Search Results (1,496)

River–lake systems are essential for sustaining ecosystems and human livelihoods. However, the complexity and variability of large river–lake systems, coupled with characteristic differences in water bodies across regions, have made quantifying their extent and changes inherently challenging. This study implements a robust water extraction method based on the multidimensional X-means clustering algorithm. This method leverages the advantages of Sentinel-2 imagery for water detection. Utilizing the X-means algorithm, it generates a new seasonal surface water area (SWA) product for the mid-lower reaches of the Yangtze River (MLRYR). The implemented method achieved an overall accuracy of 97.98%, a producer’s accuracy of 98.02%, a user’s accuracy of 96.01%, a Matthews correlation coefficient of 0.954, and a Kappa coefficient of 0.954. Analysis of water body dynamics reveals that over the past six years, the overall trend of SWA in the MLRYR has remained stable. However, within a broad range including multiple sub-basins, a decline in SWA has been observed on an inter-annual scale. Among the large lakes and reservoirs in the MLRYR, the water areas of Poyang Lake, Dongting Lake and Shijiu Lake all showed a marked decline. Among all water bodies with a significant increase in area, the Danjiangkou Reservoir is the largest. Further correlation analysis indicates that SWA exhibited the strongest correlations with precipitation and drought index in most sub-basins. In sub-basins where large lakes and reservoirs exist, the presence of river networks played a buffering role by regulating and storing water, thereby reducing the direct influence of climatic factors on lake and reservoir water extent. These findings highlight the complex interplay of climatic and hydrological factors. By integrating satellite imagery and Earth observation, this study advances understanding of MLRYR surface water dynamics, providing a robust framework for monitoring in other regions. It offers critical insights into drought impacts and informs effective water resource management and conservation strategies. Full article

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as Enhanced Vegetation Index (EVI) and gross primary productivity (GPP), this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of flash droughts and slowly evolving droughts (including seasonal and cross-seasonal droughts) in the Dongjiang River Basin of China. The results indicate the following: (1) The average occurrence frequencies of flash droughts, seasonal droughts, and cross-seasonal droughts within the basin were 4.1%, 7.8%, and 8.4%, respectively. (2) The vast majority of flash droughts (approximately 90.1%) further developed into longer-lasting, slowly evolving droughts, indicating that flash droughts serve as a critical precursor to persistent drought events. Moreover, winter was identified as the key season for the occurrence of flash droughts and their transition to slowly evolving droughts. (3) In terms of ecological response, droughts significantly suppressed vegetation growth, but ecosystem resilience exhibited notable differences: although flash droughts caused relatively mild initial suppression, they were accompanied by a severe lack of ecosystem resilience; in contrast, cross-seasonal droughts, despite inducing stronger suppression, were met with higher ecosystem resilience. This study underscores the importance of the early monitoring and warning of flash droughts, and the findings provide a scientific basis for drought risk management in humid basins. Full article

The Amazon hosts one of the richest diversities of mosquitoes in the family Culicidae, which are key both as arbovirus vectors and as environmental bioindicators. However, the state of Amapá remains poorly studied regarding its mosquito fauna. This study aimed to characterize the diversity and seasonal composition of Culicidae in the municipality of Mazagão, Eastern Amazon, within a rural landscape influenced by human activity and extreme climatic events. Three sampling campaigns were conducted between 2023 and 2024, covering rainy, intermediary, and dry periods. Mosquitoes were collected using Protected Human Attraction (PHA) and CDC light traps at both ground and canopy strata. A total of 3500 specimens were obtained, representing 38 species across 15 genera. The intermediary period yielded the highest abundance and richness, whereas the dry season presented very low diversity, probably because of severe drought and forest fires. Dominant species included Coquillettidia (Rhy.) venezuelensis, Cq. albicosta, and Mansonia titillans. There were significant differences in community diversity between dry and wetter periods, underscoring the strong role of seasonality in shaping mosquito populations. These findings represent the entomofaunistic survey of the region, contributing to biodiversity knowledge and highlighting potential public health risks, thus reinforcing the need for continuous entomological monitoring. Full article

Soil respiration (Rs), the second largest carbon flux in terrestrial ecosystems, critically regulates the turnover of soil carbon pools. However, its seasonal and annual responses to extreme events in monsoon forests remain unclear. This study used a continuous multichannel automated chamber system to monitor Rs over three years of drought (2019–2021) in an Asian monsoon forest in Taiwan. We assessed seasonal and annual Rs patterns and examined how drought influenced autotrophic (Rr) and heterotrophic (Rh) respiration through changes in soil temperature and moisture. Results showed Rs declined from 5.20 ± 2.08 to 3.86 ± 1.20 μmol CO₂ m⁻² s⁻¹, and Rh from 3.36 ± 1.21 to 3.15 ± 0.98 μmol CO₂ m⁻² s⁻¹ over the study period. Spring Rr values dropped significantly—by 29.3% in 2020 and 62.2% in 2021 compared to 2019 (p < 0.05), while Rh remained unchanged (p > 0.05). These results suggest that spring drought strongly suppresses autotrophic respiration but has minimal effect on Rh. Incorporating these dynamics into carbon models could improve predictions of carbon cycling under climate change. Our findings demonstrate that spring drought exerts a strong influence on soil carbon fluxes in Asian monsoon forests. Full article

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

This study examines the impacts of climate change on fisheries and aquaculture in Bangladesh, one of the most climate-vulnerable countries in the world. The fisheries and aquaculture sectors contribute significantly to the national GDP and support the livelihoods of 12% of the total population. Using a Critical Literature Review (CLR) approach, peer-reviewed articles, government reports, and official datasets published between 2006 and 2025 were reviewed across databases such as Scopus, Web of Science, FAO, and the Bangladesh Department of Fisheries (DoF). The analysis identifies major climate drivers, including rising temperature, erratic rainfall, salinity intrusion, sea-level rise, floods, droughts, cyclones, and extreme events, and reviews their differentiated impacts on key components of the sector: inland capture fisheries, marine fisheries, and aquaculture systems. For inland capture fisheries, the review highlights habitat degradation, biodiversity loss, and disrupted fish migration and breeding cycles. In aquaculture, particularly in coastal systems, this study reviews the challenges posed by disease outbreaks, water quality deterioration, and disruptions in seed supply, affecting species such as carp, tilapia, pangasius, and shrimp. Coastal aquaculture is also particularly vulnerable to cyclones, tidal surges, and saline water intrusion, with documented economic losses from events such as Cyclones Yaas, Bulbul, Amphan, and Remal. The study synthesizes key findings related to climate-resilient aquaculture practices, monitoring frameworks, ecosystem-based approaches, and community-based adaptation strategies. It underscores the need for targeted interventions, especially in coastal areas facing increasing salinity levels and frequent storms. This study calls for collective action through policy interventions, research and development, and the promotion of climate-smart technologies to enhance resilience and sustain fisheries and aquaculture in the context of a rapidly changing climate. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

The Brazil nut production chain, which is reliant on Amazonian environmental conditions, is significantly affected by climate change, particularly extreme droughts, which decrease production and compromise sanitary quality. This study evaluated the influence of severe drought on aflatoxin concentrations and sequence toxigenic fungi in Brazil nuts harvested during the 2023 off-season. Aflatoxins were quantified using high-performance liquid chromatography, while fungal sequencing involved DNA extraction, PCR, and sequencing analysis. Findings indicated that all Brazil nut samples collected during extreme drought contained detectable aflatoxins, with 10% exceeding the legal threshold of 10 µg/kg. Phylogenetic analysis identified four isolates as Penicillium citrinum. Additional morphological and sequencing analyses identified Aspergillus species from the Circumdati and Flavi sections, although one isolate could not be taxonomically classified. These results demonstrate the aflatoxin production by fungi in Brazil nuts in an unprecedented way under drought conditions. Furthermore, the diversity of fungal species during drought underscores the risk of contamination, emphasizing the necessity for monitoring future harvests to improve management and ensure product safety. Full article

Agricultural drought, increasingly intensified by climate change, poses a significant threat to food security and water resources in semi-arid regions, including Türkiye’s Konya Closed Basin. This study evaluates six satellite-derived indices—Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), Evapotranspiration Condition Index (ETCI), and Soil Moisture Condition Index (SMCI)—to monitor agricultural drought (2001–2024) and proposes a drought vulnerability map using a novel Drought Vulnerability Index (DVI). Integrating Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Center InfraRed Precipitation with Station (CHIRPS), and Land Data Assimilation System (FLDAS) datasets, the DVI combines these indices with weighted contributions (VHI: 0.27, ETCI: 0.25, SMCI: 0.22, PCI: 0.26) to spatially classify vulnerability. The results highlight severe drought episodes in 2001, 2007, 2008, 2014, 2016, and 2020, with extreme vulnerability concentrated in the southern and central basin, driven by prolonged vegetation stress and soil moisture deficits. The DVI reveals that 38% of the agricultural area in the basin is classified as moderately vulnerable, while 29% is critically vulnerable—comprising 22% under high vulnerability and 7% under extreme vulnerability. The proposed drought vulnerability map offers an actionable framework to support targeted water management strategies and policy interventions in drought-prone agricultural systems. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change. Full article

Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (${\mathrm{R}}_{\mathrm{p}}^2$ = 0.8740, RMSE_p = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (${\mathrm{R}}_{\mathrm{p}}^2$ = 0.7626, RMSE_p = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture. Full article

In this paper, we analyse drought variability in Spain (1950–2024) using the Standardised Precipitation–Evapotranspiration Index (SPEI) at 6-, 12-, and 24-month scales. Using 43 long-record meteorological observatories (AEMET), we compute SPEI from quality-controlled (QC), homogenised series, and derive coherent drought regions via clustering and assess trends in the frequency, duration, and intensity of dry episodes (SPEI ≤ −1.5), including seasonality and statistical significance (p < 0.05). Short-term behaviour (SPEI-6) has become more complex in recent decades, with the emergence of a “Catalonia” type and stronger June–October deficits across the northern interior; Mediterranean coasts show smaller or non-significant changes. Long-term behaviour (SPEI-24) is more structural, with increasing persistence and duration over the north-eastern interior and Andalusia–La Mancha, consistent with multi-year drought. Overall, short and long scales converge on rising drought severity and persistence across interior Spain, supporting multi-scale monitoring and region-specific adaptation in agriculture, water resources, and forest management. Key figures are as follows: at 6 months—frequency 0.09/0.08 per decade (Centre–León/Catalonia), duration 0.59/0.50 months per decade, intensity −0.12 to −0.10 SPEI per decade; at 24 months—frequency 0.5 per decade (Cantabrian/NE interior), duration 0.8/0.7/0.4 months per decade (Andalusia–La Mancha/NE interior/Cabo de Gata–Almería), intensity −0.06 SPEI per decade; Mediterranean changes are smaller or non-significant. Full article

With global warming, the increasing frequency of drought events threatens the stability of ecosystems, so the development of a rational ecological drought monitoring and assessment model is urgently needed. In this study, an evapotranspiration deficit (ED) was added for the first time into the construction of an ecological drought index. Considering atmospheric water deficit (WD), soil moisture (SM) and runoff (RF), both the Copula method and a nonparametric method were used to construct a multivariate comprehensive drought index (MCDI) to monitor ecological drought. The MCDI was evaluated using Pearson, actual drought validation, Theil–Sen, Mann–Kendall and ExtraTrees+SHAP methods, in order to assess differences between construction methods, analyze the drivers and sensitivities of ecological drought in Xinjiang, China, and specifically explore the role of ED in ecological drought. The results showed that (1) ED based on the ratio form is more suitable for capturing SM changes; (2) the performance of the composite drought index was improved in all aspects when cumulative effects were considered, and the ecological drought index based on the nonparametric method was superior to the index using the Copula method; (3) soil moisture was identified as the main contributor to ecological drought in Xinjiang, with the strongest synergistic effect occurring between SM and ED; and (4) the sensitivity of ecological drought to soil moisture within the arid regions increased nonlinearly along the decreasing SM gradient. In addition, the sensitivity to all drivers increased over time, with the largest increase observed for RF, followed by SM and then ED. The findings of this paper provide a useful reference for constructing a comprehensive drought index at the global scale, since the nonparametric method requires considerably fewer computational resources compared with the Copula method. In addition, the identified synergistic effect of ED and SM offers a new theoretical basis for ecological drought prevention and management in arid regions. Full article

Frequent and severe droughts intensify water scarcity in arid and semi-arid regions, creating an urgent need for alternative water resources in agriculture. Treated wastewater (TWW) has emerged as a sustainable option; however, its long-term use may alter soil properties and pose risks if not carefully managed. This study tested the hypothesis that long-term TWW irrigation increases soil salinity, alters fertility, and affects microbial quality, with rainfall partially mitigating these effects. Soil samples (n = 96 at each time point) were collected from two calcareous soils in Jordan, silt loam (Mafraq) and silty clay loam (Ramtha), under four treatments (control and 2, 5, and 10 years of TWW irrigation) at three depths (0–30, 30–60, and 60–90 cm). Sampling was conducted at two intervals, before and after rainfall, to capture the seasonal variation. Soil indicators included the pH, electrical conductivity (EC), sodium (Na⁺), chloride (Cl⁻), calcium (Ca²⁺), magnesium (Mg²⁺), exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR), organic matter (OM), total nitrogen (TN), and microbial parameters (total coliforms (TC), fecal coliforms (FC), and Escherichia coli). Data were analyzed using a linear mixed-effects model with repeated measures, and significant differences were determined using Tukey’s Honest Significant Difference (HSD) test at p < 0.05. The results showed that rainfall reduced Na⁺ by 70%, Cl⁻ by 86%, EC by 73%, the ESP by 28%, and the SAR by 30%. Furthermore, the TC and FC concentrations were diminished by almost 96%. Moderate TWW irrigation (5 years) provided the most balanced outcomes across both sites. This study provides one of the few long-term field-based assessments of TWW irrigation in semi-arid calcareous soils of Jordan, underscoring its value in mitigating water scarcity while emphasizing the need for monitoring to ensure soil sustainability. Full article