اولویت‌بندی زیرحوضه‌ها در کنترل سیلاب با توجه به میزان تاثیرگذاری آنها بر مشخصات سیلاب با استفاده از داده‌های ماهواره‌ای (مورد مطالعاتی: رودخانه حبله‌رود)

خانی, راضیه; حسینی, خسرو; موسوی, سیدفرهاد

doi:10.22091/wrcc.2025.12359.1009

	اولویت‌بندی زیرحوضه‌ها در کنترل سیلاب با توجه به میزان تاثیرگذاری آنها بر مشخصات سیلاب با استفاده از داده‌های ماهواره‌ای (مورد مطالعاتی: رودخانه حبله‌رود)
منابع آب و تغییر اقلیم
دوره 1، شماره 2، تیر 1404، صفحه 11-24 اصل مقاله (1.32 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22091/wrcc.2025.12359.1009
نویسندگان
راضیه خانی¹؛ خسرو حسینی^* ²؛ سیدفرهاد موسوی²
¹دانشجوی کارشناسی ارشد، گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه سمنان، سمنان، ایران.
²گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه سمنان، سمنان، ایران.
چکیده
در این پژوهش، زیرحوضه‌های حوضه آبخیز حبله‌رود از نظر سیل‌خیزی با استفاده از مدل HEC-HMS اولویت‌بندی می‌شوند. به‌دلیل نبود داده‌های بارش، از داده‌های ماهواره‌ای GPM (محصول IMERG) استفاده می‌شوند. سه واقعه سیلاب به‌منظور واسنجی و اعتبارسنجی مدل انتخاب می‌گردند. واسنجی با استفاده از دو سیلاب سال 2007 انجام می‌شود. شاخص‌هایR² و NSE برای آوریل به‌ترتیب 0.93 و 0.83 و برای ژوئن به‌ترتیب 1.86 و 3.75 بودند که نشان‌دهنده عملکرد مطلوب مدل است. پس از ساخت مدل، بارش معادل سیلاب 50 ساله وارد مدل شد. به کمک روش حذف ترتیبی زیرحوضه‌ها، میزان تأثیر آن‌ها بر دبی اوج و حجم سیلاب در ایستگاه بنکوه تحلیل گردید. نتایج نشان دادند که زیرحوضه 4 با مساحت 575.56 کیلومترمربع، دبی اوج 239 مترمکعب بر ثانیه و حجم سیلاب 12086.2 هزار مترمکعب، بیش‌ترین نقش را در سیلاب دارد، به‌طوری‌که حذف آن موجب کاهش 22.58 درصدی جریان خروجی شد. ارزیابی دقت داده‌های ماهواره‌ای نشان داد که میانگین همبستگی 0.51، ضریب اریبی 3.13 و خطای میانگین 1.92 بوده و میانگین دقت تشخیص وقوع بارش بیش از 0.86 است. هم‌چنین، مشخص شد که مساحت یا دبی بالا به‌طور حتم به مشارکت بیش‌تر در سیلاب منجر نمی‌شود و عواملی مانند موقعیت، ضریب تمرکز و تاخیر نیز نقش دارند.
کلیدواژه‌ها
بارش-رواناب؛ سیل‌خیزی؛ HEC-HMS؛ IMERG
عنوان مقاله [English]
Sub-Basin Prioritization for Flood Control Based on Their Contribution to Flood Characteristics Using Satellite Remote Sensing Data: A Case Study of the Hablehroud River Basin
نویسندگان [English]
Razie Khani¹؛ Khosrow Hosseini²؛ Farhad Mousavi²
¹M.Sc. Student, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
²Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
چکیده [English]
Abstract Objective: In this study, the sub-basins of the Hablehroud watershed were prioritized in terms of flood potential using the HEC-HMS hydrological model. Method: Due to the lack of ground-based precipitation data, satellite-based rainfall data from the Global Precipitation Measurement (GPM) mission, specifically the IMERG product, were utilized. Three flood events were selected for model calibration and validation, with calibration performed using two flood events from 2007. The coefficients of determination (R²) and Nash–Sutcliffe efficiency (NSE) for the April event were 0.93 and 0.83, respectively, while for the June event, they were 0.86 and 0.75, indicating satisfactory model performance. After model development, a 50-year return period design storm was introduced into the model. Using the sequential sub-basin elimination method, the contribution of each sub-basin to peak discharge and flood volume at the Bankouh gauging station was analyzed. Results: The results showed that Sub-basin 4, with an area of 575.56 km², a peak discharge of 239 m³/s, and a flood volume of 12,086 thousand m³, played the most significant role in flood generation, resulting in a 22.58% reduction in outlet discharge upon its removal. Evaluation of the satellite precipitation data revealed a mean correlation coefficient of 0.51, a bias coefficient of 3.13, and a mean error of 1.92. Conclusion: The average probability of precipitation detection exceeded 0.86. Furthermore, the results demonstrated that larger basin area or higher discharge do not necessarily lead to greater flood contribution, and that factors such as basin location, concentration coefficient, and time lag also play critical roles in flood generation. English Extended Abstract Introduction This study prioritizes sub-basins of the Hablehroud watershed for flood risk using the HEC-HMS model, with GPM satellite rainfall data due to the lack of ground observations. Three flood events were used for calibration and validation, showing strong model performance (e.g., R² up to 0.93). A 50-year flood simulation and sub-basin elimination method identified Sub-basin 4 as the most critical, reducing outflow by 22.58% when removed. Satellite data evaluation showed reasonable accuracy. Results indicate that factors like location and concentration time matter more than just area or peak flow. Method This section focuses on modeling the Hablehroud Watershed using the HEC-HMS hydrological model. The study area, covering approximately 326,991 hectares, spans parts of Tehran, Mazandaran, and Semnan provinces. Soil data were obtained from the HWSD database, while vegetation cover information was derived from MODIS satellite imagery. Curve Numbers (CN) for sub-basins were calculated based on soil hydrologic groups and land use types using TR-55 reference tables. Due to the unavailability of high-resolution observational rainfall data, satellite-based GPM (IMERG product) precipitation data with a 30-minute temporal resolution were utilized and converted to hourly intervals via Python programming. The SCS-CN and Clark methods were applied for runoff estimation. Model performance was evaluated using R², Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) indices. The model was calibrated and validated against three selected flood events recorded at the Bankouh hydrometric station. Results This study employed the HEC-HMS model to manage flood risk in the Hablehroud Basin. After determining Curve Numbers (CN) for sub-basins using land cover and soil data, the accuracy of IMERG satellite rainfall data was evaluated and deemed acceptable. The model was then calibrated and validated using these data, showing strong agreement between simulated and observed hydrographs (R² up to 93% and NSE up to 83%). To identify flood-prone areas, the impact of individually removing each sub-basin on peak discharge and flood volume was analyzed. Sub-basin 4, which showed the highest reduction in peak discharge, was prioritized for flood control interventions. Additionally, a 30% reduction in CN values for individual sub-basins significantly decreased peak discharge and flood volume, with model performance (NSE) improving up to 89.6%. The results highlight that land use modifications and enhanced soil infiltration can serve as effective strategies for flood mitigation. The study underscores the value of targeted sub-basin management and CN adjustment in improving watershed hydrological responses and reducing flood risk. Conclusion This study investigates the simulation of hydrological processes in a watershed using the HEC-HMS model, along with remote sensing and GIS data. The results indicate that the model performs well in simulating runoff and streamflow, and satellite data significantly enhance the simulation accuracy in the absence of ground-based stations. Land use maps, digital elevation models, and soil data were generated using GIS. The analysis shows that watershed area alone does not determine the amount of runoff; factors such as slope, elevation, time of concentration, and sub-basin location also play important roles. Sub-basin 4 is the largest in terms of area, while sub-basin 7 is the smallest. Sub-basin 4 also shows the greatest reduction in discharge, whereas sub-basin 15 has the least. Moreover, smaller sub-basins tend to produce higher peak discharges and are more sensitive to sudden rainfall events. Overall, the integration of remote sensing, GIS, and numerical modeling provides an effective tool for analyzing the hydrological behavior of watersheds.
کلیدواژه‌ها [English]
Rainfall-runoff, Flood potential, HEC-HMS, IMERG

مراجع
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اولویت‌بندی زیرحوضه‌ها در کنترل سیلاب با توجه به میزان تاثیرگذاری آنها بر مشخصات سیلاب با استفاده از داده‌های ماهواره‌ای (مورد مطالعاتی: رودخانه حبله‌رود)