Distinguishing the effects of climate change and vegetation greening on soil moisture variability along aridity gradient in the drylands of northern China

Agricultural and Forest Meteorology, Год журнала: 2023, Номер 343, С. 109786 - 109786

Опубликована: Окт. 26, 2023

Язык: Английский

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Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Journal of Hydrology, Год журнала: 2024, Номер 637, С. 131336 - 131336

Опубликована: Май 12, 2024

Язык: Английский

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Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Journal of Hydrology, Год журнала: 2024, Номер 634, С. 131102 - 131102

Опубликована: Март 22, 2024

Язык: Английский

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Response and recovery times of vegetation productivity under drought stress: Dominant factors and relationships

Journal of Hydrology, Год журнала: 2025, Номер 655, С. 132945 - 132945

Опубликована: Фев. 22, 2025

Язык: Английский

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Quantifying the Spatiotemporal Changes in Evapotranspiration and Its Components Driven by Vegetation Greening and Climate Change in the Northern Foot of Yinshan Mountain

Remote Sensing, Год журнала: 2024, Номер 16(2), С. 357 - 357

Опубликована: Янв. 16, 2024

Evapotranspiration (E), a pivotal phenomenon inherent to hydrological and thermal dynamics, assumes position of utmost importance within the intricate framework water–energy nexus. However, quantitative study E on large scale for “Grain Green” projects under backdrop climate change is still lacking. Consequently, this examined interannual variations spatial distribution patterns E, transpiration (Et), soil evaporation (Eb) in Northern Foot Yinshan Mountain (NFYM) between 2000 2020 quantified contributions vegetation greening changes Et, Eb. Results showed that (2.47 mm/a, p < 0.01), Et (1.30 Eb (1.06 0.01) all exhibited significant increasing trend during 2000–2020. Notably, emerged as predominant impetus underpinning augmentation both Eb, augmenting their rates by 0.49 mm/a 0.57 respectively. In terms meteorological factors primary catalysts, with temperature (Temp) assuming role at rate 0.35 mm/a. Temp, Precipitation (Pre), leaf area index (LAI) collectively dominated proportional accounting shares 32.75%, 28.43%, 25.01%, Within spectrum drivers influencing Temp exerted most substantial influence, commanding largest proportion 33.83%. For preeminent determinants were recognized LAI constituting portion area, 32.10% 29.50%, The pronounced direct influence no effects bare Wind speed (WS) had impact Et. Pre strong Relative humidity (RH) significantly affected directly. primarily influenced indirectly through radiation (Rad). Rad inhibitory effect These findings advanced our mechanistic understanding how its components NFYM respond greening, thus providing robust basis formulating strategies related regional ecological conservation water resources management, well supplying theoretical underpinnings constructing sustainable restoration involving region.

Язык: Английский

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Exploring the dominant drivers affecting soil water content and vegetation growth by decoupling meteorological indicators

Journal of Hydrology, Год журнала: 2024, Номер 631, С. 130722 - 130722

Опубликована: Янв. 23, 2024

Язык: Английский

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Enhanced evapotranspiration induced by vegetation restoration may pose water resource risks under climate change in the Yellow River Basin

Ecological Indicators, Год журнала: 2024, Номер 162, С. 112060 - 112060

Опубликована: Апрель 22, 2024

Quantifying the impacts of climate change, vegetation greening and human activities (CVH) on evapotranspiration (ET), surface drought intensity (ET divided by precipitation, SDI), available water (precipitation minus ET, VAW) would improve our understanding cycle processes. The Yellow River Basin (YRB) is a significant climate-sensitive region in China, resulting an obvious spatiotemporal heterogeneity SDI, VAW response to driving variables. In this study, we analysed variation characteristics YRB from 1984 2018. We also quantified direct indirect contributions CVH changes VAW, revealed influence mechanism each link. Finally, resource risks were assessed probabilistic perspective. results indicated that was primary driver ET with increase rate 1.60 mm/a, which most important influencing factor SDI decrease. Leaf area index (LAI) relative humidity (RH) jointly dominated 66 % YRB, temperature (Temp) nearly half basin, precipitation (Pre) LAI YRB. Temp indirectly influenced primarily through LAI, whereas directly. had impact while mainly RH wind speed (WS). exhibited substantial negative VAW. identified as contributing risks, probability reaching 0.8, probabilities associated other factors inducing such similar at basin level, but disparities existed among different land use types. findings study significantly enhanced role played hydrological processes, serving crucial foundation for achieving balance between ecological restoration socio-economic development

Язык: Английский

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Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Atmosphere, Год журнала: 2024, Номер 15(2), С. 155 - 155

Опубликована: Янв. 25, 2024

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production cause large economic losses. The accurate prediction of drought effectively reduce impacts droughts. Deep learning methods have shown promise in prediction, with convolutional neural networks (CNNs) being particularly effective handling spatial information. In this study, we employed deep approach to predict Fenhe River (FHR) basin, taking into account meteorological conditions surrounding regions. We used daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as evaluation index. Our results demonstrate effectiveness CNN model predicting events 1~10 days advance. evaluated predictions made by model; average Nash–Sutcliffe efficiency (NSE) between predicted true values for next 10 was 0.71. While accuracy slightly decreased longer lengths, remained stable heavy are typically difficult predict. Additionally, key variables were identified, found training these led higher than it all variables. This study approves an when considering

Язык: Английский

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Precipitation exacerbates spatial heterogeneity in the propagation time of meteorological drought to soil drought with increasing soil depth

Environmental Research Letters, Год журнала: 2024, Номер 19(6), С. 064021 - 064021

Опубликована: Май 9, 2024

Abstract The propagation of meteorological droughts to soil poses a substantial threat water resources, agricultural production, and social systems. Understanding drought process is crucial for early warning mitigation, but mechanisms the from drought, particularly at varying depths, remain insufficiently understood. Here, we employ maximum correlation coefficient method random forest (RF) model investigate spatiotemporal patterns drivers time (PT) four different depths across China 1980 2018. Our findings reveal consistently higher PT in northern lower southern with more pronounced spatial heterogeneity increasing depth. Furthermore, identify temperature precipitation as determinants surface deeper layers, respectively. Additionally, emerges dominant factor influencing changes between layers. study highlights discernible shift depth increases significant impact on exacerbating PT. This contributes an enhanced comprehension which can aid establishing practical mitigation measures

Язык: Английский

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Time-frequency insights: Uncovering the drivers of reference evapotranspiration across China

Agricultural Water Management, Год журнала: 2025, Номер 310, С. 109367 - 109367

Опубликована: Фев. 13, 2025

Язык: Английский

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