Abstract
Sianou Ezéckiel Houénafa, Lionel Cédric Gohouede, Romuald Daniel BOY-NGBOGBELE, Melissa Latella, Olatunji Olugoke Johnson, Cenk Sezen
Abstract
Authors
Institutions
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The CAMELS data set: catchment attributes and meteorology for large-sample studies
10.5194/hess-21-5293-2017 · 2017
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New global hydrography derived from spaceborne elevation data
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Uncertainty estimation with deep learning for rainfall–runoff modeling
10.5194/hess-26-1673-2022 · doi-reference
Uncertainty quantification in watershed hydrology: which method to use?
10.1016/j.jhydrol.2022.128749 · doi-reference
Propagation of structural uncertainty in watershed hydrologic models
10.1016/j.jhydrol.2019.05.026 · doi-reference
Strictly proper scoring rules, prediction, and estimation
10.1198/016214506000001437 · doi-reference
Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models
10.1002/hyp.13319 · doi-reference
Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity
10.1002/2013wr014185 · doi-reference
Effects of uncertainties in hydrological modelling: a case study of a mountainous catchment in southern Norway
10.1016/j.jhydrol.2016.02.036 · doi-reference
Probability flow solution of the Fokker–Planck equation
10.1088/2632-2153/ace2aa · doi-reference
Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
10.1016/s0022-1694(01)00421-8 · doi-reference
Development of a conceptual deterministic rainfall–runoff model
10.2166/nh.1973.0012 · doi-reference
Is precipitation responsible for the most hydrological model uncertainty?
10.3389/frwa.2022.836554 · doi-reference
The CAMELS data set: catchment attributes and meteorology for large-sample studies
10.5194/hess-21-5293-2017 · doi-reference