Major Earth system processes are non-linear and non-Gaussian, and so should be our data assimilation approaches.
Water Resources Research
Posted inEditors' Highlights
When It Rains, It Pours!
Water that falls on a forest canopy during rainfall events reaches the ground at focused locations called “pour points”. This insight has a major impact on how we view hydrologic processes on the ground.
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What Happens to Nutrients After They Leave Agricultural Fields?
To better quantify the fate of nutrients after they are released from agricultural fields, scientists examine storage and nitrate export regimes in agricultural hydrology systems.
Posted inEditors' Vox
Benefiting Society with Translational Water Research
A new special collection welcomes translational research contributions that bridge the gap between scientific knowledge and practical applications regarding water as a key societal resource or a risk.
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AGU Publications Opens Science: Making Science Accessible and Equitable
To celebrate the Year of Open Science, we highlight our efforts to make AGU journals and books more open, accessible, and inclusive.
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Advancing AI and Machine Learning Beyond Predictive Capabilities
A new cross-journal special collection invites contributions that unlock the next frontier in hydrology and Earth sciences through artificial intelligence and machine learning.
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Meteorological Uncertainty Shapes Global Hydrological Modeling
A new study examines the effects of spatiotemporal precipitation uncertainty on key hydrologic processes, including runoff and soil moisture, in a comprehensive sample of 289 cryosphere regions.
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AGU’s Journal Water Resources Research Goes Fully Open Access
AGU continues its commitment to equitable open science by converting Water Resources Research to a fully open access journal in 2024, with robust funding support for publishing fees.
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Inductive Approach Reveals Controls on Dissolved Organic Carbon
Machine learning leverages large data sets to reveal hidden patterns explaining when, where, and why dissolved organic carbon moves from hillslopes to streams.
Posted inEditors' Highlights
Playing Bricks with Neural Networks to Learn Sorption Processes
Designated neural network modules are combined to mimic numerically-discretized diffusion-sorption equations, which allows learning “missing pieces” in system understanding and their uncertainties.