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بهبود دقت برآورد دبی با تلفیق روشهای هیدرولوژیکی و دادههای سنجشازدور با تاکید بر نقش بافت خاک و کاربری اراضی در حوضه های فاقد آمار هیدرومتری | ||
مجله اکوهیدرولوژی | ||
مقاله 3، دوره 11، شماره 3، مهر 1403، صفحه 337-354 اصل مقاله (1.79 M) | ||
نوع مقاله: پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ije.2024.383293.1842 | ||
نویسندگان | ||
حافظ میرزاپور1؛ علی حقی زاده* 2؛ مهدی سلیمانی مطلق3 | ||
1دانشجوی دکتری مدیریت حوزههای آبخیز، دانشکده منابع طبیعی، دانشگاه لرستان، خرمآباد، ایران | ||
2دانشیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرمآباد، ایران | ||
3استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرمآباد، ایران | ||
چکیده | ||
مدیریت مؤثر منابع آب در مناطق با دادههای هیدرومتری محدود، نیازمند استفاده از روشهای نوین و ترکیبی است که به بررسی دقیقتر دینامیکهای هیدرولوژیکی بپردازند. این پژوهش به بررسی و تحلیل برآورد دبی زیرحوزههای دز در استان لرستان میپردازد. ابتدا با اتکا به دادههای ماهوارهای سنتینل 1 و 2 و بهرهگیری از شاخصهای SRCI و BI نقشهی بافتهای خاک، کاربری اراضی و نقشه شماره منحنی (CN) استخراج گردید. در ادامه، با تکیه بر دادههای بارش و دبی از سال 1371 تا 1402 و تحلیل آماری، دوره بازگشت بارش و دبی زیرحوزههای موردمطالعه با بهرهگیری از نرمافزار ایزی فیت محاسبه گردید. دبی هر زیر حوزه با استفاده از روش SCS و رگرسیون چندمتغیره تخمین زده شد. نتایج نشان داد رگرسیون چندمتغیره باتوجهبه مقادیر آماره دوربین واتسون (1/74) آمارههای ضریب تعیین 0/768 میانگین مربعات خطا 17/88و نش ساتکلیف 0/758 در دوره بازگشت 2 ساله مناسبترین دوره بازگشت جهت تخمین دبی ایستگاههای فاقد آمار در زیرحوزههای دز در استان لرستان میباشد. بهطورکلی، این پژوهش شیوههای کارآمدی را برای مدیریت منابع آبی و بهینهسازی هیدرولوژیکی در استان لرستان ارائه میدهد و توصیه میشود جهت صرفه جویی در هزینه و زمان از رگرسیون چندمتغیره برای تخمین دبی در زیرحوزههای آبخیز فاقد آمار بهرهبرداری گردد. | ||
کلیدواژهها | ||
گوگل ارث انجین؛ بافت خاک؛ SRCI؛ شاخص روشنایی BI؛ سنتینل | ||
عنوان مقاله [English] | ||
Improving Flow Estimation Accuracy Through the Integration of Hydrological Methods and Remote Sensing Data: Emphasizing the Role of Soil Texture and Land Use in Unguaged Sites Located Hydrometric Data | ||
نویسندگان [English] | ||
Hafez mirzapour1؛ Ali Haghizadeh2؛ mahdi soleimani-Motalgh3 | ||
1PhD. student of Watershed Management Engineering Faculty of Natural Resources Lorestan University, Khorram Abad, Lorestan, Iran | ||
2Associate Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran | ||
3Assistant Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran | ||
چکیده [English] | ||
Effective water resource management in areas with limited hydrometric data requires the application of innovative and integrated methods to examine hydrological dynamics more accurately. This study investigates and analyzes how flow was estimated in the sub-basins of the Dez in Lorestan Province. Initially, Sentinel-1 and 2 satellite data were used, along with SRCI and BI indices, to extract maps of soil textures, land use, and curve number (CN). Subsequently, Relying on rainfall and discharge data from 1992 to 2023 and statistical analysis, the return period of rainfall and flow for the studied sub-basins was calculated utilizing EasyFit software. The flow for each sub-basin was estimated using the SCS method and multivariate regression. The results indicated that multivariate regression, evaluated using the Durbin-Watson statistic (1.74), the coefficient of determination (0.768), the mean squared error (17.88), and the Nash-Sutcliffe efficiency (0.758) for a 2-year return period, was the most suitable method for estimating flow at ungauged stations within the sub-basins of the Dez River. Overall, this research presents effective approaches for water resource management and the optimization of hydrological in Lorestan Province, To optimize cost and time efficiency, the use of multivariate regression for flow estimation in ungauged hydrometric sub-basins is recommended. | ||
کلیدواژهها [English] | ||
Google Earth Engine, Soil Texture, SRCI, Brightness Index, Sentinel | ||
مراجع | ||
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