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RESEARCH ON RUNOFF SIMULATION IN NINGXIA SECTION OF THE YELLOW RIVER BASIN BASED ON IMPROVED SWAT MODEL.

Authors :
WANG, Z.
TIAN, J.
FENG, K.
Source :
Applied Ecology & Environmental Research; 2019, Vol. 17 Issue 2, p3483-3497, 15p
Publication Year :
2019

Abstract

In recent years, the Ningxia Section of the Yellow River Basin of China has become waterstressed due to the reduction of the upstream precipitation, to the serious water and soil loss in the midstream, to the increased water consumption in industrial and agricultural areas and to other reasons. To fully understand the runoff variation of the Yellow River in the Ningxia Section of China and to rationally carry out the water dispatching and the water resource management, the applicability of the SWAT model for water balance in this basin is explored. Based on the improved SWAT model, the distributed hydrological model of the Ningxia Section of the Yellow River Basin of China has been constructed. The meteorological and hydrological data of the Ningxia Section of the Yellow River Basin of China from 1990 to 2017 have been used for simulation. The relative error R<subscript>e</subscript> the correlation coefficient R² and Nash-Suttcliffe coefficient Ens have been used as the standards, while the sensitive parameter of the measured monthly runoff from 2005 to 2011 to the model used for the calibration, and the model have been validated by the measured monthly runoff from 2012 to 2017. The research results show that the simulation results basically meet the model evaluation requirements, indicating that the SWAT model is applicable to the runoff simulation of the Ningxia Section of the Yellow River Basin of China, and it can provide decision-making basis for water resource management in the Ningxia Section of the Yellow River Basin of China. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15891623
Volume :
17
Issue :
2
Database :
Complementary Index
Journal :
Applied Ecology & Environmental Research
Publication Type :
Academic Journal
Accession number :
135972083
Full Text :
https://doi.org/10.15666/aeer/1702_34833497