Climatic and non‐climatic factors affect the chemical weathering of silicate rocks, which in
turn affects the CO2 concentration in the atmosphere on a long‐term scale. However, the …

Parameter estimation is one of the most challenging tasks in large‐scale distributed
modeling, because of the high dimensionality of the parameter space. Relating model …

Land surface hydrologic models adeptly capture crucial terrestrial processes with a high
level of spatial detail. Typically, these models incorporate numerous uncertain, spatially …

The wflow_sbm hydrologic model, recently released by Deltares, as part of the Wflow. jl (v0.
6.1) modelling framework is being used to better understand and potentially address …

This chapter provides an overview of machine learning models and their applications to the
science of inland waters. Such models serve a wide range of purposes for science and …

Distributed environmental models such as land surface models (LSMs) require model
parameters in each spatial modeling unit (eg, grid cell), thereby leading to a high …

Spatial parameter fields are required to model hydrological processes across diverse
landscapes. Transfer functions are often used to relate parameters to spatial catchment …

Although decades of effort have been devoted to building Physical‐Conceptual (PC) models
for predicting the time‐series evolution of geoscientific systems, recent work shows that …

Accurate estimation of the spatio‐temporal distribution of snow water equivalent is essential
given its global importance for understanding climate dynamics and climate change, and as …

Methoden der künstlichen Intelligenz haben sich in den letzten Jahren zu essenziellen
Bestandteilen fast aller Bereiche von Wissenschaft und Technik entwickelt. Dies gilt auch für …