پديد آورندگان :
شفيعي، مريم دانشگاه تهران - پرديس كشاورزي و منابع طبيعي -گروه مهندسي آبياري و آباداني، كرج، ايران , بذرافشان، جواد دانشگاه تهران - پرديس كشاورزي و منابع طبيعي -گروه مهندسي آبياري و آباداني، كرج، ايران , ايران نژاد، پرويز دانشگاه تهران - مؤسسه ژئوفيزيك - گروه فيزيك فضا ، تهران، ايران
كليدواژه :
مدل مفهومي HBV , حوضه آبريز كرخه , واسنجي , تحليل حساسيت
چكيده فارسي :
مدل HBV (Hydrologiska Byråns Vattenbalansavedlning) يك مدل مفهومي است كه به‌طور گسترده اي
براي پيش بيني هاي آب‌شناسي و مطالعات منابع آب به‌كار ميرود. در اين مطالعه تحليل حساسيت پارامترهاي مدل HBV براي زيرحوضه‌هاي كرخه و كل حوضه كرخه در چهار بازه زماني مختلف 1، 5، 10 و 25 سال با چهار روش FAST (Fourier Amplitude Sensitivity Test)، (Regional Sensitivity Analysis) RSA،Sobol و رگرسيون بررسي شده است. پس از تعيين حساسترين پارامترها مدل با روش الگوريتم ژنتيك با مرتب سازي نامغلوب،
NSGA ) Nondominated Sorting Genetic Algorithm) واسنجي شده است. توابع هدف براي بررسي عملكرد مدل شامل NSE، RMSE، RSR و BIAS مي باشند. نتايج تحليل حساسيت پارامترها نشان ميدهد كه روشهاي Sobol و RSA به‌علت تغييرپذيري در بازه هاي زماني و زيرحوضه هاي مختل فروش هاي قابل اطمينان تري هستند. حساسترين پارامترهاي مدل HBV براي زيرحوضه ها و حوضه كرخه در روال خاك پارامتر بيشينه ذخيره رطوبت خاك (Fcap) و در روال پاسخ پارامتر بيشينه ذخيره رطوبت لايه سطحي خاك (hl1) هستند، اين پارامترها در دبيهاي كمينه بيشترين حساسيت را نشان دادهاند. پارامترهاي روال برف مخصوصاً پارامتر دماي آستانه براي يخ زدگي (ttlim) در زيرحوضه هاي قره سو و كشكان و در بازه هاي زماني كوتاه مدت (1 و 5 سال) حساسيت نشان دادهاند. مدل HBV توانايي شبيه سازي رواناب در حوضه كرخه و زيرحوضه هاي آن با دقت بالا را دارد. اين مطالعه نشان ميدهد انتخاب بازه هاي زماني كوتاهتر واسنجي، نتايج شبيه سازي بهتري ارائه ميدهد. در بازه زماني يك سال بهترين ضريب NSE، RSR و RMSE مربوط به زيرحوضه گاماسياب به‌ترتيب به‌مقدار 95/0، 21/0 و 4/1 و بهترين BIAS مربوط به زيرحوضه كشكان و حوضه كرخه به‌مقدار 13/0 است.
چكيده لاتين :
The HBV (Hydrologiska Byråns Vattenbalansavedlning) is a conceptual model widely used for hydrological forecasting and water resource studies. In this study, sensitivity analysis of parameters of the HBV model is investigated for Karkhe basin and its sub-basins for four different periods 1, 5, 10 and 25 years with four methods including FAST (Fourier Amplitude Sensitivity Test), RSA (Regional Sensitivity Analysis), Sobol and regression. After determining the most sensitive parameters, the model is calibrated using Nondominated Sorting Genetic Algorithm (NSGA) method. In all statistical periods, one year has been used for warm-up to eliminate the effects of initial conditions. In this study, the MOUSE Toolbox is used to analyze the sensitivity of the HBV model parameters. This software is based on Java programming language. To analyze the sensitivity of the HBV model parameters based on the Monte Carlo sampling method and the Halton sequence method for each of the samples (time periods) in each sub-basin separately, 1000 samples are taken for the set of input parameters with a specified range for each parameter taken. Objective functions for evaluating performance of model are NSE, RMSE, RSR and BIAS. The results of sensitivity analysis of the parameters show that Sobol and RSA are more reliable methods because of variability in time intervals and different sub-basins. Fast and regression methods in the Karkheh basin and its sub-basins for different time periods show similar results that considering the change in hydroclimate conditions in this basin, isn't practical and the results of these methods can not be used for investigating sensitivity of parameters and their identification in the studied basin. The most sensitive parameters of HBV model for Karkheh basin and its sub-basins in soil routine is maximum soil moisture content (Fcap) and in the response routine is the storage of soil surface moisture content (hl1). These parameters have shown the most sensitive factor in minimum fluxes. The snow routine parameters, especially the threshold temperature for ice freezing (ttlim), are sensitive in the sub-basins of Ghare Sou and Kashkan in short periods (1 and 5 years). For a specific sub-basin, the sensitivity of the parameters in different time periods is not completely stable and a little variability has been observed in different periods. But the most sensitive parameters (hl1 and fcap) have maintained their sustainability almost in all periods. Parameters of response and soil routines are more sensitive to the parameters of snow and routing routines. The results of the interaction between the parameters using the Sobol method in different sub-basins indicate that the strongest interactions are between the soil routine parameters, especially Fcap, with the response routine parameters and also the response routine parameters with each other. The time variability of parameters indicates that the soil routine and response parameters in the minimum discharge show the most sensitivity. Other parameters are more sensitive in the dry season of the basin (summer and autumn). The HBV model has the ability to simulate runoff in the Karkhe basin and its sub-basins with high precision. This study shows that selection of shorter period of calibration gives better simulation results. For one year's period the best NSE, RSR and RMSE are in Gamasyab sub-basin respectively 0.95, 0.21 and 1.4 and the best BIAS is in Kashkan sub-basin and Karkhe basin with 0.13.
