A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen - Water Resources Research, 2018 - Wiley Online Library
Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …
industries, daily lives, and various scientific disciplines in recent years. DL represents …
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 …
Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US
Climate, groundwater extraction, and surface water flows have complex nonlinear
relationships with groundwater level in agricultural regions. To better understand the relative …
relationships with groundwater level in agricultural regions. To better understand the relative …
[HTML][HTML] The plumbing of land surface models: benchmarking model performance
Abstract The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model
Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model …
Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model …
Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions
Recently, recurrent deep networks have shown promise to harness newly available satellite‐
sensed data for long‐term soil moisture projections. However, to be useful in forecasting …
sensed data for long‐term soil moisture projections. However, to be useful in forecasting …
A philosophical basis for hydrological uncertainty
Uncertainty is an epistemological concept in the sense that any meaningful understanding of
uncertainty requires a theory of knowledge. Therefore, uncertainty resulting from scientific …
uncertainty requires a theory of knowledge. Therefore, uncertainty resulting from scientific …
[HTML][HTML] Benchmarking of a physically based hydrologic model
The concepts of model benchmarking, model agility, and large-sample hydrology are
becoming more prevalent in hydrologic and land surface modeling. As modeling systems …
becoming more prevalent in hydrologic and land surface modeling. As modeling systems …
On hypothesis testing in hydrology: Why falsification of models is still a really good idea
KJ Beven - Wiley Interdisciplinary Reviews: Water, 2018 - Wiley Online Library
This opinion piece argues that in respect of testing models as hypotheses about how
catchments function, there is no existing methodology that adequately deals with the …
catchments function, there is no existing methodology that adequately deals with the …
[HTML][HTML] Uncertainty quantification in watershed hydrology: Which method to use?
Different paradigms have emerged in watershed hydrology to deal with the uncertainties
associated with modeling with both similarities and differences in philosophies and …
associated with modeling with both similarities and differences in philosophies and …
Equifinality and flux map**: A new approach to model evaluation and process representation under uncertainty
Uncertainty analysis is an integral part of any scientific modeling, particularly within the
domain of hydrological sciences given the various types and sources of uncertainty. At the …
domain of hydrological sciences given the various types and sources of uncertainty. At the …