This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Abstract Launched in May 2018, the Gravity Recovery and Climate Experiment Follow‐On
mission (GRACE‐FO)—the successor of the erstwhile GRACE mission—monitors changes …

The machine learning algorithm 'random forest'has been applied in many areas of water
resources research including discharge simulation. Due to low setup and operation cost …

Abstract Model development in groundwater simulation and physics informed deep learning
(DL) has been advancing separately with limited integration. This study develops a general …

Although deep learning applicability in various fields of earth sciences is rapidly increasing,
shallow multilayer-perceptron neural networks remain widely used for regression problems …

Terrestrial and airborne laser scanning and structure from motion techniques have emerged
as viable methods to map snow depths. While these systems have advanced snow …

In mountain regions, forests that overlap with seasonal snow mostly reside in complex
terrain. Due to persisting major observational challenges in these environments, the …

Accurate map** of snow amount in the mountains is critical as mountain snowpacks are
water supply for millions of people. Satellite remote sensing has been largely unable to …

Abstract The Snow Survey and Water Supply Forecasting (SSWSF) Program and the Soil
Climate Analysis Network (SCAN) of the United States Department of Agriculture's Natural …