An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
Process-based hydrological models have a long history dating back to the 1960s. Criticized
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …
by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is …
Hydrogeomorphic processes and scaling issues in the continuum from soil pedons to catchments
Understanding integrated hydrological phenomena in catchments is difficult because of the
fragmented nature of soil physical and hydrological data, given these are typically derived …
fragmented nature of soil physical and hydrological data, given these are typically derived …
A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs
Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for
managing water resources and establishing early-warning systems. However, this can be a …
managing water resources and establishing early-warning systems. However, this can be a …
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Applications of process‐based models (PBM) for predictions are confounded by multiple
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
uncertainties and computational burdens, resulting in appreciable errors. A novel modeling …
The origin of fine sediment determines the observations of suspended sediment fluxes under unsteady flow conditions
Field observations in a wide range of environments have shown that sediment availability is
a major control on the suspended sediment observations in streams. Here we examine, via …
a major control on the suspended sediment observations in streams. Here we examine, via …
Robust and efficient uncertainty quantification for extreme events that deviate significantly from the training dataset using polynomial chaos-kriging
This study presents the strengths of polynomial chaos-kriging (PCK), a new surrogate model
that merges polynomial chaos extension (PCE) and Gaussian process with kriging variance …
that merges polynomial chaos extension (PCE) and Gaussian process with kriging variance …
Climate change and uncertainty assessment over a hydroclimatic transect of Michigan
Predictions of a warmer climate over the Great Lakes region due to global change generally
agree on the magnitude of temperature changes, but precipitation projections exhibit …
agree on the magnitude of temperature changes, but precipitation projections exhibit …
Improving the accuracy of dam inflow predictions using a long short-term memory network coupled with wavelet transform and predictor selection
Accurate and reliable dam inflow prediction models are essential for effective reservoir
operation and management. This study presents a data-driven model that couples a long …
operation and management. This study presents a data-driven model that couples a long …
The role of rainfall spatial variability in estimating areal reduction factors
For the last several decades, great efforts have been put into converting point precipitation
into mean areal precipitation in the design of hydraulic and hydrologic infrastructures. The …
into mean areal precipitation in the design of hydraulic and hydrologic infrastructures. The …
An implicit friction source term treatment for overland flow simulation using shallow water flow model
Aiming at resolving the numerical problems caused by the improper friction source term
treatment when simulating overland flow using 2D shallow water flow models, a proposed …
treatment when simulating overland flow using 2D shallow water flow models, a proposed …