Delineating the predominant impact of rising temperature on the enhancement of severity in compound drought-hot events in China: An empirical Copula and path analysis-based approach DOI Creative Commons
Xiaohua Xiang, Yongxuan Li,

Xiaoling Wu

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101769 - 101769

Published: April 1, 2024

China Compound drought-hot events (CDH) inflict serious socio-economic damages on our society and the natural environment. Given inverse relationship between average summer temperature precipitation, this investigation introduces an innovative empirical copula-based compound index (CDHI). This is crafted from joint distribution of standardized precipitation evapotranspiration (SPEI) (STI). While previous research has documented a rising trend in these complex both regional global stages, scrutiny into their escalating severity remains limited. To highlight critical role climbing temperatures increasing CDH within China, utilizes CDHI tandem with path analysis to precisely assess shifts occurrences during warm season 1901 2022. study used implemented quantify response changes mean water deficit historical perspective. Our findings reveal marked escalation across much China. Path divulges that influence seen significant uptick last 60 years (1962–2022), displaying more considerable contribution rate than earlier 60-year span (1901–1961). points changing impact over recent decades. During initial interval (1901–1961), we saw 0.7% 1.7% per-decade increase areas affected by severe moderate events, respectively. Contrastingly, subsequent period (1962–2022) experienced rise, area expanding twice as much. Totally speaking, exploration enhances comprehension intensification event climatic drivers. These insights can contribute improved risk assessments development tailored adaptation mitigation strategies face ongoing climate change.

Language: Английский

High Sensitivity of Compound Drought and Heatwave Events to Global Warming in the Future DOI Creative Commons
Qin Zhang, Dunxian She, Liping Zhang

et al.

Earth s Future, Journal Year: 2022, Volume and Issue: 10(11)

Published: Oct. 27, 2022

Abstract Compound drought and heatwave (CDHW) events have received considerable attention in recent years due to their devastating effects on human society ecosystem. In this study, we systematically investigated the changes of CDHW multi‐spatiotemporal scales for historical period (1951–2014) four future scenarios (2020–2100) (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP5‐8.5) over global land by using Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The responses maximum air temperature climatic water balance variable are also examined. results show that multi‐model ensembles project a significant increasing trend characteristics almost all lands under SSP5‐8.5, especially across northern North‐America, Caribbean, Mediterranean Russian‐Arctic, there is stronger trend. A significantly risk will occur most medium long term without aggressive adaptation mitigation strategies. path analysis suggest dominant factor influencing events. Additionally, higher sensitivity warming future. Particularly, each 1°C increases duration 3 days period, but about 10 period. Overall, study improves our understanding projection impacts climate drivers various scenarios, which could provide supports assessment, strategies change.

Language: Английский

Citations

72
Spatial-temporal variations of terrestrial evapotranspiration across China from 2000 to 2019 DOI Creative Commons
Jing Fu,

Yueqi Gong,

Wenwu Zheng

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 825, P. 153951 - 153951

Published: Feb. 19, 2022

Terrestrial evapotranspiration (ET) refers to a key process in the hydrological cycle by which water is transferred from Earth's surface lower atmosphere. With spatiotemporal variations, ET plays crucial role global ecosystem and affects vegetation distribution productivity, climate, resources. China features complex, diverse natural environment, leading high heterogeneity climatic variables. However, past future trends remain largely unexplored. Thus, using MOD16 products meteorological datasets, this study examined variations of 2000 2019 analyzed what behind changes, explored trends. Climate variation was statistically significant had remarkable impact on ET. Average annual increased at rate 5.3746 mm yr-1 (P < 0.01) during period. The main drivers trend are increasing precipitation wind speed. increase can also be explained some extent temperature, decreasing sunshine duration relative humidity. zonation results show that speed, decrease humidity large positive effects growth, either promoting or inhibiting different agricultural regions. Pixel-based exhibited an overall obvious spatial volatility. Hurst exponent indicates characterized anti-persistence, with widely distributed areas expected experience decline These findings improve understanding climate variability processes, question will ultimately affect system.

Language: Английский

Citations

65
Vegetation dynamics influenced by climate change and human activities in the Hanjiang River Basin, central China DOI

Shaokang Yang,

Ji Liu, Chenghao Wang

et al.

Ecological Indicators, Journal Year: 2022, Volume and Issue: 145, P. 109586 - 109586

Published: Oct. 22, 2022

Language: Английский

Citations

51
High-resolution crop yield and water productivity dataset generated using random forest and remote sensing DOI Creative Commons
Minghan Cheng,

Xiyun Jiao,

Lei Shi

et al.

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: Oct. 21, 2022

Abstract Accurate and high-resolution crop yield water productivity (CWP) datasets are required to understand predict spatiotemporal variation in agricultural production capacity; however, for maize wheat, two key staple dryland crops China, currently lacking. In this study, we generated evaluated a long-term data series, at 1-km resolution of CWP wheat across based on the multiple remotely sensed indicators random forest algorithm. Results showed that MOD16 products an accurate alternative eddy covariance flux tower describe evapotranspiration (maize RMSE: 4.42 3.81 mm/8d, respectively) proposed estimation model accuracy local rRMSE: 26.81 21.80%, regional 15.36 17.17%, scales. Our analyses, which patterns yields can be used optimize strategies context maintaining food security.

Language: Английский

Citations

46
Untangling the effects of climate change and land use/cover change on spatiotemporal variation of evapotranspiration over China DOI
Xiaoyang Li, Lei Zou, Jun Xia

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 612, P. 128189 - 128189

Published: July 16, 2022

Language: Английский

Citations

41
Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize DOI Creative Commons
Minghan Cheng, Chengming Sun, Chenwei Nie

et al.

Agricultural Water Management, Journal Year: 2023, Volume and Issue: 287, P. 108442 - 108442

Published: July 7, 2023

Accurately monitoring the crop water conditions (CWC) is vital for agricultural management. Traditional in situ measurements are limited by inefficiency and lack of spatial information. However, development unmanned aerial vehicle (UAV) applications agriculture now provides a high throughput cost-effective method to obtain field growth Unfortunately, current UAV-based drought indices do not capture time series information, or accuracy limited. This study uses multispectral thermal information site-observed air temperature following three indices: normalized relative canopy (NRCT), vegetation index (TVDI), three-dimension (TDDI). We evaluate with which these can be used characterize CWC maize comparing them moisture contents (VMC). aims (i) pertinence TDDI characterizing VMC, (ii) compare performance that NRCT TVDI, analyze spatiotemporal variation indices. The results show best estimates VMC (r = 0.71), TVDI comparable 0.59 0.63, respectively) strongly correlated 0.92), (iii) distribution well, but multi-phase image makes it significantly better studying temporal variations than TVDI. this prove observations accurately monitor conditions. In addition, new insights into remote sensing-based

Language: Английский

Citations

30
Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China DOI Creative Commons
Jian Lü, Jian Li,

Hongkun Fu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(6), P. 794 - 794

Published: May 22, 2024

The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance CNN-LSTM-Attention model in predicting maize, rice, soybeans Northeast China compares its effectiveness with traditional models such as RF, XGBoost, CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, photosynthetically active parameters, our research examines model’s capacity capture essential spatial temporal variations. integrates Convolutional Neural Networks, Long Short-Term Memory, an attention mechanism effectively process complex datasets manage non-linear relationships within data. Notably, explores potential using kNDVI multiple crops, highlighting effectiveness. Our findings demonstrate that advanced deep-learning significantly enhance yield accuracy over methods. We advocate incorporation sophisticated technologies practices, can substantially improve production strategies.

Language: Английский

Citations

18
The Effects of Climate Change and Greening of Vegetation on Spatiotemporal Variation of Evapotranspiration in the Haihe River Basin, China DOI Creative Commons

Yang Chen,

Shaorui Chai,

Wenjie Chen

et al.

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(3)

Published: March 1, 2025

Highly accurate evapotranspiration (ET) estimation and understanding the impacts of climatic land use change on ET are essential for water resources management in Haihe River Basin (HRB). This study estimated spatial temporal changes its drivers over period 2000-2020, using Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. Validation performed with observations 11 eddy covariance sites showed that PT-JPL model can simulate high accuracy (R 2 = 0.64, RMSE 1.32 mm/day, NSE 0.57). During 21-year period, mean annual HRB was 583 mm/year an insignificant increasing trend (0.45 mm/year). Canopy transpiration (ETc, 2.96 mm/year) interception evaporation (ETi, 0.74 significantly increased whereas soil (ETs, -3.25 decreased. The net radiation (Rn), relative humidity (Rh), wind speed (Ws) decreasing trends. In contrast, air temperature (Tm), vapor pressure deficit (VPD), precipitation leaf area index (LAI) demonstrated vegetation is greening. We explored relationship between components to climate parameters. results most important parameter variations. Vegetation had large ETc. greening dominates Net role ETs. Temperature were key impact parameters ETi. increase ETi mainly located region forests, which due forest protection afforestation projects HRB. highlights importance isolating contributions components, useful other regions world.

Language: Английский

Citations

2
Spatiotemporal assessment of sustainable groundwater management using process-based and remote sensing indices: A novel approach DOI

Hossein Sadeghi-Jahani,

Hamed Ketabchi, Hossein Shafizadeh‐Moghadam

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 918, P. 170828 - 170828

Published: Feb. 8, 2024

Language: Английский

Citations

9
Quantifying the Spatiotemporal Changes in Evapotranspiration and Its Components Driven by Vegetation Greening and Climate Change in the Northern Foot of Yinshan Mountain DOI Creative Commons
Zijun Wang, Yangyang Liu, Zhenqian Wang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 357 - 357

Published: Jan. 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.

Language: Английский

Citations

8