Analysis of runoff variation trend of the Dongting Lake in China

2021 ◽  
Vol 826 (1) ◽  
pp. 012004
Author(s):  
Guo Wei ◽  
Shao Jun ◽  
Ou Yangshuo ◽  
Yao Liqiang ◽  
Wu Guangdong ◽  
...  

2017 ◽  
Vol 08 (2) ◽  
pp. 77-91
Author(s):  
Dehua Mao ◽  
◽  
Chang Feng ◽  
Hui Zhou ◽  
Guangwei Hu ◽  
...  

2013 ◽  
Vol 19 (3) ◽  
pp. 434-443 ◽  
Author(s):  
Meiwen ZHANG ◽  
Bo LI ◽  
Yong WANG ◽  
Daosong JIANG ◽  
Huang HUANG ◽  
...  

2010 ◽  
Vol 30 (4) ◽  
pp. 400-405 ◽  
Author(s):  
Xian-Yan QIN ◽  
Yong-Hong XIE ◽  
Xin-Sheng CHEN

2006 ◽  
Vol 34 (3) ◽  
pp. 117-123
Author(s):  
Shouhei Takeuchi ◽  
Yuesheng Li ◽  
Yongkang He ◽  
Huan Zhou ◽  
Moji Kazuhiko ◽  
...  

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.


Sign in / Sign up

Export Citation Format

Share Document