Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Mao, Yuna; Bai, Jiaxin; Wu, Guocan; Xu, Lin; Yin, Changjian; Feng, Fei; He, Yanyi; Zhang, Zhengtai; Wang, Kaicun

doi:10.1029/2023jd039387

Cited by 2 publications

(1 citation statement)

References 190 publications

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“…We use the Global Land Evaporation Amsterdam Model (GLEAM) V3.7 ET product (Martens et al, 2017) as reference, which has been shown to exhibit relatively high accuracy (Jia et al, 2022;Mao et al, 2024). GLEAM ET data set is available on a 0.25°regular grid and at daily temporal resolution.…”

Section: Model and Datamentioning

confidence: 99%

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

Sun,

Pan,

Duan

2024

Water Resources Research

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Land surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single‐value objective function. To address this problem, we propose a novel Generative Adversarial Network‐based Parameter Optimization (GAN‐PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network‐based surrogate models to make the physical model differentiable, we employ GAN‐PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN‐PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN‐PO excels in maintaining spatial consistency, outperforming the state‐of‐the‐art differentiable parameter learning (dPL) method in terms of model spatial performance.

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Section: Model and Datamentioning

confidence: 99%

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

Sun,

Pan,

Duan

2024

Water Resources Research

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Natural assets, changes, and variations of the socioeconomic-environmental systems along the Asian drylands belt

Chen,

John,

Kolluru

et al. 2024

Environ. Res.: Ecology

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The amount of and changes in a country's natural assets are crucial for developing national strategic plans and policies due to their foundational role in determining the resilience of social-environmental systems (SES), especially under a changing climate. Many integrated metrics on natural assets have been proposed based on individual measures to provide insights into the state of national natural resources. This is particularly true for countries experiencing extreme environmental stresses. Drawing on longitudinal data spanning from 1980 to 2020, the objective of this study is to analyze nuanced distinctions across 23 political entities (PEs) in the dryland regions of mid-latitudinal Asia, investigating their interannual variabilities over the four-decade study period and discerning potential driving forces. We examined three key integrated measures of the SES: ecosystem water use efficiency (WUE), human appropriation of net primary production (HANPP), and human appropriation of water resources (HAWR). We found increased variability as well as spikes in extreme values in each of these three measures of SES function among the 23 PEs over the study period. Water stress played an increasing and more important role than temperature in influencing the magnitude and variations of the three measures. Our results also indicate that human interventions may help increase the efficiency of water use in this dryland region of the world.

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Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Cited by 2 publications

References 190 publications

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network

Natural assets, changes, and variations of the socioeconomic-environmental systems along the Asian drylands belt

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