simulation of hydrological processes in watersheds are two fundamental applications of rainfall-runoff models that play a crucial role in water resource planning and management. Accordingly, this research was conducted using the HEC-HMS model to examine the impact of and performance of road-crossing water on flood crises in several selected watershed in the Yazd province. Materials and Methods The present study was conducted due to the presence of multiple intersecting structures and the history of damage during the floods of 2022 in six selected watersheds in the counties of Taft, Ashkazar, and Mehriz, including the Khamsian, Darbe Raz, Dashtak, Roobaz, Ghavam-Abad, and Konj-Kuh watersheds in Yazd province. The aim was to gather and obtain necessary information about corresponding rainfall-runoff events from relevant sources and extract precise details of intersecting structures such as culverts and small bridges to gain a comprehensive view of the physical, hydraulic, and structural conditions. This serves as the basis for evaluating capacity, analyzing hydraulic performance during floods, and assessing the efficiency of the studied route's drainage system. After identifying the number of culverts in the watersheds, the HEC-HMS software was used to determine the volume and amount of flood. To determine the compatibility of their flow capacity with the flood discharge using the HEC-HMS model, hydrological parameters were first extracted from the watershed and then, using numerical hydraulic models, the flow behavior at culvert sections was analyzed. Results and Discussion The results of comparing the hydraulic capacities of selected culverts with the design flood discharge at various return periods revealed that the hydraulic behavior of structures in response to increased return periods is non-linear and highly sensitive; in most cases, as intensity and frequency of rainfall increase, ratio (Qc/Qd) decreases rapidly, leading the structure into the critical. The watersheds of Khamsian, Dashtak, and Roobaz have minimum relative capacities and enter an unstable state from 5 to 10-year periods. A Qc/Qd <0.5 in these areas severe hydraulic section deficiencies, significant energy at the culvert outlet, and the risk of overflow from the culvert. Such conditions are mainly observed in structures with reduced hydraulic performance due toation, increased roughness, and geometric shapes. In the Darbe Raz and Dashtk watersheds, the structure's condition is acceptable up to 10-25-year return periods, but beyond 25 years, there is a likelihood of flooding and upstream reversal. The rate of capacity reduction to Qd decreasing from 0.09 to below 0.5 the 25-50- range indicates that the flood is entering unstable state and the outlet is starting saturate. In the Ghavam-Abad watershed, the ratio (Qc/Qd) above 1 in all return periods, making it the only structure evaluated as from a hydraulic design perspective. Ac/Qd value of 4.1 indicates a significant excess capacity resulting the larger outlet dimensions and the suitable longitudinal slope of the inlet channel. However, for extreme events above 500 years, this ratio decreases to around 1, indicating that the flow has reached the threshold of the final capacity. Given the potential for severe rainfall events with return periods exceeding 500 years in 2022, it necessary to reassess the design range for this structure as well. Conversely, the Konj-Kuh culvert exhibits the poorest performance, entering a critical state even at the 2-year return period, meaning that normal annual rainfall could trigger overflows. Conclusion In view of the flood rainfall in April 2022 in Yazd province, as one of the most intense recorded rainfall events in the contemporary statistical period; the analysis of ratios (Qc/Qd) showed that with an increase in the return period from 5 to 500 years, the average ratio of design capacity to flow rate from around 0.75 to less than 0.20 percent. Among the existing structures in the studied watersheds, only 1 structure was evaluated safe (Ghavam-Abad) and 8 structures are in critical conditions in one of the return periods less than an equal 25 years. This pattern that the initial design of culverts based on return periods lower than the of publication 415 (1 to 25 years). زیرآبگذر‌ها از جمله رایج‌ترین و مهم‌ترین سازه‌های انتقالی هستند که برای عبور جریان آب، مصالح، یا تأسیسات مدفون در زیر خاک به‌کار می‌روند. بنابراین در نظر گرفتن عوامل مختلف تأثیرگذار در طراحی و جانمایی این سازه‌ها به‌منظور بهبود عملکرد آن‌ها ضروری است. بر این اساس پژوهش حاضر با هدف بررسی تأثیر دبی طراحی و عملکرد سازه‌های آبی تقاطعی جاده‌ای در بحران سیل در تعدادی از حوزه‌های آبخیز منتخب استان یزد انجام گرفت. در این راستا پس از جمع‌آوری و اخذ اطلاعات و آمارهای مورد نیاز مشخصات دقیق زیرآبگذر‌ها بررسی شده سپس با استفاده از مدل HEC-HMS اقدام به تعیین مقدار و حجم سیلاب شد و رفتار جریان در مقاطع این زیرآبگذر مورد بررسی قرار گرفت. یافته‌ها نشان داد که ظرفیت هیدرولیکی زیرآبگذر‌های منتخب در برابر افزایش دوره بازگشت رفتار غیرخطی داشته به‌طوری که با افزایش شدت و فراوانی بارش نسبت Qc/Qd به سرعت کاهش یافته و منجر به ورود سازه مورد نظر به شرایط بحرانی می‌شود. بر این اساس در تعداد نه سازه مورد بررسی در شش حوزه آبخیز منتخب فقط زیرآبگذر جدیدالاحداث قوام‌آباد ایمن بوده و سایر زیرآبگذر‌ها بر اساس دوره بازگشت‌های مختلف بررسی شده در شرایط بحرانی قرار داشتند. بنابراین به‌منظور مدیریت و پیشگیری از تخریب و آسیب‌های احتمالی لازم است نسبت به ایمن‌سازی این سازه‌ها (ارتقای ظرفیت عبور جریان) بر اساس دوره بازگشت‌های استاندارد اقدامات مورد نیاز انجام شود.

https://mmws.uma.ac.ir/article_4462_1aabc00f32a6003a0b53501c64102aac.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Evaluation of MICE-based machine learning models for reconstructing missing climate data in the Urmia Lake basin ارزیابی مدل‌های یادگیری ماشین مبتنی بر MICE در بازسازی داده‌های گمشده اقلیمی در حوضه دریاچه ارومیه 181 199 4466 10.22098/mmws.2026.19270.1777 FA محمد شایان‌نژاد گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران محمد جمالی گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران Journal Article 2026 01 26 Extended Abstract Introduction Complete and continuous climatic datasets are fundamental for reliable analyses in hydrology, climate change assessment, water resources management, and environmental modeling. However, observational climate records frequently suffer from missing values due to instrument malfunction, station relocation, data transmission errors, or long-term interruptions in measurements. If not appropriately addressed, missing data can introduce bias, reduce statistical power, and compromise the reliability of subsequent modeling and decision-making processes. This challenge is particularly critical in regions with complex climatic variability and environmental sensitivity, such as the Lake Urmia Basin in northwestern Iran. Traditional approaches for handling missing climatic data, including listwise deletion or simple statistical substitution (e.g., mean or median imputation), are computationally convenient but often distort the statistical structure of the data and fail to capture inter-variable dependencies. In response to these limitations, advanced multivariate and machine-learning-based imputation methods have gained increasing attention. Among them, Multiple Imputation by Chained Equations (MICE) has emerged as a robust framework that accounts for uncertainty and exploits relationships among multiple variables. Recent studies suggest that integrating MICE with machine learning algorithms can further enhance imputation accuracy, particularly for non-linear and highly interdependent climatic variables. Nevertheless, comprehensive evaluations comparing different MICE-based hybrid models across multiple climatic variables and stations remain limited. Therefore, this study aims to systematically assess and compare the performance of standard MICE and four hybrid approaches MICE-Linear Regression (MICE–LR), MICE- Decision Tree (MICE–DT), MICE-K-Nearest Neighbor (MICE–KNN), and MICE- Support Vector Machine (MICE–SVM), across a wide range of climatic variables and meteorological stations within the Lake Urmia Basin. Materials and Methods This study was conducted using daily climatic data from six synoptic meteorological stations located in the Lake Urmia Basin. The dataset includes a diverse set of climatic variables representing thermal conditions, atmospheric moisture, cloudiness, wind characteristics, radiation and energy balance, and sea-level pressure. To ensure consistency and robustness, all variables were preprocessed through quality control procedures, including outlier detection and temporal consistency checks. Missing data were reconstructed using five imputation approaches: standard MICE and four hybrid MICE-based models (MICE-LR, MICE-DT, MICE-KNN, and MICE-SVM). The imputation procedure was implemented iteratively within the chained equations framework to ensure convergence and stability of the reconstructed values. Model performance was evaluated using multiple complementary statistical metrics, including the coefficient of determination (R²), normalized root means square error (NRMSE), Kling–Gupta Efficiency (KGE), and percent bias (PBIAS). These metrics collectively assess accuracy, variability representation, correlation structure, and systematic bias. In addition to predictive performance, computational efficiency was assessed by measuring the average execution time of each model. The evaluation framework was designed to enable comparisons from three perspectives: climate-variable-based, model-based, and station-based analyses. Results and Discussion The comparative analysis revealed substantial differences in imputation performance among the evaluated models, depending on the type of climatic variable and station characteristics. Overall, hybrid MICE-based models demonstrated superior performance compared to the standard MICE approach, particularly for temperature-related variables and atmospheric moisture parameters. Among the hybrid models, MICE–DT achieved comparatively higher KGE values for several variables, highlighting its ability to model non-linear interactions. Nevertheless, both MICE–DT and MICE–LR provided a more balanced trade-off between reconstruction accuracy and computational efficiency. In contrast, MICE–KNN and MICE–SVM exhibited variable performance, with notable sensitivity to station-specific conditions and variable type. While these models performed reasonably well for certain variables, their performance deteriorated for others, especially in cases involving higher variability or weaker spatial coherence. Standard MICE and MICE–LR showed comparable results, suggesting that linear assumptions may be insufficient for fully representing the dynamics of complex climatic systems. The station-based analysis highlighted spatial heterogeneity in model performance, emphasizing the influence of local climatic and topographic conditions. Furthermore, the computational analysis indicated that while hybrid models generally required longer execution times than standard MICE, MICE–DT provided a favorable balance between accuracy and computational efficiency. These findings underscore the importance of selecting imputation methods based on both data characteristics and practical constraints. Conclusion This study provides a comprehensive evaluation of standard and hybrid MICE-based imputation methods for reconstructing missing climatic data in a multi-variable and multi-station framework. The results demonstrate that incorporating machine learning algorithms within the MICE framework substantially improves reconstruction accuracy, particularly for variables characterized by non-linear behavior. Among the evaluated models, MICE–DT emerged as the most robust and efficient approach, offering consistently high performance across different climatic variables and stations. Despite these strengths, certain limitations were identified. The performance of some hybrid models, particularly MICE–KNN and MICE–SVM, showed sensitivity to station-specific conditions and increased computational demand, which may limit their applicability in large-scale studies. These findings suggest that no single imputation method is universally optimal, and model selection should be tailored to the characteristics of the dataset and research objectives. From a practical perspective, the proposed framework provides valuable guidance for researchers and practitioners seeking reliable methods for handling missing climatic data. The results have direct implications for hydrological modeling, climate trend analysis, and environmental impact assessments in data-scarce regions. Future research should explore the integration of deep learning approaches within the MICE framework and assess model performance under varying missing-data scenarios and spatial scales. وجود داده‌های گمشده در شبکه‌های ایستگاه‌های هواشناسی یکی از چالش‌های مهم در مطالعات هیدرولوژیکی و اقلیمی است که می‌تواند موجب افزایش عدم‌قطعیت و کاهش دقت تحلیل‌ها شود. انتخاب روش مناسب برای بازسازی این داده‌ها نقش تعیین‌کننده‌ای در بهبود کیفیت نتایج دارد. در این پژوهش، عملکرد روش MICE و چهار رویکرد ترکیبی آن شامل MICE–LR، MICE–DT، MICE–KNN و MICE–SVM در بازسازی داده‌های گمشده متغیرهای اقلیمی مورد ارزیابی قرار گرفت. تحلیل‌ها با استفاده از داده‌های شش ایستگاه منتخب حوضه دریاچه ارومیه انجام شد که تنوع مکانی و اقلیمی مناسبی را پوشش می‌دهند. ارزیابی عملکرد مدل‌ها بر اساس شاخص کارایی کلینگ–گوپتا (KGE)، درصد اریبی (PBIAS) و زمان اجرای محاسباتی صورت گرفت. نتایج نشان داد که تمامی مدل‌ها توانایی قابل قبولی در بازسازی داده‌های گمشده دارند؛ با این حال، بهبود عملکرد نسبت به روش پایه MICE وابسته به نوع مدل بوده است. در این میان، مدل‌های MICE–DT و MICE–SVM عملکرد برتری نسبت به مدل پایه از خود نشان دادند؛ به‌طوری‌که مدل MICE–DT در بازسازی متغیرهای دمایی، ابرناکی و فشار و مدل MICE–SVM در متغیرهای رطوبتی دقت بالاتری را ارائه کردند. با این وجود، روش پایه MICE در متغیرهای تابش و انرژی عملکردی قابل رقابت با مدل‌های ترکیبی داشت. در این میان، مدل‌های MICE–LR و MICE–DT با مقادیر کم‌تر PBIAS و زمان اجرای پایین‌تر، توازن مناسبی بین دقت بازسازی و کارایی محاسباتی برقرار کردند. در مقابل، مدل MICE–KNN به دلیل نیاز به محاسبه فاصله بین نمونه‌ها در هر تکرار، زمان اجرای بیشتری نسبت به مدل‌های خطی داشت. همچنین، مدل MICE–SVM به علت فرآیند بهینه‌سازی تکرارشونده در آموزش و تنظیم پارامترهای کرنل در هر مرحله از الگوریتم MICE، زمان اجرای قابل توجهی را به خود اختصاص داد؛ از این‌رو، در داده‌های حجیم و کاربردهای عملی در مقیاس‌های بزرگ گزینه‌ای کم‌کارآمدتر محسوب می‌شود. به‌طور کلی، نتایج این مطالعه نشان می‌دهد که استفاده از رویکردهای ترکیبی ساده‌تر مبتنی بر MICE می‌تواند راهکاری مؤثر و کارآمد برای بازسازی داده‌های گمشده اقلیمی باشد.

https://mmws.uma.ac.ir/article_4466_6f797adaa92d8e94a7d59aa5f9b74a7d.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Classification of critical flood areas based on artificial intelligence algorithms and combined with crowd wisdom methods (Case study: Rakaat Dezpart watershed) کلاسه‌بندی مناطق بحرانی سیل مبتنی بر الگوریتم‌های هوش مصنوعی و تلفیق با روش‌های خرد جمعی (مطالعه موردی: حوضه رکعت دزپارت) 200 216 4529 10.22098/mmws.2026.19213.1772 FA قاسم شهپری فر دانش آموخته کارشناسی ارشد، گروه زمین شناسی مهندسی، ‌دانشکده علوم، دانشگاه اصفهان، اصفهان،‌ ایران 0009-0002-1995-1343 امین ذرتی پور دانشیار، گروه مهندسی طبیعت، دانشکده کشاورزی،‌ دانشگاه علوم کشاورزی و منابع طبیعی خوزستان،‌ خوزستان،‌ ایران. اشکان یوسفی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، خوزستان، ایران. Journal Article 2026 01 07 Extended Abstract Introduction Flooding is a major natural disaster, according to the UN, endangering lives, property, and societies more than any other. Mapping floodplains and modeling floods in mountain basins is essential for development projects, helping identify critical areas and control damage. A key 21st-century tool is satellite imagery, which provides valuable flood-related data. By processing these images, various information such as flooded areas, vegetation, lithology, slope, soil moisture, etc. can be calculated and estimated. In addition, machine learning algorithms have made it possible to estimate very complex relationships between various parameters and floods. However, these models require complex calibration and extensive data. Recently, many flood susceptibility models have been developed. Combining statistical and decision-making models with remote sensing and GIS has gained attention for improving predictive ability. Today, machine learning algorithms such as artificial neural networks, generalized linear algorithms, support vectors, and random forest models are used.Machine learning models are used in two aspects;one is to process and identify flooded areas, and the other is to zone and examine the importance of flood-intensifying parameters. Materials and Methods In recent decades, new methods have been used to identify the risk of flooding in basins and prepare maps of sensitivity to its occurrence, such as the use of multivariate statistical models, data mining, random forest methods, and machine learning methods. This study aimed to evaluate the performance of five machine learning models including random forest model, support vector model, generalized ensemble model, generalized linear model, classification and regression tree, and augmented tree regression (RF, SVM, BRT, CART, and GLM) in modeling flood probability in the northern mountainous basin of Khuzestan province. Also, to increase the stability and accuracy of the models, four ensemble methods were used, including simple mean, weighted mean, committee mean, and median. in the first step, thirteen different parameters were used as factors affecting the flood phenomenon, and using the collinearity test between the parameters, it was ensured that there was no strong relationship between each of them and other parameters. The factors studied are distance from the river, distance from the dam lake, curvature of the longitudinal profile of the waterway, curvature of the waterway plan, shape factor, river density, basin area, geology, vegetation, erosion factor, SPI index, TWI index, and curve number value. In this regard, the digital elevation model (DEM) of the region with an accuracy of 30 meters was extracted from the USGS website. Results and Discussion The AUC values for RF, BRT, SVM, CART and GLM models were estimated to be 0.932, 0.929, 0.885, 878 and 0.855, respectively. The results indicate that the random forest (RF) model has higher accuracy than other models in predicting flood risk in the study area. The results showed that all the models used showed acceptable performance; however, tree-based models had a significant advantage over linear and SVM models. In particular, the random forest (RF) model achieved the best overall performance in predicting flood occurrence in the region, with the highest AUC value of 0.932. The boosted tree regression (BRT) model was followed by the least accurate model with an AUC of 0.929. In contrast, the generalized linear model (GLM) had the lowest accuracy among the individual models with an AUC of 0.855. In addition, the results from ensemble methods also showed that the values of AUC, TPR, and FPR parameters of these four methods are in the ranges of 0.919 to 0.926, 0.826 to 0.857, and 0.072 to 0.079. Among the methods studied, the average and weighted average methods have higher accuracy than the other two methods. Conclusion These findings emphasize the need to pay special attention to the spatial and hydrological characteristics of the basin in flood risk management planning, and introduce the RF model and microaggregate approaches as effective strategies for preparing more accurate zoning maps. The use of four ensemble methods (Mean, WMean, Median, and Committee Averaging) resulted in more stable risk maps. Overall, the performance of these methods, effectively reduced the uncertainty resulting from the selection of a single model. The simple mean (Mean) and weighted mean (WMean) methods provided more favorable results than the other ensemble methods due to their high accuracy (AUC = 0.926) and rapid convergence of results. This indicates that combining the outputs, with or without appropriate weighting (WMean), resulted in a more robust and repeatable estimate. This is consistent with similar studies that confirm the effectiveness of averaging methods in improving the performance of classification models. امروزه پهنهبندی پدیده سیلاب در حوضههای کوهستانی و مناطق صعب العبور، پیش نیاز توسعه در هر کشوری محسوب میشود و بایستی قبل از اجرای پروژههای مختلف صنعتی، عمرانی، شهرسازی و غیره؛ به وضعیت سیلاب حوضه و تعیین نقاط بحرانی توجه خاصی شود. این پژوهش با هدف ارزیابی عملکرد پنج مدل یادگیری ماشین شامل، مدل‌جنگل‌ تصادفی، مدل‌بردار پشتیبان، مدل‌ جمعی تعمیمیافته، مدل‌خطی تعمیمیافته، درخت طبقهبندی و رگرسیون، رگرسیون تقویت شده درختی (RF ، SVM، BRT، CART و GLM) مدل‌سازی احتمال سیل‌ در حوضه کوهستانی رکعت دزپارت در استان خوزستان انجام گرفت. همچنین، برای افزایش پایداری و دقت مدلها، از چهار تکنیک خرد جمعی (Ensemble Methods)، شامل میانگین ساده، میانگین وزنی، میانگین کمیته‌ای و میانه استفاده شد. نتایج نشان داد تمامی مدلهای مورد استفاده عملکردی قابل قبول از خود نشان دادند؛ با این حال، مدلهای مبتنی بر درخت، برتری محسوسی نسبت به مدلهای خطی و SVM داشتند. به‌طور خاص، مدل جنگل تصادفی (RF) با کسب بالاترین مقدار AUC برابر با 932/0، بهترین عملکرد کلی را در پیش‌بینی وقوع سیل در منطقه به خود اختصاص داد. پس از آن، مدل رگرسیون تقویت‌شده درختی (BRT) با AUC برابر با 929/0 در رتبه دوم قرار گرفت. در مقابل، مدل خطی تعمیمیافته (GLM) با AUC برابر با 855/0، کمترین دقت را در میان مدلهای منفرد از خود نشان داد. همچنین تکنیک‌های خردجمعی عملکرد مدلها را بهبود بخشیدند، به‌طوری که مقادیر AUC برای روش‌های تلفیقی در محدوده 919/0 تا 926/0 قرار گرفت و پایداری پیش‌بینی‌ها را تقویت نمود. این یافته‌ها بر لزوم توجه ویژه به ویژگی‌های مکانی و هیدرولوژیکی حوضه در برنامه‌ریزی‌های مدیریت ریسک سیلاب تأکید می‌کند و مدل RF و رویکردهای خردجمعی را به عنوان استراتژی‌های مؤثر برای تهیه نقشه‌های پهنه‌بندی دقیق‌تر معرفی می‌نماید.

https://mmws.uma.ac.ir/article_4529_5f65a576780a463c3e7227f20740169a.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Spatial estimation of soil erosion and sediment yield using the RUSLE and SEDD models in Chehelchay watershed, Golestan Province برآورد مکانی فرسایش و رسوب با مدل‌های RUSLE و SEDD در آبخیز چهل‌چای استان گلستان 217 234 4585 10.22098/mmws.2026.19535.1794 FA حبیب نظرنژاد دانشیار، گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران فرخنده آقاجان لیافو دانشجوی دکتری، گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران Journal Article 2026 03 30 Extended Abstract Introduction Soil erosion is one of the most serious and pervasive environmental challenges worldwide, directly and significantly threatening the stability of natural ecosystems, the quality of drinking water and agricultural resources, and long-term food security. In many mountainous regions of Iran, the combination of climatic conditions, steep slopes, geological instability, and increasing anthropogenic pressures has exacerbated this process. The Chehelchay watershed, located in Golestan Province, is a prime example of a watershed susceptible to erosion. This basin, covering an area of 25683 hectares and receiving an average annual rainfall of 766 mm, has witnessed extensive land-use changes, with a significant portion of its natural forest cover being converted into agricultural lands. Despite numerous studies conducted in this region and similar areas in the past, limited efforts have been made to integrate empirical models (such as RUSLE) with process-based or distributed models (such as SEDD) for the simultaneous estimation of the spatial pattern of soil erosion and sediment transfer, using reliable spatial data. Therefore, this study aims (i) to accurately quantify the spatial distribution of soil erosion and (ii) to estimate sediment transfer rates, and to identify priority and critical erosion areas within the Chehelchay watershed, by integrating the Revised Universal Soil Loss Equation (RUSLE) model and the Sediment Delivery Distributed (SEDD) model. Few studies have simultaneously mapped soil erosion and sediment delivery using integrated empirical and distributed models in data‑limited mountainous watersheds. This integrated approach, which simultaneously accounts for processes of surface sediment generation and its transfer along the river network, provides a robust and comprehensive framework for supporting conservation and management planning in data-limited watersheds. Materials and Methods To estimate surface erosion and sediment yield in the Chehelchay watershed, the RUSLE and the SEDD model were employed. For estimating surface erosion, the RUSLE model was utilized, incorporating five factors: Rainfall erosivity (R), Soil erodibility (K), Slope-Length (LS), Land cover and management (C), and Support practices (P). Spatial input maps were generated for each RUSLE factor in ArcGIS. The R-factor map was extracted using long-term daily precipitation data and the modified Fournier index. Physicochemical properties of the soil formed the basis for preparing the K-factor map. The LS factor map was calculated by processing the Digital Elevation Model (DEM) in a GIS environment. The interpretation of Landsat 8 satellite imagery and vegetation cover maps enabled the preparation of the C-factor map. The P-factor was determined based on land use type and farming practices. After preparing the RUSLE parameter layers, the annual water erosion map was generated by multiplying these layers in ArcGIS software. Subsequently, the SEDD model was used to quantitatively estimate sediment yield and its transfer ratio to the watershed outlet. This model, by spatially calculating the Sediment Delivery Ratio (SDR), establishes a link between the erosion calculated by RUSLE and the sediment data measured at the Lezoreh hydrometric station. Suspended sediment data from the Lezoreh station were analyzed using the sediment rating curve method and an intermediate data approach. The accuracy of the curve calibration was confirmed with a coefficient of determination R² = 0.79. Finally, the sediment yield, SDR, and erosion maps derived from the models were integrated and spatially analyzed to identify priority areas with severe erosion and high sediment yield potential. Results and Discussion The RUSLE-based assessment indicated that the average annual soil erosion rate in the Chehelchay watershed is approximately 4 t ha⁻¹ yr⁻¹. However, its spatial distribution exhibited considerable heterogeneity and was found to be significantly controlled by topographic factors, particularly the LS factor (slope and slope length), and land use type. The highest erosion rates were observed in agricultural fields situated on steep slopes; this pattern is primarily attributed to minimal soil conservation measures and the direct exposure of soil to intense rainfall events. In contrast, forested areas displayed strong protective effects, substantially reducing surface erosion due to dense vegetation cover and soil structural stability. These findings underscore the critical importance of preserving and expanding forest cover in mountainous regions. Based on data collected from the Lezoreh sediment monitoring station, the average annual sediment yield was estimated at 1.78 t ha⁻¹ yr⁻¹. The overall watershed SDR was calculated to be approximately 44%. This relatively high SDR indicates the presence of efficient sediment transfer pathways, largely facilitated by steep slopes and discontinuous vegetative cover across the watershed. Integrated mapping of erosion potential and sediment delivery ratios revealed that areas with the highest erosion potential and sediment generation are primarily concentrated in the middle and upper sections of the watershed, where slope gradients exceed 30% and agricultural expansion has replaced natural forest cover. The spatial correspondence between high erosion potential and elevated SDR highlights the crucial importance of simultaneously considering both sediment generation (erosion) and sediment transport (SDR) processes when designing management interventions. Conclusion Ultimately, the outcomes of this research can serve as a practical guide for developing and implementing sustainable water and soil resource management plans in the Chehelchay watershed and in other regions with comparable ecological and data conditions. The overall study results emphasize the pivotal role of topographic factors and land use; specifically, terrain slope, land use type, and vegetation condition were identified as the most determinant factors of erosion intensity. Notably, the negative consequences of land use changes, particularly the degradation of forest cover and its conversion to agricultural or other uses on steep slopes, lead to a significant increase in soil erosion and, consequently, a higher volume of sediment transported downstream. This study affirms the critical importance of principled land use planning, taking into full account the inherent topographical constraints of mountainous regions. The effective implementation of conservation measures, including the preservation and restoration of natural vegetation and the application of sustainable management practices in agricultural lands, is deemed essential for controlling and reducing sediment transport in priority areas. Despite the satisfactory accuracy of the employed models, limitations such as sensitivity to the quality and precision of input data and the lack of comprehensive field data for complete validation still persist. These limitations create valuable opportunities for future research to significantly improve prediction accuracy by integrating extensive field sampling, high-resolution soil data, and the application of more advanced process-based modeling approaches. فرسایش خاک به‌عنوان یکی از چالش‌های چندبعدی زیست‌محیطی، امنیت اکولوژیک و غذایی را در سطح جهانی تهدید می‌کند. آبخیز چهل‌چای در شهرستان مینودشت استان گلستان با مساحت 25683 هکتار، به‌دلیل شیب متوسط بالای 30 درصد، بارندگی زیاد (میانگین سالانه 766 میلی‌متر)، سازندهای زمین‌شناسی حساس به فرسایش و تغییرات گسترده کاربری اراضی (به‌ویژه تبدیل جنگل به اراضی زراعی)، از مناطق مستعد فرسایش و رسوب در شمال ایران محسوب می‌شود. این پژوهش با هدف برآورد مکانی فرسایش خاک و تولید رسوب و اولویت‌بندی مناطق بحرانی، با تلفیق مدل‌ اصلاح شده جهانی فرسایش خاک(RUSLE) و مدل توزیعی نرخ تحویل رسوب (SEDD) انجام شد. داده‌های مورد نیاز شامل نقشه‌ فرسایندگی باران، فرسایش‌پذیری خاک، توپوگرافی (طول شیب)، کاربری اراضی و پوشش گیاهی و اقدامات حفاظتی بود. نتایج مدل RUSLE نشان داد میانگین فرسایش سالانه آبخیز 4 تن در هکتار است که توزیع مکانی آن تحت تأثیر شدید عامل توپوگرافی (LS) و کاربری اراضی قرار دارد. بیشترین میزان فرسایش در اراضی زراعی واقع در شیب‌های تند مشاهده شد، در حالی‌که پوشش جنگلی تأثیر محافظتی قابل توجهی در کاهش فرسایش داشت. میانگین تولید رسوب سالانه در ایستگاه لزوره در خروجی آبخیز با استفاده از منحنی سنجه رسوب (روش حد وسط داده‌ها با ضریب تبیین 79/0) برابر با 11/24 تن در هکتار در سال و نسبت تحویل رسوب کل آبخیز 23 درصد برآورد شد. با به‎‌کارگیری مدل SEDD نقاط با نرخ تحویل رسوب بالا و بحرانی آبخیز از نظر رسوب تعیین شد. این مطالعه بر ضرورت حفظ و توسعه پوشش گیاهی طبیعی، به‌ویژه در مناطق شیبدار، و اعمال ملاحظات توپوگرافی در برنامه‌ریزی کاربری اراضی کشاورزی برای کاهش فرسایش تأکید می‌کند. مدل‌های مورد استفاده با وجود ساده‌سازی‌های ذاتی، ابزار کارآمدی برای شناسایی مناطق بحرانی و اولویت‌بندی اقدامات حفاظتی در شرایط کمبود داده‌های میدانی ارائه می‌دهند.

https://mmws.uma.ac.ir/article_4585_56a00b7ef421200e4c6f242edf1baa02.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Estimating flood discharge in compound river channels using minimum gauging data تخمین دبی سیلاب در مقاطع مرکب رودخانه‌ای با حداقل داده‌های میدانی 235 258 4613 10.22098/mmws.2026.19574.1801 FA عبدالرضا ظهیری دانشیار گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران جواد قلی نژاد دانشجوی دکتری علوم و مهندسی آب، گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران Journal Article 2026 04 18 Extended Abstract Introduction Floods are among the most complex hydrological phenomena which are known to be the main factor of natural disasters and costs in many river basins, especially in regions with arid and semi-arid climate. Due to the nonlinear nature, high intensities and low durations of floods, there are large uncertainties in their predicting, forecasting, modeling, and managing. Therefore it is necessary to have measured characteristics of flood flows occurred in the river to calibrate and validate mathematical and hydraulic models. However, measuring river flow during floods and inundation of floodplains is very difficult, costly, and dangerous. For this reason, methods based on extrapolation of stage-discharge rating curve are usually used to estimate river flood discharge. Different methods can be used for this purpose, most of which require a lot of field data. For example, in most of these methods, the Manning roughness coefficients of the main channel and floodplains must be known, which has many limitations in this regard. In this study, a simple but practical method has been used to estimate flood discharge of compound river channels, which, while having appropriate accuracy, requires minimal gauging data. It only requires that the river channel geometry, flood stage, and the river stage-discharge rating curve be known. Materials and Methods To estimate flood discharges from stage–discharge curve, three methods were evaluated and compared. The first is the rating curve extension, which fits Q=a(h−h0)b to non‑flood measurements data and then extrapolated to higher water levels. This method, introduces considerable error in rivers with wide and rough floodplains. The second method is the conveyance-slope approach, which is specifically developed for compound sections. The cross‑section is divided into the main channel and floodplains and then the conveyance factor Ki=AiRi2/3/ni is computed for each water level. The river energy slope Sf=(Q/K)2 is then derived and extrapolated to flood stages. A major limitation of this method is the need for reliable Manning’s roughness coefficients for both the main channel and the floodplains. To obtain these coefficients, the quasi‑2D model of Shiono and Knight (1991) was employed. The third method is the alpha (α) method, which combines energy slope and Manning’s n into one parameter as α=Q/(AR2/3). The Manning formula is usually face with many uncertainties in riverbed Manning roughness coefficient and energy slope, especially for compound channels with natural features or dense vegetation. This study discusses flood discharge estimation using a calibrated α parameter for alluvial rivers in Iran (Golestan province) and England. Parameter α accounts for the simultaneous effects of energy slope and Manning’s roughness. The method computes α from measured discharges and geometric properties (area A, hydraulic radius R), then fits a regression curve to α-R. From this curve, α is predicted for any flood stage, and flood discharge is subsequently calculated. Results and Discussion To evaluate three flood discharge extrapolation methods, some statistical metrics (RMSE, MAE, and R²) were used. For the new alpha method, the mean absolute errors were 7.4%, 12.4%, and 5.2% at Arazkooseh, Aghghala, and River Severn, respectively, which are quite acceptable for flood control engineering applications. The conveyance slope method yields MAE values of 20.7%, 42.1%, and 20.8%, while the stage-discharge curve extension method gives 26.3%, 6.0%, and 10.3% for these three river stations. The alpha coefficient method performs more accurately than the other two methods. Its superiority is confirmed by higher R² and lower RMSE error measure, demonstrating better reproduction of actual stage-discharge behavior under floods. The high errors of the conveyance slope method reflect its inability to capture nonlinearity during high flows and floodplain inundation. The rating curve extension method works acceptably only at some stations (e.g., Aghghala). From a hydraulic perspective view, the alpha parameter simultaneously considers variations in discharge (Q), cross-sectional area (A), and hydraulic radius (R), providing a more consistent physical description. Its superiority comes from incorporating concurrent changes in A, R and the nonlinear Q–h curve. When the floodplains become inundated, abrupt changes in velocity distribution and effective roughness occur. Methods relying on slope of rating curve or energy slope extension cannot fully represent this nonlinearity. The alpha parameter accounts for the rate of change of Q with respect to hydraulic cross-section characteristics, offering greater flexibility in modeling flow regime transitions. Therefore, the alpha-based method is proposed as a reliable framework for extrapolating stage-discharge curves, particularly for flood-prone rivers. Conclusion (1) In this study, a simple method proposed for flood discharge prediction in alluvial rivers with floodplains conveying flood flows which overtax the main river channel. The main reason behind the development of this method is its requirement for minimum river data recorded at the time of river flood flows, including river bathymetry (even before flood passage), flood stage and the common stage-discharge rating curve (and independent to riverbed Manning roughness coefficients of main channel and floodplains). (2) The obtained results of flood flow discharges based on the new developed method are quite satisfactory for all three selected gauge stations in comparison with the classic available methods for flood prediction.(3) The proposed method may faces with physical limitations or problem in rivers with prismatic or manmade main channel and in these cases, at least one data or record of flood flow (stage and discharge) should be given.(4) The implication of this new method is recommended for flood flow prediction in meandering compound river channels and also in multi-stage compound channels which have been recently become a topic of interest to researchers. اندازهگیری مستقیم دبی جریان رودخانهها در زمان سیلاب و ورود جریان به دشتهای سیلابی پرهزینه و خطرناک است. به همین دلیل معمولاً از روشهای برونیابی از منحنی دبی-اشل برای تخمین دبی سیلاب استفاده میشود. اغلب این روشها به دادههای میدانی زیادی نیاز دارند. در این مطالعه، از یک روش ساده و کاربردی بهنام ضریب آلفا برای تخمین دبی سیلاب مقاطع مرکب رودخانهای استفاده شده است که به حداقل دادههای میدانی (هندسه رودخانه، تراز سیلاب و منحنی دبی-اشل رودخانه در شرایط غیرسیلابی) نیاز دارد. پایه و اساس این روش، فرمول مانینگ است. در این روش، ابتدا بر اساس دادههای منحنی دبی-اشل رودخانه (دبی و تراز سطح آب) و نیز هندسه رودخانه، ضریب آلفا (نسبت شیب انرژی به ضریب زبری مانینگ) محاسبه میشود. سپس منحنی تغییرات این ضریب نسبت به شعاع هیدرولیکی برازش داده میشود. بهکمک این برازش، دبی جریان در هر تراز سطح آب دلخواه قابل برآورد است. کاربرد این روش در چند رودخانه مرکب طبیعی در استان گلستان (ایستگاههای هیدرومتری ارازکوسه و آققلا) و رودخانه سورن در انگلستان نشان داد که دقت نتایج این روش در مقایسه با روشهای معمول برونیابی منحنی دبی-اشل قابل قبولتر است. نتایج روش فاکتور انتقال-شیب انرژی همواره با بیشبرآوردی همراه بوده و روش امتداد شیب منحنی دبی-اشل نیز فقط در ایستگاه آققلا دارای دقت مناسبی است. میانگین خطای (MAE) روش آلفا در این رودخانهها بهترتیب 4/7، 4/12و 2/5 درصد بهدست آمد که در مباحث مهندسی و صنعت آب قابل قبول است. برای این سه ایستگاه، میانگین خطا برای روش فاکتور انتقال-شیب انرژی بهترتیب 7/20، 1/42 و 8/20 درصد و برای روش امتداد منحنی دبی-اشل بهترتیب 3/26، 6 و 3/10 درصد میباشد. از روش پیشنهادی این تحقیق میتوان برای تخمین دبی سیلاب رودخانههای با مقطع مرکب استفاده نمود.

https://mmws.uma.ac.ir/article_4613_c28ab4d4c9b63574ef1390264bd6d05c.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Assessment of the determinants of salinity in the Gotvand Dam reservoir and the contribution of outlet structures to its regulation ارزیابی عوامل موثر بر شوری مخزن سد گتوند و نقش تخلیه‌کننده‌ها در کنترل آن 259 278 4665 10.22098/mmws.2026.19647.1806 FA اشکان فرخ نیا پژوهشکده انرژی، پژوهشگاه علوم و تکنولوژی پیشرفته و علوم محیطی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران مرتضی افتخاری پژوهشکده مطالعات و تحقیقات منابع آب، موسسه تحقیقات آب، تهران، ایران آمنه میان آبادی گروه اکولوژی، پژوهشکده علوم محیطی، پژوهشگاه علوم و تکنولوژی پیشرفته و علوم محیطی، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران Journal Article 2026 04 29 Introduction Salinity accumulation and the consequent deterioration of water quality in reservoir systems represent a critical challenge in water resources management, particularly in arid and semi-arid regions where evaporation rates are high and freshwater availability is limited. In such environments, even moderate increases in salinity can significantly reduce the usability of stored water for agricultural, domestic, and industrial purposes. The problem becomes more severe in reservoirs located downstream of evaporite geological formations, where continuous dissolution of salt-bearing strata introduces persistent and often difficult-to-control saline inflows. These processes not only degrade water quality but also complicate reservoir operation, ecological stability, and downstream water allocation. The Gotvand Dam reservoir in southwestern Iran is a well-documented and particularly severe example of this phenomenon. Since its design and construction phases, the reservoir has been recognized as vulnerable to salinity intrusion due to its proximity to the Gachsaran Formation, specifically the Anbal salt section. Following impoundment, rapid salinization of reservoir water confirmed concerns raised in early feasibility studies, making it one of the most significant salinity-impacted reservoirs in the region. Previous studies have primarily focused on identifying salinity sources and simulating its distribution; however, there remains a lack of long-term, field-based evaluations of salinity control strategies. This study aims to address this gap by investigating the temporal evolution of salinity within the reservoir and evaluating the effectiveness of outlet structures, particularly the bottom outlet and GRP pipe, in controlling salinity over a 13-year operational period. By integrating extensive field measurements with mass balance and stratification analyses, this research aims to provide a comprehensive understanding of both natural processes and operational interventions influencing reservoir salinity dynamics. Materials and Methods The study was based on an extensive and continuous dataset collected over 13 years from August 2011 to October 2024, comprising more than 1,400 sampling days. The collected data included inflow and outflow discharge measurements, salinity concentrations of released water, vertical salinity profiles at multiple depths, reservoir water level fluctuations, and detailed operational records of outlet structures. Salinity observations were primarily conducted at the deepest point near the dam body, which was confirmed through preliminary analysis to be representative of the overall vertical salinity structure of the reservoir. This location provided a reliable proxy for assessing stratification dynamics and the effectiveness of withdrawal operations across different depth layers. The reservoir system receives its primary inflow from regulated releases of the upstream Masjed Soleyman Dam, supplemented by contributions from intermediate sub-basin areas. Outflows from the reservoir occur through four main structures: the hydropower intake, the spillway, a bottom outlet located at an elevation of 123 meters, and a GRP pipe installed at an elevation of 90 meters. To analyze salinity behavior, the study employed a combination of time-series analysis, vertical stratification assessment, and salt mass balance modeling. Stratification assessments were used to evaluate vertical salinity gradients and the stability of density-driven layering under different hydrological and thermal conditions. The mass balance approach quantified salt inputs, outputs, and storage variations within the reservoir system, enabling a system-scale understanding of salinity accumulation and removal processes. Furthermore, the performance of outlet structures was evaluated under different operational scenarios by correlating changes in salinity profiles with discharge regimes. Results and Discussion The results indicated that reservoir salinity dynamics were governed by a combination of hydrological, thermal, and operational factors. Water level fluctuations, inflow characteristics, and seasonal temperature variations significantly influenced salinity stratification. During warm periods, inverse stratification was observed in upper layers due to evaporation and inflow of warmer saline layers, whereas colder seasons exhibited more uniform salinity profiles near the surface. Flood events played a dual role by both inducing vertical mixing and introducing additional salt loads, particularly from intermediate sub-basin interacting with saline formations. However, the intensity of salt dissolution from the Anbal formation appeared to decrease over time, suggesting a reduction in readily soluble salt sources. A key finding of this study was the differentiated performance of outlet structures in salinity management. The bottom outlet had proven highly effective in removing saline water from intermediate layers (approximately 120–160 m elevation), thereby reducing salinity gradients and preventing upward migration of saline layers toward the power intake level. Sustained operation of this outlet (even at moderate discharges of 5–10 m³/s) significantly stabilized salinity conditions in these layers. In contrast, the GRP pipe, designed to evacuate highly saline water from deeper layers, exhibited limited effectiveness due to its low discharge capacity. While it can locally reduce salinity near its intake elevation (90–100 m), its overall impact on reservoir-scale salinity control was negligible. Moreover, its operation contributed to increased salinity in downstream systems. The salt mass balance analysis further corroborated these findings. While the total salt load entering the reservoir from upstream flows was substantial, its concentration remained relatively low. Conversely, salt inflow from the Gachsaran Formation showed a decreasing trend over time, except during major flood events. The total salt storage in the reservoir increased sharply during the initial impoundment phase but stabilized in subsequent years, reflecting the effectiveness of controlled outlet operations, particularly after 2019. Conclusion This study provided robust, long-term empirical evidence on salinity dynamics and management in a reservoir affected by evaporite formations. The findings demonstrated that while natural factors such as hydrology and temperature influenced salinity distribution, operational strategies played a decisive role in its control. Among the evaluated measures, selective withdrawal through the bottom outlet emerged as the most effective strategy for managing salinity, particularly in intermediate layers. In contrast, the GRP pipe had limited practical utility despite its intended purpose. The results highlighted that optimizing the operational regime of the bottom outlet was essential for sustainable salinity management in the Gotvand Dam reservoir. Furthermore, the study underscored the importance of continuous monitoring and improved data availability, especially for intermediate inflows, to enhance the reliability of future assessments and support informed reservoir management decisions. ورود نمک از سازند گچساران عنبل، چالش اصلی کیفیت آب در مخزن سد گتوند از مرحله طراحی بوده است. قبل از آبگیری سد و برای مدیریت چالش شوری ورودی به مخزن از سازند عنبل، روش‌های مختلف سازه‌ای و مدیریتی بررسی شده و در نهایت بر اساس مدل‌سازی، مدیریت شوری مخزن از طریق تخلیه‌کننده تحتانی در تراز 123 متر و تعبیه یک لوله GRP در تراز ۹۰ متر در اولویت قرار گرفت. مطالعه حاضر با تحلیل داده‌های میدانی اندازه‌گیری شده طی 13 سال (از مرداد 1390 تا مهر 1403) بهره‌برداری از سد شامل دبی ورودی و خروجی، شوری خروجی و شوری اعماق مختلف مخزن، تراز آب و برنامه بهره‌برداری از مخزن، میزان موفقیت مدیریت شوری مخزن سد گتوند و نقش این دو تخلیه‌کننده را به تفکیک مورد ارزیابی قرار داده است. نتایج نشان می‌دهد که اگرچه عواملی چون تراز آب، جریان سیلابی ورودی و دما بر میزان شوری مخزن سد تاثیر دارد، اما تخلیه‌کننده‌های ذکر شده می‌تواند شوری مخزن را کنترل کند. بیلان نمک ارائه شده در این پژوهش نیز نقش تخلیه‌کننده‌ها را در کنترل شوری تایید می‌کند. با این‌حال اثربخشی آنها در کنترل شوری متفاوت است. بر اساس نتایج حاصل، بین تخلیه از آبگیر تحتانی (با دبی پایدار ۵–۱۰ متر مکعب بر ثانیه) و تثبیت شوری در لایه‌های میانی در محدوده ارتفاعی ۱۲۰–۱۶۰ متر همبستگی مشاهده می‌شود که این امر می‌تواند از انتقال شوری به لایه‌های بالایی جلوگیری نماید. لولهGRP با دبی بسیار کم ۵۰–۱۵۰ لیتر بر ثانیه تنها اثرات محلی محدودی در لایه ۹۰–۱۰۰ متر داشته و تأثیر قابل‌توجهی در مقیاس کل مخزن ندارد. تحلیل بیلان جرم نمک نشان داد که ورودی نمک از سازند گچساران روند کاهشی داشته و ذخیره نمک مخزن پس از مرحله اولیه آبگیری به‌شدت افزایش یافته و سپس تثبیت شده است، به‌ویژه از سال ۱۳۹۸ به بعد که مدیریت بهره‌برداری از خروجی تحتانی به‌طور واضح بهبود یافته است. نتایج این مطالعه شواهد عینی برای تایید اثربخشی استراتژی تخلیه آب شور لایه‌های میانی از طریق تخلیه‌کننده تحتانی ارائه نموده و تاکید می‌کند که بهره‌برداری مناسب از دریچه تخلیه‌کننده تحتانی مخزن سد گتوند کلید مدیریت پایدار شوری آب در این مخزن است.

https://mmws.uma.ac.ir/article_4665_60f719bb0e7ed07bba6ab5f4c1db5d87.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Urban water crisis foresight through scenario planning: A structural and managerial analysis of Ardabil city آینده‌پژوهی بحران آب شهری با رویکرد سناریونویسی: تحلیل ساختاری و مدیریتی شهر اردبیل 279 294 4677 10.22098/mmws.2026.19654.1807 FA بهنام باقری استادیار، گروه جغرافیا، دانشکده علوم اجتماعی، دانشگاه پیام نور، تهران، ایران ابراهیم َعبدی استادیار، گروه اقتصاد، دانشکده مدیریت، اقتصاد و حسابداری، دانشگاه پیام نور، تهران، ایران Journal Article 2026 05 02 Introduction According to United Nations reports, Iran is among the ten countries experiencing the highest levels of water stress worldwide, with annual per capita renewable water resources falling below 1,000 cubic meters, thereby exceeding the critical threshold. Excessive extraction of water resources, declining precipitation caused by climate change, and rapid urban and agricultural population growth have intensified the water crisis across many regions of the country. Furthermore, climate change impacts—particularly the increasing intensity and frequency of droughts—have significantly increased the recurrence of critical conditions and complicated long-term water resource forecasting. Reduced access to water resources not only exacerbates poverty and livelihood vulnerability but also contributes to forced migration, especially from rural to urban areas, thereby deepening regional inequalities. Empirical evidence suggests that water scarcity and its unequal distribution disproportionately affect low-income populations, intensifying social and economic disparities at both local and global scales. In Ardabil, the water crisis is not limited to quantitative shortages; water quality has also become a serious concern. In addition to natural processes, human activities—particularly those associated with the agricultural sector—have contributed to the contamination of water resources with heavy metals and other pollutants. Materials and Methods This study is applied in terms of purpose and descriptive–analytical in nature, employing a futures studies approach. The primary objective is to develop plausible scenarios for the future of the water crisis in Ardabil, considering developments and uncertainties up to the horizon year 2035. The research methodology is based on a combination of the modified Delphi method and cross-impact analysis, with data collected through expert questionnaires. The study population consisted of 30 experts in the fields of water resource management, urban planning, environmental science, agriculture, and climatology, selected through purposive sampling. In the first phase, a two-round modified Delphi method was employed to identify the variables influencing the water crisis. During the first round, a semi-structured questionnaire was distributed among experts, including sections related to environmental, economic, social, and managerial variables, while also allowing respondents to introduce additional factors through open-ended responses. The purpose of this stage was to extract an initial set of influential variables. In the second round, the identified variables were presented to the experts, who were asked to evaluate their importance and influence using a Likert scale. Based on the aggregated responses, the final set of variables was selected. Subsequently, these variables were analyzed using a cross-impact matrix to determine their levels of influence and interdependence. Finally, Scenario Wizard software was utilized to process the data and generate plausible future scenarios. Results and Discussion Based on the outputs generated by scenario wizard, six robust scenarios were identified for the future of the water crisis in Ardabil. Among these, scenario 1 represents the most favorable and optimal condition. The analysis indicates that the future of Ardabil’s water system can be categorized into three distinct yet interconnected groups, each reflecting different levels of governance effectiveness, infrastructural capacity, and the interaction between water supply and demand. The first group, which includes scenarios 1 and 2, represents conditions of relative to optimal stability. In these scenarios, integrated water resource management, targeted infrastructure investment, and increased public awareness and participation contribute to a relative balance between available resources and consumption. Under such circumstances, the system demonstrates sufficient resilience to cope with climatic fluctuations and maintain stability through adaptive mechanisms. In contrast, the second group, represented by Scenario 3, reflects a transitional phase toward instability. In this scenario, early signs of water stress—such as reduced precipitation, declining infrastructure efficiency, and weak institutional coordination—gradually emerge. Although the system remains functional, the accumulation of these pressures increases vulnerability and reduces adaptive capacity over time. The third group, consisting of scenarios 4, 5, and 6, represents critical and highly unstable conditions. These scenarios are characterized by severe water shortages, deteriorating infrastructure performance, ineffective governance, increasing demand pressures, and declining institutional capacity. Under such circumstances, the imbalance between water supply and demand intensifies, leading to heightened social, economic, and environmental challenges. Conclusion The findings indicate that the future of Ardabil’s urban water system is shaped by a complex interaction of managerial, infrastructural, social, and climatic factors and cannot be attributed solely to natural variations such as precipitation levels. Even under conditions of relatively improved rainfall, weaknesses in governance, inefficient infrastructure, and limited managerial capacity may still intensify the crisis. This suggests that the roots of the water crisis lie more in structural and institutional deficiencies than in absolute resource scarcity. Furthermore, the developed scenarios demonstrate that the future trajectory of the system is non-linear and highly dependent on governance quality, institutional coordination, investment levels, and consumption patterns. In the sustainable scenarios, integrated management, strengthened infrastructure, and enhanced social participation contribute to balancing supply and demand and improving system resilience. Conversely, in the critical scenarios, accumulated inefficiencies, poor planning, and inadequate demand management result in persistent water shortages, even when water resources are relatively abundant. Scenario 3, representing the transitional phase, is particularly significant as a strategic turning point, providing opportunities for effective intervention at lower cost and with greater impact. Once the system enters more advanced stages of crisis, the complexity of the situation increases, making a return to stable conditions considerably more difficult. In conclusion, the future of Ardabil’s water resources depends more on governance quality and managerial capacity than on climatic conditions alone. Achieving sustainability therefore requires the adoption of integrated water resource management approaches, the strengthening of technical infrastructure, improved institutional coordination, increased social participation, and the reform of consumption patterns. Additionally, incorporating foresight and scenario-based approaches into decision-making processes can facilitate the timely identification of emerging threats, prevent the system from shifting toward critical scenarios, and support sustainable urban development. بحران آب شهری در ایران به‌عنوان یکی از چالش‌های پیچیده و چندبعدی، تحت تأثیر هم‌زمان عوامل اقلیمی، مدیریتی، اقتصادی و اجتماعی شکل گرفته و شهر اردبیل نیز در سال‌های اخیر با تشدید این بحران مواجه بوده است. پژوهش حاضر با هدف تحلیل آینده‌های محتمل بحران آب شهری اردبیل، با بهره‌گیری از رویکرد آینده‌پژوهی و روش سناریونویسی تا افق 1414، مبتنی بر تحلیل اثرات متقاطع انجام شده است. در این راستا، ابتدا با استفاده از روش دلفی اصلاح‌شده، 38 متغیر در مرحله اول و سپس در گام دوم دلفی، 11 متغیر کلیدی به‌عنوان پیشران‌های اصلی بر پایه نظر خبرگان و با استفاده از طیف لیکرت انتخاب شدند. در ادامه، با تعریف 33 وضعیت برای این متغیرها و براساس ماتریس اثرات متقابل امتیاز دهی شده و با بکارگیری نرم‌افزار سناریوویزارد، 6 سناریوی سازگار و محتمل استخراج گردید. نتایج نشان می‌دهد که سیستم آب شهری اردبیل ماهیتی آستانه‌ای و شکننده دارد؛ به‌طوری‌که تغییرات نسبتاً محدود در متغیرهای حکمرانی می‌تواند به گذار سریع از وضعیت پایدار به شرایط بحرانی منجر شود. علاوه بر این، تحلیل‌ها حاکی از آن است که ریشه اصلی بحران آب در اردبیل، بیش از آنکه ناشی از کمبود مطلق منابع آبی باشد، در نارسایی‌های ساختاری و نهادی نهفته است. سناریوهای طراحی‌شده نیز نشان می‌دهند که مسیر آینده این سیستم به‌طور قابل توجهی وابسته به کیفیت حکمرانی، میزان هماهنگی نهادی، سطح سرمایه‌گذاری و الگوهای مصرف است.

https://mmws.uma.ac.ir/article_4677_06a1aa3c09b020679905917c4a02aab0.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Development and field evaluation of a biphasic portable streamer trap (BPST) for phase-resolved measurement of bedload and suspended load on the Southern Caspian coast توسعه و ارزیابی میدانی تله رسوبگیر دوفازی برای اندازه‌گیری تفکیکی بار بستر و بار معلق در سواحل جنوب خزر 295 312 4678 10.22098/mmws.2026.19665.1808 FA رسول کوهزاد دانشجوی کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران رضا دزواره رسنانی دانشیار، دانشکده مهندسی عمران، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران Journal Article 2026 05 02 Introduction Coastal sediment transport, particularly longshore sediment transport (LST), governs shoreline evolution and port sedimentation. Accurate prediction requires separate quantification of bedload and suspended load—a capability absent in traditional single-chamber streamer traps, which merge both phases into one sample. This technological gap prevents independent calibration of two-phase numerical models, introducing significant uncertainty into coastal management decisions. This study addresses the gap by developing and field-testing a novel Biphasic Portable Streamer Trap (BPST) equipped with an internal horizontal baffle that physically separates bedload from suspended load at the point of capture. The BPST provides, for the first time, phase-resolved field data essential for advancing sediment transport modeling and enabling evidence-based Integrated Coastal Zone Management (ICZM). Materials and Methods The BPST was developed by modifying the full-depth streamer trap design with a stainless-steel horizontal baffle installed 150 mm above the base plate, creating independent lower (bedload) and upper (suspended load) compartments. The 150-mm height was determined from bedload layer thickness theory. A 100-µm polyester mesh covers the frame. An extended-handle configuration with a boat-based mooring system was developed for deployments deeper than 1.0 m. Field tests were conducted over one year (December 2022–December 2023) on the sandy southern Caspian coast near Nowshahr Port, Iran. The bimodal wave climate comprises calm conditions (Hs < 0.5 m, 85% of the year) and storm events (Hs > 0.5 m, 15%). Twenty tests were performed at depths of 0.3–1.3 m. Samples from both compartments were separately collected, desalinated, oven-dried, and weighed to 0.1 g precision. Wave, current, and wind data were obtained from a nearshore ADCP and ERA5 reanalysis. Results and Discussion The BPST successfully separated the two transport phases across all 20 field tests. The mean total captured sediment was 1665 g per deployment, comprising 1543 g bedload (92.7%) and 122 g suspended load (7.3%). Decisive proof of selective performance was the simultaneous recording of substantial bedload (up to 4890 g) with zero suspended load during calm conditions, confirming complete physical separation. During storm events, suspended load increased significantly, reaching up to 560 g, demonstrating that high-energy waves drive sediment suspension. The extended-handle configuration performed stably in water depths up to 1.3 m, overcoming traditional depth limitations. Analysis of phase-resolved data against synchronous wave measurements confirmed that suspended sediment mobilization occurs primarily during storms, while bedload dominates the annual transport budget. These phase-resolved datasets provide the missing empirical foundation for independently calibrating bedload and suspended load components in two-phase sediment transport models. Conclusion This study successfully developed and validated the Biphasic Portable Streamer Trap as a reliable instrument for phase-resolved measurement of coastal sediment transport. The internal baffle design proved effective in physically separating bedload and suspended load, verified by the selective capture of bedload during calm conditions. On the studied coast, bedload accounts for approximately 93% of total annual LST, with suspended load mobilized primarily during storm events. The BPST bridges the critical gap between two-phase numerical models and field observations by delivering the first phase-resolved datasets for this environment. Minor limitations include the fixed 150-mm baffle height, which may be exceeded by saltating grains in extreme plunging breakers. The BPST offers a robust, cost-effective monitoring platform for Iranian coastal waters and supports evidence-based ICZM. Future work should deploy BPST arrays across broader spatiotemporal scales to construct regional phase-resolved sediment transport atlases and systematically integrate these data into numerical model calibration routines. مناطق ساحلی تحت تأثیر پیوسته اندرکنش امواج، جریان‌ها و انتقال رسوب قرار دارند. انتقال رسوب در امتداد ساحل، فرآیند غالب شکل‌دهنده خطوط ساحلی و عامل تعیین‌کننده در پایداری سازه‌های بندری و مدیریت یکپارچه سواحل است. شکاف عمیقی میان مدل‌های عددی دو-فازی و داده‌های میدانی وجود دارد که ریشه آن نه در ضعف مدل‌ها، بلکه در نبود فناوری‌های اندازه‌گیری تفکیک‌گر برای ثبت هم‌زمان بار بستر و بار معلق در نقطه نمونه‌برداری است. تله‌های جریانی نسل‌های پیشین مجموع بار را جمع‌آوری می‌کردند و از ارائه داده‌های تفکیکی ضروری برای کالیبراسیون مستقل مدل‌های دوفازی عاجز بودند. هدف این مطالعه، پر کردن این خلأ از طریق طراحی، ساخت و ارزیابی یک سامانه بومی نوآورانه تحت عنوان تله رسوبگیر دوفازی قابل حمل است. نوآوری اصلی این سامانه، الحاق یک صفحه جداکننده افقی داخلی در ارتفاع ۱۵۰ میلی‌متری از کف محفظه است که جریان ورودی را به‌طور فیزیکی به محفظه بار بستر (تحتانی) و بار معلق (فوقانی) تفکیک می‌کند. همچنین، جهت غلبه بر محدودیت سنتی تله‌های جریانی به آب‌های کم‌عمق، پیکربندی ارتقایافته‌ای با دسته‌های طویل‌شده و سیستم مهار شناور برای عملیات در اعماق بیش از ۱ متر توسعه یافت. این سامانه طی یک کمپین میدانی یک‌ساله در سواحل ماسه‌ای جنوب خزر (نوشهر) تحت شرایط آرام و طوفانی در اعماق 3/0 تا 3/1 متر آزمون شد. نتایج ۲۰ آزمون میدانی، موفقیت کامل طراحی در تفکیک فازها را اثبات کرد. میانگین وزن کل رسوب ۱۶۶۵ گرم (دامنه ۱۵۰ تا ۵۴۵۰ گرم) بود که ۱۵۴۳ گرم (۹۲٫۷%) در محفظه بار بستر و ۱۲۲ گرم (۷٫۳%) در محفظه بار معلق جمع‌آوری شد. شاهد قطعی دقت تشخیصی سامانه، ثبت وزن‌های بار بستر قابل توجه (تا ۴۸۹۰ گرم) هم‌راه با وزن صفر در محفظه بار معلق در شرایط آرام بود، در حالی که در طوفان‌ها وزن بار معلق تا ۵۶۰ گرم افزایش می‌یافت. تحلیل همبستگی نشان داد که شرایط طوفانی محرک اصلی تحرک بار معلق است. این پژوهش با معرفی تلهگیر دوفازی به‌عنوان یک سکوی مشاهداتی بومی و استاندارد، علاوه بر رفع یک خلأ سخت‌افزاری دیرینه، داده‌های تفکیکی ضروری برای کالیبراسیون مؤلفه‌محور مدل‌های دو-فازی را فراهم کرده و گامی اساسی در جهت مدیریت یکپارچه و مبتنی بر شواهد سواحل شمال ایران است.

https://mmws.uma.ac.ir/article_4678_0ad0b7f5c5a13aba58d5884a448cd077.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Dynamics of changes in soil quality properties in a semi‑arid ecosystem following poplar wood biochar application under field conditions پویایی تغییرات ویژگی‌های کیفیت خاک یک زیست‌بوم نیمه‌خشک پس از کاربرد زغال زیستی چوب صنوبر در شرایط صحرایی 313 328 4692 10.22098/mmws.2026.19749.1812 FA احسان پورحسین دانشجوی کارشناسی ارشد، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران حسین خیرفام دانشیار، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، یران کامران زینال‌زاده دانشیار، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران رضا اسماعیل‌نژاد دانش‌آموخته دکتری، علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ارومیه، ایران هانیه فرامرزی دانشجوی دکتری، علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران Journal Article 2026 05 11 Introduction The rapid expansion of arid and semi-arid regions, driven by climate change and unsustainable land management, has led to severe soil degradation, fertility loss, and increasing threats to global food security. In this challenging context, biochar, as a highly stable carbon-based soil amendment, holds significant potential for improving critical soil chemical properties. However, most existing research focuses on short-term effects, and the long-term temporal dynamics of key soil indicators following a single biochar application in semi-arid ecosystems remain poorly understood. Current evidence suggests that initial agronomic benefits may gradually decline over time due to surface aging processes and nutrient leaching. Therefore, determining the optimal application rate and effective time horizon to sustain soil health is crucial for sustainable land restoration. This study aimed to monitor the three-year effects of a single application of poplar wood biochar at 25 and 50 t/ha on soil organic matter, total nitrogen, carbon-to-nitrogen ratio, pH, and electrical conductivity under semi-arid field conditions. The main hypothesis was that soil chemical responses to biochar would follow a non-linear trajectory, with higher application rates demonstrating greater functional stability over the three-year monitoring period. This targeted approach provides a robust scientific framework for cost-effective and ecologically sound decision-making in degraded land restoration programs worldwide. Materials and Methods This field experiment was conducted at the Urmia University research farm (37°39′N, 44°58′E; 1362 m a.s.l.) under a cold semi-arid climate with ~340 mm annual rainfall and 11.5°C mean temperature. A completely randomized design with three replications evaluated single applications of poplar wood biochar at 0 (control), 25, and 50 t/ha across nine 10-m² plots. Biochar was produced via slow pyrolysis at 450°C (120 min, 10–15°C/min heating rate), sieved to 0.5–1.0 mm, and uniformly incorporated into the top 25 cm of loam-clay soil (texture verified by hydrometer) at trial initiation, with no further amendments over three years. Composite soil samples were collected annually at the end of each cropping cycle. Soil organic matter was determined via the Walkley-Black wet oxidation method (Van-Bemmelen conversion factor 1.724), total nitrogen by standard Kjeldahl digestion-distillation-titration, and the C/N ratio was subsequently calculated. Soil pH and electrical conductivity were measured in saturated paste extracts following established laboratory protocols. Data normality and variance homogeneity were confirmed using Shapiro-Wilk and Levene’s tests. Temporal and treatment effects were analyzed through repeated-measures ANOVA, with mean separations performed using the LSD test at p≤0.05 in SPSS v27. All experimental procedures strictly followed standardized agronomic and soil analytical guidelines. Results and Discussion Repeated measures ANOVA revealed significant effects of biochar application and time on all soil chemical properties (p<0.05). Organic matter increased significantly in both biochar treatments: at 25 t/ha, values were 197%, 120%, and 95% higher than control in years one, two, and three, respectively, rising from 2.88% to 1.77% (w/w); at 50 t/ha, increases reached 267%, 195%, and 151%, with values increasing from 3.56% to 2.29% (w/w). Total nitrogen showed significant increases only at 50 t/ha across all three years (0.26%, 0.22%, 0.20%; representing 108%, 97%, and 67% above control), whereas the 25 t/ha rate yielded smaller gains of 65%, 42%, and 22%. The C/N ratio increased by 75%, 53%, and 62% at 25 t/ha and by 71%, 46%, and 52% at 50 t/ha relative to control, with no significant difference between application rates. pH increased modestly by 2.3%, 1.5%, and 1.6% at 25 t/ha and by 4.3%, 2.9%, and 2.2% at 50 t/ha versus control, converging by year three. Electrical conductivity rose within the non-saline range: at 25 t/ha, values increased from 1.37 to 1.08 dS/m, representing a decline in relative enhancement from 40% to 5.2% above control; at 50 t/ha, values increased from 1.65 to 1.11 dS/m, with relative enhancement decreasing from 70% to 8.1%. The significant treatment×time interaction for organic matter and electrical conductivity indicates temporal sensitivity, whereas non-significant interactions for nitrogen, C/N ratio, and pH suggest stable, uniform effects. These quantitative patterns demonstrate that 25 t/ha suffices for structural and regulatory improvements, while 50 t/ha is necessary for sustained nitrogen retention in semi-arid soils over a three-year horizon. Conclusion The findings demonstrate that biochar’s influence on soil chemical parameters follows a dynamic, non-linear trajectory over a three-year period. Initial application yields peak improvements, gradually transitioning toward surface aging and functional equilibrium by year three. While soil organic matter maintained significant enhancements at both 25 and 50 t/ha, long-term total nitrogen stabilization exclusively required the higher rate. Conversely, the C/N ratio, pH, and electrical conductivity reached practical saturation thresholds by the final year, indicating that doubling the application from 25 to 50 t/ha offered no additional agronomic benefit for these indicators. This divergent response highlights the necessity of a targeted, objective-driven biochar strategy in semi-arid agroecosystems. Farmers prioritizing structural improvement, chemical buffering, and runoff mitigation can confidently adopt 25 t/ha as a cost-effective solution. In contrast, low-input systems requiring sustained nitrogen autonomy must apply 50 t/ha to maintain adequate nutrient reservoirs across multiple cropping cycles. Uniform application guidelines without specific restoration objectives risk significant economic inefficiencies. Given the study’s constraints to a single feedstock, pyrolysis temperature, and loam-clay texture, future research should emphasize multi-year monitoring, isotopic tracing of nutrient fluxes, and integration with complementary nature-based practices. These efforts will refine stability models and establish optimized, site-specific protocols for resilient dryland agriculture. Adaptive management frameworks remain essential for scaling implementation. در اراضی نیمه‌خشک، کیفیت پایین‌ خاک یکی از موانع اصلی تولید پایدار است. زغال زیستی به‌عنوان یک اصلاح‌کننده کربن-بنیان، گرچه در بازه یک‌ تا دو ساله بر کیفیت خاک مؤثر است، اما پایداری اثرات آن در مقیاس سه ساله هم‌چنان عدم قطعیت دارد. از این‌رو پژوهش حاضر با هدف بررسی تأثیر زغال زیستی حاصل از گرماکافت تدریجی چوب صنوبر در ۴۵۰ درجه سلسیوس در سطوح کاربرد صفر (شاهد)، ۲۵ و ۵۰ تن بر هکتار در سه بازه زمانی یک، دو و سه ساله بر ویژگی‌های مهم شیمیایی خاک در مزرعه بدون گیاه در کرت‌های 10 مترمربعی واقع در دشت ارومیه تحت طرح کاملاً تصادفی طراحی و داده‌ها با آزمون تجزیه واریانس اندازه‌گیری‌های مکرر و آزمون تعقیبی حداقل اختلاف‌های معنی‌دار تحلیل شدند. نتایج نشان داد پاسخ شاخص‌ها خطی نبوده و در سال اول به اوج اثرگذاری رسید، در سال دوم روندی کاهشی اما پایدار داشت و در سال سوم به تعادل نزدیک شد. پس از سه سال، ماده آلی در سطوح ۲۵ و ۵۰ تن بر هکتار به‌ترتیب 95 و 151 درصد و نیتروژن کل تنها در سطح ۵۰ تن با 67 درصد نسبت به شاهد افزایش معنی‌دار یافت. در تیمارهای ۲۵ و ۵۰ تن بر هکتار، نسبت کربن به نیتروژن از ۷۸/۵ در شاهد به ۳۷/۹ و ۸۲/۸، pH خاک از ۶۹/۷ به ۸۱/۷ و ۸۶/۷ و هدایت الکتریکی از 03/۱ به ۰۸/۱ و ۱۱/۱ دسی‌زیمنس بر متر افزایش یافت، اما تفاوت معنی‌داری بین دو سطح مصرف نبود. یافته‌ها نشان داد برای تنظیم pH و شوری خاک، سطح ۲۵ تن بر هکتار در چرخه سه‌ساله کافی و اقتصادی است و برای حفظ نیتروژن در بلندمدت نیازمند ۵۰ تن بر هکتار است. این پویایی زمانی و پاسخ شاخص‌ها به زغال زیستی، انتخاب سطح مصرف بهینه را بر اساس هدف مدیریتی و افق زمانی پایش در کشاورزی مناطق نیمه‌خشک به‌طور علمی و عملیاتی امکان‌پذیر می‌سازد.

https://mmws.uma.ac.ir/article_4692_82e20a2b8e552e0b3a782ec694d4371b.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 Assessing the relationship between environmental variables, vegetation indices, and soil properties on rainfed wheat yield ارزیابی ارتباط بین متغیرهای محیطی، شاخص‌های گیاهی و خصوصیات خاک بر عملکرد گندم دیم 329 350 4693 10.22098/mmws.2026.19728.1811 FA آزاده سلطانی دانشجوی دکتری، گروه بیابان‌زدایی، دانشکده مطالعات کویر، دانشگاه سمنان، سمنان، ایران علی اصغر ذوالفقاری دانشیار، گروه بیابان‌زدایی، دانشکده مطالعات کویر، دانشگاه سمنان، سمنان، ایران سید حسن کابلی دانشیار، گروه بیابان‌زدایی، دانشکده مطالعات کویر، دانشگاه سمنان، سمنان، ایران جوزف لونگو مینولو استادیار پژوهشی، دانشکده کشاورزی، غذا و محیط زیست (Di3A)، دانشگاه کاتانیا، ایتالیا Journal Article 2026 05 09 Introduction Rainfed wheat is one of the most important strategic crops in arid and semi-arid regions, where crop production is highly dependent on rainfall variability and environmental conditions. In these systems, spatial and temporal differences in soil properties, vegetation dynamics, and topographic conditions can significantly affect crop growth and final yield. Soil physical and chemical characteristics influence water retention, nutrient availability, aeration, and root development, while vegetation indices derived from satellite imagery can reflect crop vigor and seasonal growth conditions. Similarly, topographic variables such as elevation, slope, and moisture-related indices affect runoff generation, soil moisture distribution, erosion processes, and local microclimatic conditions. In semi-arid regions of Iran, including Semnan Province, identifying the dominant environmental factors controlling rainfed wheat yield is essential for improving agricultural management and reducing production risks. Previous studies have often investigated soil, vegetation, or topographic factors separately; however, fewer studies have simultaneously evaluated their combined effects and relative importance under field conditions. In addition, the interrelationships among environmental variables may complicate the interpretation of their independent effects on yield. Therefore, the present study was conducted in the Kalpoosh Plain to investigate the relationships among soil properties, NDVI, topographic indices, and rainfed wheat yield using correlation analysis and Boruta feature selection. The study also evaluated multicollinearity among environmental variables to improve the interpretation of variable importance and environmental interactions. Materials and Methods This study was conducted in rainfed wheat fields of the Kalpoosh Plain located in northeastern Mayami County, Semnan Province, Iran. Data were collected from 112 wheat fields during the growing season. Wheat yield was measured using 5 × 5 m plots, and soil samples were collected from the 0–30 cm layer at the center of each plot. Geographic coordinates of sampling points were recorded using GPS. Soil analyses included texture fractions (sand, silt, and clay), bulk density (BD), pH, electrical conductivity (EC), equivalent calcium carbonate (TNV), organic carbon (OC), total nitrogen (N), soluble potassium (K), sodium (Na), calcium plus magnesium (Ca+Mg), and sodium adsorption ratio (SAR). Laboratory analyses were performed using standard procedures. Monthly NDVI values from April to July were extracted from Sentinel-2 imagery with 10 m spatial resolution after atmospheric correction and cloud removal. Topographic variables including elevation, slope, topographic wetness index (TWI), LS-factor, plan curvature, profile curvature, valley depth, and relative slope position (RSP) were derived from the 30 m SRTM digital elevation model. Pearson correlation analysis was used to investigate relationships among environmental variables and wheat yield. Correlation matrices and heatmaps were generated for graphical interpretation. Because strong correlations among predictor variables can influence statistical interpretation, multicollinearity was evaluated using Variance Inflation Factor (VIF) and Tolerance (TOL) indices. Variable importance and sensitivity analysis were performed using the Boruta algorithm based on Random Forest in the R-Studio environment. Results and Discussion The results showed that many soil properties were strongly interrelated, whereas their direct relationships with wheat yield were generally weak. Among soil variables, TNV (r = −0.25) and soil pH (r = −0.21) showed the relatively weak to moderate negative correlations with yield. Soil texture fractions also exhibited very strong interrelationships, particularly between sand and silt (r = −0.97), while their correlations with yield were not significant. NDVI values across all months showed positive correlations with yield. The highest correlation was observed in May (r = 0.53), followed by June (r = 0.46), July (r = 0.43), and April (r = 0.42), highlighting the importance of vegetation conditions during stem elongation and reproductive growth stages. Strong correlations among monthly NDVI values also indicated strong temporal continuity in crop growth dynamics. Among topographic variables, elevation showed the strongest (though weak) negative correlation with yield (r = −0.29), whereas TWI showed a weak positive correlation (r = 0.24). The negative effect of elevation may be associated with lower temperatures and shorter growing periods at higher altitudes. Multicollinearity analysis indicated that most variables did not exhibit serious collinearity; however, sand, clay, soluble sodium, and NDVI (month 2) showed high VIF values, suggesting the presence of multicollinearity among these predictors. Therefore, these variables should be interpreted with caution in model-based inference due to potential redundancy and overlapping information. Boruta analysis identified NDVI, elevation, TNV, pH and Ca+Mg the most influential variables affecting rainfed wheat yield. Conclusion This study identifies four key factors affecting rainfed wheat yield: altitude as a microclimatic factor, TNV, pH, and Ca+Mg as the main soil chemical constraints, and NDVI as the optimal remote sensing indicator for the critical growth stage. The insignificant contribution of soil texture and most topographic indicators highlights the nonlinear and interactive nature of these environmental factors, which can complicate linear modeling approaches. Theoretically, these findings emphasize the improvement of the efficiency of the feature selection algorithm (Boruta). In practical terms, targeted monitoring of the four identified variables (altitude, TNV, pH, Ca+Mg and NDVI) can support sustainable management through methods such as soil amendment to reduce lime and salinity, combined with seasonal remote sensing monitoring. In general, in semi-arid regions, rainfed wheat yield depends more on a few dominant environmental factors than on a wide range of variables. Future research should integrate dynamic climate data and use advanced models (e.g., neural networks) for yield prediction. Such approaches will help reduce yield variability, enhance food security, and support climate adaptation strategies in rainfed agriculture. عملکرد گندم دیم در مناطق خشک و نیمه‌خشک به عوامل محیطی نظیر توپوگرافی، پوشش گیاهی و خصوصیات خاک وابسته است. این مطالعه باهدف شناسایی ارتباط و اهمیت این عوامل در دشت کالپوش استان سمنان انجام شده است. داده‌های عملکرد و نمونه‌های خاک از ۱۱۲ مزرعه برداشت و عملکرد در پلات‌های ۵×۵ متر و نمونه‌های خاک از عمق ۰–۳۰ سانتی‌متری همان پلات‌ها جمع‌آوری گردید. ویژگی‌های فیزیکی و شیمیایی خاک شامل بافت، چگالی ظاهری، کربن آلی، نیتروژن کل، اسیدیته، هدایت الکتریکی، کربنات کلسیم معادل، نسبت جذب سدیم و غلظت عناصر محلول اندازه‌گیری شد. شاخص NDVI از تصاویر Sentinel-2 با تفکیک ۱۰ متر برای ماه‌های فروردین تا تیر استخراج گردید. شاخص‌های توپوگرافی شامل شیب، ارتفاع، شاخص رطوبت توپوگرافی، LS-factor، انحنای سطح، عمق دره و موقعیت شیب نسبی نیز از مدل رقومی ارتفاع بادقت 30 متر استخراج شد. تحلیل داده‌ها در محیط R انجام شد. نتایج نشان داد خاک منطقه دارای بافت لوم شنی - رسی با میانگین pH برابر ۶/۷، شوری 4/1 dS/m،کربنات کلسیم معادل برابر ۳/۸ درصد، کربن آلی ۱/۱ درصد، سدیم محلول 96/5 ppm و SAR حدود ۶۶/۱ است. نتایج نشان داد که کربنات کلسیم معادل با 25/0-=r و اسیدیته با 21/0-=r همبستگی معنی‌دار در سطح ۰۵/۰ با عملکرد داشتهاند. NDVI در تمام ماه‌ها رابطه مثبت معنیدار با عملکرد نشان داد و بیشترین همبستگی در اردیبهشت (53/0=r) مشاهده شد. در بین متغیرهای توپوگرافی ارتفاع، شاخص رطوبت توپوگرافی و موقعیت نسبی شیب به ترتیب با 29/0-=r ، 24/0= r و 23/0=r همبستگی معنیدار با عملکرد داشتند. تحلیل هم‌خطی نشان داد متغیرهای درصد شن و رس، سدیم محلول و NDVI ماه دوم دارای هم‌خطی بالا هستند و از لیست دادهها حذف گردیدند. بر اساس الگوریتم Boruta، متغیرهای NDVI بهجز اردیبهشت، ارتفاع، مجموع کلسیم - منیزیم، کربنات کلسیم معادل و اسیدیته مهم‌ترین عوامل مؤثر بر عملکرد گندم دیم بودند. در مجموع، نتایج نشان داد که عملکرد گندم دیم در دشت کالپوش حاصل برهم‌کنش عوامل محیطی مختلف از جمله ویژگی‌های شیمیایی خاک (به‌ویژه آهکی بودن) و شاخص پوشش گیاهی در طول فصل رشد است. نتایج نشان داد که متغیرهای مرتبط با پوشش گیاهی و ویژگی‌های شیمیایی خاک، به‌ویژه NDVI و کربنات کلسیم معادل، میتوانند نقش مهمتری در تبیین تغییرات عملکرد گندم دیم داشتهباشند.

https://mmws.uma.ac.ir/article_4693_8ad12cb25dc45e5656dc76df04077a78.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 3 2026 08 23 The relationship between teleconnection indices and Aerosol Optical Depth (AOD) at selected stations in Sistan and Baluchistan Province ارتباط شاخص‌های پیوند از دور با شاخص عمق اپتیکی آئروسل در ایستگاه‌های منتخب استان سیستان و بلوچستان 351 368 4461 10.22098/mmws.2026.19012.1755 FA ابوالفضل داوری دانشجوی دکتری، گروه مهندسی منابع‌طبیعی، دانشکدة کشاورزی و منابع‌طبیعی ، دانشگاه هرمزگان، بندرعباس، ایران رسول مهدوی دانشیار، گروه مهندسی منابع‌طبیعی، دانشکدة کشاورزی و منابع‌طبیعی، دانشگاه هرمزگان، بندرعباس، ایران 0000-0002-5573-4736 مرضیه رضایی دانشیار، گروه مهندسی منابع‌طبیعی، دانشکدة کشاورزی و منابع‌طبیعی، دانشگاه هرمزگان، بندرعباس، ایران ام البنین بذرافشان استاد، گروه مهندسی منابع‌طبیعی، دانشکدة کشاورزی و منابع‌طبیعی، دانشگاه هرمزگان، بندرعباس، ایران علیرضا شهریاری دانشیار، گروه فضای سبز، دانشکدة جغرافیا و برنامه‌ریزی محیطی، دانشگاه سیستان و بلوچستان، زاهدان، ایران Journal Article 2025 12 20 Extended Abstract Introduction Dust storms, which are particularly prevalent in arid and semi-arid regions such as Sistan and Baluchestan Province in Iran, pose significant environmental and health challenges. These storms are influenced by climatic factors and large-scale atmospheric patterns known as teleconnections, which modulate dust activity by affecting wind patterns, precipitation, and temperature. This study investigates the relationship between teleconnection indices and Aerosol Optical Depth (AOD) at two stations, Iranshahr and Zabol, aiming to improve the understanding and prediction of dust storms in the region. By leveraging machine learning models, the research seeks to identify key climatic drivers and develop accurate predictive tools for dust storm management. Materials and Methods The study utilized meteorological and climatic data from local weather stations, satellite sources (e.g., MODIS), and global teleconnection indices obtained from NOAA’s Physical Sciences Laboratory. Data preprocessing involved normalization and standardization to enhance model performance. Relationships between teleconnection indices and AOD were examined using Pearson correlation analysis. Feature selection was performed with the Boruta method, followed by the application of five machine learning algorithms Bagged CART, LightGBM, Gradient Boosting, Random Forest, and XGBoost for AOD prediction. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Furthermore, Shapley values, Sobol sensitivity analysis, and Partial Dependence Plots (PDPs) were employed to assess variable importance and interpret model behavior. Results and Discussion Correlation analysis revealed distinct patterns between teleconnection indices and Aerosol Optical Depth (AOD) at the two study stations. At Iranshahr, a strong negative correlation (-0.437) was observed with the Atlantic Meridional Mode (AMM), while the North Atlantic Oscillation (NAO) showed a positive correlation (0.236). In contrast, the most influential indices at Zabol were the Trans-Niño Index (TNI) and the Western Hemisphere Warm Pool (WHWP).Feature selection identified AMM, WHWP, and the Tropical Northern Atlantic index (TNA) as critical drivers for Iranshahr, whereas TNI and WHWP emerged as dominant predictors for Zabol. The applied machine learning models demonstrated strong predictive performance for AOD, with XGBoost and Gradient Boosting achieving the highest accuracy (R²=1 for Iranshahr and R²=0.99 for Zabol). Sensitivity analyses confirmed nonlinear and threshold-dependent relationships between teleconnection indices and AOD. Both Shapley and Sobol analyses highlighted AMM as the dominant factor, particularly at short-term lags, while Partial Dependence Plots (PDPs) further corroborated the threshold-dependent and nonlinear nature of these interactions. Conclusion The analysis of results from the Iranshahr and Zabol stations indicates that teleconnection indices significantly influence Aerosol Optical Depth (AOD) variations in these regions. This influence stems from the indices' impact on atmospheric circulation patterns, dust transport pathways, and regional moisture conditions. While similar general patterns have been observed elsewhere, the intensity and direction of these relationships vary due to the unique geographical characteristics of each location. Pacific Ocean indices dominate AOD variations at Zabol, with increasing influence over longer lags, whereas Atlantic indices are the primary drivers at Iranshahr, due to distinct local wind and geographical conditions. From a modeling perspective, boosting-based algorithms (e.g., XGBoost, Gradient Boosting) outperformed bagging models, demonstrating higher efficiency in capturing the nonlinear relationships between climatic indices and AOD. This study advances the understanding of AOD control mechanisms by identifying key teleconnection drivers and developing accurate predictive models. It also accounts for spatial variations in influential factors, which can support the design of region-specific early warning systems for dust storms. The identification of threshold-dependent relationships and critical behavioral thresholds in the indices can significantly improve the accuracy of both short-term and long-term AOD predictions. Furthermore, these results provide a robust scientific foundation for adaptive management planning in sectors such as water resources, agriculture, public health, and transportation. By leveraging this enhanced understanding of regional climatic mechanisms, policymakers and planners can develop more targeted and effective strategies to mitigate the impacts of dust storms. شاخص‌های پیوند از دور تأثیر بسزایی بر شدت و فراوانی طوفان‌های گرد و غبار دارند. در این پژوهش به بررسی ارتباط بین شاخص‌های پیوند از دور و تغییرات عمق اپتیکی آئروسل در ایستگاه‌های ایرانشهر و زابل در سیستان و بلوچستان پرداخته شده است. داده‌های ماهانه AOD (۲۰00-۲۰21) از سنجنده MODIS و شاخص‌های پیوند از دور از پایگاه NOAA استخراج شد. تحلیل اولیه با همبستگی پیرسون انجام گرفت. سپس، با استفاده از الگوریتم Boruta، متغیرهای مؤثر برای هر ایستگاه انتخاب شدند. پنج الگوریتم یادگیری ماشین شامل Bagged CART، LightGBM، Gradient Boosting، Random Forest و XGBoost برای مدل‌سازی استفاده شد و با معیارهای RMSE، MAPE و R² مورد ارزیابی قرار گرفتند. در نهایت، برای تفسیر مدل‌های یادگیری ماشین از روش‌های SHAP، تحلیل حساسیت Sobol و نمودارهای Partial Dependence Plots (PDP) استفاده گردید. نتایج همبستگی بین شاخص‌های پیوند از دور و AOD نشان داد شاخص Atlantic Meridional Mode (AMM) با همبستگی منفی (437/0-) و North Atlantic Oscillation (NAO) با همبستگی مثبت (236/0) بیشترین تأثیر را بر عمق اپتیکی آئروسل ایرانشهر دارند، درحالی‌که در زابل، شاخص‌های(TNI) Trans-Niño Index و Western Hemisphere Warm Pool (WHWP) نقش اصلی را ایفا می‌کنند. تحلیل نتایج انتخاب ویژگی‌های اقلیمی در ایستگاه ایرانشهر نشان داد، شاخص‌های مرتبط با اقیانوس اطلس از جمله AMM،Tropical Northern Atlantic (TNA) و Atlantic Multi-decadal Oscillation (AMO) به همراه شاخص‌های منطقه‌ای مانند Tropical Southern Atlantic (TSA) و NAOبیشترین تأثیر را بر شرایط اقلیمی دارند. در مقابل، در ایستگاه زابل شاخص‌های وابسته به اقیانوس آرام شامل TNI و WHWPنقش تعیین‌کننده‌تری ایفا کردند. مدل‌سازی با پنج الگوریتم یادگیری ماشین نشان داد مدل‌های XGBoost و Gradient Boosting به‌عنوان بهترین مدل‌ها قادر به پیش‌بینی دقیق عمق اپتیکی آئروسل هستند. تحلیل جامع ارزیابی اهمیت متغیرها در دو ایستگاه ایرانشهر و زابل نشان داد که عوامل اقلیمی مؤثر در هر منطقه کاملاً متمایز هستند. در ایرانشهر، شاخص AMM به‌عنوان مؤثرترین عامل، به‌ویژه در تأخیرهای کوتاه‌مدت و در هر دو روش SHAP و Sobol شناخته شد. در مقابل، در زابل شاخص‌های اقیانوس آرام (TNI و WHWP) نقش غالب داشتند و تأثیر آن‌ها با افزایش مدت تأخیر، تشدید شد. همچنین، تحلیل‌های PDP در هر دو ایستگاه، روابط غیرخطی را آشکار کرد که نشان می‌دهد تغییرات در مقادیر شاخص‌ها در نقاط بحرانی می‌تواند اثرات نامتناسبی بر سیستم داشته باشد. این یافته‌ها می‌تواند مبنایی برای توسعه سیستم‌های پیش‌بینی دقیق‌تر و برنامه‌ریزی مدیریتی در برابر پدیده‌های اقلیمی باشد.

https://mmws.uma.ac.ir/article_4461_238d25e83b01f30acabe25e518aa8eef.pdf