[HTML][HTML] Sensitivity analysis of environmental models: A systematic review with practical workflow
Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model
can be attributed to variations of its input factors. SA is increasingly being used in …
can be attributed to variations of its input factors. SA is increasingly being used in …
A decade of Predictions in Ungauged Basins (PUB)—a review
Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …
Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores
A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe
efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is …
efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is …
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
The mean squared error (MSE) and the related normalization, the Nash–Sutcliffe efficiency
(NSE), are the two criteria most widely used for calibration and evaluation of hydrological …
(NSE), are the two criteria most widely used for calibration and evaluation of hydrological …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the
hydrological sciences. The problem currently is that traditional hydrological models degrade …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
What role does hydrological science play in the age of machine learning?
This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall …
Modeling soil processes: Review, key challenges, and new perspectives
The remarkable complexity of soil and its importance to a wide range of ecosystem services
presents major challenges to the modeling of soil processes. Although major progress in soil …
presents major challenges to the modeling of soil processes. Although major progress in soil …
Karst water resources in a changing world: Review of hydrological modeling approaches
Abstract Karst regions represent 7–12% of the Earth's continental area, and about one
quarter of the global population is completely or partially dependent on drinking water from …
quarter of the global population is completely or partially dependent on drinking water from …
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation
JA Vrugt - Environmental Modelling & Software, 2016 - Elsevier
Bayesian inference has found widespread application and use in science and engineering
to reconcile Earth system models with data, including prediction in space (interpolation) …
to reconcile Earth system models with data, including prediction in space (interpolation) …
The abuse of popular performance metrics in hydrologic modeling
The goal of this commentary is to critically evaluate the use of popular performance metrics
in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling …
in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling …