دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Zoning the Potential Flood Hazard and Its Relationship with Hydro-Geomorphological Indices Using the MFFPI Model in the Samian Watershed پهنه‌بندی پتانسیل خطر وقوع سیلاب و ارتباط آن با شاخص‌های هیدروژئومورفولوژی با استفاده از مدل MFFPI در حوزه آبخیز سامیان 1 16 3796 10.22098/mmws.2025.16491.1537 FA موسی عابدینی استاد، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران امیرحسام پاسبان دانشجوی دکتری، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران Journal Article 2025 01 05 Extended Abstract Introduction Flash floods are one of the most dangerous natural phenomena, causing significant loss of life and property due to their rapid and unpredictable nature. These floods claim thousands of lives each year and damage infrastructure, particularly in agricultural areas and along riverbanks. Key factors that exacerbate the damage include the lack of accurate flood hazard maps, insufficient preventive measures, dense drainage networks, and steep slopes. To mitigate the effects of floods and preserve ecosystems, it is essential to use flood models with spatial and watershed scales. Hydrological modeling, along with advanced technologies such as remote sensing and Geographic Information Systems (GIS), are powerful tools in this field. These methods provide precise spatial data and can simultaneously analyze factors such as slope, vegetation, and land use, enabling the production of more accurate flood hazard maps. Among these, numerical models like the FFPI model, based on physiographic parameters, are particularly useful in evaluating the risk of flash floods and managing crises. Numerous studies have shown that these models can effectively help identify high-risk areas and predict flood occurrences. In specific regions, such as the Samian watershed in Ardabil Province, due to its unique geographic features and climate change, accurate flood hazard assessment and mapping are crucial. This study, using the MFFPI model, analyzes factors such as slope, soil type, and vegetation, aiming to improve flood risk management and reduce potential damages. Materials and Methods This study's research methodology, designed to assess flood risk in the Samian watershed, is based on the analysis of geographical and hydrogeomorphological data. The Samian watershed, with an area of 4,200 square kilometers, is located in Ardabil province and includes units of the foothill plains. The data used in this study include 1:25,000 digital maps from the National Cartographic Center, a 30-meter resolution Digital Elevation Model (DEM), monthly and annual rainfall statistics from the Meteorological Organization, Landsat OLI 9 satellite images, 1:25000 soil texture maps, and 1:100,000 geological maps. The software used for data analysis includes ArcGIS 10.7, ENVI 5.3, Google Earth, SPSS, and Excel. In this study, hydrogeomorphological indices such as slope length, stream power, topographic moisture, slope curvature, and surface curvature were used to evaluate the topographical and geological features related to flood occurrence. Specifically, slope length and stream power indices were considered key factors influencing the hydrological response of the watershed. Additionally, the MFFPI model was employed to analyze the flash flood risk in the Samian watershed. This model uses parameters such as slope, stream density, soil texture, land cover, and land use. Pearson’s correlation test, applied in SPSS, was used to analyze the relationship between hydrogeomorphological indices and floods. This test examined the relationship between independent and dependent variables and analyzed the impact of each index on flood occurrence. Finally, these analyses were applied to assess the flood potential and identify flood-prone areas in the Samian watershed. Results and Discussion This study examined various physiographic factors influencing flash floods in the Samian watershed. The results show that land slope, flow accumulation, soil permeability, slope curvature, land use, lithological features, and vegetation significantly affect flood potential in this region. Land slope, especially in low-slope areas, is directly related to water accumulation and the likelihood of flood occurrence. Flow accumulation is also higher in areas with high stream density, such as main channels, and these areas have a higher flood potential. Soil permeability and lithological features play important roles in controlling runoff and flooding. Areas with less permeable soils and impermeable lithological features have the greatest potential for flooding. On the other hand, vegetation cover and land use, especially in residential and agricultural areas, increase runoff and decrease permeability, thereby intensifying flood risk. Correlation analysis shows that heavy rainfall (SPI) has the strongest positive relationship with flood risk. Finally, the MFFPI model was used to predict flood hazard zonation in the Samian watershed, producing a map with five flood risk classes, from low to high potential. This research emphasizes the importance of accurately analyzing physiographic factors in water resource management and flash flood prediction. Conclusion This study emphasizes the importance of hydrogeomorphological indices in flood hazard zoning for sudden floods in the Samian watershed using the MFFPI model. The model includes factors such as slope, stream density, soil permeability, land use, slope curvature, and soil texture, with flood hazard maps prepared in five risk categories. The results showed that areas with gentle slopes, poor vegetation cover, impermeable soils, and inappropriate land use are most prone to floods. Statistical analysis (Spearman and Pearson correlations) revealed that slope and soil texture had the highest significant positive impacts (0.78 and 0.70, respectively), and SPI, as the most influential index, with a coefficient of 0.64, determined flood severity. The TWI index was effective in areas with low slopes and water retention capacity, while NDVI and LSF showed no significant impact. The findings showed that SPI is useful for identifying high-risk areas in steep regions, while TWI is better suited for flatter areas. This study highlights the importance of advanced numerical models like MFFPI in flood risk management and damage reduction using GIS and remote sensing data and provides recommendations such as improving drainage infrastructure, restoring vegetation cover, and developing early warning systems. هدف از این پژوهش پهنه‌بندی پتانسیل خطر وقوع سیلاب و ارتباط آن با شاخص‌های هیدروژئومورفولوژی با استفاده از مدل توسعه یافته سیلاب ناگهانی (MFFPI) در حوزه آبخیز سامیان در استان اردبیل است. در این راستا ابتدا پس از استخراج لایه‌های شیب، تجمع جریان، کاربری اراضی، نفوذپذیری سنگ، انحنای دامنه و بافت خاک در محیط GIS اقدام به وزن‌دهی این لایه‌ها شد و در گام بعد با هم‌پوشانی این لایه‌ها نقشه پتانسیل سیلاب ناگهانی برای حوزه سامیان استخراج و بر اساس احتمال وقوع سیلاب به پنج دسته خطر خیلی کم، کم، متوسط ، زیاد و خیلی زیاد، به ترتیب 314، 926، 752، 1251 و 990 کیلومتر مربع تقسیم‌بندی شد. نتایج نشان داد مناطقی با شیب تند، پوشش‌گیاهی کم، خاک‌های سفت و کاربری‌های نامناسب مانند شهرها و مزارع، بیش‌ترین خطر وقوع سیلاب را دارند. این مناطق به‌دلیل نفوذ کم آب در خاک، رواناب زیاد و ظرفیت کم نگهداری آب در زمان بارندگی‌های شدید، مستعد سیل‌گیری هستند. برعکس، مناطقی با شیب ملایم، پوشش‌گیاهی انبوه، خاک‌های نرم و کاربری‌های مناسب مانند جنگل و مرتع، کم‌ترین خطر وقوع سیلاب را دارند. پوشش‌گیاهی با جذب باران و کاهش سرعت جریان آب، و خاک‌های نرم با نفوذ بهتر آب به داخل زمین، از وقوع سیلاب جلوگیری می‌کنند. با استفاده از آزمون آماری اسپیرمن میزان هم‌بستگی لایه‌های مدل MFFPI با سیلاب ناگهانی مشخص شد. نتایج نشان داد که لایه‌های شیب و بافت خاک به‌ترتیب با مقدار 78/0 و 70/0 درصد دارای بیش‌ترین هم‌بستگی مثبت معنی‌داری با سیلاب ناگهانی است. هم‌چنین شاخص‌های هیدروژئومورفولوژی شامل توان آبراهه، رطوبت توپوگرافی، پوشش‌گیاهی و توپوگرافی با استفاده از نقشه‌های پایه در محیط ArcGIS تهیه و استخراج شدند. به‌منظور بررسی ارتباط و میزان اثرگذاری هر یک از این شاخص‌ها در سیلاب حوزه از آزمون هم‌بستگی پیرسون استفاده شد. نتایج نشان داد شاخص توان آبراهه با ضریب هم‌بستگی مثبت و معنادار (r = 0.64) نقش اصلی را در افزایش شدت سیلاب‌های ناگهانی ایفا می‌کند.

https://mmws.uma.ac.ir/article_3796_6b0d7abc69c31b1f165b823bf2b6df0e.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Application and Comparison of Missing Groundwater Level Data Interpolation Methods with an Emphasis on DeepMVI Performance (Case Study: Ajabshir Plain) کاربست و مقایسه روش‌های درون‌یابی داده‌های گمشده‌ تراز آب زیرزمینی با تأکید بر عملکرد DeepMVI (منطقه مورد مطالعه: دشت عجب‌شیر) 17 36 3864 10.22098/mmws.2025.17457.1601 FA آیناز وفایی ممقانی دانشجوی کارشناسی ارشدمنابع آب ، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران اسماعیل اسدی استادیار/گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران صابره دربندی دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران محمدتقی ستاری دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران 0000-0002-5139-2118 Journal Article 2025 05 18 Groundwater is a vital water resource, especially in arid and semi-arid regions such as northwestern Iran. It plays a crucial role in agriculture, drinking water supply, and industrial activities. Therefore, reliable monitoring and management of groundwater levels are essential for sustainable development. However, missing data in groundwater level time series caused by factors like equipment failure, inaccessible terrain, or extreme weather can hinder accurate analysis and prediction. To address this, interpolation techniques are used to estimate missing values based on observed data. The reliability of these techniques depends on the quantity, spatial distribution, and temporal resolution of the available data. In recent years, machine learning and deep learning methods have shown promise in handling complex, nonlinear, and high-dimensional datasets. This study evaluates the effectiveness of five interpolation methods Kriging, Inverse Distance Weighting (IDW), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Random Forest Spatial Interpolation (RFSI), and Deep Missing Value Imputation (DeepMVI) to reconstruct missing groundwater level data. The focus is on improving data completeness and accuracy for subsequent groundwater analyses. The case study is the Ajabshir aquifer, where long-term data from 29 piezometric wells are used. The objective is to compare the performance of traditional and modern interpolation approaches and to determine the most accurate method for handling missing groundwater level data. Materials and Methods In this study, groundwater level data from 29 piezometric wells in the Ajabshir aquifer in northwest Iran were analyzed monthly over a 17-year period (2006–2022). Due to various operational and environmental constraints, numerous gaps were observed in the dataset. To estimate the missing values, five interpolation methods were evaluated: Kriging, Inverse Distance Weighting (IDW), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Random Forest Spatial Interpolation (RFSI), and Deep Missing Value Imputation (DeepMVI). Kriging uses semivariograms to model spatial dependence and provides statistically unbiased estimates. IDW is a deterministic technique based on the inverse distance to known values. PCHIP maintains the monotonicity and continuity of time-series data. RFSI applies the Random Forest algorithm to capture nonlinear spatial relationships, and DeepMVI utilizes deep learning to model complex temporal and multivariate dependencies in the data. The dataset was randomly divided into training (70%) and testing (30%) subsets. The performance of each method was assessed using the correlation coefficient (R), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE). Results and Discussion The evaluation results show significant variation in model accuracy. The Kriging method, while widely used, showed poor performance in this study due to the sparse and irregular distribution of observation wells. Its results included a low correlation (R = 0.37), high RMSE (417.91), and low NSE (0.11), indicating that this method is not suitable under conditions with extensive missing data and limited spatial continuity. The IDW method improved over Kriging but still yielded moderate accuracy (R = 0.56, RMSE = 365.51, NSE = 0.30). The PCHIP method performed considerably better, reflecting its ability to handle temporal data smoothly. It achieved R = 0.89, RMSE = 7.52, and NSE = 0.72, making it the second most accurate method. The method preserved the shape of the original groundwater level trends and was effective in reconstructing long sequences of missing data. The RFSI method, which leverages machine learning, showed better accuracy than Kriging and IDW (R = 0.63, RMSE = 11.06, NSE = 0.40), although it was outperformed by PCHIP and DeepMVI. This suggests that while machine learning can improve performance, spatial interpolation with sparse data remains challenging. The DeepMVI method outperformed all other methods, achieving the highest correlation (R = 0.92), lowest RMSE (6.44), and highest NSE (0.80). Its ability to capture both spatial and temporal relationships using a hybrid deep neural architecture makes it highly effective in imputing missing groundwater data, especially when the dataset includes complex time-dependent patterns and multivariate interactions. The final comparison of time series plots across 29 piezometric wells also visually confirmed the accuracy of the DeepMVI model in maintaining original trends and minimizing noise or abrupt changes. These results demonstrate that deep learning models offer a promising approach for improving the quality and reliability of groundwater monitoring datasets. Conclusion This research evaluated the performance of five interpolation methods for reconstructing missing groundwater level data from 29 piezometric wells in the Ajabshir aquifer over a 17-year period. Among the methods tested, DeepMVI outperformed all others, providing the most accurate and reliable results. Its ability to model complex temporal and spatial dependencies makes it particularly suitable for environmental datasets with high variability and missing values. PCHIP and RFSI also performed well and could serve as viable alternatives when deep learning infrastructure is not available. Although Kriging and IDW are widely used in hydrogeological studies, their lower performance in this study suggests that their application may be limited under conditions of sparse or irregular data. The study highlights the importance of selecting appropriate interpolation methods based on data characteristics. DeepMVI, with its robust architecture, holds significant promise for future groundwater studies and can enhance the quality of groundwater monitoring systems by providing more complete and accurate datasets. This, in turn, can improve water resource management and planning in regions facing water scarcity and environmental stress. آب زیرزمینی یکی از منابع حیاتی در مناطق خشک و نیمه‌خشک محسوب می‌شود و تکمیل داده‌های مفقود آن نقش مهمی در مدیریت منابع آبی دارد. هدف پژوهش حاضر، ارزیابی عملکرد پنج روش درونیابی شامل کریجینگ، فاصله معکوس وزنی (IDW)، جنگل تصادفی مکانی (RFSI)، چندجمله‌ای تکه‌ای هرمیت مکعبی (PCHIP) و مدل یادگیری عمیق DeepMVI برای بازسازی داده‌های تراز آب زیرزمینی در آبخوان دشت عجب‌شیر طی دوره آماری 1385 تا 1401 است. داده‌های ماهانه از 29 ایستگاه گردآوری و به نسبت 70 به 30 برای آموزش و ارزیابی مدل‌ها تقسیم شدند. نتایج مدل‌سازی نشان داد که مدل‌های سنتی مانند کریجینگ و IDW با مقادیر ضریب همبستگی (R) به ترتیب برابر با 37/0 و 56/0 و خطای RMSE بالا (به ترتیب 91/417 و 51/365) دقت پایینی در بازسازی داده‌ها داشتند. مدل RFSI با R برابر 63/0 و RMSE برابر 06/11 عملکرد بهتری نسبت به روش‌های کلاسیک داشت، اما همچنان از دقت لازم برخوردار نبود. مدل PCHIP با R برابر 89/0 و RMSE برابر 52/7 عملکرد قابل‌قبولی ارائه داد. با این حال، مدل DeepMVI با ضریب همبستگی بالا (92/0R =)، کم‌ترین مقدار RMSE (44/6) و بیشترین ضریب نش-ساتکلیف (8/0NSE=) بهترین عملکرد را در بین تمامی روش‌ها نشان داد. این نتایج نشان می‌دهد که استفاده از روش‌های مبتنی بر یادگیری عمیق می‌تواند دقت بازسازی داده‌های تراز آب زیرزمینی را به طور چشمگیری افزایش داده و ابزار مناسبی برای مدیریت بهینه منابع آب در مناطق دارای داده‌های ناقص فراهم آورد.

https://mmws.uma.ac.ir/article_3864_0ea31aeb01d1ed103765dfa375cb2dd8.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Non-stationary modeling of the meteorological drought index SPIt using generalized additive models for location, scale and shape شبیه سازی ناایستای خشکسالی هواشناسی بر مبنای شاخص SPIt با استفاده از مدل تعمیم یافته جمعی پارامترهای مکان، مقیاس و شکل 37 53 3880 10.22098/mmws.2025.17463.1602 FA محمدرضا شریفی دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران. کامران قیصری موزرمی دانش‌آموخته کارشناسی ارشد منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران. حیدر زارعی دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران 0000-0003-3414-1816 مهرداد تقیان سازمان آب و برق خوزستان، گروه برنامه ریزی منابع آب کارون بزرگ، اهواز، ایران Journal Article 2025 05 18 Introduction Traditional hydrological time series analyses often assume stationarity, particularly in the estimation of drought indices such as the Standardized Precipitation Index (SPI). However, increasing climate variability and anthropogenic influences have introduced significant non-stationarity into hydrological processes. This challenges the reliability of stationary-based assessments and highlights the need for models that can adapt to changing conditions. Generalized Additive Models for Location, Scale, and Shape (GAMLSS) offer a flexible framework for modeling such dynamics by allowing distribution parameters to vary over time or in relation to covariates. Recent studies suggest that non-stationary modeling improves drought characterization, particularly at longer time scales. Yet, findings remain mixed: while some report better accuracy with non-stationary approaches, others find stationary models still perform adequately, depending on regional and climatic factors. Given these variations, regional validation becomes essential. This study evaluates the performance of a non-stationary SPI-based index, referred to as SPIt, in comparison with the traditional stationary SPI. The case study is the Karkheh river basin in western Iran—a region with distinct climatic conditions compared to where SPIt was originally developed. Drought monitoring is conducted using monthly precipitation data from five stations, focusing on seasonal periods outside the dry summer months due to limited rainfall during that time. By comparing the two indices, the study aims to assess whether accounting for non-stationarity leads to more accurate drought representation in semi-arid climates. Materials and Methods The study focuses on five meteorological stations: Pol-Zal, Bostan, Pol-Kohneh, Noorabad and Halilan, spanning a historical data period ranging from 1971 to 2022 (C. E.) depending on rain gauge station availability. These stations were selected due to their diverse altitudes, geographical spread within the Karkheh basin (The latitude range is 47 to 48 degrees north and longitude 31 to 34 degrees east), and sufficiently long precipitation records, especially for the winter season (December to February), which accounts for the bulk of annual rainfall in the region. Precipitation data were analyzed for stationarity using the non-parametric Mann-Kendall trend test. Stations exhibiting significant trends were modeled using non-stationary GAMLSS, where the shape parameter of the gamma distribution was allowed to vary with time using polynomial functions optimized through the Akaike Information Criterion (AIC). The study employed a two-parameter gamma distribution to model winter precipitation in both stationary and non-stationary conditions. For drought assessment, two indices were used. SPI that Assumes stationary gamma-distributed precipitation, standardized to a normal distribution and SPIt that extends SPI by allowing the shape parameter of the gamma distribution to vary over time, thereby accommodating non-stationarity. Results and Discussion The Mann-Kendall test revealed significant decreasing trends in precipitation at Polzal and Bostan stations at 95% confidence level, and a similar albeit weaker trend at Norabad. No significant trends were detected at Pol-Kohneh and Holilan. The goodness-of-fit tests (Kolmogorov-Smirnov and Chi-square) confirmed that the gamma distribution was appropriate for all stations. GAMLSS modeling showed that non-stationary models outperformed stationary ones at stations with evident trends. For instance, AIC values were reduced by 5, 6, and 1 unit at Pol-Zal, Bostan, and Norabad, respectively, indicating better fit under non-stationary conditions. Time series analysis of the shape parameter in GAMLSS revealed temporal variability at all stations, supporting the hypothesis of non-stationarity. Worm plots for residual analysis confirmed model adequacy in both conditions, but improvements in model residuals under non-stationarity were evident at trend-affected stations. A comparison of SPI and SPIt indices indicated substantial differences in drought classification over time. At Polzal, years such as 1352 and 1354 showed no drought under SPI but were classified as moderate drought (D1) under SPIt. Similarly, years with similar rainfall amounts (e.g., 127 mm in 1971 vs. 125 mm in 2010) were categorized differently in SPIt, highlighting the model's sensitivity to underlying non-stationarity. At Holilan station, where no significant trend was observed, SPI and SPIt provided nearly identical results, reaffirming the utility of SPIt in trend-sensitive environments. A station-wise drought frequency comparison between SPI and SPIt further revealed that non-stationary modeling generally results in higher estimated drought frequencies at trend-affected stations. For example, the frequency of droughts at Polzal increased from 48% (SPI) to 52% (SPIt). Similar increases were noted at Bostan and Noorabad. Conversely, at Holilan and Polkohneh, where no significant trends were detected, the drought frequency remained the same or slightly decreased under SPIt. Moreover, the frequency of severe droughts (D4) decreased under the non-stationary model, with D4 events dropping from 2% to 0% at Pol-Zal, from 3% to 0% at Bostan, and from 6% to 4% at Noorabad. This suggests that the SPI may overestimate drought severity when stationarity is incorrectly assumed. Conclusion Long-term drought monitoring at various rain gauge stations highlights the importance of considering changes in precipitation when making decisions and setting policies in watersheds. When significant trends are present, drought analysis can be performed under either stationary or non-stationary assumptions, depending on the objective. If the primary concern is drought frequency, non-stationary analysis is strongly recommended. Results showed that at stations with trends such as Polzal, Bostan, and Norabad the frequency of droughts was underestimated under stationary analysis compared to non-stationary models. For example, frequencies increased from 48, 48, and 43 (stationary) to 52, 52, and 47 (non-stationary), respectively. However, in non-trending stations, stationary models may still provide reliable results for frequency estimation. In contrast, if the focus is on severe droughts, stationary models may outperform non-stationary ones at trend-affected stations. Non-stationary analysis yielded zero severe drought events, while stationary models identified 2, 3, and 6 cases in Palzal, Bostan, and Norabad, respectively. In non-trend stations like Polkohneh and Holilan, non-stationary analysis was more effective in detecting severe events. These findings align with previous research suggesting that while non-stationary models, such as those using GAMLSS, offer better parameter estimation, stationary models may sometimes better reflect reality in future projections. Therefore, although non-stationary modeling is essential under climate variability, the choice of model should depend on the monitoring goal. It is also recommended to incorporate time-varying variance and alternative probability distributions for better drought characterization under potential extreme rainfall events. اگرچه در نظر گرفتن خصوصیت ناایستایی سری‌های زمانی در مقایسه با ایستا فرض نمودن آن، منجر به ارتقای برآورد پارامترهای توزیع احتمالاتی متغیر می‌شود، برتری شبیه سازی ناایستا نسبت به ایستا و به‌دنبال آن پیش بینی وضعیت در آینده، تحت تأثیر محل و در نتیجه عوامل موثر بر ناایستایی، نتایج متفاوتی به‌دست داده است. از این‌رو در مطالعة حاضر با هدف ارزیابی نتایج حاصل از شرایط ناایستایی بر پایش خشک‌سالی، اقدام به مدل‌سازی ناایستای خشک‌سالی هواشناسی، با شاخص خشک‌سالی SPIt، با استفاده از مدل تعمیم یافتة جمعی پارامترهای مکان، مقیاس و شکل GAMLSS در پنج ایستگاه باران سنجی پل‌زال، بستان، پل‌کهنه، نورآباد و هلیلان، در حوزة آبریز کرخه و مقایسة آن با مدل ایستای شاخص خشک‌سالی SPI، شد. دورة زمانی مورد مطالعه، از حداقل 31 ساله (1396-1365) در ایستگاه نورآباد تا حداکثر 52 ساله (1401-1350) در ایستگاه پل‌زال، شامل بارش تجمعی فصل زمستان است. نتایج نشان داد تخمین نا ایستای پارامترها، در ایستگاه‌های دارای روند، در مقایسه با تخمین ایستایی در آن‌ها، دارای دقت بیش‌تری است. به‌طوری‌که تخمین ناایستا، سبب 5، 6 و 1 واحد کاهش مقدار آکاییک به‌ترتیب، در ایستگاهای پل‌زال، بستان و نورآباد، نسبت به شرایط ایستا، شد. هم‌چنین در نظر گرفتن شرایط ناایستایی در ایستگاه‌های دارای روند، سبب افزایش فراوانی خشک‌سالی شد. به‌طوری‌که درصد فراوانی خشک‌سالی ایستگاه‌های پل زال، بستان و نورآباد به‌ترتیب از 48، 48 و 43 در حالت ایستا به 52، 52 و 47 درصد در حالت ناایستا، افزایش یافت. این در حالی است که درصد فراوانی خشک‌سالی‌های شدید (کلاس D4) در حالت ناایستا در مقایسه با ایستا، کاهش نشان داد. به‌طوری‌که در ایستگاه‌های پل‌زال، بستان و نورآباد، درصد فراوانی خشک‌سالی‌های شدید (کلاس D4)، در شرایط نایستا به‌ترتیب صفر، صفر و 4 درصد و در شرایط ایستا به‌ترتیب، 2، 3 و 6 درصد به‌دست آمد. از این‌رو بسته به هدف پایش خشک‌سالی در مدیریت حوزه‌های آبریز، مبنی بر این‌که مسأله بحرانی ناشی از خشک‌سالی، فراوانی وقوع یا فراوانی شدیدترین خشک‌سالی مد نظر باشد، در علیرغم وجود ناایستایی، به‌ترتیب تحلیل ناایستا و ایستا، نتایج توام با ریسک کم‌تری به‌دست خواهد داد.

https://mmws.uma.ac.ir/article_3880_7c8da9a6a27af69a5a31ef973ef4a3bb.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 The effect of applying river environmental requirements on the design and operation characteristics of storage reservoirs using the Monte Carlo method (case study: Nazlou reservoir dam) اثر اعمال نیاز محیط زیستی رودخانه بر مشخصات طرح و بهره برداری مخازن ذخیره با استفاده از روش مونت کارلو (مطالعه موردی: سد مخزنی نازلو) 54 76 3938 10.22098/mmws.2025.17691.1617 FA جواد حیدری کهلی دانشجوی دکترای مهندسی منابع آب، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران مجید منتصری استاد، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران سمیه حجابی دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران Journal Article 2025 06 22 Introduction In recent years, one of the most important challenges in planning and operating reservoir systems is the estimation of actual (sufficient) environmental flows in rivers (downstream) during operation years and evaluating its effect on the performance of such reservoir systems. Failure to comply with the environmental flows of the rivers downstream has had long-term destructive consequences for socio-ecological systems. Therefore, accurate and timely estimation of environmental flow and its application in the operation of existing and new reservoir systems is of particular importance. Several studies have been conducted to assess environmental flows and the resulting changes in river ecology after the construction of reservoir dams. All studies have investigated and evaluated the flow regime before and after dam construction on rivers and have emphasized the need for timely allocation of environmental flows and changes in reservoir system operation patterns. Studies of the environmental flows of rivers have mostly focused on estimating environmental flows using various methods and comparing the river flow regime before and after dam construction in both current and ecosystem-compatible conditions in the historical period. However, the effect of timely release of environmental demand compatible with the ecosystem on the long-term behavior of various important characteristics of the storage reservoir system (such as useful volume, command curve, hydrological behavior, critical period, evaporation losses, etc.) has not been considered in previous studies, and this could be an innovative approach in this field, which has been carried out in the present study. For this purpose, in this study, a comprehensive Monte Carlo simulation approach is carried out to compare several important common methods for estimating the environmental demand of the river (dam downstream) and evaluate their effects on important design characteristics (such as useful volume) and operation (such as the command curve) in Nazlou reservoir system. The Nazlou river is one of the important rivers of the Lake Urmia basin. After providing drinking and agricultural water to the lands under its coverage, it is discharged into Lake Urmia. Materials and Methods In this study, the Nazlou Reservoir Dam and the Nazlou River in Urmia were selected as a case study. The Nazlou River is one of the important rivers of the Lake Urmia basin. After providing drinking and agricultural water to the lands under its coverage, it is discharged into Lake Urmia. Methods for determining environmental demands in the river downstream are classified into three groups: hydrological, hydraulic, and habitat simulation, but hydrological methods, with the availability of the required data, are used by researchers as reliable and common methods. In this study, five common hydrological methods, which are preferred by researchers, namely Desktop Reserve Model, flow duration curve, Smakhtin, Tessman, and Tennant methods, were used to estimate environmental demands. This comprehensive study is based on a Monte Carlo simulation approach with a very large process (924,000 times) of stochastic simulation of the storage system, i.e. 1,000 time series of production flow, 7 demands (DAnnual=0.1 to 0.4 MAF, step 0.05), 11 time reliability indices (Rel=0.9 to 1.0 step 0.01), 2 vulnerability indices (Vul=0.0 and 0.3), and 6 methods of determining environmental demand (the 5 above-mentioned methods + 1 Method without applying environmental demands). The aforementioned Monte Carlo simulation approach is based on a hybrid model combining the Valencia-Shaake disaggregation stochastic model and the Modified Sequent Peak Algorithm (SPA) simulation model. Results and Discussion The main results of this study are summarized as follows: a) Comparison of the environmental demand values obtained from the five applied methods showed that the environmental demand based on the Tennant and Tessman method were 20% (as minimum value) and 53% (as maximum value) of average annual flow, respectively, and the obtained values of the other three methods are approximately close to each other and are about 28% of the average annual flow. The monthly variation of the obtained environmental flow in river also indicated that the results of the three methods Tessman, FDC shifting (C) and DRM (C) had a similar behavior with the mean monthly historical flow values. b) The results showed that the performance of the used stochastic model in reproducing the statistical parameters of historical flow data at both annual and monthly levels and reproducing the correlation structure between the flows of different months and the correlation between the annual and monthly flows is quite desirable, and this plays a key role for a reservoir system simulation in real conditions. c) The results indicate a systematic long-term behavior of main reservoir storage characteristics (i.e., active storage, resiliency index, evaporation loss, and critical period) against demand for two condition, without and with including environmental flow, with the exception of the Tessman method. Conclusion The main outcome of investigation could be summarized as follows: a) The ecological system of rivers often adapts to its monthly flow regime over time, therefore, using a method of estimating the ecological demands of a river in accordance with the monthly flow regime and with an appropriate amount (30-40% of the average monthly flow) has a more relative advantage than other methods. So, FDC Shifting (C) method, with an average of 32 percent of the average monthly flow and the minimum coefficient of variation (CV=0.20), was the most appropriate method for estimating the environmental demand in tested reservoir dam. b) The combination of the annual AR(1) model and the monthly Valencia-Shaake disaggregation model has the ability to reproduce the statistical characteristics of the historical streamflow values at both annual and monthly levels sufficiently, and this provides a reliable framework with high accuracy in the stochastic analysis of a reservoir system. c) The long-term storage-demand-performance relationships of the reservoir system due to environmental demand, with the exception of the Tessman method, show a similar systematic behavior change for different methods of estimating environmental demand. d) By applying environmental demands to the analysis of storage reservoir systems, the active storage, evaporation loss, and critical period increase by 2 to 10 times. اغلب سیستم‌های مخازن ذخیرة موجود در سراسر دنیا بدون اعمال نیاز محیط زیستی پایاب سد در رهاسازی مورد بهره‌برداری قرار می‌گیرند. این امر اثرات مخربی در اکوسیستم رودخانه در پایاب چنین سیستم مخازن ذخیره، از جمله در حوضة آبریز دریاچه ارومیه، به‌وجود آورده است. در حال حاضر بر روی تمامی رودخانه‌های حوضة آبریز دریاچه ارومیه، سدهای مخزنی متعدد برای کنترل و تنظیم جریان رودخانه برای مصارف شرب و کشاورزی احداث یا در شرف احداث بوده و این سیستم‌ها غالباً بدون اعمال نیاز محیط زیستی پایاب رودخانه، خصوصاً حق‌آبة دریاچة ارومیه مورد بهره‌برداری قرار گرفته و احتمالاً موجب بحران خشک شدن دریاچه ارومیه شده است. در این مطالعه نوآورانه اثر اعمال نیاز محیط زیستی بر مشخصات طراحی و بهره‌برداری مخزن ذخیره فرضی رودخانه نازلوچای بر اساس پنج روش هیدرولوژیکی، Tennant، Tessman، Smakhtin، DRM و FDC Shifting مورد بررسی و ارزیابی قرار گرفته است. بدین منظور یک روش مونت کارلو گسترده با تولید 1000 سری جریان ماهیانه 30 ساله با استفاده از رویکرد استوکاستیک توزیعی (ترکیب دو مدل AR(1) و Valencia and Schaake) برای محدودة وسیع از تقاضا (7 مورد)، شاخص اعتمادپذیری (11) و شاخص آسیب‌پذیری (2) انجام پذیرفته است. نتایج نشان داد که روش FDC Shifting (C) با میزان متوسط 32 درصد میانگین جریان ماهیانه و حداقل ضریب تغییرات (CV=0.20) مناسب‌ترین روش برآورد نیاز محیط زیستی پایاب مخزن ذخیرة مورد مطالعه بوده و تخصیص 95 میلیون مترمکعب در سال از سیستم ذخیره را لازم دارد. با اعمال نیاز محیط زیستی در تحلیل سیستم مخازن ذخیره، تغییرات حجم مفید، حجم تلفات تبخیر و دوره بحرانی با افزایش تقاضا به‌طور نمایی افزایش یافته و این افزایش در حدود 2 برابر برای روش Tennant تا 10 برابر برای روش Tessman نسبت به شرایط بدون اعمال نیاز محیط زیستی است. به‌طور خلاصه، یک روش برآورد نیاز محیط زیستی رودخانه در انطباق با رژیم جریان ماهیانه یا مبتنی بر الگوی رژیم جریان ماهیانه و با میزان مناسب (40-30 درصد متوسط جریان ماهیانه) مانند روش FDC Shifting (C) دارای مزیت نسبی بسیار بالاتری نسبت روش‌های دیگر است.

https://mmws.uma.ac.ir/article_3938_921e68a94e2b1e40dca2540a71dd2da3.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Analytical comparison of soil particle size distribution in different land uses/covers of the Vaz watershed using laser granulometry مقایسه تحلیلی توزیع اندازه ذرات خاک در کاربری‌ها/پوشش‌های مختلف اراضی حوزه آبخیز واز با استفاده از دانه‌بندی لیزری 77 93 3959 10.22098/mmws.2025.17786.1622 FA سعید درختی دانشجوی دکتری، گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران. لیلا غلامی دانشیار، گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران. عطااله کاویان استاد، گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران. عبدالواحد خالدی درویشان دانشیار، گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران Journal Article 2025 07 07 Introduction The distribution of soil particle sizes is a key physical characteristic that affects soil structure and is closely related to soil texture and behavior. This feature enhances the exchange of matter and energy in the soil environment. The distribution of soil particle sizes not only influences soil permeability and organic matter content but also affects soil fertility, erosion susceptibility, soil conservation, moisture and nutrient movement, vegetation productivity, environmental remediation, and land degradation. On the other hand, human interventions and the improper use of soil based on its inherent potential have led to changes in various land uses, resulting in floods, erosion, sediment yield, and soil transformation. Therefore, understanding the distribution of soil particle sizes and determining the sensitivity of a region's soil to erosion can help illustrate the pattern of erosion distribution and ultimately assist managers in making decisions regarding watershed management actions. Thus, in this study, we analyze soil particle size distribution in various land uses/covers in the Vaz watershed in Mazandaran province using laser particle size analysis and statistical indices. Materials and Methods In the present study, the land use map of the watershed was prepared using Landsat satellite images in remote sensing software (ENVI). Then, using this map, the area of land uses and access routes to the region were determined, resulting in 47 sampling points, including 25 points for forest, 13 points for rangeland, 4 points for agriculture, and 5 points for residential areas. Samples were then collected in the field. The samples were exposed to open air to dry their moisture; subsequently, approximately 50 grams of each sample were placed in an oven for final drying after removing straw, chaff, and other waste materials for 24 hours. Finally, 30 grams of each were placed in Falcon tubes and sent to the laboratory for laser analysis. After receiving the laser particle size distribution results, these results were examined in terms of clay, silt, sand, pebble, mean particle size, sorting, skewness, kurtosis, 10d, 50d (median), and 90d. To extract particle size statistics and percentages of particle fractions, the GRADISTAT macro written in Excel was used. Results and Discussion The results showed that the average particle size of clay, silt, sand, and pebble in forest land use was 1.03, 51.52, 45.39, and 2.06, respectively; in rangeland use, it was 0.62, 52.41, 45.21, and 1.76; in agricultural land use, it was 1.33, 75.2, 23.39, and 0.08; and in residential land use, it was 0.57, 36.96, 54.5, and 7.97. The particle size distribution in forest and rangeland uses indicates that silt and sand fractions have the highest percentages, reflecting a balanced soil structure. This may be due to the influence of vegetation and the accumulation of organic matter. Agricultural land use has a significant percentage of silt (75.2%), indicating a finer texture, likely resulting from agricultural practices such as tillage. Residential land use shows a higher proportion of sand (54.5%) and pebble (7.97%), which is associated with construction activities in this land use. The evaluation of grain size statistics reveals that the mean particle size in residential land use is 2.70 Φ, in forest land use 3.99 Φ, in rangeland use 4.04 Φ, and in agricultural land use 5.37 Φ, corresponding to fine sand, very fine sand, silt, and silt textures, respectively. The presence of sand and pebble mines in the area and construction activities are the main reasons for the coarser mean particle size in residential land use. The sorting (standard deviation) of soil particles in agricultural land use is 1.78 Φ, in residential land use 2.21 Φ, in forest land use 2.21 Φ, and in rangeland use 2.24 Φ. According to Folk's classification, residential, forest, and rangeland uses have very poor sorting, while agricultural land use has poor sorting. The skewness of particles in agricultural land use is -0.41, in rangeland use -0.07, in forest land use -0.03, and in residential land use 0.23. Based on Folk's classification, skewness is nearly symmetrical in forest and rangeland uses, skewed toward very coarse particles in agricultural land use, and skewed toward coarse particles in residential land use. Conclusion In general, analyzing soil particle size is crucial for identifying erosion-sensitive areas. In this study, we conducted soil particle size analysis in the Vaz watershed using sampling from various land uses/covers and laser technology. The predominance of silt and sand in the watershed indicates its sensitivity to erosion. Given that the Vaz watershed has a steep slope and considering the construction activities throughout the watershed, as well as the low presence of stable particles (coarse particle size), it is recommended that some watershed management actions be implemented in the upper part of the watershed. Also, the results obtained and the distribution the map of soil particles can assist in prioritizing soil conservation measures and controlling erosion in the Vaz watershed. ______________________________________ توزیع اندازة ذرات خاک، ویژگی فیزیکی مهمی است که بر ساختار خاک تأثیر می‌گذارد و ارتباط نزدیکی با بافت و رفتار خاک دارد. بنابراین، در این پژوهش با استفاده از دانه‌بندی لیزری و شاخص‌های آماری به تحلیل دانه‌بندی خاک در کاربری‌های/پوشش‌های مختلف اراضی در حوزة آبخیز واز در استان مازندران پرداخته شد. در پژوهش حاضر 47 نقطة نمونه‌گیری شامل 25 نقطه برای جنگل، 13 نقطه برای مرتع، چهار نقطه برای کشاورزی و پنج نقطه برای مسکونی مشخص و نمونه‌ها از عمق 5 سانتی‌متری و به مقدار تقریبی 500 تا 700 گرم برداشت شد. سپس نمونه‌ها در معرض هوای آزاد خشک شده و بعد از آن با خارج کردن مواد زائد حدود 50 گرم از هر نمونه‌ در آون قرار داده شدند. در نهایت، مقدار 30 گرم از آن‌ها در لوله فالکون ریخته شد و برای آنالیز لیزری به آزمایشگاه انتقال داده شدند. پس از دریافت نتایج دانه‌بندی لیزری، این نتایج از نظر مقدار رس، سیلت، ماسه، سنگ‌ریزه، میانگین اندازه ذرات، جورشدگی، چولگی، کشیدگی، 10d، 50d (میانه) و 90d مورد بررسی قرار گرفتند. نتایج نشان داد که میانگین اندازة ذرات رس، سیلت، ماسه و سنگ‌ریزه به‌ترتیب در کاربری جنگل برابر 03/1، 52/51، 39/45 و 06/2، در کاربری مرتع برابر 62/0، 41/52، 21/45 و 76/1، در کاربری کشاورزی برابر 33/1، 2/75، 39/23 و 08/0، و در کاربری مسکونی برابر 57/0، 96/36، 5/54 و 97/7 درصد است. توزیع اندازة ذرات در کاربری‌های جنگل و مرتع نشان می‌دهد که بخش‌های سیلت و ماسه دارای بیش‌ترین درصد هستند، که نشان‌دهندة ساختار متعادل خاک است. با توجه به اینکه حوزة آبخیز واز دارای شیب زیاد است و از طرفی با توجه به ساخت و ساز در آن پیشنهاد می‌شود اقدامات آبخیزداری متناسب در بالادست حوضه انجام ‌شود. نتایج به‌دست آمده و نقشة توزیعی ذرات خاک می‌تواند به اولویت‌بندی اقدامات حفاظت خاک و هم‌چنین کنترل فرسایش در حوزه آبخیز واز کمک کند.

https://mmws.uma.ac.ir/article_3959_6b67ee475a31b113bb82a47f89e564a6.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Analysis of the relationship between land use contributions to sediment yield with landscape metrics and soil erosion factors in the Kasilian watershed تحلیل ارتباط سهم کاربری‌های اراضی در تولید رسوب با سنجه‌های سیمای سرزمین و عوامل مؤثر بر فرسایش خاک در حوزه آبخیز کسیلیان 94 108 3964 10.22098/mmws.2025.17776.1621 FA فاطمه اکبری امام زاده دانشجوی دکتری گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران عبدالواحد خالدی درویشان دانشیار گروه مهندسی آبخیزداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران مهدی وفاخواه استاد گروه آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور، ایران کاظم نصرتی استاد گروه جفرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران Journal Article 2025 07 07 Introduction Soil erosion and sedimentation are integral components of natural geomorphological cycles, playing a pivotal role in regulating watershed functionality, preserving water quality, maintaining agricultural productivity, and ensuring the long-term sustainability of ecosystems. Under natural conditions, these processes contribute to landscape evolution and nutrient redistribution. However, human activities combined with climate change have greatly accelerated erosion and sedimentation rates, causing severe environmental damage and substantial economic losses worldwide. Factors such as land use changes, deforestation, overgrazing, and improper land management have intensified soil degradation and sediment transport, seriously threatening both terrestrial and aquatic ecosystems across diverse regions. In response to these growing challenges, understanding sediment source dynamics has become critically important. Identifying main sediment contributors and accurately measuring their relative impacts are vital for developing effective conservation and management strategies. Additionally, analyzing the landscape structure through landscape metrics which assess spatial patterns, connectivity, and fragmentation of land uses offers important insights into how landscape features directly affect erosion and sediment movement processes. Integrating such comprehensive landscape level analyses significantly improves our ability to predict erosion risks and design targeted interventions to effectively protect vital soil and water resources, ultimately supporting environmental health and promoting sustainable land management practices for future generations. Materials and Methods This study aimed to examine the effects of landscape metrics and erosion-related factors on suspended sediment generation across different land use types in the Kasilian Watershed, located in Mazandaran Province, northern Iran. The area lies on the southern slopes of the Alborz Mountains and drains into the Caspian Sea. It features a complex land use mosaic including natural forest, agriculture, rangeland, and plantation forest, each with distinct structural and ecological characteristics affecting erosion potential. A sediment fingerprinting approach using 59 geochemical tracers was employed. These tracers were analyzed in 36 source samples and 8 suspended sediment samples collected at the watershed outlet. Discriminant function analysis was performed using the FingerPro package in R. Potassium, sodium, and lead were identified as the most effective tracers for source discrimination. These elements were then applied in a multivariate model to estimate the proportional contributions of each land use type. Simultaneously, spatial analysis using the Revised Universal Soil Loss Equation (RUSLE) was conducted. RUSLE factors included soil erodibility, rainfall erosivity, topographic slope-length and steepness, cover management, and conservation practices. These were mapped with spatial data and overlaid with land use distribution. Landscape structure was also quantified using FRAGSTATS 4.2 to calculate metrics such as patch density, mean patch size, edge contrast, and nearest neighbor distance. Results and Discussion The sediment source apportionment results revealed a striking dominance of rangelands, which contributed an estimated 76% of the suspended sediment load. In contrast, natural forests, agricultural lands, and plantation forests were responsible for only 9%, 6%, and 6% of sediment contributions, respectively. This disproportionate contribution from rangelands is particularly noteworthy given their relatively remote position from the watershed outlet. Several factors account for this observation: (1) Rangelands exhibited the highest values for the cover-management factor (C), indicating sparse and degraded vegetation cover; (2) The slope-length and steepness factor (LS) was also elevated in rangeland areas, signaling steep terrain prone to rapid runoff and detachment; (3) Soil erodibility (K) was greater in these zones, further exacerbating erosion potential. Additionally, a high mean Euclidean nearest neighbor distance (ENN_MN) in rangeland parcels suggests that these patches are more isolated and less buffered by adjacent vegetative covers, reducing their resilience to erosive forces. These results underscore the complexity of sediment production processes and highlight the importance of considering both biophysical and spatial variables in watershed-scale erosion studies. Despite the geographical distance of rangelands from the watershed outlet, their structural fragility, topographic exposure, and low vegetative cover make them particularly susceptible to erosion and major contributors to downstream sediment loads. Conclusion From a management perspective, the integration of landscape metrics with erosion related biophysical parameters provides a comprehensive framework for identifying critical sediment source areas. This approach goes beyond traditional point-based monitoring and enables a broader understanding of sediment dynamics across the landscape. The insights gained from this study offer a solid scientific basis for prioritizing conservation actions such as reforestation, rangeland restoration, slope stabilization, and the implementation of soil conservation practices tailored to specific local conditions. Moreover, this research contributes meaningfully to the broader conversation on sustainable watershed management in the face of climatic uncertainty and increasing human pressures. By combining sediment fingerprinting techniques, geospatial analysis, and landscape ecology principles, this study presents a replicable methodology that can be applied in other watersheds facing similar challenges. In conclusion, the findings emphasize the urgent need for integrated assessment frameworks that link land use, topography, and landscape structure with sediment generation and transport processes. These approaches are essential for guiding targeted, evidence-based policies and building ecological resilience. As climate change continues to intensify hydrological extremes and land degradation, proactive sediment management informed by science and supported by spatial tools will be vital to maintaining the productivity, sustainability, and environmental health of watersheds. اجرای برنامه‌های مؤثر در حفاظت از منابع آب‌ و خاک و کنترل رسوب، نیازمند درک دقیق از منابع رسوب و سهم نسبی آن‌ها در فرآیندهای تولید رسوب است؛ چرا که این اطلاعات در شناسایی مناطق بحرانی و هدایت اقدامات مدیریتی نقش اساسی ایفا می‌کنند. پژوهش حاضر با هدف تحلیل ارتباط سهم کاربری‌های مختلف اراضی در تولید رسوب معلق با سنجه‌های سیمای سرزمین و سایر عوامل مؤثر بر فرسایش خاک است. در حوزة آبخیز کسیلیان استان مازندران انجام شد. بدین منظور 59 ردیاب ژئوشیمی در 36 نمونة منابع رسوب شامل کاربری‌های جنگل طبیعی، کشاورزی، مرتع و جنگل دست کاشت و 8 نمونة رسوب معلق اندازه‌گیری شد. با بهره‌گیری از آزمون‌های آماری دامنه، کروسکال-والیس و تحلیل تابع تفکیک در بستة نرم‌افزاری FingerPro در محیط R، ردیاب‌های بهینه برای منشأیابی رسوبات معلق تعیین شدند؛ به‌طوری‌که عناصر پتاسیم (K)، سدیم (Na) و سرب (Pb) به‌عنوان ردیاب‌های مؤثر انتخاب شدند. تحلیل سنجه‌های سیمای سرزمین با استفاده از نرم‌افزار FRAGSTATS انجام شد. نتایج حاصل از منشأیابی رسوبات معلق نشان داد که سهم نسبی کاربری‌های مرتع، جنگل طبیعی، کشاورزی و جنگل دست‌کاشت در تولید رسوب به‌ترتیب 76، 9، 6 و 6 درصد است. هم‌چنین نتایج نشان داد که سهم بالای کاربری مرتع در تولید رسوب، با توجه به مقادیر بالای ضریب پوشش گیاهی (33/0C=)، فرسایش‌پذیری خاک (03/0K=)، عامل توپوگرافی (58/25LS=)، و شاخص پراکندگی لکه‌ها (3/575ENN-MN=)، قابل‌تبیین است. این نتایج می‌تواند پایه‌ای علمی برای شناسایی مناطق بحرانی، اولویت‌بندی اقدامات حفاظتی، و تدوین راهبردهای مؤثر در مدیریت جامع منابع آب و خاک باشد.

https://mmws.uma.ac.ir/article_3964_794b5e3dee75f600c46a063ebe35597e.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Rainfall-runoff prediction using the GR2M Hydrological Model under Sixth IPCC Scenarios: A Case Study of Lazoreh and Jangaldeh Watersheds پیش‌بینی بارش-رواناب با مدل هیدرولوژیکی GR2M تحت تأثیر سناریوهای گزارش ششم تغییر اقلیم (مطالعه موردی: حوزه‌های آبخیز لزوره و جنگلده) 109 131 4023 10.22098/mmws.2025.17923.1634 FA خلیل قربانی عضو هیات علمی گروه مهندسی آب دانشگاه علوم کشاورزی و منابع طبیعی گرگان موسی حسام گروه مهندسی آب. دانشکده مهندسی آب و خاک. دانشگاه علوم کشاورزی و منابع طبیعی گرگان. گرگان. ایران. لاله رضائی قلعه دانش آموخته دکتری-گروه مهندسی آب. دانشکده کشاورزی. دانشگاه ارومیه. ارومیه. ایران فریبا نیرومند فرد دانش آموخته دکتری. گروه مهندسی آب. دانشکده کشاورزی. دانشگاه بیرجند. بیرجند. ایران میثم سالاری جزی گروه مهندسی آب. دانشکده مهندسی آب و خاک. دانشگاه علوم کشاورزی و منابع طبیعی گرگان. گرگان. ایران Journal Article 2025 07 27 Introduction Climate change, one of the major challenges of the 21st century, has far-reaching impacts on natural systems and human societies. These changes, including changing precipitation patterns, increasing the intensity of droughts and floods, and changing temperatures and evaporation, pose serious uncertainties for the sustainable management of water resources. Runoff, as a key component of the hydrological cycle, plays a vital role in agricultural water supply, groundwater recharge, and river flow, and its disruption has direct consequences for aquatic ecosystems and human livelihoods. Golestan Province, and in particular the Gorganrood Basin, with its geographical and climatic diversity, is considered a region sensitive to climate change. The two sub-basins of Lazoreh and Jangaldeh are of particular importance because they provide significant surface water resources, and the economic and agricultural activities of the region depend on them. In this study, a simple and valid GR2M precipitation-runoff model was used to assess the impact of climate change on monthly runoff. This model, with minimal data required, allows for accurate simulation of hydrological processes and analysis of future scenarios. Such a level of research, focusing on sub-basins, will help policymakers and water resource managers design solutions that are adaptable to future climate conditions. These measures will not only help reduce agricultural vulnerability and ensure food security, but will also be effective in reducing social tensions caused by water scarcity. Overall, this study aims to provide a scientific and practical understanding of sustainable water resource management in the face of climate change. Materials and Methods In the present study, the GR2M precipitation-runoff model was used to simulate monthly runoff. This model is considered a suitable option for analysis at the monthly scale due to its simple structure and minimal data requirements. To predict future climate conditions (2023–2100), the outputs of the ACCESS-ESM1-5 global climate model were used under three scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. These outputs were downscaled with the help of the LARS-WG8 random weather generator to better reflect local characteristics. In order to calibrate and validate the model, daily temperature (minimum and maximum) and precipitation data from the Minudasht evapotranspiration station during 1993 to 2022, and monthly river flow data from two hydrometric stations during 2011 to 2022 were used. Also, potential evapotranspiration was estimated using the Thornthwaite method. Combining observational data, climate projections, and hydrological modeling has enabled a more detailed analysis of the impacts of climate change on water resources and provided scientific insights for sustainable water management in the region. Results and Discussion The results from the ACCESS-ESM1-5 model output and under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the future period (2023-2100) show that the average minimum and maximum temperatures in the study area in the future period (2023-2100) under all three scenarios have increased compared to the observation period (1993-2022). The results show that the average minimum temperature in the observation period (1993-2022) is 12.66 °C. While the increase in the average minimum temperature under the SSP1-2.6 scenario in the future period compared to the observation period in the time period (2077-2100) is 14.28 °C, and under the SSP2-4.5 and SSP5-8.5 scenarios, the highest increase in the minimum temperature in the time period is also in the time period (2077-2100) at 15.48 and 17.28 °C, respectively. The results show that the average maximum temperature in the observation period is 25.08 °C, but the highest increase in the maximum temperature for all three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, will occur on average in the final period (2077-2100) at 26.88, 28.07, and 29.89 °C, respectively. The results show that the minimum and maximum temperatures under all three scenarios will increase in the future period compared to the observation period. The precipitation parameter in the observation period is also 441.06 mm, with the highest increase in precipitation compared to the observation period under all three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, in the time interval (2077-2100) being 493.07, 489.53, and 513.75 mm, respectively. The output results of the GR2M model for simulating the flow of the Chehelchay and Narmab rivers at the hydrometric stations of Lazoreh and Jangaldeh during the observation period show that the model performs well in both calibration and validation periods, as shown by the Kling-Gupta values and the root mean square error. The Kling-Gupta efficiency criterion and the root mean square error in the validation period for the Lazore watershed are 0.68 and 14.95, and in the Jangaldeh watershed are 0.68 and 24.52. Therefore, the results in the observational section show that the model performs well in simulating the flow rate. The GR2M model estimated the monthly flow reasonably well, but in some months, there were differences between the observed and simulated values, indicating overestimation or underestimation. The results of future flow predictions under the SSP1-2.6 and SSP2-4.5 scenarios show that after 2040, the river flow will decrease due to increasing temperature, but in the final years, the river changes correspond well to the rainfall fluctuation pattern. In the SSP5-8.5 scenario, it is observed that in the period 2040 to 2060, increasing temperature and precipitation have largely caused the flow to increase, but then in the period 2060 to 2100, the flow velocity decreases significantly with increasing temperature and decreasing precipitation. Conclusion The results show that climate change, especially under high emission scenarios, will have a negative impact on surface water availability in the two watersheds of Lazoreh and Jangaldeh. This underscores the urgent need for adaptive water management strategies, including improved irrigation efficiency, water allocation planning, and drought preparedness. Furthermore, given the satisfactory performance of the GR2M model in this study, its simplicity, low data requirements, and ease of implementation make it a suitable tool for hydrological modeling and climate impact assessment in other data-scarce basins across Iran and similar regions. This research contributes to the growing body of evidence on climate change impacts in semi-arid regions and provides actionable insights for regional water authorities and policymakers aiming to ensure water security under future climatic uncertainty. مدل‌های بارش-رواناب ابزار مهمی برای شبیه‌سازی جریان رودخانه و درک فرآیندهای هیدرولوژیک هستند، اما انتخاب آن‌ها نیازمند شناخت محدودیت‌ها و قابلیت‌هاست. در این تحلیل‌ها باید اثر تغییرات اقلیمی، گازهای گلخانه‌ای و نوسانات اقلیمی نیز لحاظ شود تا نتایج به واقعیت نزدیک‌تر باشند. بنابراین، در این پژوهش ابتدا با استفاده از مدل هیدرولوژیکی GR2M به شبیه‌سازی بارش-رواناب ماهانه دو رودخانة چهل‌چای و نرماب در دو حوزة آبخیز لزوره و جنگلده پرداخته شده است. سال‌های آماری (2011-2022) به‌عنوان دورة مشاهداتی مشترک هر دو ایستگاه هیدرومتری و سال‌های (2023-2100) به‌عنوان دورة آینده برای پیش‌بینی دبی جریان تحت جدیدترین سناریوهای گزارش ششم تغییر اقلیم SSP1-2.6، SSP2-4.5 و SSP5-8.5 در نظر گرفته شده است. معیار کارایی کلینگ-گوپتا و میانگین ریشة مربعات خطا در دورة صحت‌سنجی برای حوزة آبخیز لزوره برابر با 68/0 و 95/14 و در حوزة آبخیز جنگلده برابر با 68/0 و 52/24 است. بررسی نتایج نشان می‌دهد که مدل عملکرد خوبی در شبیه‌سازی دبی جریان دارد. در ادامه نتایج پیش‌بینی جریان تحت تأثیر تغییر اقلیم در آینده نشان می‌دهد که دبی جریان هر دو ایستگاه تحت دو سناریوی SSP1-2.6 و SSP2-4.5 در بازة زمانی 2020-2060 کاهشی است و در بازة زمانی 2080-2100 افزایش خواهد یافت. در حالی که دبی جریان هر دو ایستگاه تحت سناریوی بد‌بینانه SSP5-8.5 در بازة زمانی 2040-2060 روندی افزایشی دارد و در بازة زمانی 2060-2100 روند جریان نیز کاهشی است. در نهایت مهم‌ترین نکته‌ای که باید مورد توجه قرار گیرد این است که تحت هر سه سناریو مورد بررسی مقدار جریان تحت تأثیر تغییرات اقلیمی به مراتب نسبت به دورة مشاهداتی کاهش خواهد یافت که این موضوع لزوم توجه بیش‌تر به برنامه‌ریزی منابع آب در پایاب این حوضه‌ها را برجسته می‌کند.

https://mmws.uma.ac.ir/article_4023_caf5465d5b16295f400f46790d4d1840.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Modeling soil water repellency in loess soils of northern Iran using machine learning مدل‌سازی آب‌گریزی خاک‌های لسی شمال ایران با الگوریتم‌های یادگیری ماشین 132 148 4027 10.22098/mmws.2025.17919.1633 FA علی محمدیان بهبهانی دانشیار، گروه مدیریت مناطق بیابانی، دانشکدة مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گلستان، ایران کهزاد حیدری استادیار پژوهشی بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات، آموزش کشاورزی و منابع طبیعی خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز، خوزستان، ایران محسن حسینعلی زاده دانشیار، گروه مدیریت مناطق بیابانی، دانشکدة مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گلستان، ایران Journal Article 2025 07 26 Extended Abstract Introduction A major hydrological and physical event affecting surface runoff, erosion, and water infiltration is soil water repellency (SWR). Hydrophobic soils reject wetting, therefore causing water droplets to linger on the surface instead of penetrating the soil profile. Particularly in sloping environments and arid ecosystems, this disease causes more overland flow, less water retention, and higher sensitivity to soil loss. SWR is progressively seen in northern Iran, especially in the loess-derived soils of Golestan and Mazandaran provinces, as a result of a mix of climatic conditions and changes in land use. Fine silty to silty-clay textures characterize loess soils in these areas; together with environmental stressors including drought cycles, agricultural development, and deforestation, they aid in the formation and magnification of SWR. Central in the water repellency are organic compounds, especially hydrophobic plant-derived substances like waxes and lignins. Emphasizing the significance of soil chemical composition, many studies have shown that soil organic carbon is strongly positively linked to SWR intensity. Variations in clay content, pH, and electrical conductivity (EC) can also affect SWR patterns. Although SWR is very important in soil degradation processes, little research has been done employing sophisticated data-driven techniques to forecast its spatial variability. Machine learning (ML) algorithms like Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) provide strong means for modeling complicated soil behavior. Using these algorithms, the current study attempts to forecast SWR in loess soils based on a thorough set of physicochemical parameters, therefore helping to justify improved soil management and erosion control measures. Materials and Methods Northern Iran served as the location for the study, which looked at specific loess terrains in Golestan and Mazandaran provinces. From many sites including Gorgan, Maraveh Tappeh, Neka, Sari, and Amol, 45 surface soil samples (depth 0–10 cm) were gathered. While minimizing confounding effects, sampling places were chosen to record changes in topography, land use, and vegetation. Key soil physicochemical characteristics assessed were organic carbon (OC), organic matter (OM), electrical conductivity (EC), pH, mean weight diameter (MWD) of soil aggregates, and particle size distribution (sand, silt, clay). With infiltration time recorded up to 3000 seconds, WDPT tests involved dropping 50 μL distilled water droplets on air-dried soil surfaces at room temperature. WDPT values were used as the target variable in model development. Procedures used in laboratories complied with the same criteria used in earlier research. R software was used for data preprocessing. Outlier detection based on interquartile range (IQR), Z-score normalization of numerical variables, and multicollinearity analysis utilizing the Variance Inflation Factor (VIF) were included in this. Categorical variables like soil texture classes were converted to dummy variables using one-hot encoding. Three machine learning approaches—Decision Tree (CART approach), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were applied to the dataset, which was randomly separated into 70% training and 30% testing parts. Models were implemented using R packages rpart, randomForest, and xgboost. Through repeating 10-fold cross-validation, hyperparameter tuning was carried out to enhance prediction accuracy. Results and Discussion Initial model performance using default settings revealed limited predictive ability across all algorithms. The Decision Tree (DT) model yielded the weakest results with RMSE = 19.55 and R² = 0.02, indicating poor capacity to capture the variability in WDPT values. After hyperparameter optimization, both Random Forest (RF) and XGBoost (XGB) showed significant improvements. The RF model achieved RMSE = 15 and R² = 0.42, while XGB recorded RMSE = 14.7 with the same R², highlighting their comparable predictive power. Feature importance analysis revealed that organic carbon was the most influential predictor of WDPT across all models. Additional influential variables included clay content, sand fraction, EC, OM, and pH, though their relative importance varied by algorithm. In RF, organic matter and sand had high predictive value, whereas in XGB, clay and EC gained prominence. These differences reflect each model's inherent sensitivity to nonlinear interactions. Spatial analysis showed that areas with higher organic carbon content aligned with regions of higher WDPT, confirming the key role of hydrophobic organic compounds in driving soil water repellency. Uncertainty assessment using Bootstrap and Monte Carlo simulations demonstrated that RF was the most stable model, showing the lowest RMSE variability and higher resilience to noisy input data. Overall, the results confirm that machine learning algorithms, especially RF and XGB, can effectively model and interpret the complex interactions influencing soil water repellency in loess landscapes. Conclusion This study demonstrated the applicability of advanced machine learning algorithms for modeling soil water repellency (SWR) in loess-derived soils of northern Iran. Among the three tested models, Random Forest (RF) provided the most reliable and stable predictions, with optimal performance metrics (RMSE = 15, R² = 0.42) and low sensitivity to data uncertainty. XGBoost (XGB) also yielded competitive results but showed slightly lower stability under uncertain conditions. The Decision Tree (DT) model, while interpretable, lacked sufficient predictive accuracy for complex, nonlinear relationships. The results confirmed that organic carbon is the dominant driver of SWR in the study area, supporting previous findings regarding the hydrophobic nature of plant-derived organic compounds. Other variables such as clay content, sand fraction, pH, and EC also played important roles depending on the model structure. Differences in variable importance highlight the benefit of using multiple algorithms to obtain a comprehensive understanding of the underlying mechanisms. Uncertainty analysis showed that RF is less susceptible to overfitting and data noise, making it a more robust choice for environmental modeling. Spatial patterns of WDPT and key soil variables revealed strong regional correlations, suggesting the feasibility of using geospatial ML models for site-specific soil management. Future research should explore hybrid models (e.g., RF-XGB) and deep learning architectures (e.g., CNNs or ActionFormer) to enhance predictive power, particularly in dynamic or post-disturbance soil systems. Moreover, integrating multi-temporal datasets could improve the understanding of SWR variability under different environmental and management conditions. یکی از ویژگی‌های مهم خاک که بر چگونگی حرکت آب، نفوذ آن و فرسایش سطحی اثر می‌گذارد، خاصیت آب‌گریزی آن است. این پژوهش باهدف بررسی و تخمین میزان آب‌گریزی خاک بر اساس شاخص WDPT و با استفاده از روش‌های یادگیری ماشینی مثل درخت تصمیم، جنگل تصادفی و XGBoost در خاک‌های لسی شمال ایران انجام شد. به‌این منظور، از ۴۵ موقعیت مختلف نمونه‌برداری خاک انجام گرفت و ویژگی‌های فیزیکی و شیمیایی آن‌ها، شامل میزان کربن آلی، مواد آلی، هدایت الکتریکی، pH، اندازة ذرات خاک، درصد ماسه، رس و سیلت اندازه‌گیری شد. عملکرد مدل‌ها با استفاده از معیارهای آماری RMSE، MAE و R² و هم‌چنین با تحلیل حساسیت و عدم قطعیت سنجیده شد. در ابتدا، عملکرد مدل‌ها چندان مطلوب نبود، اما با تنظیم دقیق پارامترها، دقت پیش‌بینی به شکل چشم‌گیری بهبود یافت. پس از بهینه‌سازی، مدل جنگل تصادفی با RMSE برابر با ۱۵، MAE برابر با 11/93 و R² برابر با 42 بهترین نتیجه را ارائه داد. مدل XGBoost نیز پس از تنظیم پارامترها با RMSE برابر با 7/14 و R² برابر با 42 در رتبة دوم قرار گرفت. مدل درخت تصمیم همچنان ضعیف‌ترین عملکرد را داشت. تحلیل عدم قطعیت با روش‌های بوت‌استرپ و مونت‌کارلو نشان داد که مدل جنگل تصادفی کم‌ترین پراکندگی و نوسان RMSE را دارد و از پایداری بیش‌تری برخوردار است. تحلیل حساسیت مدل‌ها نشان داد که کربن آلی مهم‌ترین عامل در پیش‌بینی WDPT در همة مدل‌ها است. سایر عوامل مانند رس، مواد آلی، هدایت الکتریکی و pH نیز در مدل‌های مختلف نقش مهمی ایفا کردند. نتایج این پژوهش نشان می‌دهد که الگوریتم‌های پیشرفتة یادگیری ماشین، به ویژه مدل جنگل تصادفی، می‌توانند ابزارهای قوی و قابل اعتمادی برای مدل‌سازی و مدیریت پدیده‌های پیچیده مانند آب‌گریزی خاک در مناطق حساس مانند خاک‌های لسی شمال ایران باشند. یافته‌های این پژوهش می‌تواند به‌عنوان مبنایی برای ارزیابی و مدیریت آب‌گریزی خاک‌های لسی و بهینه‌سازی راهکارهای کاهش فرسایش و بهبود نفوذپذیری در مناطق مشابه به‌کار رود.

https://mmws.uma.ac.ir/article_4027_870af0adf271967f1ffeaee55b4041c3.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 The effect of wind erosion on soil physicochemical properties and microbial activity response in the sustainability of the Sistan Plain ecosystem تأثیر فرسایش بادی بر ویژگی‌های فیزیکوشیمیایی خاک و پاسخ فعالیت میکروبی در پایداری اکوسیستم دشت سیستان 149 163 4048 10.22098/mmws.2025.18242.1664 FA مرتضی صابری دانشیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران رسول خطیبی استادیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران ایران محمدرضا دهمرده قلعه نو دانشیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران Journal Article 2025 09 02 Introduction Soil degradation is closely related to increased wind erosion. Wind erosion has multifaceted and widespread effects on arid and semi-arid ecosystems. This phenomenon weakens soil structure and fertility by decreasing the soil's physicochemical quality, reducing organic matter and nutrients, and causing significant biological changes, thereby limiting the ecosystem's capacity. In the Sistan Plain, the occurrence of long and strong winds, known as the 120-day winds, is a prominent example of this erosion that has numerous consequences for the environment and human societies. These winds, by moving soil particles and reducing microbial activity, not only disrupt ecosystem performance but also threaten people's physical health through the spread of polluted dust, leading to increased respiratory problems, allergies, and chronic diseases. Moreover, continuous exposure to these conditions can create psychological and social stress, reduce agricultural productivity and quality of life, and result in economic and migration consequences. These conditions highlight the importance of carefully examining the effects of wind erosion on the physicochemical properties and microbial activities of the soil and its broad implications for ecosystem sustainability and regional challenges. Materials and Methods In the present study, the region was categorized into four levels of soil erosion intensity as the main treatment: no erosion, low, moderate, and severe erosion. To investigate the effects of these treatments on changes in soil properties, sampling was conducted based on a completely randomized design in the fall of 2023. At each erosion level, five homogeneous areas with similar physiographic conditions were selected, and five soil samples were collected from each area at a depth of 0 to 30 cm. The soil samples were combined using a composite sampling method. Some samples were immediately transported to the laboratory in sealed containers and stored in a refrigerator after collection to measure microbial characteristics while maintaining initial humidity. The remaining samples were air-dried and passed through a 2 mm sieve to determine physical and chemical properties. Physical characteristics assessed included soil texture, bulk density, and porosity, chemical characteristics included organic carbon, total nitrogen, available phosphorus and potassium, acidity, electrical conductivity, calcium carbonate, sodium absorption ratio. Microbial characteristics were catalase enzyme activity, basal and stimulated microbial respiration, microbial biomass carbon and nitrogen, and microbial contribution. Data were analyzed using one-way analysis of variance (ANOVA) in SPSS software version 26, and means were compared using Duncan's test at a 95% confidence level. Additionally, correlations among the studied characteristics were examined using R software. Results and Discussion The results of this study demonstrated that the intensity of wind erosion had significant and widespread effects on the physicochemical and microbial properties of the Sistan Plain soil. As erosion intensity increased, the levels of organic carbon, total nitrogen, available phosphorus and potassium, as well as soil porosity, decreased significantly. Conversely, bulk density, electrical conductivity, calcium carbonate, and sodium absorption ratio showed a significant increase. This pattern indicated the removal of fine particles rich in organic matter and nutrients from the soil surface, while coarser particles, richer in calcium carbonate and salts, remained as a result of wind erosion. Changes in surface soil pH were not significant. Additionally, the results indicated a significant decline in microbial activity and soil biological indicators. Catalase enzyme activity, basal and stimulated microbial respiration, microbial biomass carbon and nitrogen, and microbial population all decreased progressively with increasing erosion intensity. The highest values were observed in non-eroded soils, while the lowest values occurred in soils subjected to severe erosion. The ratio of microbial biomass carbon to nitrogen exhibited significant changes but no clear trend. The relative contribution of microbial carbon to total organic carbon increased at high erosion intensities, likely due to a sharp decline in total organic carbon. Correlations between erosion intensity and microbial indices were strongly negative (-0.94 to -0.99), whereas the microbial C/N ratio showed a moderate positive correlation, and the soil microbial contribution exhibited a weak negative correlation. These findings highlight that wind erosion directly limits microbial activity, organic matter decomposition, and soil nutrient cycling, posing a severe threat to the sustainability of soil fertility and ecosystem function in the Sistan Plain. Implementing appropriate soil management practices is essential to reduce erosion, preserve soil microbiota, and ensure ecosystem stability. Conclusion The present study indicates that wind erosion not only alters the physical and chemical properties of the soil but also significantly impacts the activity and dynamics of the microbial community. The reduction of organic matter, changes in bulk density, porosity, nutrient loss, and decreased microbial activity suggest that wind erosion limits the ecosystem's capacity to maintain biological function and sustainability. These findings underscore the importance of soil resource management and the development of conservation strategies in arid regions. They can also inform the design of sustainable agricultural programs aimed at mitigating the adverse effects of wind erosion. Future research should explore the interactions between soil physicochemical and microbial changes over extended time scales, as well as the combined effects of climate change and wind erosion, to develop more effective strategies for protecting dryland ecosystems. فرسایش بادی به‌عنوان یکی از عوامل اصلی تخریب خاک، تأثیر قابل‌توجهی بر فعالیت میکروبی خاک در دشت سیستان دارد. مطالعة حاضر در پاییز ۱۴۰۲ با هدف بررسی اثر شدت فرسایش خاک بر ویژگی‌های فیزیکی، شیمیایی و میکروبی خاک انجام شد. شدت فرسایش در چهار سطح بدون فرسایش، کم، متوسط و شدید دسته‌بندی شد و نمونه‌برداری از عمق صفر تا ۳۰ سانتی‌متری خاک در چهار منطقة همگن با شرایط فیزیوگرافی مشابه انجام شد. نتایج نشان داد که با افزایش شدت فرسایش، کربن آلی از ۵۳/۰ به ۱۴/۰ درصد، نیتروژن کل از ۰۶۱/۰ به ۰۱۲/۰ درصد، پتاسیم قابل جذب از ۶/۱۵۲ به ۵/۶۳ میلی‌گرم در کیلوگرم و فسفر قابل جذب از ۹/۶ به ۵/۱ میلی‌گرم در کیلوگرم کاهش یافت. تخلخل خاک نیز از ۳/۵۵ به ۳۳ درصد افت کرد، در حالی‌که وزن مخصوص ظاهری از ۱۲/۱ به ۵۴/۱ گرم بر سانتی‌متر مکعب، هدایت الکتریکی از ۰۴/۲ به ۶/۸ دسی‌زیمنس بر متر، کربنات کلسیم از ۹ به ۳/۱۹ درصد و شاخص جذب سدیم از ۹/۳ به ۵/۸ افزایش یافتند. تغییر pH خاک معنا‌دار نبود و خاک‌ها ماهیت نسبتاً خنثی خود را حفظ کردند. شاخص‌های میکروبی شامل فعالیت آنزیم کاتالاز، تنفس پایه و برانگیخته، کربن و نیتروژن زیست‌توده میکروبی و جمعیت میکروارگانیسم‌ها با شدت فرسایش کاهش قابل‌توجه داشتند. به‌طوری که کربن زیست‌توده میکروبی ۱۷۹ به ۳/۳۹ میلی‌گرم در کیلوگرم و نیتروژن زیست‌توده از ۶/۱۸ به ۲/۳ میلی‌گرم در کیلوگرم کاهش یافت. نسبت C/N زیست‌تودة میکروبی تغییر معنا‌دار داشت اما روند مشخصی نشان نداد و سهم نسبی میکروبی خاک در شدت‌های بالا افزایش یافت. همبستگی بین فرسایش و شاخص‌های میکروبی منفی و بسیار قوی بود. یافته‌ها نشان می‌دهند که فرسایش بادی توان اکوسیستم را در حفظ عملکرد و پایداری زیستی محدود می‌کند و مدیریت مناسب خاک برای کاهش فرسایش و حفظ میکروبیوتای خاک ضروری است. این مطالعه می‌تواند راهنمای طراحی برنامه‌های پایدار کشاورزی و حفاظت از اکوسیستم‌های خشک باشد.

https://mmws.uma.ac.ir/article_4048_ae9f8ad22a7caf872a754fa098cead44.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Assessment of the impacts of climate change on land surface temperature and drought risk in agricultural land use in Khuzestan province ارزیابی اثرات تغییر اقلیم بر دمای سطح زمین و خطر خشکسالی در کاربری کشاورزی استان خوزستان 164 191 4096 10.22098/mmws.2025.18202.1658 FA نیلوفر محمدی دانشجوی دکتری آب و هواشناسی، گروه آب و هواشناسی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران زهرا حجازی زاده استاد، گروه آب و هواشناسی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران پرویز پرویز ضیاییان فیروزآبادی استاد، گروه آموزشی سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران علیرضا کربلایی درئی استادیار آب و هواشناسی، گروه آب و هواشناسی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران اسلام گله بان دانشجوی دکتری سنجش از دور، گروه آموزشی سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیایی، دانشگاه تهران، تهران، ایران کاظم علوی پناه استاد، گروه آموزشی سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم جغرافیایی، دانشگاه تهران، تهران، ایران Journal Article 2025 08 27 Introduction Climate change is intensifying global droughts, posing severe threats to the environment, agriculture, and human livelihoods. The Earth's temperature is rising at twice the global average, driving significant changes in planetary conditions. Escalating land surface temperatures have degraded ecosystems, depleted soil quality, and reduced water availability, leading to the expansion of arid regions. These shifts stress vegetation and diminish agricultural yields, exacerbating food insecurity, particularly in developing nations. Drought and water scarcity are now prominent trends in arid and semi-arid regions. Altered precipitation patterns, influenced by climate change, disrupt rainfall quantity and distribution, further straining water resources. In countries like Iran, with predominantly dry climates, these climatic shifts are especially acute. Iran ranks among the top six nations for natural disasters, with over 83 percent of its crises tied to earthquakes, floods, and droughts. Drought, a recurring issue, inflicts significant damage on Iran's society, water, and soil resources. Khuzestan Province, a key agricultural and economic hub in southwestern Iran with rich water resources, is significantly affected by climate change. Rivers like Karun, Karkheh, and Jarahi, vital for drinking water, agriculture, and industry, are impacted by these changes. This study evaluates the impact of climate change on land surface temperature and drought risk in the agricultural regions of Khuzestan Province, aiming to address these critical challenges. Materials and Methods This study utilized two primary data sources to assess climate change impacts in Khuzestan Province. Climate parameters, including precipitation, radiation, and maximum/minimum temperatures, were obtained from the Abadan synoptic station (1985–2015) from the Meteorological Organization. Additionally, satellite data for 19 stations, covering temperature, radiation, and precipitation, were sourced from the ERA5 database for 1950–2025. Due to incomplete data from 21 meteorological stations, satellite data were used for the first time in this region to model and forecast temperature and precipitation. The Mann-Kendall Z-statistic test, applied at 95 and 99 percent significance levels, analyzed trends in temperature and precipitation changes. To address the low resolution of general circulation models for regional studies, the LARS-WG8 statistical model was employed for downscaling. This model used daily time series data, including precipitation (mm), maximum/minimum temperatures (C°), and radiation (MJ/m²/day), with a base period of 1985–2015, aligned with CMIP6 models. Projections were based on the CanESM5 model under the high-emission SSP5-8.5 scenario, downscaled for short-term (2021–2040) and medium-term (2031–2050) periods. Trends in modeled data were also evaluated using the Mann-Kendall test. Model performance was assessed in Excel using percentage error, correlation coefficient (R), and root mean square error (RMSE). Additionally, satellite data from Google Earth Engine were analyzed to calculate land surface temperature (LST), normalized difference vegetation index (NDVI), standardized precipitation index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). Results and Discussion An analysis of monthly and annual temperature trends from 1950 to 2025 across 19 stations in Khuzestan shows a significant upward trend in nearly all months. Projections using the LARS-WG8 model under the SSP585 scenario CanESM5 indicate an approximate 3 C° increase in both the near future (2021–2040) and medium-term future (2031–2050) compared to the 1985–2015 baseline. Data from the Abadan synoptic station and satellite observations confirm this warming, with satellite data predicting even hotter summers. In contrast, precipitation is projected to decline across all months in both future periods, with December and January remaining the wettest months. The 24- year NDVI record shows substantial vegetation cover fluctuations, with the lowest values in 2000, 2008, and 2009, likely linked to low rainfall and drought. A recovery has been observed since 2010, possibly reflecting improved climatic or hydrological conditions. Land surface temperature has shown a steady rise since 2010. The Standardized Precipitation Index (SPI) indicates severe droughts in 2000–2004, 2010, 2021, and 2022. The Standardized Precipitation Evapotranspiration Index (SPEI) shows that from 1980 to 1999, conditions were generally normal with alternating mild to moderate droughts. From 2000 to 2024, mild to moderate droughts predominated, while 2008–2022 marked a shift toward severe drought conditions. According to the SPEI index, the most extreme droughts occurred in December 2010 (−2.53) and December 2021 (−2.48). Conclusion This study comprehensively analyzes the drought trend in Khuzestan Province from 1950 to 2025 using meteorological and remote sensing data. Initial findings show a significant increase in temperature in all months except November, along with a decrease in precipitation in the long term, which is consistent with the CanESM5 model predictions of a 3 C° increase in temperature and a decrease in precipitation. These results confirm that global warming and human activities, especially in industrial cities such as Ahvaz and Abadan, have exacerbated drought conditions. The SPI and SPEI indices show severe drought periods between 2000 and 2022, accompanied by a decrease in NDVI and adverse effects on agriculture and vegetation. These findings highlight the impact of non-precipitation factors, such as artificial irrigation, in mitigating the effects of drought. However, limitations include uncertainties in the SSP5-8.5 scenarios, reliance on data from specific weather stations, and weak correlations between SPI and NDVI. To address these issues, future research should use hybrid climate models, extend data collection to rural areas, and integrate artificial intelligence for advanced analysis. Practical recommendations include promoting drought-resistant crops, optimizing irrigation systems, and educating local communities. This study provides a basis for water resources management and climate policies, and emphasizes the urgent need for adaptive measures to cope with intensifying droughts. تغییر اقلیم خشکسالی‌ها را شدیدتر کرده و چالشی جدی برای کشاورزی ایجاد نموده است. این پژوهش با هدف ارزیابی اثرات تغییر اقلیم بر دمای سطح زمین و ریسک خشکسالی در کاربری کشاورزی استان خوزستان انجام شد. داده‌های ماهواره‌ای (دمای بیشینه و کمینه، بارش و تابش) از 19 ایستگاه در خوزستان از پایگاه ERA5 برای دوره 2025-1950 جمع‌آوری گردید. برای پیش‌بینی دما و بارش در دوره‌های آتی نزدیک (2040-2021) و میان‌مدت (2050-2031)، از مدل CANESM5 تحت سناریوی SSP5-8.5 در مدل LARS-WG8 استفاده شد. پایش خشکسالی، پوشش گیاهی و دمای سطح زمین با داده‌های MOD11A1 در Google Earth Engine تحلیل گردید. بیش‌ترین مقدار آماره(Z) برابر با 42/5 در ایستگاه آبادان، ماه (جولای) همراه با شیب تغییرات معادل 024/0 سانتی‌گراد در سال بود. در مقابل کم‌ترین مقدار برابر با 26/0- در ایستگاه دهدز با شیب تغییرات 002/0- سانتی‌گراد مشاهده شد. این امر با گرمایش جهانی، آئروسل‌های انسانی و تغییرات کاربری اراضی در مناطق شهری سازگار است. شاخص پوشش گیاهی نوسانات قابل توجهی داشت و در سال‌های 2008 و 2009 به حداقل رسید. دمای سطح زمین از 2010 افزایشی بود. شاخص خشکسالی SPI نشان داد که خوزستان در سال‌های 2004 تا 2000، 2010، 2021 و 2022 خشکسالی‌های شدیدی را تجربه کرد. شاخص SPEI حاکی از آن است که از 1999 تا 1980، شرایط نرمال با خشکسالی‌های خفیف تا متوسط متناوب بود، اما از 2000 تا 2024، خشکسالی‌های خفیف تا متوسط غالب شد و از 2022 تا 2008 خشکسالی‌های شدید را تجربه کرده است. شدیدترین خشکسالی‌ها طبق شاخص SPEI در دسامبر 2010 (53/2-) و دسامبر 2021 (48/2-) ثبت شد. نتایج نشان‌دهنده تشدید خشکسالی در دوره‌های آتی تحت سناریوی SSP5-8.5 و حرکت مناطق کشاورزی به سمت بیابان‌زایی است. بنابراین، تدوین برنامه‌ریزی جامع اقلیمی برای سازگاری و مدیریت منابع آب در خوزستان و بحران‌های زیست محیطی ضروری است.

https://mmws.uma.ac.ir/article_4096_92acd4070f2d7158f96f386c7196a886.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Evaluation of point pedotransfer functions in estimating field capacity and permanent wilting point water content ارزیابی توابع انتقالی نقطه‌ای در برآورد رطوبت ظرفیت مزرعه و نقطه پژمردگی دائم 192 207 4097 10.22098/mmws.2025.18164.1652 FA پریسا کهخا مقدم استادیار گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران Journal Article 2025 08 23 Introduction The amount of available water in the soil for crop use, called available water (AW), is described as the capacity of the soil to retain water and make it available for root uptake. Irrigation management, agricultural projects, and soil water balance are some of the well-known practical applications related to AW. It is generally accepted that matric potentials at -33 kPa and -1500 kPa represent FC and PWP, respectively. An attractive alternative to direct and often laborious measurements of these soil hydraulic properties is their estimation using soil pedotransfer functions (PTFs). The efficiency of PTFs is not the same in different locations and conditions, so the performance of each PTFs should be evaluated according to the soil characteristics of each region. The Sistan Plain is one of the strategic border areas of eastern Iran and the edge of the desert. The main objective of this study is to evaluate the performance of 23 different PTFs models in predicting soil water retention at matric potentials of -33 kPa and-1500 kPa in the Sistan Plain. In order to determine the best PTF that is appropriate for the conditions of the study area, 100 soil samples from the Sistan region were used. Materials and Methods In order to conduct this study, 100 soil samples were collected from a part of the Sistan Plain for laboratory analysis and transported to the laboratory. Particle size distribution (sand, silt, and clay) was measured by hydrometric method, organic carbon by Walkey-Black method, and bulk density by cylinder method. Twenty-three PTFs were evaluated to predict water content (gravimetric and volumetric), including twelve functions for matric potential of -33 kPa and eleven functions for matric potential of -1500 kPa. PTFs can also be categorized based on the type of prediction, namely point and continuous PTFs, and in this study, point PTFs were evaluated. The selection of PTFs is limited to PTFs that use soil properties that are present in the data set of the present study. The sum of squared error method was used to calibrate the models under study. In this study, NRMSE, ME, r, and RES criteria were used to fully evaluate the models. Results and Discussion The results showed that the changes in the amounts of sand, silt, and clay ranged from 14 to 56, 27 to 52, and 14 to 57 percent, respectively, which resulted in the creation of textural classes of loam, clay loam, sandy clay loam, clay, silty loam, and sandy loam. The function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148, respectively, had the least closeness to the measured values of θ33. The results also show that among the PTFs examined for estimating θ33, the PTF developed by dos Reis et al. (2024) with NRMSE, ME, r and RES values of 0.10, 0.01, 1.04 and -0.012, respectively, and in the next rank and with a slight difference, the PTFs developed by Oliveira et al. (2002) and Minasny and Hartemink (2011) with NRMSE, ME and r values of 0.10, 0.02 and 0.05 (same for both functions) had the best agreement with the measured values of θ33. This is while the function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148 respectively had the least closeness to the measured values of θ33. The summary of the evaluation of eleven PTFs for estimating θ1500 shows that the Dijkerman (1988) and Aina and Periaswamy (1985) functions provided the best performance among the functions studied with ME of 0.00 and -0.01, NRMSE values of 0.15 and 0.16, and r of 1.02 and 0.95, respectively. The function developed by Oliveira et al. (2002) showed good performance for estimating θ33, but it was the least close to the measured values of θ1500 with NRMSE, ME, r and RES values of 0.84, 0.14, 1.83 and -0.138, respectively. Then with the aim of improvement, the high-performance economic functions were selected and recalibrated. In estimating θ33, the performance of both the dos Reis et al. (2024) and Oliveira et al. (2002) functions improved (albeit only slightly) by reducing NRMSE (0.08) and ME (0.00) to reach r of 1.00. The Dijkerman (1988) and dos Reis et al. (2024) functions also improved the estimation of θ1500 with NRMSE, ME, and r of 0.14, 0.00, and 1.00, respectively. Conclusion The results showed that among the PTFs used to estimate θ33, the functions developed by dos Reis et al. (2024) and Oliveira et al. (2002) show the best agreement with the measured values. The functions developed by Dijkerman (1988) and Aina and Periaswamy (1985) also provide the highest performance in estimating θ1500. The results of this study also show that some of the new functions presented in this field can provide good performance compared to the basic functions in predicting soil moisture content. The performance of PTFs for the study area is not affected by the number of their input components, and PTFs that require fewer inputs will not necessarily have lower performance, and this is because PTFs depend on the location where they are developed and also on the soil structure. In order to improve the results of the functions of dos Reis et al. (2024) and Oliveira et al. (2002) were recalibrated to estimate θ33 and the functions of Dijkerman (1988) and dos Reis et al. (2024) were recalibrated to estimate θ1500. The selection of the aforementioned PTFs was based on high performance and minimum required input components, or in other words, the economy of the function. Recalibration further improved the performance of the aforementioned functions. θ33, θ1500 and AW are key components in a wide range of studies such as crop modeling, hydrological modeling, water resources management, soil nutrient cycle modeling and soil pollution modeling, therefore the results presented in this study can be used in predicting soil moisture content and AW for the study area, using the soil transfer function technique as an input parameter in various modeling. مدل‌های گیاهی و هیدرولوژیکی اغلب به مؤلفه‌های ورودی در مورد آب قابل‌دسترس برای گیاه (AW) نیاز دارند. با این وجود به‌دست آوردن چنین مؤلفه‌هایی پرزحمت و پرهزینه است. توابع انتقالی خاک (PTFs) یکی از روش‌های جایگزین برای تعیین خواص فیزیکی خاک مانند محتوای رطوبت خاک است. لذا هدف این پژوهش ارزیابی بیست و سه PTFs نقطه‌ای برای تخمین رطوبت خاک در پتانسیل‌های ماتریک ۳۳- (θ33) و ۱۵۰۰- (θ1500) کیلوپاسکال، برای صد نمونه از خاک‌های دشت سیستان است. در این پژوهش تلاش بر این بوده است که از جدیدترین توابعی که در این زمینه ارائه شده است نیز استفاده شود. نتایج حاکی از آن بود که تابع دوس‌ریس با مقادیر NRMSE، ME، r و RES به‌ترتیب برابر 10/0، 01/0، 04/1 و 012/0- و سپس تابع اولیویرا و همکاران با مقادیر NRMSE، ME، r و RES به‌ترتیب برابر 10/0، 02/0، 05/1 و 016/0-، بیش‌ترین انطباق را با مقادیر اندازه‌گیری شده θ33 داشته است. برای تخمین θ1500 نیز تابع دیجکرمن با مقادیر NRMSE، ME، r و RES به‌ترتیب برابر 15/0، 00/0، 02/1 و 003/0- و در رتبة دوم تابع آینا و پریاسوامی با مقادیر NRMSE، ME، r و RES به‌ترتیب برابر 16/0، 01/0-، 95/0 و 009/0- بهترین عملکرد را ارائه کردند. هم‌چنین نتایج این پژوهش نشان می‌دهد بهترین توابع در تخمین θ33 و θ1500 به‌ترتیب تنها به درصد شن و درصد رس بستگی دارند، لذا برای منطقة مورد مطالعه PTFs که به ورودی‌های کم‌تری نیاز دارند، لزوماً عملکرد پایین‌تری نخواهند داشت، بلکه عواملی مانند تعداد نمونه‌های خاک، ساختمان خاک و مکانی که توابع مورد بررسی توسط آن‌ها توسعه یافته‌اند نیز بر عملکرد آنها مؤثر است. در ادامه PTFs اقتصادی‌تر و با عملکرد بالاتر مورد واسنجی مجدد قرار گرفتند. در تخمین θ33 عملکرد هر دو تابع دوس‌ریس و اولیویرا و همکاران با کاهش NRMSE (08/0) بهبود یافته‌اند. توابع دیجکرمن و دوس‌ریس نیز با مقادیر NRMSE برابر 14/0، تخمین θ1500 را بهبود بخشیده‌اند. θ33، θ1500 و AW مؤلفه‌های کلیدی در طیف وسیعی از مطالعات مانند مدل‌سازی گیاهی، مدل‌سازی‌ هیدرولوژیکی، مدیریت منابع آب، مدل‌سازی چرخه مواد غذایی خاک و مدل‌سازی آلودگی خاک هستند؛ بنابراین، نتایج این پژوهش می‌تواند در مباحث مربوط به مدیریت آبیاری و حفاظت از خاک در منطقه مورد مطالعه به‌کار گرفته شود.

https://mmws.uma.ac.ir/article_4097_3a30d37e4bacf86af02534dda4bd20a6.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Analysis of changes in maximum snow cover duration in Northwest Iran تحلیل تغییرات بیشینه ماندگاری متوالی برف‌پوش در شمال غرب ایران 208 225 4111 10.22098/mmws.2025.18162.1651 FA برومند صلاحی استاد، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران. امیرحسین حلبیان دانشیار، گروه جغرافیا، دانشگاه پیام نور، تهران، ایران عباس کاشانی دانشجوی دکتری آب و هواشناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران Journal Article 2025 08 22 Extended Abstract Introduction Variations in snow cover, along with its phenological aspects such as the duration, onset, and cessation of snowfall, are critical to understanding mountainous ecosystems. These changes heavily influence water resource accessibility in nearby regions. Snow cover serves as a vital component in energy and temperature regulation and is intricately connected to hydrological, biological, chemical, and geological processes. Furthermore, it plays a significant role in both the hydrological cycle and the overall energy balance of the environment. Recent research indicates that mountain regions are undergoing temperature increases at twice the global average rate, with this trend intensifying at higher altitudes. As a result, mountains have come to be regarded as critical indicators for monitoring climate change over the past few decades. This study focused on examining variations in the maximum snow cover persistence in the northwest of Iran, analyzed across monthly, seasonal, and annual time scales. Materials and Methods This study utilized MOD10A1 and MOD10A1 version 6 products from the Terra and Aqua satellites to conduct a daily analysis of snow phenology spanning the years 2003 to 2020. These datasets, accessible as digital network data derived from the NDSI index, were retrieved through Earthdata's online portal at earthdata.nasa.gov. A threshold value of 0.1 or higher was applied for snow cover estimation to facilitate the conversion of snow cover data into binary format. All adjustments, data processing, and analytical procedures were carried out using Python. Cloudiness reduction was accomplished using data fusion algorithms, spatial neighborhood filtering, and temporal filtering techniques. By combining the Terra and Aqua databases and applying spatio-temporal filtering with a threshold range of 0.1 to 1, a binary database of daily snow cover was generated. This database served as the basis for evaluating snow cover persistence and maximum snow cover persistence parameters, which were analyzed across monthly, seasonal, and annual intervals. Results and Discussion During January, the maximum consecutive snow cover pattern is marked by two prominent peaks in the Sahand and Sabalan Mountains, specifically around Ghoch-Goli Daghi and Sultan-Sabalan, with intervals spanning 20 to 30 days. In February, the MaxSCDur indicates a noticeable rise in snow cover duration, influenced by various surface roughness features and an expansion of areas showing high persistence. By March, this pattern shifts as regions of high persistence shrink and areas with low persistence expand. As the cold season concludes, the snow cover significantly decreases. The spatial pattern observed in April reveals a notable reduction in snow cover, with snow retreating from mid- and low-altitude areas while remaining concentrated on high peaks. This pattern underscores the crucial influence of altitude on the persistence of snow cover during the melting season. Between May and September, spatial patterns for MaxSCDur exhibit a gradual decline in duration, decreasing from 20.87 to 24.24 days in May to just 5.91 to 7.5 days by September. During August and September, the maximum consecutive snow cover is confined exclusively to the summit of the Sabalan Glacier. From October to December, MaxSCDur spatial patterns display an upward trend in frequency, with durations increasing from 20.84 to 23.24 days in October to approximately 30 to 31.25 days by December. The MaxSCDur pattern for October highlights a notable increase in snow cover range and duration as autumn sets in, marked by declining temperatures, especially at higher altitudes. Analysis of the spatial distribution for winter’s MaxSCDur reveals the highest snow cover duration at the summits of Sabalan and Sahand, ranging between 25.6 and 29.25 days. The spring pattern shows a reduction in MaxSCDur, decreasing progressively from southern to northern parts of the studied area when compared to winter. During summer, the maximum consecutive snowfall period is limited to 0–2 days across most regions, except for notable peaks such as Sabalan (4–20 days), Sahand, Avarin, Barda-Rash, Kale-Shin, and Qandil (2–4 days). The autumn MaxSCDur pattern records durations of 4–20 days in Sabalan and its slopes, 4–8 days in the Sahand and Bazgush mountains, and 4–6 days in Qara-Dagh, Barda-Rash, Dalampar, and Kale-Shin. On an annual scale, areas with extended MaxSCDur have shown strong regression towards central and high troughs during 2010 and 2018. Conversely, years such as 2008, 2012, and 2017 exhibit visible rock formations predominantly at higher altitudes, encompassing foothills and slopes within these geomorphologic units. Conclusion The MaxSCDur map for February reveals that areas with high snow cover persistence have expanded and intensified when compared to January. An evaluation of the MaxSCD models for November and December indicates a marked increase in both the thickness and duration of snow cover across all snow types as the cold season advanced. December serves as the turning point when full winter conditions are established in the study area. In a seasonal context, winter is characterized by peak precipitation levels and prolonged snow cover, especially across rugged terrains throughout the region analyzed. As spring arrives, rising temperatures lead to the gradual retreat and melting of snow cover. By summer, most regions are entirely devoid of snow, with only fleeting patches visible on the highest summits. Autumn brings a shift as falling temperatures initiate the gradual accumulation of snow at elevated altitudes, resulting in a higher maximum snowfall compared to summer. These patterns reflect a transition from warmer to colder conditions and signal the region's preparation for increased winter precipitation. Historical observations show that minimum MaxSCD values occurred in 2010 and 2018, while peak values were recorded in 2008, 2012, and 2017 within the study area. در این پژوهش با هدف شناسایی و واکاوی تغییرات فضایی- زمانی بیشینة ماندگاری متوالی برف‌پوش در شمال غربی ایران از فرآورده‌های روزانه برف مادیس (MOD10A1 و MYD10A1) در دورة 2020-2003 استفاده شد. ابتدا، داده‌ها بر اساس آستانة حداقل 1/0 باینری شد. در گام بعدی اثر ابرناکی با استفاده از ترکیب داده‌ها، فیلتر مکانی و زمانی کاهش داده شد. سپس، بیشینة ماندگاری برف‌پوش (MaxSCDur) در هر یاخته در شبکة رستری در بازظ ماهانه، فصلی و سالانه محاسبه و نقشه‌های پهنه‌ای ترسیم شد. نتایج نشان می‌دهد که پهنه‌های با ماندگاری بالا در فوریه نسبت به ژانویه گسترش و افزایش یافته است. در این ماه بیشینة میزان MaxSCDur با مقادیر 28-20 روز متعلق به قلل کوهستانی و کمینة آن با 3-0 روز مربوط به نواحی پست شمال شرقی کوه‌های طارم است. الگوی مارس کاهش گستره پهنه‌های با ماندگاری بالا و گسترش مناطق با ماندگاری کم را نشان می‌دهد. الگوی آوریل نشان از کاهش چشم‌گیر ماندگاری برف و تمرکز برف در قلل مرتفع دارد. الگوهای ماه‌های می تا سپتامبر کاهش مقادیر این نمایه را نشان می‌دهد. بر اساس الگوهای اکتبر تا دسامبر، بیشینه میزان MaxSCDur در قلل ارتفاعات از 23-84/20روز به 31-30 روز افزایش یافته است. کمینة میزان آن در این دورة مربوط به نواحی پست با 3-0 روز است. الگوی زمستانه، بیشینة میزان MaxSCDur را در قلل سبلان و سهند با مقادیر 6/29-25 روز و کمینة آن را در نواحی پست با مقادیر 20-0 روز نشان می‌دهد. الگوی بهاره نسبت به زمستانه نشان از افت MaxSCDur از جنوب به‌طرف شمال در پهنه مطالعاتی دارد. بر اساس الگوی تابستانه، MaxSCDur فقط در قله سبلان با 20-4 روز، در سهند و قلل ارتفاعات غربی منطقه با 24-2 روز نمودی آشکار دارد. بیشینة ماندگاری برف‌پوش پاییزه نسبت به الگوی تابستانه فقط در سرزمین‌های مرتفع کوهستانی افزایش نشان می‌دهد و همچنان خط‌الرأس سبلان با 20-4 روز از بیش‌ترین مقدار برخوردار است. بر اساس الگوهای سالانه MaxSCDur سال‌های 2010 و 2018 در فاز کمینه و سال‌های 2008، 2012 و 2017 در فاز بیشینه قرار داشته است. یافته‌های این پژوهش می‌تواند در مقولة منابع آبی، توان‌های اکولوژیکی و مدیریت نواحی کوهستانی مورد استفاده قرار گیرد.

https://mmws.uma.ac.ir/article_4111_60cbab9ffc3459f7853ba243ad33b00d.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Estimating the water balance of the Kowsar Dam watershed using the SWAT hydrological model and satellite data برآورد بیلان آب حوزه آبخیز سد کوثر با استفاده از مدل هیدرولوژیک SWAT و داده‌های ماهواره‌ای 226 240 4149 10.22098/mmws.2025.18378.1685 FA علی طالبی استاد گروه علوم و مهندسی آب، دانشکده مهندسی کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران سارا پرویزی دکتری علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی دانشگاه یزد، یزد، ایران سمیه طالبی اسفندارانی استادیار مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران، تهران، ایران جمال آغایاری استادیار مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران، تهران، ایران آرش زارع گاریزی استادیار گروه آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران حمید رضا پورقاسمی استاد گروه علوم خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران Journal Article 2025 09 17 Estimating the water balance is a key factor in water resource management and environmental planning. The water balance refers to the equilibrium between water inputs and outputs in a specific area, including factors such as precipitation, evaporation, runoff, and water withdrawal. The most important elements of the water balance from the perspective of water resource management are precipitation, evapotranspiration, surface runoff, groundwater flow, and lateral flow. A correct understanding of this balance can help optimize water resource use, predict climate change, and assess human impacts on ecosystems. Given the increasing population and the growing need for water, accurate estimation of the water balance has become crucial for informed management decisions and sustainable development in the agricultural, industrial, and urban sectors. Therefore, dynamic and coherent planning, along with the implementation of appropriate management and conservation measures, is essential for the optimal use of the country's water and soil resources. In this context, employing mathematical models and field data can enhance the analysis of water resource status and future planning. Today, hydrological models are utilized to study and plan for the sustainable and effective comprehensive management of watersheds. An example of a physically based hydrological model is SWAT, which simulates large-scale processes and monitors them based on the characteristics of the watershed and its climatic conditions. Materials and Methods In this study, to model the hydrological conditions of the Kowsar Dam basin using the SWAT model, we first imported a digital elevation model with a resolution of 30 meters into the model software environment (ArcSWAT). The output location was then specified, and the watershed boundary was established. Next, we overlaid land use, soil, and slope class maps to obtain hydrological response units (HRUs) for the region. At this stage, the basin was divided into 35 sub-basins and 184 HRUs. To run the model, we utilized daily climatic data from the meteorological stations, which included precipitation, maximum and minimum temperatures, and relative humidity. The model was calibrated using the SUFI2 program, based on the data from 2007 to 2019. Initially, to identify the parameters influencing runoff in the region, a sensitivity analysis was conducted using the One Parameter at a Time (OAT) method, which helped identify the sensitive parameters for model calibration. By implementing the SUFI2 algorithm, we determined the optimal values for these sensitive parameters. Model validation was carried out using the modified parameter values obtained during the calibration stage. To evaluate model performance during both the calibration and validation phases, we used the coefficient of determination (R²) and the Nash-Sutcliffe coefficient (NS). Results and Discussion The results showed that the SWAT model simulated the water balance components of the Kowsar Dam watershed with acceptable accuracy. For this reason, the values of the R² and NSE indices were relatively high. Based on the results, it was also determined that in the studied basin, most of the precipitation occurred in the fall and winter seasons, with the maximum occurring in November (Aban) and the lowest precipitation in June (Khordad). The amount of surface runoff in November (Aban) gradually begins with the onset of autumn and winter precipitation, so that the highest amounts of surface runoff were observed in November (Aban) and February (Bahman). The temporal changes in base flow throughout the year showed that its highest amount was related to late winter and its lowest amount was related to October (Mehr). Actual evapotranspiration gradually increases from November (Aban) with the onset of precipitation, so that from late winter, as the weather warms up, the actual evapotranspiration rate increases and reaches its maximum in May (Ardibehesht). In relation to potential evapotranspiration, unlike actual evapotranspiration, this parameter will increase with increasing temperature and decreasing precipitation. The lowest potential evapotranspiration rate is in January (Day) due to the sharp decrease in temperature in this month, and the highest rate is in early summer, that is, July (Tir). Conclusion By implementing the SWAT model in the Kowsar Dam basin, we were able to simulate the monthly flow for the studied period. Statistical comparisons of this modeling demonstrated acceptable results. The comparison of the simulated and observed hydrographs showed a strong correlation according to the Nash-Sutcliffe criterion. Therefore, we can conclude that the SWAT physical model performs acceptably in the Kowsar Dam basin, based on the simulation results. By comparing the appearance and statistics of the observed hydrograph with those of the simulated hydrograph, we found a high similarity between the two during the study period. There is good agreement between the hydrographs regarding important characteristics such as peak discharge, runoff volume, and time to reach peak discharge. Overall, the results indicate that the SWAT model has the ability and acceptable accuracy to simulate the monthly runoff discharge of the Kowsar Dam watershed. In this study, the model's calibration and validation results showed its efficiency in estimating the water balance in the Kowsar Dam watershed. The final results showed that on average, about 51 percent of precipitation enters the atmosphere as evaporation and transpiration, about 21 percent as surface runoff, 5 percent as lateral flow, and 16 percent as return flow directly into waterways. In total, about 26 percent of water enters the soil layers and aquifer. The results indicate the effectiveness of the SWAT model in simulating the water balance of the Kowsar Dam watershed. بیلان آب نشان‌دهندة تعادل بین ورودی و خروجی آب در یک منطقه است و نقش مهمی در مدیریت منابع، توسعه پایدار و کاهش اثرات انسانی بر اکوسیستم دارد. با افزایش جمعیت و تقاضای آب، تخمین دقیق آن برای کشاورزی، صنعت و شهرها ضروری است. بهره‌گیری از مدل‌های ریاضی و داده‌های میدانی ابزار مؤثری برای تحلیل وضعیت و برنامه‌ریزی آینده منابع آبی محسوب می‌شود. در این پژوهش، مؤلفه‌های بیلان آبی حوزة آبخیز سد کوثر با استفاده از مدل هیدرولوژیکی SWAT و سنجش از دور شبیه‌سازی شد. هدف این پژوهش، آزمون کارایی مدل و قابلیت استفاده از آن به‌عنوان شبیه‌ساز بیلان آب در حوزة آبخیز سد کوثر است. در این پژوهش برای به‌دست آوردن نقشه کاربری اراضی از تصاویر ماهواره‌ی سنتینل-2 سال (2020) استفاده شد. برای واسنجی مدل از الگوریتم SUFI2 در نرم‌افزار SWAT CUP استفاده شد. فرآیند انجام مدل برای بازة زمانی 1386 تا 1398 انجام شد. دقت شبیه‌سازی رواناب ماهانه با استفاده از شاخص ضریب تعیین (R2) و نش-ساتکلیف (NSE) در ایستگاه نازمکان و سید آباد به‌ترتیب 55/0، 54/0 و 62/0، 6/0 به‌دست آمد. نتایج نهایی نشان داد که به‌طور متوسط حدود 51 درصد بارش به‌صورت تبخیر و تعرق وارد اتمسفر می‌شود، حدود 21 درصد آن به‌صورت رواناب سطحی، 5 درصد به‌صورت جریان جانبی و 16 درصد به‌صورت جریان بازگشتی مستقیماً به آبراهه‌ها وارد می‌شود. در مجموع حدود 26 درصد آب وارد لایه‌های خاک و آبخوان می‌شود. نتایج حاصل نشان‌دهندة کارایی مدل SWAT در شبیه‌سازی بیلان آبی حوزه آبخیز سد کوثر است.

https://mmws.uma.ac.ir/article_4149_14d908ceace19b1a37c53f111e91a7c2.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 The impact of drought stress on the growth, yield, and water use efficiency of white sweet potato in autumn cultivation تاثیر تنش خشکی بر رشد، عملکرد و کارایی مصرف آب سیب‌زمینی شیرین سفید در کشت پاییزه 241 251 4153 10.22098/mmws.2025.18475.1694 FA زهرا ذاکری درباغی فارغ التحصیل کارشناسی ارشد باغبانی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران محمدرضا ترکی زاده دانشجوی کارشناسی گروه علوم و مهندسی آب، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس، ایران مهدیه نورالدینی دانشجوی کارشناسی گروه علوم و مهندسی آب، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس، ایران حمیدرضا کمالی استادیار گروه علوم و مهندسی آب، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس، ایران Journal Article 2025 09 30 Introduction With the reduction of water resources in Iran, increasing the efficiency of water use in the agricultural sector has attracted the attention of researchers. Regarding water requirements, irrigation requirements, and crop water productivity, it is important for managers and decision-makers to adjust the cultivation information patterns of each region. In Iran, limited research has been conducted on sweet potatoes, and the extent of its production within the country remains unclear. Sweet potatoes are mostly grown in the cities of Minab, Jask, and parts of Sistan and Baluchestan province. In the study by Shamili et al. (2016), the effect of soil texture and irrigation method on improving yield and yield components of two sweet potato cultivars was investigated. Sweet potato is also used as a potential crop for animal feed and raw material in industry. Therefore, the research of Naseri et al. (2014) was conducted with the aim of cultivating sweet potato in the Minab region to produce fresh fodder, which shows the suitable ability of this plant for producing animal fodder. Given that Minab and Jask counties are significant producers of sweet potatoes in the nation, this study aimed to examine the impact of water stress on the growth and yield of sweet potato in the tropical region of Minab county. Materials and Methods This study was conducted in the research lysimeters of Minab Higher Education Center in 2024-2025 in a completely randomized design. The study area is located at an altitude of 29 meters above sea level at the latitude and longitude coordinates of 27° 5' 28" and 57° 5' 42", respectively. The study area is a tropical and humid region with a temperate climate in winter, and therefore most of the cultivation is done in the fall and winter. White sweet potato plant cuttings were planted at 40 cm intervals on the ridge on 2024/4/11. The lysimeters were irrigated to meet soil moisture deficiency. Irrigation treatments included (I1) 120, (I2) 100, (I3) 80, and (I4) 60 percent of the moisture requirement, and were performed in three replications. For proper establishment of the plants, the same irrigation rate was applied for all four treatments up to 40 days after planting, and then irrigation was performed based on moisture stresses. The irrigation rate was determined before irrigation and by using soil sampling from different depths. At the end of the growing season on 27/2/1404, the entire root crop as well as the plant were collected and weighed. Considering that not all produced tubers were suitable for the market and economically viable, the weight, diameter, and length of each sweet potato tuber were evaluated to determine their marketability. Those that met USDA standards were classified as marketable products. To calculate water use efficiency, the definition of irrigation water use efficiency (IWUE) was used. Results and Discussion Some parameters such as root weight, root dry weight, and total dry matter were significantly affected by drought stress, and other parameters such as irrigation water use efficiency based on tuber and total dry matter were not significant. The weight of the harvested tubers for treatments I1 to I4 was found to be 5846, 5224, 2852, and 2069 kg ha-1, respectively. Additionally, the IWUE for all harvested tubers for treatments I1 to I4 were determined to be 1.20, 1.26, 0.97, and 0.83 kg m-3, respectively. Drought stress up to 80% moisture supply (I3) had no significant effect on yield reduction. A similar trend was observed for foliage fresh weight, with the difference that drought stress only caused a significant difference between the maximum stress (I4) and minimum stress (I1) treatments. Excessive water consumption in treatment I1 only increased the weight of foliage and had no effect on the total weight of harvested tubers. Despite the fact that water stress had no effect on the IWUE of all tubers, the IWUE value for marketable tubers was significant. According to the results, the IWUE value of treatments I1 and I2 was at the same level, but applying stress in treatments I3 and I4 significantly reduced the IWUE of marketable tubers. The amount of applied water in treatment I1 was 479 mm, treatment I2 was 408 mm, treatment I3 was 304 mm, and treatment I4 was 241 mm, which was applied based on the supply of soil moisture deficiency. The actual evapotranspiration amount during the growing season was found to be 565, 494, 390, and 334 millimeters for treatments I1 to I4, respectively. Conclusion The effect of drought stress on the fresh weight of harvested tubers was significant. However, this effect was not significant on the IWUE. From this, it can be concluded that the sweet potato plant has the ability to adapt to drought conditions, such that with increasing drought stress, the IWUE remained constant and did not decrease. The product obtained in the present study was also evaluated in terms of marketability. The results showed that by separating the marketable product, the IWUE decreases. Also, by applying water stress, the IWUE decreases significantly. In other terms, water stress leads to a reduction in the quantity of marketable products, which has resulted in a notable decline in the Irrigation Water Use Efficiency (IWUE). Given the importance of economic issues, it is recommended that an economic analysis of this research be conducted to determine the impact of drought stress on crop profitability. Also, the response of other sweet potato cultivars to drought stress also requires a more comprehensive study. با کاهش روزافزون منابع آبی در ایران، افزایش بهره‌وری مصرف آب در بخش کشاورزی همواره از دغدغه‌های پژوهشگران بوده است. اطلاعات مربوط به نیاز آبی، نیاز آبیاری و بهره‌وری آب گیاهان زراعی کمک قابل توجهی به مدیران و تصمیم گیران جهت تنظیم الگوی کشت هر منطقه می‌کند. در پژوهش حاضر به بررسی اثر تنش آبی بر رشد و عملکرد گیاه سیب‌زمینی شیرین پرداخته شده است. این مطالعه در لایسیمترهای پژوهشی مجتمع آموزش عالی میناب در سال زراعی 1404-1403 و در قالب طرح کاملا تصادفی انجام شد. تیمارهای آبیاری شامل (1I)120، (2I)100، (3I)80، (4I)60 درصد نیاز رطوبتی بود که در سه تکرار اعمال شد. آبیاری لایسیمترها به صورت غرقابی جوی و پشته‌ای و بر اساس تامین کمبود رطوبتی خاک ناحیه‌ی ریشه ، تا حد زراعی، بود. نتایج نشان داد برخی پارامترها مانند وزن تر و خشک غده، وزن تر و خشک شاخه و کل ماده‌ی خشک تحت تاثیر معنی‌دار تنش خشکی قرار گرفته، و بقیه‌ی پارامترها از جمله کارایی مصرف آب آبیاری معنی‌دار نشده است. تنش خشکی تا سطح 20 درصد (3I) تاثیر معنی‌داری بر کاهش وزن تر غده و شاخه نداشت. مقدار وزن تر غده‌ی برداشت شده برای تیمارهای 1I تا 4I بترتیب برابر5846، 5224، 2852 و 2069 کیلوگرم بر هکتار بدست آمد. همچنین مقادیر کارایی مصرف آب برای کل غده‌های برداشت شده برای تیمارهای 1I تا 4I بترتیب برابر 20/1، 26/1، 97/0 و 83/0 کیلوگرم بر مترمکعب بدست آمد. مشخص شد که تنش آبی باعث کاهش تعداد غده‌ها شده است. چون مبحث بازار پسندی محصولات، از نظر اقتصادی مهم است، پس از جداسازی غده‌های بازار پسند بر اساس دستورالعمل USDA، مجددا تحلیل آماری انجام شد و نتایج نشان داد که تنش آبی تاثیر معناداری در سطح آماری 5 درصد بر کاهش کارایی مصرف داشته است بطوریکه کارایی مصرف آب برای غده‌های بازارپسند برای تیمارهای 1I تا 4I بترتیب برابر 90/0، 73/0، 43/0 و 70/0 کیلوگرم بر مترمکعب بدست آمد. نتایج نشان داد که علاوه بر تاثیر تنش خشکی بر مقدار محصول، تاثیر تنش خشکی بر بازارپسندی محصول نیز حایز اهمیت است و بایستی مورد بررسی قرار گیرد.

https://mmws.uma.ac.ir/article_4153_6d3a62855495935d69501eb8d8977b88.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Optimization of cropping pattern in Minab plain using linear programming under water scarcity conditions بهینه‌سازی الگوی کشت دشت میناب با استفاده از برنامه‌ریزی خطی و با لحاظ محدودیت‌های طرح تعادل‌بخشی منابع آب زیرزمینی 252 269 4194 10.22098/mmws.2025.18423.1691 FA مجتبی محمدی گروه مدیریت و کنترل بیابان، دانشکده علوم محیط زیست، برنامه‌ریزی و توسعه پایدار، دانشگاه سراوان، سراوان، ایران 0000-0002-5614-8376 Journal Article 2025 09 24 Extended Abstract Introduction Introduction The increasing pressure of population growth has placed significant constraints on finite land and water resources. In arid and semi-arid regions like Iran, the combination of recurrent droughts and decades of groundwater over-extraction has led to a severe water crisis. The Minab plain in Hormozgan province, a critical agricultural hub, exemplifies this challenge. It has experienced a drastic groundwater level drop of nearly 14 meters, resulting in land subsidence and water quality deterioration. To address this crisis, the “Groundwater Resources Revival and Balancing Plan” was initiated to enforce strict limits on water extraction. However, a critical gap exists between this top-down policy and on-farm implementation. The primary novelty of this study lies in bridging this gap. While numerous studies have applied Linear Programming (LP) for general crop optimization, this research specifically utilizes the LP model as a practical tool to operationalize the water balancing policy. It addresses the critical question of how farmers can adapt their cropping patterns to comply with the new legal water withdrawal limits while simultaneously maximizing their economic returns. This research, therefore, provides a scientifically-grounded strategy that aligns the goals of environmental sustainability with the economic viability of agriculture in the Minab plain. Accordingly, the following sections detail the materials and methods employed, present and discuss the results from the optimization model, and provide concluding remarks and policy recommendations. Materials and Methods The optimization problem was formulated as a Linear Programming (LP) model. The objective function was designed to maximize the total net profit (Z) from all agricultural activities, defined as: Maximize Z = Σ (BCi * Xi) where Xi are the decision variables representing the area (ha) allocated to each crop i, and BCi is the net profit per hectare of that crop. This maximization is subject to three key constraints: (1) Water Availability: Total annual water consumption (Σ Vi * Xi) must not exceed the sum of available surface water and the legally mandated groundwater limit of 57.92 MCM. (2) Land Availability: The total cultivated area (Σ Xi) must be less than or equal to the total arable land. (3) Cultivation Bounds: The area for each crop (Xi) must remain within its observed historical minimum and maximum levels to ensure market stability and reflect practical farming conditions.This study was conducted in the Minab plain, located in southern Iran, which covers an area of approximately 652 km². The region’s water supply is derived from both surface sources, primarily the Esteghlal Dam on the Minab River, and groundwater extracted from the alluvial aquifer. To develop the optimization model, a comprehensive dataset was compiled for twenty major crops cultivated in the area, covering grains, vegetables, melons, and forage crops.This data, sourced from the Jihad-e-Agriculture Organization, the Regional Water Authority, and national statistical databases, included historical cultivation areas (minimum and maximum bounds), crop yield, production costs, and market prices. Crop water requirements were calculated using the NetWat software, which considers local climatic conditions and specific plant characteristics. A Linear Programming (LP) model was formulated to solve the resource allocation problem. The model’s objective function was designed to maximize the total net profit, calculated as the sum of net returns from all cultivated crops. The model was subjected to a set of critical constraints: (1) Water resource availability, limiting total water consumption to the sum of surface water allocation and the permissible groundwater withdrawal of 57.92 million cubic meters (MCM) as stipulated by the balancing plan; (2) Land availability, ensuring the total cultivated area does not exceed the available arable land; (3) Crop area limits, restricting the cultivation area for each crop to be within its observed historical minimum and maximum levels to ensure market stability and crop diversity. To evaluate the impact of water management practices, the model was executed under two distinct irrigation efficiency scenarios: Scenario 1 with 53% efficiency, representing the current situation, and Scenario 2 with 65% efficiency, representing an improved condition achievable through modern irrigation technologies. Results and Discussion The results of the Linear Programming model provided distinct optimal cropping patterns for the two irrigation efficiency scenarios. Under Scenario 1 (53% efficiency), the optimal solution allocated a total of 13,949 hectares for cultivation, generating a maximum net profit of 8.37 trillion Rials. In this scenario, the cultivation areas for most crops were set at their minimum allowable limits, with the exception of highly profitable crops like leafy vegetables, which were allocated a larger area. This indicates that under current efficiency levels, water scarcity is the primary limiting factor for agricultural expansion. In contrast, under Scenario 2 (65% efficiency), the model recommended a significantly larger total cultivation area of 21,382 hectares, resulting in an increased net profit of 10.55 trillion Rials. This represents a 53% increase in cultivated land and a 26% rise in profit compared to the baseline scenario. The improved water use efficiency allowed for the expansion of high-value crops such as green beans, peppers, cucumbers, and leafy vegetables to their maximum allowable cultivation limits. A crucial finding is that both optimal scenarios successfully operated within the mandated groundwater withdrawal limit of 57.92 MCM. This is a substantial reduction compared to the average historical consumption of approximately 65.9 MCM between 2011 and 2014. Furthermore, the net profits generated under both optimized scenarios were significantly higher than the profits recorded in those historical years. This demonstrates that a scientifically planned cropping pattern can simultaneously achieve the dual goals of reducing groundwater over-extraction and enhancing farmers’ economic returns. Conclusions This study successfully demonstrated the effectiveness of Linear Programming as a powerful tool for optimizing cropping patterns to address the intertwined challenges of water scarcity and economic viability in the Minab plain. The findings conclusively show that it is possible to increase farmers’ net profits while adhering to strict groundwater withdrawal limits necessary for aquifer restoration. The research highlights that improving irrigation efficiency is the most critical lever for unlocking the region’s agricultural potential. A transition from the current 53% efficiency to a more achievable 65% can substantially increase both the cultivated area and farmer income, creating a strong economic incentive for adopting sustainable practices. The practical implications of this work are significant; the developed model serves as a robust decision-support tool for policymakers and water resource managers. It is recommended that local agricultural authorities promote the outputs of this model and facilitate the adoption of modern irrigation technologies through supportive policies and subsidies. By aligning the financial interests of farmers with the long-term goal of water resource sustainability, this approach offers a tangible and effective pathway to reviving the Minab aquifer while ensuring a prosperous agricultural future for the region. کمبود منابع آب و افت شدید سطح آب‌های زیرزمینی در دشت میناب، پایداری کشاورزی منطقه را با چالش جدی مواجه کرده است. هدف این پژوهش، تدوین الگوی کشت بهینه با استفاده از مدل برنامه‌ریزی خطی (LP) به منظور حداکثرسازی سود خالص کشاورزان، با درنظرگرفتن محدودیت‌های منابع آب بر اساس طرح تعادل‌بخشی منابع آب زیرزمینی است. برای این منظور، اطلاعات مربوط به بیست محصول زراعی عمده منطقه شامل غلات، صیفی‌جات، سبزیجات و محصولات علوفه‌ای، جمع‌آوری شد. مدل برنامه‌ریزی خطی با تابع هدف حداکثرسازی سود و با محدودیت‌های مربوط به آب قابل‌دسترس (سطحی و زیرزمینی)، مساحت اراضی و حداقل و حداکثر سطح زیر کشت هر محصول فرموله گردید. سپس عملکرد مدل تحت دو سناریوی راندمان آبیاری ۵۳ درصد (شرایط فعلی) و ۶۵ درصد (شرایط بهبودیافته) ارزیابی شد. نتایج نشان داد که در سناریوی راندمان ۵۳ درصد، سطح زیر کشت بهینه 949/13 هکتار با سود خالص 37/8 تریلیون ریال به دست آمد. با افزایش راندمان آبیاری به ۶۵ درصد، سطح زیر کشت بهینه به 382/21 هکتار و سود خالص به 55/10 تریلیون ریال افزایش یافت. این یافته‌ها بیانگر آن است که اجرای الگوی کشت بهینه، به‌ویژه هم‌زمان با بهبود راندمان آبیاری، نه‌تنها منجر به افزایش قابل‌توجه درآمد کشاورزان می‌شود، بلکه امکان رعایت کامل محدودیت برداشت از منابع آب زیرزمینی و حرکت به‌سوی پایداری کشاورزی و احیای آبخوان دشت میناب را فراهم می‌آورد. بر این اساس، پیشنهاد کاربردی پژوهش، تدوین بسته‌های سیاستی حمایتی، شامل ارائه یارانه‌های هدفمند برای گذار به سیستم‌های نوین آبیاری و ترویج کشت محصولات شناسایی‌شده در الگوی بهینه، جهت تحقق همزمان اهداف اقتصادی و زیست‌محیطی در منطقه است.

https://mmws.uma.ac.ir/article_4194_490452969002e5b41e96253ba09470c8.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Assessing the effect of climate change and irrigation management on yield and water use efficiency of canola cultivars in Khuzestan province ارزیابی تأثیر تغییرات اقلیمی و مدیریت آبیاری بر عملکرد و کارایی مصرف آب ارقام کلزا در استان خوزستان 270 286 4227 10.22098/mmws.2025.18649.1708 FA عبدالمجید سهیل نژاد استادیار، گروه کشاورزی، دانشگاه پیام نور، تهران، ایران سجاد رحیمی مقدم استادیار، گروه مهندسی تولید و ژنتیک گیاهی، دانشکده کشاورزی، دانشگاه لرستان، خرم آباد، ایران Journal Article 2025 10 25 Introduction Climate change is one of the most pressing challenges for global agriculture, particularly in arid and semi-arid regions such as southwestern Iran. Increases in temperature, alterations in rainfall distribution, and the rise in atmospheric CO₂ concentrations strongly affect crop growth, yield formation, and water use efficiency (WUE). Rapeseed (Brassica napus L.), as a C₃ plant and a major oilseed crop, is highly sensitive to both climatic fluctuations and water availability. In Khuzestan province, rapeseed has become increasingly important in recent years, but its productivity is constrained by limited water resources and the growing threat of climate stress. Therefore, it is crucial to evaluate the combined effects of cultivar choice and irrigation strategies under both current and projected climate conditions to support sustainable rapeseed production. This study aimed to assess the performance of various rapeseed cultivars and irrigation regimes under future climate conditions. Materials and Method The research was conducted using the APSIM model to simulate the growth and yield of rapeseed at five sites in Khuzestan Province (Shush, Shushtar, Izeh, Dezful, and Behbahan) under both baseline and future periods. Climate projections for the future periods were obtained from five Global Circulation Models (GCMs: MPI-ESM1-2-LR, ACCESS-ESM1-5, MRI-ESM2-0, HadGEM3-GC31-LL, and CNRM-CM6-1) under three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The LARS-WG software (version 8) was employed for statistical downscaling to generate future climate data. Three widely cultivated rapeseed cultivars, namely Hyola401, Hyola308, and RGS003, together with five irrigation treatments, were considered to simulate their responses to future climate conditions in terms of yield, WUE, ETc, and to identify the optimal combinations for the study sites. Irrigation treatments were defined based on soil field capacity (FC) levels, including FC50, FC60, FC70, FC80, and FC90. These treatments corresponded to the replenishment of soil moisture whenever the available water content was depleted to 50%, 60%, 70%, 80%, and 90% of field capacity, respectively. Statistical analyses, including regression analysis and Duncan’s multiple range test, were applied at the 0.05 and 0.01 probability levels. Results and Discussion The results showed that, under the baseline period, the cultivar Hyola401 achieved the highest grain yield in the FC90 treatment (an average of 4631 kg ha⁻¹ across all regions). The highest WUE was obtained by Hyola401 under the FC50 treatment (1.1 kg m⁻³) in Izeh. Regression analysis revealed a positive and significant relationship between grain yield and WUE (R² = 0.91; p-value < 0.05). During the future period, mean temperature increased by 1.53 °C compared with the baseline, while the length of the growing season decreased by about 5%, leading to reductions in both yield and WUE in most regions. For instance, in Izeh, a slight improvement in yield was observed for the cultivar RGS003 under FC50 and FC60 irrigation (by 0.65% and 0.34%, respectively) under the SSP1-2.6 scenario. In Shushtar, however, yield improvements were associated with the cultivar Hyola401 across all scenarios and irrigation treatments, with yield increases ranging from 2.5% to 17%. The results highlight the trade-off between grain yield and water use efficiency under different irrigation levels. While FC90 maximized grain yield, it also increased ETc beyond 6000 m3, resulting in lower WUE. Conversely, deficit irrigation (FC50) improved WUE but caused a significant yield reduction. The optimal balance was achieved at intermediate irrigation levels (FC60–70), particularly when combined with high-performing cultivars such as Hyola401. Future climate scenarios suggested that the negative effects of increased temperature and shorter growth duration would outweigh the positive effects of CO₂ fertilization in most warm regions. The increase in temperature and its negative association with grain yield were observed in in regression analyses, where the relationship between mean temperature and grain yield was significantly negative (R² = -0.68; p-value < 0.05). The largest yield reductions were predicted in Shush, confirming that heat stress during the reproductive stage is the most critical constraint. Nonetheless, in cooler regions such as Izeh the positive impact of elevated CO₂ partially offset the yield decline, confirming the spatial heterogeneity of climate change impacts. These findings are consistent with previous reports indicating that elevated CO₂ may mitigate some of the negative consequences of rising temperatures in C₃ crops, but cannot fully compensate for severe heat stress. Conclusion This study demonstrated that both cultivar selection and irrigation management are key determinants of sustainable rapeseed production under climate change. Under baseline conditions, Hyola401 × FC90 achieved the highest grain yield, while Hyola401 × FC50 achieved the highest WUE. However, under future climate conditions, yield reductions of up to -9% were projected, particularly in warmer areas such as Shush under SSP5-8.5. In contrast, yield and WUE improvements were observed in Izeh and Shushtar, largely due to the positive effects of increased atmospheric CO₂. Overall, intermediate irrigation strategies (FC60–70) combined with resilient cultivars such as Hyola401 and Hyola308 were found to offer the most sustainable balance between yield and WUE under future climate conditions. These results emphasize the importance of climate-smart strategies, including adaptive irrigation management, the selection of stress-tolerant cultivars, and the adjustment of planting schedules, to mitigate the adverse impacts of climate change and ensure sustainable rapeseed production in Khuzestan. هدف این پژوهش بررسی اثر سطوح آبیاری بر عملکرد دانه و WUE ارقام مختلف کلزا در شرایط اقلیمی پایه و آینده استان خوزستان بود. برای این منظور، از مدل APSIM به‌منظور شبیه‌سازی و بررسی واکنش سه رقم کلزا شامل Hyola401، Hyola308 و RGS003 به پنج سطح آبیاری (FC50، FC60، FC70، FC80 و FC90) در قالب سه سناریوی اقلیمی SSP1-2.6، SSP2-4.5 و SSP5-8.5 استفاده شد. داده‌های اقلیمی دورة پایه از سازمان هواشناسی کشور جمع‌آوری شد و برای تولید داده‌های اقلیمی آینده از نرم‌افزار LARS-WG نسخه 8 استفاده شد. نتایج نشان داد در دورة پایه، رقم Hyola401 توانست بیشنظ عملکرد دانه را در تیمار FC90 (به‌طور میانگین 4631 کیلوگرم در هکتار) به‌طور میانگین در سراسر مناطق بدست آورد. بیشینة WUE را رقم Hyola401 و FC50 (1/1 کیلوگرم بر مترمکعب) در منطقة ایذه داشت. تحلیل رگرسیون نشان داد رابطة منفی و معنی‌داری میان عملکرد دانه و میانگین دما در طول فصل رشد (86/0R2=؛ p-value<0.05) وجود داشت. در دورة آینده، میانگین دما نسبت به پایه 53/1 درجة سانتی‌گراد افزایش و طول دورة رشد حدود 5 درصد کاهش یافت که منجر به کاهش عملکرد و WUE در بیش‌تر مناطق شد. به‌عنوان مثال در شهرستان ایذه، بهبود جزئی عملکرد در رقم RGS003 و آبیاری‌های FC50 و FC60 (به‌ترتیب 65/0 و 34/0 درصد) تحت سناریوی SSP1-2.6 مشاهده‌ شد و در منطقة شوشتر، بهبود عملکرد مربوط به رقم Hyola401 تحت تمامی سناریوها و تیمارهای آبیاری بود، که افزایش عملکردی از 5/2 تا 17 درصد نشان داد که ناشی از اثرات مثبت دی اکسید کربن بود. به‌طور کلی، نتایج نشان داد که در مناطق گرم و تحت سناریوهای آینده، استفاده از تیمارهای پرآب (FC90) به‌دلیل افزایش ETc و کاهش WUE توجیه‌پذیر نیست. در مقابل، تیمارهای میانی (FC60–70) همراه با ارقام Hyola401 و Hyola308 می‌توانند بهترین توازن بین عملکرد و WUE را برقرار کنند. این یافته‌ها اهمیت مدیریت انعطاف‌پذیر آبیاری و انتخاب رقم مناسب را برای پایداری تولید کلزا در شرایط آینده اقلیمی تأیید می‌کنند.

https://mmws.uma.ac.ir/article_4227_f7955399b7f8ec4b6b40fe7a68810a19.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Effect of Haloxylon plantation age on soil carbon and nitrogen stocks: management implications for arid land restoration تأثیر سن تاغ‌کاری بر ذخایر کربن و نیتروژن خاک: پیامدهای مدیریتی برای احیای اراضی خشک 287 302 4231 10.22098/mmws.2025.18631.1706 FA مرتضی صابری دانشیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران محمدرضا دهمرده قلعه نو دانشیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران وحید کریمیان استادیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه یاسوج. یاسوج، ایران Journal Article 2025 10 21 Introduction The Iranian plateau, especially in arid and semi-arid regions, has sensitive and fragile conditions due to its specific climatic characteristics. In these regions, soil erosion and desertification are among the most important factors that severely threaten water and soil resources. In such conditions, the restoration of degraded rangelands through revegetation with compatible species such as Haloxylon spp. can play an effective role in reducing soil erosion, improving soil quality, and increasing carbon and nitrogen stocks. Due to its high resistance to drought and salinity, this species is one of the key options for stabilizing mobile sands and restoring desert ecosystems in Iran. In addition to its ecological role, the establishment of Haloxylon not only helps to increase soil organic carbon content and reduce atmospheric carbon dioxide but also can strengthen the livelihoods of local communities by improving soil conditions and providing economic opportunities. However, the dynamics of carbon and nitrogen stocks over time and with increasing plantation age are still not fully understood and require careful scientific investigation. Therefore, the present study aimed to investigate the effect of Haloxylon plantation age on soil carbon and nitrogen stocks in arid regions, to provide a basis for the optimal management of dryland restoration and the development of carbon sequestration schemes by explaining the relationships between soil properties and vegetation age. Materials and Methods This study aimed to evaluate the effect of Haloxylon plantation age on soil carbon and nitrogen stocks in dry lands, in three areas located in the Merak area of Birjand County (South Khorasan province). The study areas included two Haloxylon plantations with ages of 34 and 26 years and a control area without Haloxylon cultivation, which are located about 10 km northeast of Birjand city. The altitude of the area varies between 1610 and 1810 meters above sea level, and its climate is dry and desert. Sampling operations were carried out in June 2024 using a systematic random method. In each area, six replicates were collected from three soil depths (0–15, 15–30, and 30–45 cm), and a total of 54 soil samples were used for physical and chemical analyses. Physicochemical properties, including pH, electrical conductivity, porosity, bulk density, organic carbon, and total nitrogen were measured, and soil carbon and nitrogen stocks were also determined. After checking for normality and homogeneity of variances, the resulting data were analyzed using a factorial design and one-way analysis of variance (ANOVA) in SPSS software. Also, the relationships between variables were examined using Pearson's correlation coefficient in R software. Results and Discussion The results of the analysis of variance showed that the effects of both area (plantation age) and soil depth on physicochemical properties, including pH, electrical conductivity, bulk density, organic carbon, and total nitrogen were statistically significant (p<0.01). Comparison of means showed that with increasing age of the Haloxylon plantation, pH decreased slightly, and electrical conductivity was higher in the 26-year-old site. The decrease in bulk density and increase in soil porosity indicated improved soil structure and greater activity of roots and soil organisms. One-way analysis of variance of carbon and nitrogen stocks at each depth showed that these variables were significantly higher in the 34- and 26-year-old Haloxylon plantation sites than in the control area. The age of Haloxylon planting had a significant effect on soil carbon and nitrogen accumulation, with this effect varying by soil depth. In the 0–15 cm layer, the highest carbon stocks were observed in the 26-year-old site (4.46 t ha⁻¹), while in the 15–30 and 30–45 cm layers, the 34-year-old site exhibited the highest stocks (5.78 and 3.35 t ha⁻¹, respectively), indicating the role of vegetation growth stage and microbial activity in organic matter accumulation and stabilization, and highlighting the importance of long-term management and drought-resistant species in enhancing soil nutrient storage capacity. Principal component analysis (PCA) and correlation coefficients also showed that organic carbon, total nitrogen, and nutrient stocks are co-directional and correlated with each other, while bulk density has an inverse relationship. These findings emphasize that over time, Haloxylon cultivation improves the physical and chemical quality of the soil and increases the nutrient storage capacity and environmental sustainability of the soil in the drylands of the Merak region of Birjand County. Conclusion The findings of this study indicate that the age of Haloxylon plantation plays a decisive role in improving soil quality and increasing carbon and nitrogen stocks in the drylands of Birjand County. Over time, the gradual accumulation of carbon and nitrogen in the surface and subsurface soil layers increased, and bulk density decreased, indicating improved soil structure, increased porosity, and higher activity of roots and soil microorganisms. Principal component analysis and correlation coefficients confirmed that increased soil organic matter is the main driver of the simultaneous increase in carbon and nitrogen stocks, and that the surface layers show the greatest sensitivity to management changes. These results indicate that long-term Haloxylon plantation can enhance soil ecological processes, including carbon and nitrogen cycling, and increase the environmental sustainability of drylands. From a management perspective, the results of the study indicate that selecting appropriate areas for Haloxylon plantation and continuously monitoring the age of vegetation cover can help increase soil fertility and improve nutrient storage capacity. Therefore, Haloxylon plantation is not only a soil restoration strategy, but with proper management, it plays an important role in the sustainable development of drylands. اراضی خشک و نیمه‌خشک ایران به دلیل محدودیت منابع آب و شرایط اقلیمی حساس، در معرض فرسایش خاک و کاهش حاصل‌خیزی قرار دارند. استفاده از گونه‌های مقاوم مانند زرد تاغ (Haloxylon persicum) برای تثبیت خاک و بهبود کیفیت آن، به‌ویژه از نظر ذخایر کربن و نیتروژن، اهمیت اکولوژیکی و مدیریتی دارد. این پژوهش با هدف بررسی تأثیر سن تاغ‌کاری بر ویژگی‌های فیزیکوشیمیایی خاک و ذخایر کربن و نیتروژن در شهرستان بیرجند انجام شد. بدین منظور، سه منطقه شامل دو تاغ‌زار با قدمت‌های ۳۴ و ۲۶ سال و یک منطقه شاهد (فاقد تاغ‌کاری) مورد بررسی قرار گرفتند. در هرسایت، با استفاده از روش نمونه‌برداری تصادفی سیستماتیک تعداد ۶ نمونه خاک از سه عمق صفر تا ۱۵، ۱۵ تا ۳۰ و ۳۰ تا ۴۵ سانتی‌متر برداشت گردید. نمونه‌ها پس از انتقال به آزمایشگاه، جهت اندازه‌گیری ویژگی‌های فیزیکوشیمیایی شامل pH، EC، وزن مخصوص ظاهری، تخلخل، کربن آلی و نیتروژن کل و همچنین تعیین ذخایر کربن و نیتروژن خاک مورد تجزیه و تحلیل قرار گرفتند. سن تاغ‌کاری اثر قابل‌توجهی بر تجمع کربن و نیتروژن خاک دارد و این اثر بسته به عمق متفاوت است، در عمق ۰-۱۵ سانتی‌متر بیشترین ذخایر کربن در سایت ۲۶ ساله (۴۶/۴) و در عمق‌های ۱۵-۳۰ و ۴۵-۳۰ سانتی‌متر در سایت ۳۴ ساله (۷۸/۵ و ۳۵/۳) مشاهده شد، که نشان‌دهنده نقش مرحله رشد پوشش و فعالیت میکروبی در تجمع و تثبیت مواد آلی است و اهمیت مدیریت طولانی‌مدت و گونه‌های مقاوم به خشکی برای افزایش ظرفیت ذخیره عناصر غذایی را برجسته می‌کند. کاهش وزن مخصوص و افزایش تخلخل نشان‌دهنده بهبود ساختار خاک و فعالیت بیشتر ریشه‌ها و میکروارگانیسم‌ها است. تحلیل مؤلفه‌های اصلی و همبستگی‌ها نشان داد که کربن آلی و نیتروژن کل هم‌جهت بوده و همبستگی بالایی با ذخایر عناصر غذایی دارند، در حالی که تراکم خاک رابطه معکوس با آن‌ها دارد. یافته‌ها تأکید می‌کنند که تاغ‌کاری بلندمدت موجب بهبود کیفیت فیزیکی و شیمیایی خاک، تقویت چرخه کربن و نیتروژن و افزایش پایداری اکولوژیکی اراضی خشک می‌شود. از منظر مدیریتی، انتخاب مناطق مناسب و پایش مستمر سن پوشش گیاهی، کلید ارتقای حاصل‌خیزی خاک و ظرفیت ذخیره عناصر غذایی و تضمین موفقیت برنامه‌های احیای اراضی خشک است.

https://mmws.uma.ac.ir/article_4231_9a59be0919386c6df1c3d4b91163932c.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Factors influencing farmers’ participation in the implementation of modern irrigation: A C5 decision tree approach in Aq Qala county عوامل مؤثر بر مشارکت کشاورزان در اجرای آبیاری نوین: رویکردی مبتنی بر درخت تصمیم C5 در شهرستان آق‌قلا 303 316 4233 10.22098/mmws.2025.18686.1712 FA محمد غریب کارشناسی ارشد منابع آب، گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران خلیل قربانی استاد، گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران میثم سالاری جزی دانشیار، گروه مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران فریبا نیرومندفرد دانش‌آموخته دکتری علوم و مهندسی آب، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران Journal Article 2025 10 28 Introduction This study aimed to investigate the factors influencing farmers’ participation in the implementation of modern irrigation systems and to identify the key determinants of their involvement in such projects. The significance of this research lies in the crucial role of farmers’ active cooperation in achieving sustainable water resource management, particularly in agricultural regions such as Aq Qala, located in Golestan Province. The primary objective was to examine individual, economic, and institutional characteristics that affect farmers’ willingness or reluctance to participate in these initiatives. Key factors considered included education, economic status, land ownership, access to water sources, farm size, and familiarity with participatory concepts and local institutions. By modeling these factors using the C5 decision tree algorithm, the study sought to provide a reliable predictive tool for policymakers and agricultural planners to design targeted interventions, enhance sustainable water resource management, and promote effective and enduring farmer participation. Additionally, the research aimed to offer practical recommendations to increase farmer engagement in modern irrigation programs, ultimately contributing to improved water management, enhanced agricultural productivity, and long-term resilience of local farming communities. The insights gained from this study may also inform future strategies for the adoption of innovative agricultural technologies in similar socio-economic and environmental contexts. Materials and Methods This research is applied in nature and employs a survey-based approach for data collection. For modeling and analysis, the C5 decision tree algorithm was used. The classification method of this algorithm is based on dividing data into smaller and more homogeneous subsets. Its advantages include simplicity, high interpretability, and the use of a small number of parameters, which enhances its efficiency and computational performance. The implementation of the C5 decision tree algorithm in this study was carried out using the RapidMiner software environment. After designing and distributing the questionnaire among farmers, agricultural experts, and relevant authorities, the collected data were analyzed using the C5 decision tree modeling method in RapidMiner. This approach was employed to identify the key factors influencing farmers’ willingness or unwillingness to participate in the implementation of modern irrigation systems. The research sample consisted of 68 farmers from the Aqqala region in Golestan Province, Iran. Their responses to the questionnaire were analyzed to determine the most significant factors affecting their decision to participate in modern irrigation projects. The validity of the questionnaire was confirmed by academic supervisors, professional experts, and officials from the Golestan Regional Water Company. The reliability of the questionnaire was also verified through Cronbach’s alpha coefficient, which was calculated using SPSS version 22. The independent variables used in this study included literacy level, education, age, occupation, economic status, cultivated land area, land ownership type (agricultural or orchard), water sources, familiarity with the concept of participation, and awareness of local institutions and organizations. The dependent variable was defined as the farmers’ willingness or unwillingness to participate in the implementation of modern irrigation projects. Results and Discussion The results revealed that farmers’ willingness to participate in modern irrigation systems is shaped by a combination of economic, social, and institutional factors. Key individual and economic drivers included literacy, economic status, land ownership, and familiarity with participatory practices. Educated, financially secure, and land-owning farmers exhibited higher participation, whereas illiterate, low-income, or tenant farmers were more cautious. Institutionally, access to legal water sources and trust in governmental Authorities, along with economic incentives such as free installation and after-sales service guarantees, were significant motivators. The interaction between age and education showed that younger farmers and educated older farmers were more actively engaged. Conversely, distrust of governmental institutions and complex water regulations acted as barriers. The C5 decision tree effectively modeled these complex relationships and quantified the predictive accuracy of farmers’ participation willingness. Without economic support, it is difficult to achieve sustainable participation, especially among low-income groups. In the social dimension, age and education level play a reinforcing role, and the younger generation and the educated elderly have shown a greater willingness to participate in modern irrigation system projects. On the other hand, distrust in government institutions and the complexity of laws related to water resources are considered to be factors inhibiting effective participation. Accordingly, promoting farmer participation requires a combination of economic support, legal facilitation, targeted education, and institutional trust-building. Conclusion Enhancing farmer participation in modern irrigation systems requires a multifaceted strategy encompassing economic support, targeted education, legal facilitation, and institutional trust-building. Financial status and governmental support emerged as the strongest predictors of participation, indicating that sustainable engagement is unlikely without economic backing. Social factors such as age and education play a supplementary role, with literate populations, particularly youth, more inclined to participate. Distrust of authorities and regulatory complexity remain significant obstacles. Policy recommendations include providing low-interest financial support, implementing targeted training and outreach programs, and establishing sustainable post-implementation technical services. Such measures are expected to transform minimal or conditional participation into active, voluntary, and enduring engagement, promoting optimal and sustainable water resource management in agriculture. با توجه به بحران کمبود منابع آب در بخش کشاورزی، به‌کارگیری سیستم‌های نوین آبیاری به‌عنوان راهکاری مؤثر برای افزایش بهره‌وری آب و ارتقای پایداری تولید مورد توجه قرار گرفته است. موفقیت این طرح‌ها تا حد زیادی به مشارکت فعال کشاورزان وابسته است. ازاین‌رو، هدف پژوهش حاضر شناسایی عوامل مؤثر بر مشارکت مردمی در اجرای سیستم‌های نوین آبیاری در شهرستان آق‌قلا است. این پژوهش از نظر هدف، کاربردی و از نظر روش، پیمایشی است. جامعة آماری شامل ۶۸ نفر از کشاورزان شهرستان آق‌قلا است که از طریق پرسشنامه به جمع‌آوری داده‌ها پرداخته شد. برای تحلیل داده‌ها و مدل‌سازی روابط میان متغیرها از الگوریتم درخت تصمیم C5 استفاده شد. یافته‌ها نشان داد بیش از ۹۰ درصد کشاورزان در صورت دریافت حمایت مالی و تضمین خدمات پس از فروش، تمایل به مشارکت در اجرای طرح‌های آبیاری نوین را دارند. مهم‌ترین عوامل مؤثر بر میزان مشارکت در اجرای سیستم‌های نوین آبیاری نوع منبع تأمین آب است. کشاورزانی که از منابع آب مجاز برخوردارند، تمایل به مشارکت در اجرای طرح مذکور را دارند. هم‌چنین وضعیت مالی، آگاهی از مزایای فناوری و حمایت‌های دولتی از متغیرهای پیش‌بینی‌کننده تمایل به مشارکت محسوب می‌شوند. نتایج بیانگر آن است که ترکیب آموزش‌های ترویجی، مشوق‌های اقتصادی و خدمات پس از فروش می‌تواند زمینه‌ساز افزایش اعتماد و مشارکت پایدار کشاورزان در اجرای سیستم‌های نوین آبیاری باشد.

https://mmws.uma.ac.ir/article_4233_24d97ab954ac731406da1e07f14a82da.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Evaluation of Soil Water Integral Energy Estimation Using Linear and Non-linear Models ارزیابی برآورد انرژی انتگرالی آب خاک با استفاده از مدل‌های خطی و غیرخطی 317 331 4279 10.22098/mmws.2025.18678.1711 FA مجتبی علی‌محمدی دانشجوی دکتری، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران داوود زارع حقی دانشیار، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران محمدرضا نیشابوری استاد، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران محمدعلی قربانی استاد، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران Journal Article 2025 10 27 Extended Abstract Introduction Soil moisture or water content, and the fraction of that water available to plants, are among the most critical aspects of soil water management. The concept of plant-available water was first introduced nearly a century ago by Veihmeyer and Hendrickson (1927), derived from the difference between Field Capacity (FC) and Permanent Wilting Point (PWP). The concepts of the Unrestricted Water Content Range and the Minimum Restricted Water Content Range were proposed by DaSilva et al. (1994). In this framework, in addition to the two moisture limits (FC and PWP), soil aeration and the effect of soil penetration resistance on water availability to the plant are considered using simple relationships. A limitation or defect of the LLWR concept is that it treats the boundary values for aeration porosity, penetration resistance, and soil water potential as abrupt or discontinuous in restricting water availability. In an effort to overcome the shortcomings of these preceding concepts, Minasny and McBratney (2003) proposed the Soil Water Integral Energy (IE) as a criterion for estimating plant-available water in soil, replacing the focus on soil moisture content. Soil water integral energy is a measure of the energy required to extract water from the soil over a specified range of soil water content. Under this concept: firstly, the plant-available water is not solely confined to the PWP and FC range; secondly, the effect of rapid drainage at high moisture contents, which reduces the opportunity for soil water supply to the plant, is taken into account; and thirdly, the limitation imposed by soil hydraulic conductivity at low moisture contents on water flow towards the root and subsequent absorption is incorporated. They utilized various weighting functions across a wide range of soil water potentials, encompassing the potential effect of all limiting physical characteristics on soil water availability. The most significant limitation in employing this index is the time-consuming and costly process of obtaining the soil moisture characteristic curve, the soil penetration resistance curve, and the accuracy or reliability of the coefficients used in defining the proposed weighting functions. Furthermore, in addition to time and expense, errors present in soil sampling and measurement can impose constraints on the application of IE (Integral Energy). Materials and Methods The study area includes a part of the Tabriz plain. For estimation of IE using the deep learning method, Artificial Neural Network (ANN), and Multiple Linear Regression (MLR), Mathematica Wolfram software version 14.1.0 was utilized. The input features for all three models (MLR, ANN, and Deep ANN) were identical. The data were randomly divided into two groups: training (67 data points) and testing (30 data points). The input features for the models included: 1- Percentage of water-stable aggregates 2- Soil bulk density 3- Porosity 4- Saturated hydraulic conductivity of the soil 5- Percentage of soil texture particles 6- Equivalent calcium carbonate 7- Penetration resistance at saturated moisture 8- Saturated moisture. Results and Discussion The created models were evaluated using the evaluation statistics of the coefficient of determination R2, the adjusted coefficient of determination R2adj, the root mean square error RMSE, the relative error RMSEr, the model efficiency coefficient NSE, and the average percentage of relative error RME.The results showed that the deep learning method with the highest adjusted coefficient of determination (training: 0.998, test: 0.661) and the lowest root mean square error (training: 15.943, test: 118.593), the artificial neural network method (training: 0.945, test: 0.514) and root mean square error (training: 45.347, test: 139.267), and the linear multivariate regression method (training: 0.544, test: 0.317) and root mean square error (training: 126.955, test: 239.264), respectively, provide the best estimate of the IE index. Conclusion This study underscores the importance of soil water management and the precise assessment of Plant Available Water (PAW). Given the limitations of traditional concepts like PAW and LLWR, particularly their reliance on discontinuous boundaries, the newer the soil water Integral Energy (IE) criterion is adopted as a more accurate measure for estimating plant water availability in soil.The results demonstrated that IE can be effectively and accurately estimated in the studied area (the Tabriz plain) using deep learning techniques (Artificial Neural Networks), relying on a comprehensive set of key soil properties, including the percentage of water-stable aggregates, bulk density, porosity, and saturated hydraulic conductivity. These findings pave the way for applying advanced, data-driven modeling approaches to optimize soil water resource management in arid and semi-arid regions. مشخص کردن فراهمی آب برای گیاهان در انواع خاک‌ها از چالش‌های مهم مدیریت آبیاری در کشاورزی است. تعیین فراهمی آب خاک با استفاده از مفاهیم مختلفی همچون آب قابل‌استفاده (AWC)، دامنه آب (رطوبت) با حداقل محدودیت (LLWR)، گنجایش انتگرالی آب خاک و انرژی انتگرالی آب خاک (IE) پیشنهاد گردیده است. شاخص انرژی انتگرالی آب خاک (IE) نسبت به سایر معیارها بهتر است زیرا مستقیماً نیروی نگه‌دارندگی آب خاک را اندازه‌گیری می‌کند و نه صرفاً حجم آب موجود در خاک. باوجوداین مزیت، اما به علت وقت‌گیر بودن و هزینه بربودن، اندازه‌گیری مستقیم آن‌ عملاً مقرون‌به‌صرفه نبوده و در عرصه میدانی کاربرد چندانی نداشته است. در این پژوهش سه روش رگرسیون چندمتغیره خطی (MLR)، شبکه عصبی مصنوعی (ANN) و یادگیری عمیق (DL) برای ایجاد و ارزیابی توابع انتقالی در برآورد IE بکار گرفته شد. برای این منظور از داده‌های خاک در ۹۷ مکان از اراضی زراعی و مرتعی حاشیه‌ای دریاچه ارومیه استفاده شد. درصد خاکدانه‌های پایدار در آب، جرم مخصوص ظاهری، تخلخل کل، هدایت هیدرولیکی اشباع، درصد ذرات شن، رس و سیلت، مقاومت فروروی در رطوبت اشباع، درصد کربنات کلسیم معادل و رطوبت اشباع خاک به‌عنوان ورودی مدل‌ها مورداستفاده قرار گرفت. مدل‌های ایجادشده با استفاده از آماره‌های ارزیابی مانند ضریب تبیین R2، ضریب تعیین تعدیل‌شده R2adjusted، جذر میانگین مربعات خطا RMSE، خطای نسبی RMSEr، ضریب کارایی مدل NSE، میانگین درصد خطای نسبی RME ارزیابی شد. نتایج نشان داد که روش یادگیری عمیق با بیشترین ضریب تبیین تعدیل‌شده (آموزش: ۹۹۸/۰، آزمون: ۶۶۱/۰) و کمترین مقدار جذر میانگین مربعات خطا (آموزش: 943/15، آزمون: 593/118) روش شبکه عصبی مصنوعی (آموزش: ۹۴۵/۰، آزمون: ۵۱۴/۰) و جذر میانگین مربعات خطا (آموزش: 347/45، آزمون: 267/139) و روش رگرسیون چندمتغیره خطی (آموزش: 544/۰، آزمون: ۳۱۷/۰) و جذر میانگین مربعات خطا (آموزش: 955/126، آزمون: 239/264) به ترتیب بهترین برآورد را از شاخص IE ارائه می‌دهند.

https://mmws.uma.ac.ir/article_4279_e8183141115175036978c9c6d944dfc4.pdf

دانشگاه محقق اردبیلی مدل سازی و مدیریت آب و خاک 2783-2546 6 1 2026 03 21 Estimation of Groundwater Levels in Arid Climates Using Machine Learning and Fuzzy Intelligent Systems برآورد سطح آب زیرزمینی در اقلیم خشک با رویکرد یادگیری ماشین و سامانه‌های هوشمند فازی 332 349 3998 10.22098/mmws.2025.17978.1637 FA سپیده زراعتی نیشابوری دانشجوی دکتری، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران عباس خاشعی سیوکی استاد، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران محمد قاسم اکبری دانشیار، گروه آمار، دانشکده علوم ریاضی و آمار، دانشگاه بیرجند، بیرجند، ایران Journal Article 2025 08 03 Abstract Introduction The escalating global demand for water, driven by population growth, urbanization, climate change, and excessive use of fertilizers and pesticides, has significantly impacted groundwater levels, leading to soil salinization and desertification. Continuous groundwater extraction exacerbates aquifer depletion, increasing pumping costs and limiting production capacity. Accurate groundwater level estimation is critical for effective water resource management, especially in arid and semi-arid regions like South Khorasan, Iran, where water scarcity is pronounced. This study aims to address the challenge of predicting monthly groundwater level fluctuations in the Birjand plain using advanced modeling techniques. Traditional physical and numerical models, while reliable, face limitations such as high computational demands, data dependency, and inability to handle nonlinear conditions effectively. In contrast, data-driven and artificial intelligence-based models offer simplicity, speed, and reasonable accuracy, particularly when historical data is available (Norouzi Khatiri et al., 2023). Fuzzy logic-based models, especially nonlinear fuzzy regression, excel in handling uncertainty and complex relationships in hydrological systems (Asadollahi, 2023). This research evaluates three fuzzy-based models—nonlinear fuzzy support vector regression (NLF-SVR), fuzzy nonlinear autoregressive regression (FNAR), and fuzzy linear least squares regression (FLSR)—using climatic variables (temperature, precipitation, humidity, and evapotranspiration) to enhance prediction accuracy and support sustainable groundwater. Materials and Methods The study was conducted in the Birjand plain, South Khorasan, Iran, a region characterized by an arid climate with an average annual rainfall of 169 mm in the plain and 216 mm in the highlands. The Birjand aquifer, spanning 3155 km², is heavily exploited, making it a critical case study for groundwater management. A comprehensive dataset covering daily climatic variables—mean air temperature (Tave), precipitation (Prc), relative humidity (RH), and evapotranspiration (ETo)—from April 1998 to March 2017 was compiled from regional meteorological and water authority stations. After quality control and preprocessing, daily data were aggregated into monthly values. The dataset was split into 70% for model training and 30% for validation. Three fuzzy-based models were developed: (1) Fuzzy Linear Least Squares Regression (FLSR), which extends classical regression to handle fuzzy data; (2) Nonlinear Fuzzy Support Vector Regression (NLF-SVR), combining fuzzy logic with support vector machines for nonlinear relationships; and (3) Fuzzy Nonlinear Autoregressive Regression (FNAR), designed for multi-variable fuzzy predictions. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency (NSE). Fuzzy numbers, particularly triangular fuzzy numbers, were used to model uncertainty, with Gaussian kernels applied for FNAR and NLF-SVR. Parameter optimization was achieved using grid search and generalized cross-validation (GCV). Results and Discussion The analysis revealed that the NLF-SVR model outperformed both FNAR and FLSR in predicting monthly groundwater levels in the Birjand plain, achieving an average RMSE of 0.15 m, MAE of 0.37 m, and NSE of 0.99. The model effectively captured complex, nonlinear relationships between climatic variables (Tave, Prc, RH, ETo) and groundwater levels, particularly during warmer months (July–September), where regular water consumption and evapotranspiration patterns enhanced predictability. In contrast, performance dipped in colder months (November–February) due to irregular precipitation and delayed groundwater recharge, aligning with findings by Zhang et al. (2022). The FNAR model showed acceptable performance, with higher sensitivity to seasonal climatic shifts, while FLSR struggled to model nonlinear dynamics, exhibiting higher errors and lower NSE. Nonlinear fuzzy models demonstrated robustness against outliers and noisy data, maintaining approximately 85% of their initial accuracy even with 30% noise, compared to a 60% accuracy drop in classical models. These findings align with studies by Sahoo et al. (2017) and Kumar et al. (2023), confirming the superiority of fuzzy-based approaches in handling hydrological uncertainties. Limitations include the study’s focus on a single region and reliance on climatic inputs alone, suggesting future inclusion of factors like land use and geological characteristics. The NLF-SVR model’s high accuracy supports its application in early. Conclusion This study demonstrates that nonlinear fuzzy models, particularly NLF-SVR, provide superior accuracy for predicting monthly groundwater levels in the Birjand plain, with RMSE of 0.15 m and NSE of 0.99, outperforming FNAR and FLSR. The ability of NLF-SVR to model complex, nonlinear relationships and handle data uncertainties makes it a robust tool for hydrological forecasting in arid climates. Key findings include the models’ enhanced performance during warmer months and reduced accuracy in colder seasons due to variable precipitation patterns. The robustness of fuzzy models against outliers and noisy data eliminates extensive preprocessing, preserving valuable information. Practically, NLF-SVR supports sustainable groundwater management through applications like drought warning systems and optimized irrigation scheduling. However, limitations such as region-specific data and the exclusion of non-climatic factors (e.g., land use, geology) suggest caution in generalizing results. Future research should test these models across diverse hydrogeological settings, integrate deep learning techniques, and incorporate additional variables like climate indices and satellite data. Developing multi-scale models to capture both short- and long-term fluctuations and creating decision-support systems for real-time water management are recommended. This study underscores the potential of fuzzy intelligent systems to enhance groundwater management in water-scarce regions, paving the way for advanced hydrological modeling. پایش دقیق تراز آب زیرزمینی، به‌ویژه در شرایط بهره‌برداری بی‌رویه در کشورهای در حال توسعه، برای مدیریت پایدار منابع و پیشگیری از پیامدهایی چون کاهش توان پمپاژ، نشست زمین و تراکم آبخوان‌ها ضرورتی انکارناپذیر است. با توجه به پیچیدگی فرآیندهای هیدرولوژیکی و عدم قطعیت‌های موجود در داده‌های اقلیمی، این مطالعه با هدف ارزیابی و مقایسه عملکرد مدل‌های فازی برای پیش‌بینی ماهانة تراز آب زیرزمینی دشت بیرجند انجام شد. سه مدل شامل رگرسیون بردار پشتیبان غیرخطی فازی (NLF-SVR)، رگرسیون تجمعی غیرخطی فازی (FNAR) و رگرسیون کم‌ترین مربعات خطی فازی (FLSR) با استفاده از داده‌های اقلیمی دما، رطوبت نسبی، بارش و تبخیر و تعرق طراحی و پیاده‌سازی شدند. نتایج نشان داد که مدل NLF-SVR با متوسط RMSE 0/15 متر، MAE 0/37 متر و NSE نزدیک به 99/0 عملکرد برتری نسبت به سایر مدل‌ها دارد. بر اساس معیارهای ارزیابی، مدل FNAR در جایگاه دوم قرار گرفت و حساسیت بیش‌تری نسبت به تغییرات فصلی نشان داد؛ در حالی که مدل FLSR به‌دلیل ماهیت خطی خود کم‌ترین دقت را داشت و قادر به بازنمایی پیچیدگی‌های فرآیند هیدرولوژیکی نبود. بیش‌ترین دقت مدل‌ها در ماه‌های گرم و ضعیف‌ترین عملکرد در ماه‌های سرد مشاهده گردید. این الگو ناشی از پایداری نسبی روابط بین متغیرهای اقلیمی و سطح آب زیرزمینی در فصول گرم و پیچیدگی‌های غیرخطی ناشی از تغییرات ناگهانی دما، یخبندان و نوسانات شدید بارش در فصول سرد است. در مجموع، مدل NLF-SVR به‌عنوان ابزاری کارآمد برای توسعه سامانه‌های هشدار زودهنگام، بهینه‌سازی برنامه‌ریزی کشاورزی و مدیریت پایدار منابع آب در مناطق خشک پیشنهاد می‌شود.

https://mmws.uma.ac.ir/article_3998_bf22b01fd6f50fb0a3b3f6557350e59d.pdf