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Article

Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling

1
Hydro-Climatic Extremes Lab, Ghent University, 9000 Ghent, Belgium
2
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
3
Department of Geography, Ruhr-University Bochum, 44801 Bochum, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: David Pulido-Velázquez
Water 2021, 13(11), 1573; https://doi.org/10.3390/w13111573
Received: 7 May 2021 / Revised: 25 May 2021 / Accepted: 28 May 2021 / Published: 2 June 2021
(This article belongs to the Section Hydrology)
Bias adjustment of climate model simulations is a common step in the climate impact assessment modeling chain. For precipitation intensity, multiple bias-adjusting methods have been developed, but less so for precipitation occurrence. Intensity-bias-adjusting methods such as ‘Quantile Delta Mapping’ can adjust too many wet days, but not too many dry days. Some occurrence-bias-adjusting methods have been developed to resolve this by the addition of the ability to adjust too dry simulations. Earlier research has shown this to be important when adjusting on a continental scale, when both types of biases can occur. However, the newer occurrence-bias-adjusting methods have their weakness: they might retain a bias in the number of dry days when adjusting data in a region that only has too many wet days. Yet, if this bias is small enough, it is more practical and economical to apply the newer methods when data in the larger region are adjusted. In this study, we consider two recently introduced occurrence-bias-adjusting methods, Singularity Stochastic Removal and Triangular Distribution Adjustment, and compare them in a region with only wet-day biases. This bias adjustment is performed for precipitation intensity and precipitation occurrence, while the evaluation is performed on precipitation intensity, precipitation occurrence and discharge, which combines the former two variables. Despite theoretical weaknesses, we show that both Singularity Stochastic Removal and Triangular Distribution Adjustment perform well. Thus, the methods can be applied for both too wet and too dry simulations, although Triangular Distribution Adjustment may be preferred as it was designed with a broad application in mind. View Full-Text
Keywords: climate change impact; bias adjustment; occurrence-bias-adjustment; hydrological impact climate change impact; bias adjustment; occurrence-bias-adjustment; hydrological impact
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MDPI and ACS Style

Van de Velde, J.; Demuzere, M.; De Baets, B.; Verhoest, N.E.C. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling. Water 2021, 13, 1573. https://doi.org/10.3390/w13111573

AMA Style

Van de Velde J, Demuzere M, De Baets B, Verhoest NEC. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling. Water. 2021; 13(11):1573. https://doi.org/10.3390/w13111573

Chicago/Turabian Style

Van de Velde, Jorn, Matthias Demuzere, Bernard De Baets, and Niko E.C. Verhoest 2021. "Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling" Water 13, no. 11: 1573. https://doi.org/10.3390/w13111573

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