Assessment of climate change impacts on the construction of homogeneous climate zones and climate projections during the twenty first century over Pakistan DOI
Talha Farooq, Firdos Khan, Hamd Ullah

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(10), P. 3987 - 4011

Published: June 19, 2023

Language: Английский

Agricultural drought assessment in data-limited arid regions using opensource remotely sensed data: a case study from Jordan DOI
Muhammad Rasool Al‐Kilani, Jawad Al‐Bakri, Michel Rahbeh

et al.

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(2)

Published: Jan. 10, 2025

Language: Английский

Citations

1
A new deep learning method for meteorological drought estimation based-on standard precipitation evapotranspiration index DOI
Sercan Yalçın, Musa Eşit, Önder Çoban

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 124, P. 106550 - 106550

Published: June 12, 2023

Language: Английский

Citations

21
Exploring dynamic response of agrometeorological droughts towards winter wheat yield loss risk using machine learning approach at a regional scale in Pakistan DOI
Sana Arshad, Syed Jamil Hasan Kazmi, Foyez Ahmed Prodhan

et al.

Field Crops Research, Journal Year: 2023, Volume and Issue: 302, P. 109057 - 109057

Published: July 25, 2023

Language: Английский

Citations

21
Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study DOI Creative Commons
Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(5)

Published: May 1, 2024

Abstract Droughts are among the most devastating natural hazards, occurring in all regions with different climate conditions. The impacts of droughts result significant damages annually around world. While drought is generally described as a slow‐developing hazardous event, rapidly developing type drought, so‐called flash has been revealed by recent studies. rapid onset and strong intensity require accurate real‐time monitoring. Addressing this issue, Generative Adversarial Network (GAN) developed study to monitor over Contiguous United States (CONUS). GAN contains two models: (a) discriminator (b) generator. architecture employs Markovian discriminator, which emphasizes spatial dependencies, modified U‐Net generator, tuned for optimal performance. To determine best loss function four networks functions, including Mean Absolute Error (MAE), adversarial loss, combination Square (MSE), MAE. Utilizing daily datasets collected from NLDAS‐2 Standardized Soil Moisture Index (SSI) maps, network trained SSI Comparative assessments reveal proposed GAN's superior ability replicate values Naïve models. Evaluation metrics further underscore that successfully identifies both fine‐ coarse‐scale patterns abrupt changes temporal important identification.

Language: Английский

Citations

8
Analysis of drought intensity, frequency and trends using the spei in Turkey DOI
Hıdır Serkendiz, Hasan Tatlı, Ayşegül Kılıç

et al.

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 155(4), P. 2997 - 3012

Published: Dec. 20, 2023

Language: Английский

Citations

17
Soil erosion prediction using Markov and CA-Markov chains methods and remote sensing drought indicators DOI

Marzieh Mokarram,

Abdol Rassoul Zarei

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102386 - 102386

Published: Nov. 25, 2023

Language: Английский

Citations

15
Effects of temperature and precipitation on drought trends in Xinjiang, China DOI
Jianhua Yang, Yaqian Li, Lei Zhou

et al.

Journal of Arid Land, Journal Year: 2024, Volume and Issue: 16(8), P. 1098 - 1117

Published: Aug. 1, 2024

Language: Английский

Citations

6
Impacts of climate and land coverage changes on potential evapotranspiration and its sensitivity on drought phenomena over South Asia DOI
Shahzad Ali, Abdul Basit, Muhammad Umair

et al.

International Journal of Climatology, Journal Year: 2024, Volume and Issue: 44(3), P. 812 - 830

Published: Jan. 9, 2024

Abstract Understanding the spatiotemporal historical drought pattern and their sensitivity effect on potential evapotranspiration (PET) vegetation coverage changes is essential for efficient mitigation policies under climate change. In this study, we used standardized precipitation index (SPEI) at multiple timescales, such as SPEI‐01, SPEI‐03, SPEI‐06, SPEI‐09 SPEI‐12; explored regional‐scale dry wet annual across seven sub‐regions of South Asia from 1902 to 2018. Results suggest that 1981 2018, extreme SPEI has increased in Asia, which mostly affects summer winter growing seasons, is, SPEI‐06 SPEI‐12 Asia. The frequency events during had an extremely year starting 1998 affected region. Data past 18 years showed land changing detection forests, cultivated land, arid savanna farmland; by contrast, there been significantly reduced permanent ice snow, mixed open shrub, grasslands, wetlands, water bodies evergreen broadleaf forests. Seasonal presented diverse characteristics showing a trend Afghanistan, India, Pakistan Sri Lanka autumn winter. Afghanistan Bhutan are compared with other occurring 45.3% 44.4%. Lanka, India driest regions due high frequency, duration intensity. correlation between PET crop stress (CWSI), regional ET p reduction (Er) indicated considerably negative correlation, while positive was found CWSI Er, NDVI Er. This study provides comprehensive assessment PET, SPEI, can help formulating long‐term adaptive strategies reduce cumulative impacts droughts.

Language: Английский

Citations

3
Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation DOI Open Access
Saihua Huang,

Heshun Zhang,

Yao Liu

et al.

Water, Journal Year: 2024, Volume and Issue: 16(11), P. 1466 - 1466

Published: May 21, 2024

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite index that can comprehensively characterize meteorological and hydrological In study, the was established by combining standardized precipitation (SPI) baseflow (SBI) for Jiaojiang River Basin (JRB) using copula function. The prediction model training random forests past data, driving force behind combined explored through LIME algorithm. results show combines advantages of SPI SBI in forecasting. monthly annual droughts JRB showed an increasing trend from 1991 2020, but temporal characteristics changes each subregion were different. accuracies trained forest heavy Baizhiao (BZA) Shaduan (SD) stations 83% 88%, respectively. Furthermore, Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified essential precipitation, baseflow, evapotranspiration features affect This study provides reliable valid multivariate indicators monitoring applied other regions.

Language: Английский

Citations

3
Mitigating the effects of extreme weather on crop yields: insights from farm management strategies in the Netherlands DOI Creative Commons
Sinne van der Veer, Raed Hamed, Hande Karabiyik

et al.

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(10), P. 104042 - 104042

Published: Aug. 23, 2024

Abstract Weather extremes can drive substantial crop losses. Farm-level management strategies play a critical role in mitigating the impacts of and consequences for farmer livelihoods food security. While extreme weather on yields are well documented recent studies, these predominantly focused expansive geographical scales commonly overlooked practices modulating dynamics weather-crop sensitivities. We fill this gap literature by using unique dataset that explores timely relationship between at farm level Netherlands. cover 10 types crops elucidate soil types, irrigation nutrient application crops, estimating fixed-effects regression models. show from drought during growing- harvesting period excessive precipitation planting- growing period. Severe droughts significant ( p ⩽ 0.05 ) reductions yield all lead to up 24 percent relative average Meanwhile, eight due severe water excess planting period, with 18 percent. Soils such as sand or loess amplify negative impact yield, while softening precipitation. Irrigation lesser extent shown moderately decrease yield. Our findings contribute valuable insights guide local adaptation priorities which given projected increase intensity frequency under climate change.

Language: Английский

Citations

3