Evapotranspiration on a greening Earth

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(9), P. 626 - 641

Published: Aug. 22, 2023

Language: Английский

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Differentiable modelling to unify machine learning and physical models for geosciences

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(8), P. 552 - 567

Published: July 11, 2023

Language: Английский

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A deep learning-based hybrid model of global terrestrial evaporation

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: April 8, 2022

Terrestrial evaporation (E) is a key climatic variable that controlled by plethora of environmental factors. The constraints modulate the from plant leaves (or transpiration, E

Language: Английский

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Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 613, P. 128444 - 128444

Published: Sept. 13, 2022

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, plays a critical role in earth system. ET remains one of most problematic components cycle to be determined due heterogeneity landscape complexity driving factors. The satellite-based observation expected provide information at large-scales. However, accurate global information, with spatially temporally continuous coverage moderate-to-high resolution, still scarce. In this paper, combined model, called ETMonitor, multi-process parameterizations, was improved applied estimate ET, mainly using biophysical hydrological parameters/variables retrieved from satellite observations. ETMonitor model several aspects study generate datasets during 2000–2019 daily/1-km including: 1) adopting high temporal resolution cover snow/ice as input, simulate impact their seasonal change on variation; 2) parameterizing soil moisture plant transpiration evaporation moisture, which downscaled 1-km coarse data microwave remote sensing observation; 3) involving better heat flux estimation reduce its uncertainty estimated ET; 4) being calibrated based ground observations achieve accuracy. daily validated situ site scale across various ecosystems, overall correlation (0.75), low bias (0.08 mm d-1), root mean square error (0.93 d-1). It had good ability partition total indicated by agreement isotope measurements growing season northwest China. cross-validated comparing other existing products, it showed could capture patterns both space time. also superiority product following aspects: capability capturing dynamics waterbody sublimation; performance spatial variation irrigated cropland regions mountain complex terrain than e.g., GLEAM MOD16 products; component partitioning (1-km) (daily) resolutions transpiration, evaporation, canopy rainfall interception loss, body snow sublimation accounted for 61.54 % (±0.44 %), 19.08 (±0.54 13.54 (±0.49 5.84 (±0.24 respectively, average. dataset important studies terrestrial energy cycles climate studies, resources management regional scales.

Language: Английский

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Rewards, risks and responsible deployment of artificial intelligence in water systems

Nature Water, Journal Year: 2023, Volume and Issue: 1(5), P. 422 - 432

Published: May 11, 2023

Language: Английский

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Iterative integration of deep learning in hybrid Earth surface system modelling

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(8), P. 568 - 581

Published: July 11, 2023

Language: Английский

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Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 289, P. 113519 - 113519

Published: March 2, 2023

Language: Английский

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Diminishing carryover benefits of earlier spring vegetation growth

Nature Ecology & Evolution, Journal Year: 2024, Volume and Issue: 8(2), P. 218 - 228

Published: Jan. 3, 2024

Language: Английский

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A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations

Frontiers in Science, Journal Year: 2024, Volume and Issue: 1

Published: March 5, 2024

Climate change is profoundly affecting the global water cycle, increasing likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict monitor environmental disasters optimally manage resources. These must integrate advances in remote sensing, situ , citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information communication technologies, high-performance computing. Digital Twin (DTE) models a ground-breaking solution offering digital replicas simulate processes unprecedented spatiotemporal resolution. Advances observation (EO) satellite technology pivotal, here we provide roadmap for exploitation these methods DTE hydrology. The 4-dimensional Hydrology datacube now fuses EO data advanced modeling soil moisture, precipitation, evaporation, river discharge, report latest validation Mediterranean Basin. This can be explored forecast flooding landslides irrigation precision agriculture. Large-scale implementation such will require further assess products across different regions climates; create compatible multidimensional datacubes, retrieval algorithms, that suitable multiple scales; uncertainty both models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open principles; harness AI/machine learning. We outline how various planned missions facilitate hydrology toward benefit if scientific technological challenges identify addressed.

Language: Английский

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Recent global decline in rainfall interception loss due to altered rainfall regimes

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Dec. 10, 2022

Evaporative loss of interception (E

Language: Английский

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