Study on snowmelt runoff simulation in the Kaidu River basin

2007 ◽  
Vol 50 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
YiChi Zhang ◽  
BaoLin Li ◽  
AnMing Bao ◽  
ChengHu Zhou ◽  
Xi Chen ◽  
...  
2016 ◽  
Vol 48 (4) ◽  
pp. 1100-1117 ◽  
Author(s):  
J. Sun ◽  
Y. P. Li ◽  
G. H. Huang ◽  
C. X. Wang

Uncertainties in spatial data associated with basin topography, drainage networks, and land cover characteristics may affect the performance of runoff simulation. Such uncertainties are mainly derived from selection of digital elevation model (DEM) resolution and basin subdivision level. This study focuses on assessing the effects of DEM resolution and basin subdivision level on runoff simulation with a semi-distributed land use-based runoff process model. Twenty-four scenarios based on various DEM resolutions and subdivision levels are analyzed for the Kaidu River Basin. Results can be used for quantifying the uncertainty of input data about spatial information on model simulation, disclosing the interaction between DEM resolution and subdivision level, as well as identifying the optimal system inputs. Results show that the model performance could be enhanced with the increased subdivision level. Results also reveal that the interaction of DEM resolution and subdivision level has slight effects on modeling outputs. Multi-objective fuzzy evaluation is used to further examine the uncertainty in DEM resolution and basin subdivision level on model performance. The results indicate an optimal combination of input parameters is suitable for Kaidu River Basin which could lead to more reliable results of the hydrological simulation.


2010 ◽  
Vol 62 (5) ◽  
pp. 1039-1045 ◽  
Author(s):  
Yan Dou ◽  
Xi Chen ◽  
Anmin Bao ◽  
Lanhai Li

2021 ◽  
Vol 38 ◽  
pp. 100968
Author(s):  
Bingqian Zhao ◽  
Huaiwei Sun ◽  
Dong Yan ◽  
Guanghui Wei ◽  
Ye Tuo ◽  
...  

1989 ◽  
Vol 20 (3) ◽  
pp. 167-178 ◽  
Author(s):  
B. Dey ◽  
V. K. Sharma ◽  
A. Rango

In the Snowmelt-Runoff Model (SRM), the estimate of discharge volume is based on temperature condition in the form of degree days which are used to melt the snowpack in the area of the basin covered by snow as observed from satellites. Precipitation input is used to add any rainfall runoff to the snowmelt component. When SRM was applied to the large, international Kabul River basin, initial simulations were much above the observed stream flow values. Close inspection revealed several problems in the application of SRM to the Kabul Basin that were easily corrected. Foremost among the corrections were determination of an appropriate lapse rate, substitution of a more representative mean elevation for extrapolation of temperature data, and use of an automatic streamflow updating procedure. These improvements led to a simulation for 1976 that was comparable to other simulations on large, inaccessible basins. As SRM is applied to more basins similar to the Kabul River, the determination of suitable parameters for new basin will be enhanced. Additional improvements in simulations would result from installation of climate stations at the mean elevation of basins and work to assure delivery of timely and reliable satellite snow cover data.


2009 ◽  
Vol 13 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Q. Zhao ◽  
Z. Liu ◽  
B. Ye ◽  
Y. Qin ◽  
Z. Wei ◽  
...  

Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.


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