Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
A review of catchment-scale water quality and erosion models and a synthesis of future prospects
Catchment-scale water quality models have become important tools for water quality
management, planning and reporting worldwide. In this review, we synthesise recent …
management, planning and reporting worldwide. In this review, we synthesise recent …
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (eg, hydrology and ecosystem
sciences) strongly depend on spatially-varying parameters that need calibration. A well …
sciences) strongly depend on spatially-varying parameters that need calibration. A well …
Evaluating uncertainty estimates in distributed hydrological modeling for the Wen**g River watershed in China by GLUE, SUFI-2, and ParaSol methods
Hydrological models always suffer from different sources of uncertainties. As the distributed
hydrological models play a very important role in water resource management, reliable …
hydrological models play a very important role in water resource management, reliable …
A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling
Failure to consider major sources of uncertainty may bias model predictions in simulating
watershed behavior. A framework entitled the Integrated Parameter Estimation and …
watershed behavior. A framework entitled the Integrated Parameter Estimation and …
Determining the importance of model calibration for forecasting absolute/relative changes in streamflow from LULC and climate changes
Land use/land cover (LULC) and climate changes are important drivers of change in
streamflow. Assessing the impact of LULC and climate changes on streamflow is typically …
streamflow. Assessing the impact of LULC and climate changes on streamflow is typically …
[HTML][HTML] Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam
DN Khoi, VT Thom - Global ecology and conservation, 2015 - Elsevier
Hydrological models play vital roles in management of water resources. However, the
calibration of the hydrological models is a large challenge because of the uncertainty …
calibration of the hydrological models is a large challenge because of the uncertainty …
Assessing the importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and …
The sub-tropical countries like India experience large-scale land degradation due to erosion
of the surface soil. So, there is a direct impact of monsoon climate in this region; large-scale …
of the surface soil. So, there is a direct impact of monsoon climate in this region; large-scale …
Identifying erosion hotspots in Lake Tana Basin from a multisite Soil and Water Assessment Tool validation: Opportunity for land managers
Extensive catchment degradation throughout the Ethiopian Highlands induced by long‐term
intensified land use, erosion‐prone topography, and climate causes substantial soil erosion …
intensified land use, erosion‐prone topography, and climate causes substantial soil erosion …
Learning and optimization under epistemic uncertainty with Bayesian hybrid models
Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …
science and engineering. Grey-box models use data-driven constructs to incorporate …