Uncertainty assessment DWB model by using GLUE method (Case study: Andrabi and Farvbrman catchments)

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Saeed  Emamifar ¹ (دانشجوی دکتری آبیاری و زهکشی؛گروه مهندسی آب؛ دانشکده کشاورزی؛ دانشگاه فردوسی مشهد؛ مشهد؛ ایران)
Kamran  Davari ² (استاد؛ گروه مهندسی آب؛ دانشکده کشاوری؛ دانشگاه فردوسی مشهد؛ مشهد؛ ایران)
Hussain  Ansari ³ (دانشیار؛ گروه مهندسی آب؛ دانشکده کشاوری؛ دانشگاه فردوسی مشهد؛ مشهد؛ ایران)
Bijan  Ghahraman ⁴ (استاد؛ گروه مهندسی آب؛ دانشکده کشاوری؛ دانشگاه فردوسی مشهد؛ مشهد؛ ایران)
Seyed Mahmoud  Hosseini ⁵ (استاد؛ گروه مهندسی عمران؛ دانشکده فنی مهندسی؛ دانشگاه فردوسی مشهد؛ مشهد؛ ایران)
Mohsen  Nasseri ⁶ (استادیار؛ گروه مهندسی عمران؛ دانشکده فنی مهندسی؛ دانشگاه تهران؛ تهران؛ ایران)

Received: 2016-03-16 Accepted : 2016-05-22 Published : 2016-09-22

Keywords: GLUE, Runoff, Budyko framework, DWB model,

Abstract :

The analysis uncertainty of hydrological models and identify the statistical properties them basis of existing relationships between of the model parameters and inputs is numerous of the most important hydrological modeling. In this context one of the known methods for modeling the water balance at the catchements scale is Dynamic water balance model (DWB) that By Zhang et al. (2008) developed. In this study, the model calibration and uncertainty assessment in the Andrab and Farvbrman catchments from the Kalshvr River branches (Nishapur catchment) respectively at the zarandand and Eyshabad hydrometery stations, located in semi-arid regions by using Generalized likelihood uncertainty estimation (GLUE) method was studied. The results indicated that in terms of the ability to identify, Between four parameters of the model, Retention efficacy parameter (ω_1) and basin water storage capacity (s_max [mm]) in the calibration process was less of the ability to identify (The specific domain ptimum for their can not be found) and uncertainty in the runoff simulated by the model, have a greater role. In this direction, the efficacy parameter evapotranspiration (ω_2) and the gradual flow (d), resistant behavior of the errors in the runoff observation and are so-called conservative. Evaluation of uncertainty runoff simulated showed that in general, GLUE method is well managed that it calibrate amount of runoff from case study, so that more of the runoff data recorded (over 55%) in the range of 95 percent.

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