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dc.contributor.authorOnyutha, Charles
dc.date.accessioned2022-09-14T18:47:38Z
dc.date.available2022-09-14T18:47:38Z
dc.date.issued2022
dc.identifier.citationOnyutha, C. (2022). A hydrological model skill score and revised R-squared. Hydrology Research, 53(1), 51-64. doi: 10.2166/nh.2021.071en_US
dc.identifier.other10.2166/nh.2021.071
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4730
dc.description.abstractDespite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model’s bias. A new model skill score E and revised R-squared (RRS) are presented to combine correlation, bias measure and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and variabilitymeasure used for eachmetric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on othDespite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model’s bias. A new model skill score E and revised R-squared (RRS) are presented to combine correlation, bias measure and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and variabilitymeasure used for eachmetric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on oDespite the advances in methods of statistical andmathematical modeling, there is considerable lack of focus on improving how to judgemodels’ quality. Coefficient of determination (R2) is arguably the most widely applied ‘goodness-of-fit’ metric inmodelling and prediction of environmental systems. However, known issues of R2 are that it: (i) can be low and high for an accurate and imperfect model, respectively; (ii) yields the same value when we regress observed on modelled series and vice versa; and (iii) does not quantify a model’s bias. A new model skill score E and revised R-squared (RRS) are presented to combine correlation, bias measure and capacity to capture variability. Differences between E and RRS lie in the forms of correlation and variabilitymeasure used for eachmetric. Acceptability of E and RRS was demonstrated through comparison of results from a large number of hydrological simulations. By applying E and RRS, the modeller can diagnostically identify and expose systematic issues behind model optimizations based on other ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from ∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.ther ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from ∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.er ‘goodness-of-fits’ such as Nash–Sutcliffe efficiency (NSE) and mean squared error. Unlike NSE, which varies from ∞ to 1, E and RRS occur over the range 0–1. MATLAB codes for computing E and RRS are provided.en_US
dc.language.isoenen_US
dc.publisherHydrology Researchen_US
dc.subjectDistance correlationen_US
dc.subjectHydrological modelsen_US
dc.subjectModel performance evaluationen_US
dc.subjectNash–Sutcliffe efficiencyen_US
dc.subjectRevised R-squared (RRS)en_US
dc.subjectR-squareden_US
dc.titleA hydrological model skill score and revised R-squareden_US
dc.typeArticleen_US


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