A Distributed Hydrological Model Driven by Multi-Source Spatial Data and Its Application in the Ili River Basin of Central Asia
作者:Cai, MY (Cai, Mingyong)[ 1 ] ; Yang, ST (Yang, Shengtian)[ 1 ] ; Zeng, HJ (Zeng, Hongjuan)[ 2 ] ; Zhao, CS (Zhao, Changsen)[ 1 ] ; Wang, SD (Wang, Shudong)[ 3 ]
WATER RESOURCES MANAGEMENT
卷: 28  期: 10  页: 2851-2866
DOI: 10.1007/s11269-014-0641-z
出版年: AUG 2014

摘要
Hydrological simulation in ungauged regions is a popular topic in water resource and environmental research, and is also an important part of the international research initiative Predictions in Ungauged Basins (PUB). In this study, a multi-spatial data-based DTVGM (MS-DTVGM), combining multi-source spatial data (MS-spatial data) with the Distributed Time-Variant Gain Model (DTVGM), was built in order to reduce dependence on conventional observation, and was applied to the Ili River basin where traditional data sets are scarce. Because it utilizes MS-spatial data to measure precipitation, potential evapotranspiration, air temperature, vegetation parameters, and soil parameters, the model is driven purely by data from common platforms, thus overcoming the disadvantage of the large amounts of data typically required for distributed hydrological models. The inputs and simulation results were calibrated and validated using station or field observations. The results indicate that: 1) the MS-DTVGM is feasible in the Ili River basin; all model inputs can be acquired from multi-source spatial data and the key parameters are accurate; 2) the MS-DTVGM has good performance on a monthly time scale, and its simulation results can be used for a longer time-scale water resource analysis; and (3) daily runoff generation correlated strongly with snowmelt, the R-2 is about 0.69 indicating that the latter is an important contributor to water resources and suggesting that a snowmelt module is indispensable this area. The potential of distributed models for hydrological simulation in data-scarce regions using MS-spatial data was clearly demonstrated.

通讯作者地址: Zhao, CS (通讯作者)
Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China
[ 2 ] Yangtze Soil Conservat Monitoring Ctr, Wuhan 430010, Peoples R China
[ 3 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China