the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
Louise J. Slater
Abdou Khouakhi
Fupeng Li
Yadu Pokhrel
Pierre Gentine
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This study examines large daily river flow fluctuations in the dammed Mekong River, developing integrated 3D hydrodynamic and response time models alongside a hydrological model with an embedded reservoir module. This approach allows estimation of travel times between hydrological stations and contributions of subbasins and upstream regions. Findings show a power correlation between upstream discharge and travel time, and significant fluctuations occurred even before dam construction.
Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
user guidefor scientists and (re)insurers.
neighboring column approximationfor 3-D thermal heating and cooling rates, we show that thermal radiation changes cloud circulation and causes organization and a deepening of the clouds.
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Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.