Atmospheric Research, Journal Year: 2023, Volume and Issue: 299, P. 107193 - 107193
Published: Dec. 19, 2023
Language: Английский
Journal of Hydrology, Journal Year: 2023, Volume and Issue: 627, P. 130455 - 130455
Published: Nov. 11, 2023
Language: Английский
Citations
55
Current Climate Change Reports, Journal Year: 2024, Volume and Issue: 11(1)
Published: Oct. 2, 2024
Language: Английский
Citations
23
Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(3)
Published: Feb. 6, 2025
Abstract Understanding the spatial and temporal patterns of drought is essential for mitigating drought‐induced impacts. To date, less attention paid to characterization changes across global river basins within a 3D clustering identification framework. Here, we characterized events 59 during 1979–2020 based on standardized precipitation evapotranspiration index three‐dimensional method, together with exploration relationships between indicators. The results show that characteristics did not change significantly over time in most basins, but frequency tended decrease Middle East North Africa showed increase at high latitudes. Droughts Amazon, Nile La Plata are severer than other higher severities whole. Moreover, all affected area severity both increased duration.
Language: Английский
Citations
5
Agricultural Water Management, Journal Year: 2025, Volume and Issue: 308, P. 109291 - 109291
Published: Jan. 9, 2025
Language: Английский
Citations
2
Agricultural Water Management, Journal Year: 2025, Volume and Issue: 311, P. 109378 - 109378
Published: Feb. 22, 2025
Language: Английский
Citations
2
Journal of Hydrology, Journal Year: 2024, Volume and Issue: 631, P. 130668 - 130668
Published: Jan. 24, 2024
Language: Английский
Citations
14
Advances in Meteorology, Journal Year: 2025, Volume and Issue: 2025(1)
Published: Jan. 1, 2025
This research aims to find the best model for predicting Standardized Precipitation Index (SPI) and Evapotranspiration (SPEI) in future. The study estimates SPI SPEI at different time scales, ranging from 1 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months estimated drought indices (SPI SPEI). Accuracy error metrics like Nash–Sutcliffe efficiency (NSE), root‐mean‐square (RMSE), producer accuracy (PA), user (UA), Choen’s kappa assess models. NSE values varying scales (1, 3, 6, 9, 12, months) indicate that Bahawalpur, Rawalpindi, Murree, Sargodha stations have highest 0.1148, 0.5868, 0.8302, 0.9196, 0.9516, 0.9801, 0.9845, respectively. Similarly, RMSE these show lowest 0.6187, 0.6094, 0.4091, 0.2865, 0.2275, 0.1594, 0.1106, variance explained a 1‐month scale were found be poor, but they improved as increased. On other hand, high decreased with longer scales. exhibit various Jhelum, Mianwali, Sargodha, These 0.0784, 0.6074, 0.8353, 0.9225, 0.9542, 0.9760, 0.9896, 1.002, 0.5909, 0.3993, 0.2626, 0.2132, 0.1546, 0.0941, analysis reveals distinct pattern indicating situated higher elevations more pronounced correlation between comparison lower elevations. Notably, Sialkot, Rawalpindi demonstrate statistically significant strong SPEI. Overall, results is better index classifying monitoring meteorological However, elevations, selected provide similar information, some differences.
Language: Английский
Citations
1
Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132196 - 132196
Published: Oct. 1, 2024
Language: Английский
Citations
7
Atmosphere, Journal Year: 2024, Volume and Issue: 15(2), P. 155 - 155
Published: Jan. 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
Language: Английский
Citations
6
Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(3), P. 998 - 1017
Published: Feb. 6, 2024
Abstract Drought directly impacts the agricultural ecosystem, thus causing significant threat to regional and global food security. Investigating occurrence propagation patterns of drought events is crucial for its better understanding mitigation. The study investigates different agro-climatic regions Ganga River basin from 2001 2020 quantify meteorological using Standardized Precipitation Index (SPI). Additionally, assessment was conducted Soil Moisture (SSMI) Normalized Difference Vegetation (NDVI). For dynamics drought, Pearson Correlation Coefficient (PCC)-based approach employed compute time between types. Stronger correlations were observed SPI SSMI compared NDVI anomaly, highlighting direct connection precipitation soil moisture. results present show that ranges within 1–11 months across as inferred maximum PCC values series. rate varied 29.03 73.33% among regions. insights gained this analysis on can inform policymakers in formulating appropriate measure.
Language: Английский
Citations
6