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