Short- and mid-term forecasts of actual evapotranspiration with deep learning

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 612, P. 128078 - 128078

Published: June 17, 2022

Language: Английский

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Networking the forest infrastructure towards near real-time monitoring – A white paper

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 872, P. 162167 - 162167

Published: Feb. 11, 2023

Language: Английский

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Spatial-temporal patterns of land surface evapotranspiration from global products

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 304, P. 114066 - 114066

Published: Feb. 24, 2024

Language: Английский

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Effects of cascade hydropower stations on hydrologic cycle in Xiying river basin, a runoff in Qilian mountain

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 646, P. 132342 - 132342

Published: Nov. 17, 2024

Language: Английский

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Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

Earth system science data, Journal Year: 2024, Volume and Issue: 16(3), P. 1283 - 1300

Published: March 13, 2024

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these often estimate GPP ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics the diurnal (hourly) scales, prescribe some essential parameters (i.e., Ball–Berry slope (m) maximum carboxylation rate 25 °C (Vcmax25)) as constant, inducing uncertainties in estimates ET. In this study, we present hourly estimations datasets 0.25° resolution from 2001 to 2020 simulated with widely used diagnostic TBM – Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance machine learning approaches derive upscale seasonally varied m Vcmax25 carbon water fluxes. The estimated are validated against flux observations, sensing, learning-based across multiple spatial temporal scales. correlation coefficients (R2) slopes between tower-measured modeled fluxes R2=0.83, regression =0.92 GPP, R2=0.72, =1.04 At scale, mean 137.78±3.22 Pg C yr−1 (mean ± 1 SD) positive trend 0.53 yr−2 (p<0.001), an 89.03±0.82×103 km3 slight 0.10×103 (p<0.001) 2020. pattern our agrees well other products, R2=0.77–0.85 R2=0.74–0.90 ET, respectively. Overall, new dataset serves “handshake” among process-based models, network, reliable long-term patterns facilitating studies related functional properties, carbon, cycles. available https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) accumulated https://doi.org/10.57760/sciencedb.ecodb.00165 2023b).

Language: Английский

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Dryland self-expansion enabled by land–atmosphere feedbacks

Science, Journal Year: 2024, Volume and Issue: 385(6712), P. 967 - 972

Published: Aug. 29, 2024

Dryland expansion causes widespread water scarcity and biodiversity loss. Although the drying influence of global warming is well established, role existing drylands in their own relatively unknown. In this work, by tracking air flowing over drylands, we show that contributes to dryland downwind direction. As they dry, contribute less moisture more heat humid regions, reducing precipitation increasing atmospheric demand, which ultimately aridification. ~40% land area recently transitioned from a region into dryland, self-expansion accounted for >50% observed Our results corroborate urgent need climate change mitigation measures decelerate expansion.

Language: Английский

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How much water vapour does the Tibetan Plateau release into the atmosphere?

Hydrology and earth system sciences, Journal Year: 2025, Volume and Issue: 29(2), P. 485 - 506

Published: Jan. 23, 2025

Abstract. Water vapour flux, expressed as evapotranspiration (ET), is critical for understanding the earth climate system and complex heat–water exchange mechanisms between land surface atmosphere in high-altitude Tibetan Plateau (TP) region. However, performance of ET products over TP has not been adequately assessed, there still considerable uncertainty magnitude spatial variability water released from into atmosphere. In this study, we evaluated 22 against situ observations basin-scale balance estimations. This study also spatiotemporal total flux its components to clarify TP. The results showed that remote sensing high-resolution global data ETMonitor PMLV2 had a high accuracy, with overall better accuracy than other regional fine resolution (∼ 1 km), when comparing observations. When compared estimates at basin scale, finer GLEAM TerraClimate coarse good agreement. Different different patterns variability, large differences central western multi-year multi-product mean was 333.1 mm yr−1, standard deviation 38.3 yr−1. (i.e. plant transpiration, soil evaporation, canopy rainfall interception open-water snow/ice sublimation) available some were compared, contribution these varied considerably, even cases where similar. Soil evaporation accounts most TP, followed by transpiration while contributions sublimation cannot be negligible.

Language: Английский

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Global evaluation of terrestrial evaporation trend from diagnostic products

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132979 - 132979

Published: Feb. 1, 2025

Language: Английский

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GLEAM4: global land evaporation and soil moisture dataset at 0.1 resolution from 1980 to near present

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 10, 2025

Terrestrial evaporation plays a crucial role in modulating climate and water resources. Here, we present continuous, daily dataset covering 1980–2023 with 0.1°spatial resolution, produced using the fourth generation of Global Land Evaporation Amsterdam Model (GLEAM). GLEAM4 embraces developments hybrid modelling, learning evaporative stress from eddy-covariance sapflow data. It features improved representation key factors such as interception, atmospheric demand, soil moisture, plant access to groundwater. Estimates are inter-compared existing global products validated against situ measurements, including data 473 sites, showing median correlation 0.73, root-mean-square error 0.95 mm d−1, Kling–Gupta efficiency 0.49. land is estimated at 68.5 × 103 km3 yr−1, 62% attributed transpiration. Beyond actual its components (transpiration, interception loss, evaporation, etc.), also provides potential sensible heat flux, stress, facilitating wide range hydrological, climatic, ecological studies.

Language: Английский

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An Outlook for Deep Learning in Ecosystem Science

Ecosystems, Journal Year: 2022, Volume and Issue: 25(8), P. 1700 - 1718

Published: Oct. 17, 2022

Abstract Rapid advances in hardware and software, accompanied by public- private-sector investment, have led to a new generation of data-driven computational tools. Recently, there has been particular focus on deep learning—a class machine learning algorithms that uses neural networks identify patterns large heterogeneous datasets. These developments both hype scepticism ecologists others. This review describes the context which methods emerged, most relevant ecosystem ecologists, some problem domains they applied to. Deep high predictive performance range ecological contexts, leveraging data resources now available. Furthermore, tools offer ways learn about dynamics. In particular, recent interpretable developing hybrid approaches combining mechanistic models provide bridge between pure prediction causal explanation. We conclude looking at opportunities assess challenges interpretability applications pose.

Language: Английский

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