scholarly journals Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Sampling Strategies ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Rainfall Runoff ◽  
Management Planning ◽  
Runoff Variation ◽  
Groundwater Exchange ◽  
Monthly Runoff ◽  
Rainfall Runoff Model ◽  
Two Parameters ◽  
Runoff Model

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.

Effects of conditional parameterization on performance of rainfall-runoff model regarding hydrologic non-stationarity

2012 ◽  
Vol 26 (26) ◽  
pp. 3953-3961 ◽  
Author(s):  
Jiangmei Luo ◽  
Enli Wang ◽  
Shuanghe Shen ◽  
Hongxing Zheng ◽  
Yongqiang Zhang
Keyword(s):  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Improvement of Nonlinear Rainfall-Runoff Model

1982 ◽  
Vol 108 (7) ◽  
pp. 813-822
Author(s):  
Otto J. Helweg ◽  
Jaime Amorocho ◽  
Ralph H. Finch
Keyword(s):  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Hydrological Validation of a Radar-Based Nowcasting Technique

2005 ◽  
Vol 6 (4) ◽  
pp. 532-549 ◽  
Author(s):  
Marc Berenguer ◽  
Carles Corral ◽  
Rafael Sánchez-Diezma ◽  
Daniel Sempere-Torres
Keyword(s):  
Point Of View ◽  
Motion Field ◽  
Warning Systems ◽  
Rainfall Runoff ◽  
Weather Radars ◽  
Radar Echoes ◽  
Radar Measurements ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Better Than

Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.

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