Nag, Anita and Biswal, Basudev
(2019)
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.
Hydrology, 6 (2).
p. 32.
ISSN 2306-5338
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Abstract
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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