Modelling errors calculation adapted to rainfall – Runoff model user expectations and discharge data uncertainties

Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological information from gauged basins to ungauged basins. As a consequence, regionalization-based FDC predictions are not very reliable where discharge data are scarce quantitatively and/or qualitatively. In such a scenario, it is perhaps more meaningful to use a calibration-free rainfall‒runoff model that can exploit easily available meteorological information to predict FDCs in ungauged basins. This hypothesis is tested in this study by comparing a well-known regionalization-based model, the inverse distance weighting (IDW) model, with the recently proposed calibration-free dynamic Budyko model (DB) in a region where discharge observations are not only insufficient quantitatively but also show apparent signs of observational errors. The DB model markedly outperformed the IDW model in the study region. Furthermore, the IDW model’s performance sharply declined when we randomly removed discharge gauging stations to test the model in a variety of data availability scenarios. The analysis here also throws some light on how errors in observational datasets and drainage area influence model performance and thus provides a better picture of the relative strengths of the two models. Overall, the results of this study support the notion that a calibration-free rainfall‒runoff model can be chosen to predict FDCs in discharge data-scarce regions. On a philosophical note, our study highlights the importance of process understanding for the development of meaningful hydrological models.

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IMPLICATION OF SOM-ANN BASED CLUSTERING FOR MULTI-STATION RAINFALL-RUNOFF MODELING

Journal of Urban and Environmental Engineering ◽

10.4090/juee.2014.v8n2.198210 ◽

2015 ◽

pp. 198-210

Author(s):

Vahid Nourani ◽

Masoud Mehrvand ◽

Aida Hosseini Baghanam

Keyword(s):

Stream Flow ◽

Precipitation Event ◽

Time Lags ◽

Self Organizing Map ◽

Rainfall Runoff ◽

Ann Model ◽

Discharge Data ◽

Runoff Modeling ◽

Rainfall Runoff Model ◽

Runoff Model

In this study the performance of ANN with feed-forward neural network (FFNN) algorithm evaluated rainfall-runoff modeling in five gauging stations in Florida State. In addition, for investigating the performance of ANN in multi-station discharge prediction, self-organizing map (SOM) clustering tool employed in order to cluster the input data with similar patterns, due to the large amount of records in multiple stations. The main aim of study is to investigate capability and accuracy of ANN based methods in multi-station discharge prediction. In order to consider multiple stations effect on watershed outlet discharge, different combinations for precipitation and discharge data of all stations with antecedent values over the watershed have been taken into account. In this way, application of the representatives from each cluster led to significantly reduction in the numbers of the input variables so that the optimal ANN structure could be proposed. Therefore, ANN as a data-driven model was trained to predict daily runoff for the Peace River basin via recorded values from July 1995 to July 2011. Three scenarios conducted the aim of research; first scenario was an integrated ANN model trained by the data of rainfall and runoff at multiple stations. The second scenario was a sequential ANN model processed with upstream discharge records in addition to rainfall data as inputs and downstream discharge values as target. Finally, third scenario was a SOM-ANN model, in which rainfall and runoff data were clustered according the homogeneity of data via (SOM). The center of each cluster as the dominant component of each cluster was imposed to ANN in order to present an optimal rainfall-runoff model over the watershed. In all scenarios, different data sets at various time lags in both rainfall and stream flow data were applied as inputs in ANN-based model to predict stream flow. Results show that ANN model coupled with SOM is useful tools for forecasting multi-station discharge and precipitation event response in the watershed. Furthermore, the comparison of scenarios leads to select the most efficient and optimal inputs to ANN which subsequently, presents the optimal multi-station rainfall-runoff model over the watershed.

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Coupled modeling approach to assess climate change impacts on groundwater recharge and adaptation in arid areas

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4165-2015 ◽

2015 ◽

Vol 19 (10) ◽

pp. 4165-4181 ◽

Cited By ~ 14

Author(s):

H. Hashemi ◽

C. B. Uvo ◽

R. Berndtsson

Keyword(s):

Groundwater Recharge ◽

Groundwater Level ◽

Groundwater Resources ◽

Future Climate ◽

Rainfall Runoff ◽

Discharge Data ◽

Modeling Approach ◽

Significant Difference ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract. The effect of future climate scenarios on surface and groundwater resources was simulated using a modeling approach for an artificial recharge area in arid southern Iran. Future climate data for the periods of 2010–2030 and 2030–2050 were acquired from the Canadian Global Coupled Model (CGCM 3.1) for scenarios A1B, A2, and B1. These scenarios were adapted to the studied region using the delta-change method. A conceptual rainfall–runoff model (Qbox) was used to simulate runoff in a flash flood prone catchment. The model was calibrated and validated for the period 2002–2011 using daily discharge data. The projected climate variables were used to simulate future runoff. The rainfall–runoff model was then coupled to a calibrated groundwater flow and recharge model (MODFLOW) to simulate future recharge and groundwater hydraulic heads. As a result of the rainfall–runoff modeling, under the B1 scenario the number of floods is projected to slightly increase in the area. This in turn calls for proper management, as this is the only source of fresh water supply in the studied region. The results of the groundwater recharge modeling showed no significant difference between present and future recharge for all scenarios. Owing to that, four abstraction and recharge scenarios were assumed to simulate the groundwater level and recharge amount in the studied aquifer. The results showed that the abstraction scenarios have the most substantial effect on the groundwater level and the continuation of current pumping rate would lead to a groundwater decline by 18 m up to 2050.

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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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