A rainfall-runoff model using distributed data of radar rain and altitude.

Abstract. As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.

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Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Hydrology and Earth System Sciences ◽

10.5194/hess-19-1887-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 1887-1904 ◽

Cited By ~ 8

Author(s):

T. Berezowski ◽

J. Nossent ◽

J. Chormański ◽

O. Batelaan

Keyword(s):

Sensitivity Analysis ◽

Spatial Data ◽

Distributed Data ◽

Rainfall Runoff ◽

Spatial Sensitivity ◽

Wetspa Model ◽

Spatial Sensitivity Analysis ◽

Rainfall Runoff Model ◽

Different Response ◽

Runoff Model

Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

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Sensitivity Analysis For Control Parameters Of Hybrid And Standard PSO Algorithms: Application Via A Rainfall-runoff Model Calibration

4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings ◽

10.36287/setsci.4.6.085 ◽

2019 ◽

Author(s):

Umut Okkan ◽

Umut Kırdemir

Keyword(s):

Sensitivity Analysis ◽

Model Calibration ◽

Control Parameters ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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

Hydrological Processes ◽

10.1002/hyp.14223 ◽

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

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

Water ◽

10.3390/w13091226 ◽

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.

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