Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters

Athira, P.; Sudheer, K. P.; Cibin, Raj; Chaubey, Indrajeet

doi:10.1007/s00477-015-1190-6

Cited by 44 publications

(18 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the prediction of flow for gauged and ungauged basins is one of the fundamental goals in hydrology (Deckers, Booij, Rientjes, & Krol, 2010), robust testing of any model for gauged as well as ungauged basins is of utmost importance for establishing the credibility of the model. To test the credibility of a model for the ungauged basins, regionalization of parameters that involves the transfer of the calibrated parameter values from a gauged basin to an ungauged basin has been the subject of several studies (Ali, Tetzlaff, Soulsby, McDonnell, & Capell, 2012; Athira, Sudheer, Cibin, & Chaubey, 2016; Beck et al, 2016; Dornes et al, 2008; Götzinger & Bárdossy, 2007; He, Bárdossy, & Zehe, 2011; Huang, Liang, & Liang, 2003; Visessri & McIntyre, 2016), there appears to be no universally accepted regionalization method (Deckers et al, 2010; Jin, Xu, Yu, Zhang, & Chen, 2009). However, regionalization approaches that are based on principles of similarity by spatial proximity and similarity of basin characteristics are popularly used (Deckers et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Application of a newly developed large‐scale conceptual hydrological model in simulating streamflow for credibility testing in data scarce condition

Paul

Kumari

Gaur

et al. 2020

Natural Resource Modeling

View full text Add to dashboard Cite

Regionalization approach is adopted to test the credibility of a newly developed, large‐scale, conceptual hydrological model, namely, satellite‐based hydrological model (SHM), under data‐scarce conditions. SHM has a modular structure. There are five modules: surface water module (SW), which uses natural resources conservation service‐curve number and Hargreaves equation; forest module (F), which uses water balance and subsurface dynamics; snow module (S), which uses temperature index and radiation‐temperature index algorithms; groundwater module, which considers contributions from SW, F, and S modules and uses water level variation process; and routing module, which uses modified spatial distributed direct hydrograph model. Four neighboring basins of varying size are selected to form the reference (Subarnarekha) and the test (Brahmani, Baitarani, and Kangsabati) basins. The similarity index and hierarchical agglomerative cluster analysis show that Brahmani is the most homogeneous basin with respect to the Subarnarekha basin. The model simulations are run at 1 km resolution. The values of statistical indices Nash‐Sutcliffe efficiency, coefficient of determination, and percent BIAS, are found to be 0.82%, 0.84%, and 13.99%, respectively, during calibration (1993–1999; 1993 as a warm‐up period), and 0.61%, 0.63%, and 14.13%, respectively, during validation (2000–2004) at Jenapur gauging station of the Brahmani river basin. Subsequently, the calibrated parameters are transferred in the model setup of the Subarnarekha basin for simulating streamflow over a randomly chosen period 1998–2000. The results, obtained with both Brahmani and Subarnarekha parameters, confirm that the model performs well with the regionalized parameters, and is suitable for hydrologically homogeneous ungauged basins. The results show that the newly developed model may be used for the water balance simulations in ungauged or scantily gauged basins, leading to the development of water resources plans for better utilization of this vital resource. The results from the sensitivity analysis and uncertainty analysis show that the nonsensitive parameters have a negligible impact on the model results. Further study, however, may be needed to evaluate the model performance and to establish its universal credibility. Recommendations for Water Resource Managers and The Scientific Community This successful testing of the newly developed hydrological model implies that it may be a useful tool for water resources assessment in data‐scarce conditions. The encouraging results support the further application of the model for data‐scarce basins in different parts of India and the world. This study, for the first time, applies a tool namely “cluster 3.0” (generally used in biotechnology) for hierarchical agglomerative cluster analysis to perform homogeneity test. Any modeler and policymakers may use the tool for homogeneity test of a basin with respect to a reference basin without learning the mathematical theory, in details. The results of parametric s...

show abstract

Section: Introductionmentioning

confidence: 99%

Application of a newly developed large‐scale conceptual hydrological model in simulating streamflow for credibility testing in data scarce condition

Paul

Kumari

Gaur

et al. 2020

Natural Resource Modeling

View full text Add to dashboard Cite

show abstract

“…Patil and Stieglitz [19], using a model developed themselves, analyzed selectively transferring sensitive versus insensitive parameters on flood forecasting in ungauged basins. Athira et al [20] used SWAT model to explore the appropriate functional relationship between the parameters and basin characteristics. Waseem et al [21] compared different interpolation schemes on parameter spatial distribution in ungauged basins.…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

Kong

Liu

2019

Advances in Meteorology

View full text Add to dashboard Cite

Scarce historical flood data in ungauged basins make it difficult to establish empirical and conceptual model forecast in these areas. The physical-based distributed model TOPKAPI is introduced for flood prediction in an ungauged basin by parameter transplant. Five main parameters are selected, and the sensitivity is analyzed by the GLUE method. The Xixian basin and Huangchuan basin in the upper Huaihe basin in China are chosen as study areas. The Xixian basin is regarded as a gauged basin for parameter calibration, and the Huangchuan basin is regarded as an ungauged basin by ignoring the historical discharge data. The model is calibrated in gauged Xixian basin, and then parameters are directly transplanted to adjacent “ungauged” Huangchuan basin to simulate flood forecast in an ungauged basin. The sensitivity analysis shows that soil thickness and soil saturated water content are the most sensitive parameters, and the Manning coefficient of main channel with high Strahler also significantly affects forecast results. According to the simulation results, the TOPKAPI model exhibits good performance in building and the prediction of the ungauged basin, in which the qualified rate of volume and peaks reaches 69.23%, and the average NSE criterion is over 0.67, which is acceptable forecast accuracy and has positive implication for the hydrological forecasting research.

show abstract

“…However, traditional rainfall-runoff models have multiple free parameters. They can be used for prediction in ungauged basins only after regionalizing the model parameters using discharge data from gauged basins [32][33][34][35], which implies that FDC prediction in ungauged basins with the help of a rainfall-runoff model with regionalized parameters may not be very reliable in discharge data-scarce regions.…”

Section: Introductionmentioning

confidence: 99%

Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India

Nag

Biswal

2019

Hydrology

View full text Add to dashboard Cite

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.

show abstract

Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters

Cited by 44 publications

References 54 publications

Application of a newly developed large‐scale conceptual hydrological model in simulating streamflow for credibility testing in data scarce condition

Application of a newly developed large‐scale conceptual hydrological model in simulating streamflow for credibility testing in data scarce condition

Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India

Contact Info

Product

Resources

About