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Deep storage irrigation can recharge farmland deep soil moisture and sustain production of summer maize(Zea mays L.)through flood resources utilization in irrigation districts of northern China 认领 引用
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作者 Xiaodong Fan Xiaotao Hu +4 位作者 Yakun Wang Dianyu Chen Wene Wang Fang Wang Qing Zha 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第3期1243-1262,共20页
The irrigation districts of northern China face issues such as water scarcity,inability to effectively utilize flood resources,and groundwater overexploitation.In view of these challenges,this study proposes a new con... The irrigation districts of northern China face issues such as water scarcity,inability to effectively utilize flood resources,and groundwater overexploitation.In view of these challenges,this study proposes a new concept of deep storage irrigation through flood resources utilization.However,whether deep storage irrigation can recharge deep soil moisture and sustain crop production still requires further study.A two-year field experiment was conducted on summer maize in the Guanzhong Plain with five soil wetting layer depths(T1:60 cm;T2:90 cm;T3:120 cm;T4:150 cm;T5:180 cm)and soil saturation moisture content as the irrigation upper limit.The results presented that the ranges of deep soil moisture recharge in the100–200 cm soil profile(SMS100–200)was 73.34–267.42 and 0–150.03 mm in 2021(wet season)and 2022(normal season).When the effective precipitation and irrigation exceeded 390 mm,the SMS100–200began to linearly increase.The highest grain yield(GY)were observed at T2 and T3 treatments in 2021(11.44 t ha-1)and 2022(11.25 t ha-1),respectively.The maize GY of T4 in 2021 and T5 in 2022 were only 3.9 and 5.7%lower than the maximize GY,respectively.However,the SMS100–200for T4 and T5 were 2.4 and 5.0 times that of T2 and T3 treatments in 2021 and 2022,respectively.Overall,the further increase in irrigation amounts induced only a slight decrease in grain yield,but it significantly increased deep soil moisture recharge.Therefore,the deep storage irrigation breaks through the traditional idea of water-saving irrigation with limited water resources,which can be utilized as an effective alternative to address the issues of water scarcity,low flood resources utilization,and groundwater level declines in the irrigation districts of northern China. 展开更多
关键词 deep storage irrigation soil moisture dynamics soil moisture storage grain yield summer maize
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Spatiotemporal patterns of meteorological-soil moisture drought propagation on the Qinghai-Xizang Plateau,China 认领 引用
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作者 WANG Zegen CHEN Mi +5 位作者 LIU Yaxin RAN Yaowen YONG Zhiwei HUANG Yaoxuan WANG Yingyang ZHAO Junxin 《Journal of Arid Land》 SCIE CAS CSCD 2026年第5期774-792,共19页
Drought is among the most destructive and recurrent natural disasters worldwide.In recent decades,the frequency of drought events has increased,exerting significant impacts on socioeconomic development.The propagation... Drought is among the most destructive and recurrent natural disasters worldwide.In recent decades,the frequency of drought events has increased,exerting significant impacts on socioeconomic development.The propagation of meteorological drought(MD)to soil moisture drought(SMD)is a common natural process;however,its dynamics across different seasons and vegetation types on the Qinghai-Xizang Plateau,as well as the underlying meteorological driving mechanisms,remain insufficiently understood.This study utilized precipitation and soil moisture data from the European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis v5(ERA5)-Land reanalysis dataset for the period 1982–2022.The standardized precipitation index(SPI)and standardized soil moisture index(SSMI)were employed to characterize MD and SMD,respectively.By integrating run theory with an optimal parameter geographical detector(OPGD)model,this study systematically analyzed the average duration and propagation time of MD and SMD across the Qinghai-Xizang Plateau,and quantitatively evaluated the explanatory power of various meteorological and topographical factors influencing drought propagation.The results indicated that the mean duration of SMD across the Qinghai-Xizang Plateau from 1982 to 2022 was generally longer than that of MD.Significant seasonal differences in propagation time were observed,with the average propagation time ranked as winter(21 d)>spring(14 d)>autumn(10 d)>summer(8 d).Spatial variability of propagation time was more pronounced in spring and winter than in summer and autumn.Furthermore,the analysis of driving mechanisms revealed that drought propagation from MD to SMD on the Qinghai-Xizang Plateau was primarily influenced by precipitation(relative contribution proportion of 51.9%),followed by evaporation(15.1%)and snowmelt(13.6%),with the strongest interaction effects associated with precipitation.Although the dominant factors across different vegetation types were generally consistent with those for the entire plateau,solar radiation also showed a relatively high contribution(average 13.9%)across vegetation types.In summary,this study provides a scientific basis for improving drought early warning systems and optimizing water resource management strategies. 展开更多
关键词 meteorological drought soil moisture drought drought propagation time run theory optimal parameter geographical detector(OPGD)model standardized precipitation index(SPI) standardized soil moisture index(SSMI)
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Reducing Soil Moisture Fluctuations Significantly Improves Crop Yield and Quality:Insight into Multiomics in Soil-Plant Systems 认领 引用
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作者 Weijie Chen Naikun Kuang +5 位作者 Christoph Martin Akshit Puri Bin Liu Jing He Yunpeng Zhou Yunkai Li 《Engineering》 SCIE EI CSCD 2026年第5期104-115,共12页
Agricultural water scarcity is increasingly conflicting with demands for both crop yield and crop nutritional quality,yet current irrigation strategies are failing to achieve synergistic improvements.This study explor... Agricultural water scarcity is increasingly conflicting with demands for both crop yield and crop nutritional quality,yet current irrigation strategies are failing to achieve synergistic improvements.This study explores how reducing soil moisture fluctuations(SMFs)affects crop yield and quality,using tomato plants under three irrigation treatments:fast wetting(FW),medium wetting(MW),and slow wetting(SW).We analyzed soil moisture dynamics,yield,fruit quality,soil bacteria,and plant molecular responses.Slowing the wetting process significantly improved tomato yield by 10%-20%and increased vitamin C and lycopene content by 10%-17%and 7%-29%,respectively,while reducing the irrigation quota by 30%-35%.The results showed a significant increase in the relative abundance of Myxococcota and Chloroflexi,while the relative abundance of Actinobacteria significantly decreased.Functional prediction showed that the abundance of aerobic chemotrophic heterotrophy was suppressed,whereas nitrate reduction was promoted.Based on a joint analysis of transcriptomics and metabolomics,several genes(GME,DHAR,IDH1,crtB,and crtH)encoding key enzymes in the pathways of ascorbic acid,lycopene,and organic acid cycles were significantly affected.Structural equation modeling(SEM)revealed that the stabilized soil moisture directly increased microbial community diversity and soil fertility,which subsequently activated transcriptional pathways associated with nutrient assimilation and antioxidant biosynthesis.This cascade of biological responses ultimately mediated improvements in crop productivity and quality.These findings challenge the conventional understanding of wet-dry cycles in irrigation.Reducing SMFs offers a practical approach to simultaneously improving water-use efficiency,crop yield,and fruit quality,with potential applications in sustainable agriculture. 展开更多
关键词 Soil moisture fluctuations Wetting process Soil fertility Microbial community Crop yield and quality
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Soil moisture governs the weakening response of transpiration fraction to leaf area index increase:A spatiotemporal analysis in China's Three-North region 认领 引用
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作者 Xiao Zhang Xinxiao Yu Guodong Jia 《Forest Ecosystems》 SCIE CAS CSCD 2026年第2期369-383,共15页
Accurately assessing vegetation-hydrology interactions is crucial for water resource management,especially amidst climate change and ecological restoration.Using remote sensing observations(MODIS LAI)and GLEAM model o... Accurately assessing vegetation-hydrology interactions is crucial for water resource management,especially amidst climate change and ecological restoration.Using remote sensing observations(MODIS LAI)and GLEAM model outputs(evapotranspiration components,soil moisture(SM))from 2000 to 2023 for China's Three-North(TN)region,we quantified the sensitivity of the transpiration fraction(TF,the ratio of transpiration to total evapotranspiration)to changes in leaf area index(LAI),denoted as θ=∂TF/∂LAI.We employed an analytical approach combining SM and vapor pressure deficit(VPD)trends to evaluate the mechanisms governingθ's response to increasing vegetation cover.Results show that while the TN region experienced a significant LAI increase(0.33 m²·m⁻²·decade⁻¹),driving a continuous TF rise(1.44%decade⁻¹),the sensitivity θ markedly decreased(−3.4%year⁻¹),accumulating a 32% decline over 24 years.This reveals a clear diminishing return of LAI increase on enhancing TF.Regional VPD remained stable,with opposing effects from rising temperature and atmospheric moisture largely cancelling out.Crucially,the decline inθwas primarily governed by SM dynamics;θdecreased most sharply under soil drying conditions(Δθ up to−8%),whereas sufficient soil wetting buffered the decline.Sensitivity also varied across different combinations of SM and VPD trends,being lowest where SM increased,and VPD decreased.This study demonstrates a weakening hydrological feedback to vegetation restoration in the TN region,highlighting soil moisture availability as the key constraint limiting the ecosystem's capacity to regulate water vapor fluxes.These findings provide a critical basis for assessing ecological sustainability and informing adaptive water management strategies under future aridification. 展开更多
关键词 Vegetation transpiration Terrestrial evapotranspiration Leaf area index Soil moisture Vapor pressure deficit Three-North region
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Variations in Summer Extreme Heat Events in the Mongolian Plateau:Role of the Soil Moisture over the Inner Tibetan Plateau 认领 引用
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作者 Zhongren DENG Renhe ZHANG Zhiyan ZUO 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第2期451-459,I0032-I0034,共9页
Recent studies have suggested that rapid warming over the Mongolian Plateau(MP)may intensify extreme heat events(EHEs).However,the characteristics and mechanisms driving summer EHEs over the MP(MP-EHEs)remain unclear.... Recent studies have suggested that rapid warming over the Mongolian Plateau(MP)may intensify extreme heat events(EHEs).However,the characteristics and mechanisms driving summer EHEs over the MP(MP-EHEs)remain unclear.This study explores the interannual variations in summer MP-EHEs and their relationship with the summer soil moisture over the Inner Tibetan Plateau(TP-SM).The results reveal that changes in the MP-EHEs are linked to descending atmospheric motion induced by a local high-pressure system over the MP region.Descending motion further results in decreased mid-tolow-level cloud cover and increased shortwave radiation,thereby warming the surface and triggering summer MP-EHEs.Further analysis indicates that increased TP-SM results in a greater latent heat flux,triggering a local secondary circulation that reinforces the local high-pressure system over the MP region,thus serving to promote the occurrence of summer MPEHEs.Additionally,model results from the linear baroclinic model(LBM)and CMIP6 further confirm that variations in summer TP-SM contribute to the occurrence of the MP-EHEs. 展开更多
关键词 summer extreme heat events Mongolian Plateau soil moisture Tibetan Plateau
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Response of soil moisture to vegetation restoration:A bibliometric synthesis 认领 引用 被引量：2
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作者 GAN Shao'an HAN Lei +3 位作者 LI Yabei CHANG Yuqing ZHANG Hanxiao TUO Fengwei 《中国水土保持科学（中英文）》 CSCD 北大核心 2026年第1期270-284,共15页
[Objective]Vegetation restoration is an effective strategy for ecological improvement;however,inappropriate vegetation establishment can induce soil desiccation,thereby threatening ecosystem stability.Therefore,elucid... [Objective]Vegetation restoration is an effective strategy for ecological improvement;however,inappropriate vegetation establishment can induce soil desiccation,thereby threatening ecosystem stability.Therefore,elucidating the global response patterns of soil moisture to vegetation restoration and identifying research hotspots are critical for guiding ecological construction in arid regions.[Methods]We reviewed 6,152 articles concerning soil moisture and vegetation retrieved from the Web of Science platform.Using VOSviewer,we conducted analyses of keyword co-occurrence,publication trends,and research hotspots to systematically delineate the evolving trends in this field.[Results]The results indicate a significant increasing trend in the number of publications since 2000.Global research keywords are categorized into seven clusters,including vegetation,soil moisture,rainfall-erosion-infiltration,spatial heterogeneity,and climate change.In terms of highly cited papers in 2024,China and the United States maintain a significant lead.Global research demonstrates a strong dependency on typical regional geographical features(such as climate types and topography),exhibiting differentiated research focuses.Furthermore,studies extend beyond soil moisture itself to deeply couple with ecological processes such as vegetation restoration,soil respiration,carbon cycling,and hydrothermal conditions.[Conclusions]The long-term ecological effects of afforestation in arid regions remain unclear,and empirical data from key regions highlight the current urgency.Future research should integrate climate change dynamics,innovate monitoring methodologies,and deepen the understanding of regional differentiation to provide scientific support for the adaptive management of vegetation in arid regions. 展开更多
关键词 soil moisture vegetation bibliometric analysis VOSviewer
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Machine learning with feature selection reveals key drivers of multi-depth soil moisture content 认领 引用
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作者 Qi Wu JiaXin Bian +3 位作者 XiaoLi Wang XiaoYing Qiao Ning Wang Yue You 《Research in Cold and Arid Regions》 CAS CSCD 2026年第3期270-283,共14页
Soil moisture content(SMC)plays a vital role in agricultural productivity,water resource management,and ecosystem sustainability in semi-arid regions.Despite this importance,most existing machine learning models mainl... Soil moisture content(SMC)plays a vital role in agricultural productivity,water resource management,and ecosystem sustainability in semi-arid regions.Despite this importance,most existing machine learning models mainly rely on remote sensing data to predict the soil moisture variation in the surface soil;however,they are constrained by redundant input features and limited interpretability.To address these shortcomings,this study combines the Random Forest(RF)algorithm,Convolutional Neural Networks(CNN),and the Transformer framework to develop a hybrid RF-CNN-Transformer model.Specifically,the RF algorithm,CNN,and Transformer framework are respectively used for selecting influential features,extracting spatial patterns,and capturing long-term temporal dependencies.Applied to the Mu Us Sandy Land using data from six soil depths(5,10,20,40,70,and 87 cm),the model demonstrated high prediction accuracy and training efficiency across all layers compared to baseline models,with R2 values ranging from 0.8586 to 0.984(mean R2=0.9507).Interpretability analysis revealed a shift in the controlling mechanisms of soil moisture:shallow-layer SMC is jointly influenced by meteorological conditions and groundwater level,whereas groundwater becomes the dominant factor in deeper layers.Notably,due to the extremely dry climate,precipitation has a relatively minor impact on soil moisture dynamics across all depths.Overall,the proposed RF-CNN-Transformer model enhances both the predictive capability and interpretability of soil moisture variation,supporting precision irrigation and water resource optimization in agriculture,especially in arid and semi-arid regions. 展开更多
关键词 Soil moisture content Feature selection Machine learning Semi-arid region
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China’s 1 km Daily Surface Soil Moisture Fusion Dataset(2000–2025)Based on Explainable Machine Learning 认领 引用
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作者 Yifan DONG Huiling YUAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第7期1317-1334,共18页
High-resolution soil moisture(SM)data are critical for drought monitoring and flood forecasting.This paper describes the establishment of an interpretable machine learning(ML)-based framework for SM data fusion and ge... High-resolution soil moisture(SM)data are critical for drought monitoring and flood forecasting.This paper describes the establishment of an interpretable machine learning(ML)-based framework for SM data fusion and generates a dailyscale,1-km resolution,surface SM(0–10 cm)dataset over China(2000–2025).Four state-of-the-art ML models—Random Forest,XGBoost,LightGBM,and CatBoost—were trained based on in situ SM data from 2371 automatic observation stations across China.Model performance was optimized via Recursive Feature Elimination(RFE)and automated hyperparameter tuning using Optuna,while SHapley Additive exPlanations(SHAP)provided mechanistic interpretability of the ML models.The key findings of this study are as follows:(1)The fusion model primarily enhances SM estimation,exhibiting lower root-mean-square error than CLDAS(China Meteorological Administration Land Data Assimilation System)SM,despite marginally weaker daily temporal correlation;(2)RFE eliminated 57%of features while preserving predictive accuracy;(3)SHAP analysis revealed high-accuracy SM inputs as the most influential predictors,followed by static(terrain and soil properties)and meteorological variables.The SM fusion method developed in this study is transferable to multi-source satellite SM fusion and downscaling.The dataset is publicly available at http://gffzzd3cc09b8251d45dfspuo5v9v9wu0b6kvn.ffgz.tsg.suse.edu.cn/10.11888/Terre.tpdc.302923. 展开更多
关键词 soil moisture data fusion machine learning feature selection SHAP(SHapley Additive exPlanations)
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Variations of soil moisture and its influencing factors in arid and semi-arid areas,China 认领 引用 被引量：5
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作者 NIU Jiqiang LIU Zijian +5 位作者 CHEN Feiyan LIU Gangjun ZHOU Junli ZHOU Peng LI Hongrui LI Mengyang 《Journal of Arid Land》 SCIE CSCD 2025年第5期624-643,共20页
Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are limited.Despite its importance,understanding the spatiotemporal variations and influenci... Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are limited.Despite its importance,understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient.This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation(ETC),Mann-Kendall test,Theil-Sen estimator,ridge regression analysis,and other relevant methods.The following findings were obtained:(1)at the pixel scale,the long-term monthly SM data from the European Space Agency Climate Change Initiative(ESA CCI)exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error(RMSE)of 0.014 m3/m3;(2)from 2000 to 2022,the study area experienced significant increase in annual average SM,with a rate of 0.408×10-3m3/(m3•a).Moreover,higher altitudes showed a notable upward trend,with SM increasing rates at 0.210×10-3m3/(m3•a)between 1000 and 2000 m,0.530×10-3m3/(m3•a)between 2000 and 4000 m,and 0.760×10-3m3/(m3•a)at altitudes above 4000 m;(3)land surface temperature(LST),root zone soil moisture(RSM)(10-40 cm depth),and normalized difference vegetation index(NDVI)were identified as the primary factors influencing annual average SM,which accounted for 34.37%,24.16%,and 22.64%relative contributions,respectively;and(4)absolute contribution of LST was more significant in subareas at higher altitudes,with average absolute contributions of 0.800×10-3m3/(m3•a)between 2000 and 4000 m and 0.500×10-2 m3/(m3•a)above 4000 m.This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas,highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas,providing valuable insights for ecological research and water resource management in these areas. 展开更多
关键词 soil moisture arid and semi-arid areas remote sensing extended triple collation ridge regression analysis
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Spatio-temporal uneven distribution of soil moisture replenishment by rainfall events along a hillslope in the Liupan Mountains, Northwest China 认领 引用 被引量：1
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作者 BAI Yushi YU Pengtao +4 位作者 WAN Yanfang LIU Bingbing WANG Yanhui LIU Zebin Ashley A.WEBB 《Journal of Mountain Science》 SCIE CSCD 2025年第4期1376-1387,共12页
Mountainous areas are the priority for forest restoration in semiarid regions,with hillslopes serving as the basic units of mountains.Precipitation is the only water source in these regions,and the uneven distribution... Mountainous areas are the priority for forest restoration in semiarid regions,with hillslopes serving as the basic units of mountains.Precipitation is the only water source in these regions,and the uneven distribution of hillslope soil moisture replenishment after precipitation determines vegetation survival and growth.Therefore,in this study experiments were performed on a hillslope in the Liupan Mountains,Ningxia Hui Autonomous Region,China,to quantify the unevenness of soil moisture replenishment.Soil water content(SWC)in the 0–60 cm layer and precipitation were monitored throughout the growing season in 2020 and 2021.The results showed that(1)Annual soil moisture replenishment was the highest at the mid-slope position,with an average of 309.9 mm,especially under moderate and heavy rain grade conditions,reaching 38.7% and 30.8% of the total replenishment,respectively;(2)Vertical replenishment played a dominant role in the total replenishment,accounting for 82.8%;lateral replenishment played an important but lesser role,accounting for up to 17.2% of the total replenishment;(3)Based on a soil moisture replenishment model established in this study,the maximal replenishment occurred at 90 m from the top of the slope;(4)The dominant factors contributing to the soil moisture replenishment were rainfall amount and saturated hydraulic conductivity(Ks).These findings suggest that attention should be given to both vertical and lateral soil moisture replenishment,and the mid-slope position could be preferred for site selection to achieve precise and integrated forest-water management on hillslopes in semi-arid mountainous regions. 展开更多
关键词 Soil moisture replenishment Lateral replenishment Hillslope position Loess Plateau
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Divergent responses of vegetation productivity to soil moisture and vapor pressure deficit across China:Spatiotemporal patterns and climatic attribution 认领 引用 被引量：1
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作者 SHI Chengyue ZHOU Yuke +2 位作者 CUI Na NIU Lujia YAO Haijun 《Journal of Geographical Sciences》 SCIE CSCD 2025年第7期1405-1431,共27页
Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks.Currently,the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetatio... Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks.Currently,the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetation productivity remain inadequately understood.In this study,we analyzed soil moisture(SM),vapor pressure deficit(VPD),and gross primary productivity(GPP)to investigate their spatiotemporal patterns and the combined effects on GPP over China.The results revealed that:(1)Soil drought and meteorological drought generally exhibited temporally synchronous trends across China.(2)GPP was predominantly affected by the combined and synchronous effects of both SM and VPD,although their effects displayed directional variability differences in certain regions.(3)SM demonstrated a greater relative importance on GPP than VPD across more than half of the regions in China,whereas deciduous broadleaf forests were the only vegetation type primarily affected by VPD.(4)Under the lag effects,both SM and VPD exhibited bidirectional Granger causality with GPP,with the interaction between VPD and GPP proving more pronounced than that of SM.Our research provides valuable insights into the mechanisms through which SM and VPD influence GPP,contributing to improved predictions vegetation productivity and implementing ecological restoration. 展开更多
关键词 soil moisture vapor pressure deficit gross primary productivity spatiotemporal patterns climatic attribution causal analysis
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Exploring the influence of surface soil moisture on heatwave characteristics in the Northern Hemisphere 认领 引用
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作者 BI Pengshuai CHEN Xiao +8 位作者 PAN Zhihua GAO Riping PAN Feifei MEN Jingyu HUANG Na ZHANG Fangxiao HUANG Zhanrui YANG Rongdao WANG Jialin 《Journal of Geographical Sciences》 SCIE CSCD 2025年第11期2273-2290,共18页
Heatwaves are becoming increasingly frequent and severe,posing escalating risks to ecosystems and human well-being.While soil moisture(SM)deficits are recognized as important contributors to heatwave amplification,the... Heatwaves are becoming increasingly frequent and severe,posing escalating risks to ecosystems and human well-being.While soil moisture(SM)deficits are recognized as important contributors to heatwave amplification,their spatially heterogeneous impacts across the Northern Hemisphere remain insufficiently understood.In this study,we analyze ERA5 reanalysis data(1980-2022)to investigate trends in heatwave frequency,intensity,and duration,as well as their sensitivity to SM variability.Our results show robust increases in heatwave occurrence(0.76 events per decade),intensity(0.81℃per decade),and average duration(0.40 days per decade),with extreme events,as represented by maximum intensity and duration,rising at even faster rates(2.18℃per decade and 0.83 days per decade,respectively).Strong negative correlations are observed between SM deficits and heatwave metrics,with the magnitude of this relationship varying across land cover types and heatwave severity levels.Quantile regression reveals that SM reductions have a greater impact at higher quantiles for most indicators.Cropland exhibits the highest sensitivity to SM anomalies,whereas forests show more resilience due to their superior water retention capacities.These findings underscore the crucial role of land-atmosphere interactions in shaping heatwave extremes,providing a scientific basis for enhancing early warning and adaptation strategies in the context of ongoing climate change. 展开更多
关键词 extreme events heatwaves soil moisture quantile regression climate change
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Effects of air temperature,photoperiod,and soil moisture on leaf senescence and dormancy depth in four subtropical tree species 认领 引用
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作者 Fucheng Wang Yiming Liu +5 位作者 Heikki Hänninen Jinbin Zheng Yu Zhao Wenwen Chang Jiasheng Wu Rui Zhang 《Forestry Research》 2025年第1期281-290,共10页
Climate warming has substantially delayed the autumn phenology of trees over recent decades.As the primary focus of previous studies on autumn phenology has been on temperate tree species,the environmental regulation ... Climate warming has substantially delayed the autumn phenology of trees over recent decades.As the primary focus of previous studies on autumn phenology has been on temperate tree species,the environmental regulation of leaf senescence in subtropical trees under distinct climatic conditions remains poorly understood.To address this gap,using climate chambers,we experimentally examined the effects of air temperature,photoperiod,and soil moisture on leaf senescence and dormancy depth in seedlings of four subtropical tree species.Our results showed that low temperature served as the primary environmental cue driving leaf senescence in all four species,whereas photoperiod and soil moisture had no significant effect on senescence under low-temperature conditions.However,under high-temperature conditions,both drought and short photoperiod accelerated leaf senescence.This suggests that during warm autumns in subtropical regions when the typical senescence trigger(low temperature)is absent,drought and photoperiod are alternative cues to ensure senescence occurs before the onset of winter.Furthermore,we found that leaf senescence and dormancy induction were not closely linked processes.Overall,our experimental results reveal the dominant role of air temperature and its interactions with alternative cues(photoperiod and soil moisture)in regulating autumn leaf senescence in subtropical trees,which challenges the common assumption for a majority of temperate tree species that the primary driver of leaf senescence is short photoperiod.These findings provide valuable insights into the ways trees adapt to subtropical environments. 展开更多
关键词 dormancy depth autumn phenology soil moisture climate warming climate chamberswe subtropical trees air temperature photoperiod
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A hybrid ConvLSTM-Nudging model for predicting surface soil moisture in the Qilian Mountains,China 认领 引用
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作者 FAN Manhong XIAO Qian +1 位作者 YU Qinghe ZHAO Junhao 《Journal of Arid Land》 SCIE CSCD 2025年第11期1623-1648,共26页
Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation... Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting. 展开更多
关键词 data assimilation surface soil moisture deep neural networks Convolutional Long Short-Term Memory(ConvLSTM) Squeeze-and-Excitation(SE)attention mechanism Nudging long-term prediction
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Study on the Soil Moisture Stress Level in Regulated Deficit Irrigation Experiment 认领 引用 被引量：25
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作者 柴红敏 张巍巍 蔡焕杰 《Agricultural Science & Technology》 CAS 2009年第2期154-156,共3页
On the basis of discussing the influencing mode of plant moisture stress on plant physiological process and the division of soil moisture availability range, the water suction values partitioning soil moisture were pu... On the basis of discussing the influencing mode of plant moisture stress on plant physiological process and the division of soil moisture availability range, the water suction values partitioning soil moisture were put forward, and then the corresponding water moistures under water stress were obtained by conversing together with characteristic curve of water moisture. 展开更多
关键词 Regulated deficit irrigation Soil moisture availability Soil water suction Soil moisture characteristic curve Soil moisture stress level
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Response of Soil Moisture to Precipitation in the Source Region of the Yellow River 认领 引用
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作者 Xinyi GU Xianhong MENG +5 位作者 Xianyu YANG Yuanyuan MA Zhaoguo LI Lunyu SHANG Shaoying WANG Mingshan DENG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第9期1947-1966,共20页
The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem... The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle.However,the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data.This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR,using data from Maqu,Mado,Ngoring Lake sites,and the Maqu monitoring network(MMN),along with CN05.1 precipitation and GLEAM v3.8a soil moisture data.Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR.Surface soil at Maqu,MMN,Ngoring Lake,and Mado sites require at least 8.6,8.4,5.2,and 2.84 mm of precipitation,respectively,for effective replenishment.Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area,while at 20 cm and above in the semi-arid area.Precipitation volume is the primary factor influencing soil moisture,affecting both the increment and time lag to maximum moisture.Precipitation intensity and pre-rain moisture have no direct effect.In the central SRYR,accumulated precipitation has a greater impact.Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer,responding for up to 10 days,while surface soil moisture responds more immediately but only lasts about 5 days. 展开更多
关键词 soil water dynamics precipitation soil moisture response soil depth response time
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Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-attention models in Yutian Oasis,southern Xinjiang,China 认领 引用
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作者 Xiaobo LÜ Ilyas NURMEMET +4 位作者 Sentian XIAO Jing ZHAO Xinru YU Yilizhati AILI Shiqin LI 《Pedosphere》 SCIE CAS CSCD 2025年第5期846-857,共12页
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables... Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R2)reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone. 展开更多
关键词 arid region convolutional neural network deep learning method hybrid prediction model leaf area index long short-term memory neural network normalized difference vegetation index physical model surface soil moisture
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Efficient soil moisture estimation on the Qinghai-Xizang Plateau via machine learning and optimized feature selection 认领 引用
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作者 JIA Shichao SUN Wen +1 位作者 WEI Sihao SUN Rui 《Journal of Arid Land》 SCIE CSCD 2025年第8期1147-1167,共21页
Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China... Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R2)and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R2and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau. 展开更多
关键词 soil moisture machine learning feature selection radar and optical remote sensing polarization decomposition CatBoost model Qinghai-Xizang Plateau
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An evaluation of soil moisture from AMSR-E over source area of the Yellow River, China 认领 引用 被引量：1
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作者 TangTang Zhang Mekonnen Gebremichael +3 位作者 Akash Koppa XianHong Meng Qun Du Jun Wen 《Research in Cold and Arid Regions》 CSCD 2019年第6期461-469,共9页
In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration A... In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration Agency)and VUA(Vrije University Amsterdam and NASA)over Maqu County,Source Area of the Yellow River(SAYR),China.Re sults show that the VUA soil moisture product performs the best among the three AMSR-E soil moisture products in the study area,with a minimum RMSE(root mean square error)of 0.08(0.10)m3/m3 and smallest absolute error of 0.07(0.08)m3/m3 at the grassland area with ascending(descending)data.Therefore,the VUA soil moisture product is used to describe the spatial variation of soil moisture during the 2010 growing season over SAYR.The VUA soil moisture product shows that soil moisture presents a declining trend from east south(0.42 m3/m3)to west north(0.23 m3/m3),with good agreement with a general precipitation distribution.The center of SAYR presents extreme wetness(0.60 m3/m3)dur ing the whole study period,especially in July,while the head of SAYR presents a high level soil moisture(0.23 m3/m3)in July,August and September. 展开更多
关键词 AMSR-E soil moisture products soil moisture ground measurements source area of the Yellow River AMSR-E soil moisture products applicability
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