Search Results (629)

This research has used a multi-indices geospatial framework to combine the utilization of the Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI), and Standardized Precipitation Evapotranspiration Index (SPEI) to measure drought risk in Faisalabad Division, Pakistan (2015–2023). It integrated remote sensing, GIS analysis, and change detection in Land Use Land Cover (LULC) and used Moderate Resolution Imaging Spectroradiometer (MODIS) datasets along with SPEI grids. It was found that the spatial heterogeneity that occurred with District Jhang is at high risk because it is arid (SPEI −1.5), sparsely vegetated (NDVI 0.2), and has high thermal stress (TCI -30), whereas the central/eastern parts are resilient (NDVI 0.4) due to irrigation. Through MODIS LULC analysis, the occurrence of urban growth (13.42 km² of vegetative cover loss), agricultural intensification, and afforestation (147.34 km²) were identified. As per the risk map, 74 percent of the area was defined as low risk (74 percent), 20 percent as moderate risk, and 6 percent as high risk. The findings highlight the role of water management in climate resilience. Future research should integrate high-resolution imagery, machine learning, and socioeconomic data for improved prediction. Full article

The Pantanal, considered the world’s largest tropical wetland, is increasingly threatened by intensifying droughts driven by climate variability and climate change. Using Multi-Source Weather data (MSWX), and bias-corrected multi-model means from five Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for the years 1980–2100, we assessed historical and future drought conditions under SSP2-4.5 and SSP5-8.5 scenarios for the Pantanal. Drought conditions were identified through the Standardised Precipitation Index (SPI) and the Standardised Precipitation–Evapotranspiration Index (SPEI) across multiple timescales, and with different reference periods. A historical analysis revealed a significant drying trend, culminating in the extreme droughts of 2019/2020 and 2023/24. Future projections indicate a dual pressure of declining precipitation and rising temperatures, intensifying the severity of dry conditions. By the late 21st century, SSP5-8.5 shows persistent, severe multi-year droughts, while SSP2-4.5 projects more variable but still intensifying dry spells. The SPEI highlights stronger drying than the SPI, underscoring the growing role of evaporative demand, which was confirmed through risk ratios for drought occurrence across temperature anomaly bins. These results offer multi-scalar insights into drought dynamics across the Pantanal wetland, with critical implications for biodiversity, water resources, and wildfire risk. Thus, they emphasise the urgency of adaptive management strategies to preserve ecosystem integrity under a warmer, drier future climate. Full article

This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal parameter weighting (α = 0.5), which improved detection accuracy, achieving a Critical Success Index (CSI) of 0.78. The SM-VHI exhibited strong correlations with independent drought indicators, including the Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress responses across diverse climatic conditions. Through spatial and temporal trend analyses, we identified patterns of drought severity and recovery, which emphasized the importance of tailored management strategies. Furthermore, the analysis incorporated historical maize yield data to evaluate the effectiveness of SM-VHI in representing agricultural drought conditions. A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, suggesting that enhanced vegetation and soil moisture conditions are strongly linked to increased crop productivity. This validation demonstrates the capability of SM-VHI to effectively capture drought-induced yield variability. The findings confirm the effectiveness of SM-VHI as a reliable remote-sensing tool for monitoring drought conditions and have strong potential to inform agricultural practices and policy decisions aimed at enhancing food security in a changing climate. Full article

Drought events exacerbated by global climate change occur frequently in China. Currently, high-spatiotemporal-resolution gridded meteorological drought index datasets are generally available for single time scales (e.g., 30, 60, 90, and 150 days) and do not fully account for seasonal differences in the impact of drought on vegetation, thus limiting their accuracy when monitoring drought in different regions of China. To compensate for the limitations of existing drought index datasets, a Chinese regional daily meteorological drought comprehensive index (MCI) dataset covering 1951–2022 with a spatial resolution of 0.1 degrees was developed, and standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) datasets at 30- and 90-day scales were constructed based on ERA5-Land datasets. Compared with the existing SPI and SPEI datasets, the generated dataset exhibits a high degree of consistency with those in eastern part of China (R² > 0.5; the average biases were close to 0 and significantly smaller than RMSEs of the fitting). Additionally, the MCI dataset can more accurately reflect the changes in shallow soil moisture in the eastern part of China in a timely manner (R² > 0.7 for the 0–7 cm depth), thus providing notable empirical support for research on drought development in different ecosystems. Full article

This study assesses the impact of extreme droughts on the surface and atmospheric water balance of the Peruvian Amazon basin during 2003–2024. It extends previous work by incorporating multiple datasets for precipitation (CHIRPS, MSWEP, and ERA5) and evapotranspiration (ERA5, GLDAS, Amazon-Paca, and observations from the Quistococha flux tower) and comparing three drought indices: Maximum Cumulative Water Deficit (MCWD), Standardized Precipitation Evapotranspiration Index (SPEI), and self-calibrated Palmer Drought Severity Index (scPDSI). The study focuses on the Peruvian–Ecuadorian Amazon basin, particularly on the Amazon and Madre de Dios river basins, closing at Tamshiyacu and Amaru Mayu stations, respectively. The results confirm four extreme drought years (2004–2005, 2009–2010, 2022–2023, and 2023–2024) with major precipitation deficits in dry seasons and significant reductions in runoff and total water storage anomalies (TWSAs), physically manifesting as negative surface balances indicating net terrestrial water depletion and negative atmospheric balances reflecting reduced moisture convergence, with residuals signaling hydrological uncertainties. The study highlights significant imbalances in the water cycle during droughts and underscores the need to use multiple indicators and datasets to accurately assess hydrological responses under extreme climatic conditions in the Amazon basin. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

The Warta River basin, Poland’s third-largest basin, is highly vulnerable to drought, which occurs in both cold and warm seasons. This study examined meteorological and hydrological droughts using daily temperature and precipitation data from 211 meteorological stations and discharge data from 15 hydrological gauges for 2000–2020. Four indicators were applied: SPI and SPEI for meteorological drought, and SRI and ThLM for hydrological drought. The analysis revealed prolonged droughts and a systematic decline in SRI values, especially from March to September. The longest event, a shallow drought, lasted 555 days between 2019 and 2020 at the Sławsk gauge. The period from 2018 to 2020 was particularly severe, with drought intensity increasing and affecting 70–80% of river flows, while events persisted longer than usual. Water withdrawals, especially for municipal use, further reduced river levels. The section between Uniejów and Oborniki, located downstream of one of Poland’s largest reservoirs, proved most vulnerable to hydrological drought. Overall, results indicate a deteriorating water situation in the Warta basin, with the most significant deficits in spring and summer. These trends pose serious challenges for water management and water supply security. An improved understanding of meteorological and hydrological droughts and their impact is essential for managing the water–food–environment–energy nexus, including restrictions on water use for domestic, economic, and agricultural purposes, as well as the functioning of aquatic ecosystems. Full article

The impacts of climate change are globally evident and cause significant damage to ecosystems and human activities. These impacts intensify social and economic inequality in Southern Africa (SA), where agriculture is vital for livelihoods and economic development. This study aimed to assess long-term trends in climate elements and parameters relevant to drought regimes in SA to identify drought hotspots and relate them to socioeconomic indicators. The methods include the Theil–Sen slope estimator and the Mann–Kendall statistical significance test. The study analysed ERA5 data for the 1971–2020 to compute the Standardised Precipitation Index (SPI) and Standardised Precipitation Evapotranspiration Index (SPEI) drought indices and descriptors. Results of the trend analysis reveal (i) the existence in almost the entire SA of statistically significant trends of increasing temperature and potential evapotranspiration and decreasing precipitation; (ii) increasing drought risk hotspots in the SPI and SPEI across all timescales, in the north central rainforest region, south and southeast of SA, while decreasing in the northwest coast, central west region, and in the northeast more recently; and (iii) hotspots in the drought descriptors within the same regions, but of a smaller size. Our findings pinpoint drought hotspots in regions with moderate-to-high population density and agricultural systems that involve species vital for food security and of considerable socioeconomic and commercial importance, emphasising the significance of our results for managers and decision-makers. Full article

This study aimed to assess, compare, and validate a satellite-based plant health and water stress indicator: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) over Uganda. We used a direct agricultural drought indicator—the Standardized Precipitation and Evapotranspiration Index at scale 3 (SPEI-03)—and a plant water stress indicator—the crop water stress index (CWSI)—for the period of 1983–2013. Novel approaches such as spatial variability and trend analysis, along with correlation analysis, were used to achieve this. The results showed that there are six classes of highly variable photosynthetic activity over Uganda, dominated by class 4 (0.36–0.45). This dominant class encompassed 45% of the total land area, mainly spanning cropland. In addition, significant increases in monthly photosynthetic activity (FAPAR) and FAPAR-centered stress indicators (SFI < −1) were observed over 85% and 60% of total land area, respectively. The Standardized FAPAR Index (SFI) had a strong positive correlation with SPEI-03 over cropland, grassland, and forest lands, while SFI had a strong negative correlation with CWSI over 80% of the total area. These results highlight the state and variation in plant health and water stress, generate insights on ecosystem dynamics and functionality, and weigh in on the usability and reliability of satellite-based variables such as FAPAR in plant water monitoring over Uganda. We thus recommend satellite-based FAPAR as a robust proxy for vegetation health and water stress monitoring over Uganda, with potential application in crop yield prediction and irrigation management to inform effective agricultural planning and improve productivity. Full article

Meteorological droughts have been occurring with greater frequency and intensity, impacting water security in various regions. Between 2013 and 2015, the Paraopeba River Basin in southeast Brazil experienced its most severe drought in the last 70 years, resulting in low levels in the Paraopeba system reservoirs, which supplies 53% of the Metropolitan Region of Belo Horizonte, the third largest metropolitan area in Brazil. This study evaluated the climate models’ performance from the NEX-GDDP-CMIP6 through drought indices projections, specifically the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that seven climate models can represent the current climate in the basin. For the drought’s projection, the indices were used in two time scales (six and twelve months) for both the current climate and two future scenarios (SSP245 and SSP585). Our results highlight the intensification of droughts throughout the twenty-first century, with greater intensification in the SSP585 scenario. The SPEI indicated trends towards drier conditions, particularly under the SSP585 scenario and on the twelve-month timescale. These findings demonstrate the relevance of climate change and drought indices on the projections, supporting public policies for mitigation and adaptation, especially in strategic regions for water supply and hydro-electric generation. Full article

Oak forests provide critical ecosystem services, but are being increasingly exposed to climate variability, drought, and insect outbreaks that threaten their long-term resilience. This study aims to integrate structural canopy indicators with climate-derived indices to detect early-warning signals of decline in temperate oak stands. We monitored eight Forest Management Units in western Romania between 2017 and 2021, combining field-based assessments of crown morphology, vitality traits, defoliation, and epicormic shoot frequency with hydroclimatic indices such as the Forest Aridity Index. Results revealed strong spatial and temporal variability: several stands showed advanced canopy deterioration characterized by increased defoliation, dead branches, and epicormic resprouting, while others maintained stable conditions, suggesting resilience and suitability as reference sites. Insect defoliators, particularly Geometridae, contributed additional stress, but generally at subcritical levels. By synthesizing these metrics into conceptual models and a risk scorecard, we identified the causal pathways linking climatic anomalies and biotic stressors to structural decline. The findings demonstrate that combining structural and climatic indicators offers a transferable framework for forest health monitoring, providing robust early-warning tools to guide adaptive silviculture and resilience-based management. Beyond the Romanian context, this integrative approach supports sustainability goals by strengthening conservation strategies for temperate forests under global change. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Norway spruce (Picea abies (L.) Karst.) is experiencing large-scale decline across Central Europe, with climate warming and bark beetle (Ips typographus L.) outbreaks as primary drivers. In lowland Białowieża Forest, Poland, spruce occupies a range of habitats that differ in their suitability for long-term persistence. We hypothesized that climate change accelerates spruce decline by reducing resilience in suboptimal habitats and increasing susceptibility to bark beetle outbreaks, with long-term persistence limited to optimal hydrological sites. To address this, we analysed spruce share from 1902–2018, its distribution across suitable versus unsuitable habitats, and long-term climate records in relation to outbreaks. Historical maps, forest site classifications, and meteorological data were used to calculate hydro-climatic indices (HTC, SPEI-12, Selyaninov), and outbreak relationships were tested using Welch’s t-test and point-biserial correlation, including lag effects. Spruce share increased from 12% in 1902 to 27% in 2015 and then declined to 9% by 2018. In 2015, 75% of spruce-dominated stands occurred in unsuitable habitats. Bark beetle outbreaks were significantly associated with drought, with outbreak years showing lower precipitation (–121 mm), reduced Selyaninov k (mean 1.40 vs. 1.61), and more negative SPEI-12 values (–0.48 vs. 0.07) compared to non-outbreak years (p < 0.05). One-year lag analysis indicated drought as both a predisposing and triggering factor. These findings highlight the interaction of habitat suitability and drought as a key driver of spruce decline, supporting adaptive management strategies that retain spruce in optimal habitats while converting suboptimal stands to more drought-tolerant species. Full article

This study focuses on Greece, providing a comprehensive climatological analysis of drought conditions from 1960 to 2022. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were employed on a 1-month timescale to assess meteorological drying conditions over the study period. The Drought Occurrence Probability (DOP), Total Drought Duration (TDD) and drought severity were analyzed spatially, while temporal trends were examined using rolling time windows and the Mann–Kendall test. The findings reveal regional differences in drought characteristics and indicate more intense drought conditions under the SPEI compared to the SPI, underscoring the increasing role of temperature in drought intensification. Full article

Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management. Full article