Forecasting Next Year's Global Land Water Storage Using GRACE Data

Autor: Fupeng Li, Jürgen Kusche, Nico Sneeuw, Stefan Siebert, Helena Gerdener, Zhengtao Wang, Nengfang Chao, Gang Chen, Kunjun Tian
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geophysical Research Letters, Vol 51, Iss 17, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2024GL109101
Popis: Abstract Existing approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management.
Databáze: Directory of Open Access Journals
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