Research graph
References from S-GRHyMoLAP: A stochastic extension of the GRHyMoLAP model with different diffusion mechanisms for probabilistic rainfall-runoff modeling. Local targets link to admitted publications; unresolved targets remain external evidence.
The CAMELS data set: catchment attributes and meteorology for large-sample studies
10.5194/hess-21-5293-2017 · 2017 · External reference
Modèle hydrologique basé sur le principe de moindre action (modhypma)
2010 · External reference
Quantification of different uncertainties in streamflow simulations and their propagation across multiple catchments
2026 · External reference
Is precipitation responsible for the most hydrological model uncertainty?
10.3389/frwa.2022.836554 · 2022 · External reference
Development of a conceptual deterministic rainfall–runoff model
10.2166/nh.1973.0012 · 1973 · External reference
Unresolved reference
2012 · External 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 · 2001 · External reference
Probability flow solution of the Fokker–Planck equation
10.1088/2632-2153/ace2aa · 2023 · External reference
XGBoost: a scalable tree boosting system
2016 · External reference
Unresolved reference
1999 · External reference
Deterministic and stochastic models for analyzing the dynamics of diabetes mellitus
2025 · External reference
CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking
2024 · External reference
Effects of uncertainties in hydrological modelling: a case study of a mountainous catchment in southern Norway
10.1016/j.jhydrol.2016.02.036 · 2016 · External reference
Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity
10.1002/2013wr014185 · 2014 · External reference
Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models
10.1002/hyp.13319 · 2019 · External reference
Unresolved reference
2012 · External reference
Strictly proper scoring rules, prediction, and estimation
10.1198/016214506000001437 · 2007 · External reference
Propagation of structural uncertainty in watershed hydrologic models
10.1016/j.jhydrol.2019.05.026 · 2019 · External reference
Uncertainty quantification in watershed hydrology: which method to use?
10.1016/j.jhydrol.2022.128749 · 2023 · External reference
Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modelling
2025 · External reference
Lévy-induced stochastic differential equation models in rainfall–runoff systems for assessing extreme hydrological event risks
2025 · External reference
Streamflow prediction with aleatoric uncertainty quantification in the Yala river basin via Fokker–Planck equation-based frameworks
2025 · External reference
GRHyMoLAP: a process-driven ODE catchment hydrology model inspired by GR4J and HyMoLAP approaches
2026 · External reference
AquaFetch: a unified Python interface for water resource dataset acquisition and harmonization
2025 · External reference
QDeepGR4J: quantile-based ensemble of deep learning and GR4J hybrid rainfall–runoff models for extreme flow prediction with uncertainty quantification
2025 · External reference
Unresolved reference
1991 · External reference
Uncertainty estimation with deep learning for rainfall–runoff modeling
10.5194/hess-26-1673-2022 · 2022 · External reference
Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems
10.1002/hyp.9740 · 2013 · External reference
New global hydrography derived from spaceborne elevation data
10.1029/2008eo100001 · 2008 · External reference
A unified approach to interpreting model predictions
2017 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020 · External reference
Sources of hydrological model uncertainties and advances in their analysis
10.3390/w13010028 · 2021 · External reference
Unresolved reference
2020 · External reference
Hydrologic and water quality models: performance measures and evaluation criteria
10.13031/trans.58.10715 · 2015 · External reference
Proposal and application of a new theoretical framework of uncertainty estimation in the rainfall–runoff process based on the theory of stochastic process
10.1016/j.proeng.2016.07.556 · 2016 · External reference
River flow forecasting through conceptual models Part I—a discussion of principles
10.1016/0022-1694(70)90255-6 · 1970 · External reference
Financial modeling by ordinary and stochastic differential equations
2011 · External reference
A simplex method for function minimization
10.1093/comjnl/7.4.308 · 1965 · External reference
Estimation of the uncertainty of hydrologic predictions in a karstic Mediterranean watershed
10.1016/j.scitotenv.2020.137131 · 2020 · External reference
Physically based modeling in catchment hydrology at 50: survey and outlook
10.1002/2015wr017780 · 2015 · External reference
Improvement of a parsimonious model for streamflow simulation
10.1016/s0022-1694(03)00225-7 · 2003 · External reference
Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors
10.1029/2009wr008328 · 2010 · External reference
Unresolved reference
2011 · External reference
A Fokker–Planck–Kolmogorov equation-based inverse modelling approach for hydrological systems applied to extreme value analysis
10.2166/hydro.2017.079 · 2018 · External reference
A value for n-person games
1953 · External reference
Attributing uncertainty in streamflow simulations due to variable inputs via the quantile flow deviation metric
10.1016/j.advwatres.2018.01.022 · 2018 · External reference
Unresolved reference
2010 · External reference
Evaluation of the impact of multi-source uncertainties on meteorological and hydrological ensemble forecasting
10.1016/j.eng.2022.06.007 · 2023 · External reference
Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation
10.1029/2007wr006720 · 2008 · External reference
A framework for development and application of hydrological models
10.5194/hess-5-13-2001 · 2001 · External reference
A global, self-consistent, hierarchical, high-resolution Shoreline database
10.1029/96jb00104 · 1996 · External reference
Individual comparisons by ranking methods
10.2307/3001968 · 1945 · External reference
On the validation of models
10.1080/02723646.1981.10642213 · 1981 · External reference
A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling
10.1016/j.envsoft.2014.01.004 · 2014 · External reference
Non-Markovian superposition process model for stochastically describing concentration–discharge relationship
10.1016/j.chaos.2025.116715 · 2025 · External reference
Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity
10.1002/2013wr014185 · ExternalCitation · doi-reference
Physically based modeling in catchment hydrology at 50: survey and outlook
10.1002/2015wr017780 · ExternalCitation · doi-reference
Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models
10.1002/hyp.13319 · ExternalCitation · doi-reference
Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems
10.1002/hyp.9740 · ExternalCitation · doi-reference
River flow forecasting through conceptual models Part I—a discussion of principles
10.1016/0022-1694(70)90255-6 · ExternalCitation · doi-reference
Attributing uncertainty in streamflow simulations due to variable inputs via the quantile flow deviation metric
10.1016/j.advwatres.2018.01.022 · ExternalCitation · doi-reference
Non-Markovian superposition process model for stochastically describing concentration–discharge relationship
10.1016/j.chaos.2025.116715 · ExternalCitation · doi-reference
Evaluation of the impact of multi-source uncertainties on meteorological and hydrological ensemble forecasting
10.1016/j.eng.2022.06.007 · ExternalCitation · doi-reference
A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling
10.1016/j.envsoft.2014.01.004 · ExternalCitation · 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 · ExternalCitation · doi-reference
Propagation of structural uncertainty in watershed hydrologic models
10.1016/j.jhydrol.2019.05.026 · ExternalCitation · doi-reference
Uncertainty quantification in watershed hydrology: which method to use?
10.1016/j.jhydrol.2022.128749 · ExternalCitation · doi-reference
Proposal and application of a new theoretical framework of uncertainty estimation in the rainfall–runoff process based on the theory of stochastic process
10.1016/j.proeng.2016.07.556 · ExternalCitation · doi-reference
Estimation of the uncertainty of hydrologic predictions in a karstic Mediterranean watershed
10.1016/j.scitotenv.2020.137131 · ExternalCitation · 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 · ExternalCitation · doi-reference
Improvement of a parsimonious model for streamflow simulation
10.1016/s0022-1694(03)00225-7 · ExternalCitation · doi-reference
Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation
10.1029/2007wr006720 · ExternalCitation · doi-reference
New global hydrography derived from spaceborne elevation data
10.1029/2008eo100001 · ExternalCitation · doi-reference
Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors
10.1029/2009wr008328 · ExternalCitation · doi-reference
A global, self-consistent, hierarchical, high-resolution Shoreline database
10.1029/96jb00104 · ExternalCitation · doi-reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · ExternalCitation · doi-reference
On the validation of models
10.1080/02723646.1981.10642213 · ExternalCitation · doi-reference
Probability flow solution of the Fokker–Planck equation
10.1088/2632-2153/ace2aa · ExternalCitation · doi-reference
A simplex method for function minimization
10.1093/comjnl/7.4.308 · ExternalCitation · doi-reference
Strictly proper scoring rules, prediction, and estimation
10.1198/016214506000001437 · ExternalCitation · doi-reference
Hydrologic and water quality models: performance measures and evaluation criteria
10.13031/trans.58.10715 · ExternalCitation · doi-reference
A Fokker–Planck–Kolmogorov equation-based inverse modelling approach for hydrological systems applied to extreme value analysis
10.2166/hydro.2017.079 · ExternalCitation · doi-reference
Development of a conceptual deterministic rainfall–runoff model
10.2166/nh.1973.0012 · ExternalCitation · doi-reference
Individual comparisons by ranking methods
10.2307/3001968 · ExternalCitation · doi-reference
Is precipitation responsible for the most hydrological model uncertainty?
10.3389/frwa.2022.836554 · ExternalCitation · doi-reference
Sources of hydrological model uncertainties and advances in their analysis
10.3390/w13010028 · ExternalCitation · doi-reference
The CAMELS data set: catchment attributes and meteorology for large-sample studies
10.5194/hess-21-5293-2017 · ExternalCitation · doi-reference
Uncertainty estimation with deep learning for rainfall–runoff modeling
10.5194/hess-26-1673-2022 · ExternalCitation · doi-reference
A framework for development and application of hydrological models
10.5194/hess-5-13-2001 · ExternalCitation · doi-reference
