Search Results (264)

Earth’s frozen water reserves are inherently dynamic and play a critical role in the hydrological processes of complex, dynamic river basins. Climate-driven environmental changes have led to a significant decrease in the area and duration of seasonal snow cover, particularly in the high-altitude Asian Water Tower (Himalaya). Snow cover estimates are challenging in the high-altitude Hindu Kush-Himalayan (HKH) region, which is a core contributor to water resources that sustain the environment and diverse downstream ecosystems that support more than half of the Earth’s population. This study quantifies the snow cover area over the period of 1993–2023 across the basins of ten major snow- and rainfall-dominated rivers in the HKH region using Landsat-based Normalized Difference Snow Index (NDSI), SNOMAP, Blue Snow Threshold (BST), and Dozier and Painter methods. Additionally, this study used 178 strategically positioned points across the HKH region to explore the relationship between snow cover dynamics and environmental factors such as temperature, precipitation, and greenhouse gas concentrations. The study observed significant temporal and spatial variations in snow cover, driven by regional climatic conditions and anthropogenic influences. The results revealed declining snow cover trends of 2793.15, 2108.86, 1199.49, 247.51, 230.34, and 169.39 km²/year for the Amu Darya, Indus, Yangtze, Tarim, Mekong, Ganges, and Brahmaputra River basins, respectively. Conversely, the Salween, Yellow, and Irrawaddy River basins showed a slight increase of 1002.66, 147.06, and 20.86 km²/year, respectively. These opposing trends underline the vulnerability of snow mass to water supply and hydropower scheduling, stressing the urgent need for integrated, long-term satellite and ground-based observation platforms, as well as robust hydrological and glaciological modeling. A strategic blend of remotely sensed snow cover data and locally calibrated climate and land-use records will empower decision-makers to predict water security threats and refine reservoir operation guidelines, thereby sustaining both socio-economic and ecosystem services across the principal river corridors. Full article

Global-warming-induced shifts in precipitation regimes profoundly affect terrestrial vegetation productivity; however, the pathways through which precipitation characteristics regulate gross primary productivity (GPP) remain insufficiently understood. Using meteorological records, satellite remote-sensing products, and reanalysis data from 2000 to 2024, we quantified GPP responses to changing precipitation regimes in Central Asian drylands through superposed epoch analysis, random forest and TreeSHAP analyses, and cross-lagged structural equation modeling (CB-SEM). Against a background of increasing annual precipitation (1.79 mm yr⁻¹, p = 0.014), synchronous increases in precipitation frequency and intensity enhanced dryland productivity, with increasing GPP (0.28 g C m⁻² yr⁻¹, p = 0.726). The two precipitation-frequency metrics increased across 75.10–82.46% of the study area, whereas the intensity metrics increased across 64.16–81.67%. Frequent precipitation rapidly reduced vapor pressure deficit (VPD) and maintained shallow soil moisture, producing GPP anomaly gains ~0.5 g C m⁻² 8 d⁻¹ across extensive Central Asian plains. In contrast, high-intensity precipitation replenished deep soil moisture and sustained GPP anomaly gains of 0.5–1.5 g C m⁻² 8 d⁻¹ in the semi-arid steppe of northern Kazakhstan. CB-SEM identified VPD as the strongest direct climatic driver of GPP (−0.482), followed by soil moisture (0.290) and temperature (0.283). Precipitation frequency directly reduced VPD (−0.016), whereas precipitation intensity positively affected soil moisture (0.057). The frequency-mediated atmospheric pathway was strongest during the initial t (0–8-day) response period, while the intensity-mediated soil-moisture pathway became increasingly important after 8 days. These findings reveal complementary atmospheric-regulation and soil-moisture-buffering mechanisms and provide a process-based framework for improving the representation of dryland carbon–water coupling under changing precipitation regimes. Full article

Hydrological regulation and local engineering can modify the planform dynamics of wandering rivers, but separating their effects is difficult where pre-regulation morphology and synchronous hydraulic observations are unavailable. This study integrates discharge and water-level records (2000–2023), SRTM-derived surface topography, and nine December satellite-image composites (2015–2023) for the urban Lhasa River reach between Gates 1 and 5. Within the available 24-year discharge record, Pettitt tests identify a mid-2000s distributional shift at the three stations; sensitivity tests place the detected change in 2005–2006. Because the pre-change segment contains only six years and no precipitation-based natural-flow control was available, the result is interpreted as temporally consistent with, but not proof of an effect of, Zhikong Hydropower Station commissioning. The 2013 Pangduo project is treated separately as an engineering timeline marker because no distinct annual mean breakpoint was detected at that date. Mapped water area increased by 45.39% between 2015 and 2023, whereas exposed bar–island and riparian-land classes decreased by 36.29% and 61.13%, respectively. These values are reported as a water-surface expansion signal consistent with gate impoundment and/or acquisition-stage differences, not as confirmed sedimentary or geomorphic conversion. The comparison also contains sensor-dependent positional uncertainty: approximately ±15 m for the 2015 and 2018 Landsat-8 inputs and ±5 m for Sentinel-2 inputs. Centerline length and sinuosity varied by less than 1%. The nearest-neighbor bankline metric declined by 9.53%, whereas the node-mean channel-regime-center metric declined by 81.99%; the latter is retained only as an exploratory indicator because it is sensitive to centerline-node sampling. Overall, the observations are consistent with reduced lateral activity in an engineered reach, but the available evidence does not isolate reservoir regulation, gate operation, bank protection, climate, land use, or sediment-supply effects. Stronger causal inference requires a pre-regulation morphological baseline, synchronous discharge and water-level observations at image acquisition, fixed-interval centerline resampling, sediment data, and surveyed hydraulic geometry. Full article

In the semi-arid Cilo-Sat cryosphere of Hakkari Province, Türkiye, mountain snow is the dominant freshwater store, and a sparse ground network leaves reanalysis as the only practical source of data. This study introduces CryoSENSE-HK, an explainable machine-learning framework that forecasts ERA5-Land snow cover, snow water equivalent and snowmelt one to thirty days ahead. Every predictor is observed at or before the issue date, feature ranking uses training data alone, and the held-out period is used once. The framework reproduces the reanalysis with held-out coefficients of determination of 0.93, 0.91 and 0.73, respectively, at thirty days, and is favoured in 77 of 80 block-bootstrap paired tests against eight reference forecasts. Agreement with the ground is much weaker. Station records show the reanalysis carrying two to four times the observed snow depth, and satellite retrievals place its cover too high through the melt season. A reduced monthly emulator of it responds to warming far more weakly than physically based references do: 5.4% of snow water equivalent per degree of warming, against 21.3% for a degree-day balance and 23.4% for CMIP6 snow fields. Skill measured inside a reanalysis is in agreement with the product, not with the snowpack, and a framework of this class needs independent validation of its climate sensitivity before any scenario-driven use. Full article

The development of temporally consistent long-term normalized difference vegetation index (NDVI)climate data records is essential for global change and ecosystem research. The Medium Resolution Spectral Imager-II (MERSI-II) sensor aboard China’s Fengyun-3D (FY-3D) satellite shares similar spectral characteristics with Moderate Resolution Imaging Spectroradiometer (MODIS), offering potential for synergistic applications. However, systematic biases arising from differences in sensor design, radiometric calibration, and atmospheric correction hinder their direct combination. This study established a full-chain framework that integrated cross-calibration of surface reflectance using quasi-synchronous FY-3D/MODIS observations and a MERSI-II-specific atmospheric correction scheme based on the 6S radiative transfer model. After correction, the FY-3D NDVI shows substantially improved consistency with MODIS, achieving a reduction in root mean square error of over 25.9%, an increase in correlation coefficient of approximately 5%, and a decrease in mean absolute error of about 40%. Spatial biases are within ±0.1 over most global land areas, with robust performance across vegetation types and climate zones. Based on this technical framework, a fused FY-3D and MODIS NDVI climate data record was established, which has been operationalized at the Beijing Climate Center for global vegetation monitoring. This work provides a transferable framework for integrating Chinese Fengyun satellite data with international datasets like MODIS/VIIRS. Full article

Precision agriculture (PA) has emerged as a data-driven approach for improving maize (Zea mays L.) production through the integration of remote sensing, Geographic Information Systems (GISs), Global Navigation Satellite Systems (GNSSs), the Internet of Things (IoT), and machine learning (ML). This systematic review evaluates the application of PA for yield optimisation and resource-use efficiency in maize production between 2020 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 464 records were identified from Scopus and Web of Science, of which 129 studies met the inclusion criteria. The reviewed literature comprised field experiments (28.6%), remote sensing and PA integration studies (25.5%), machine learning applications (19.4%), climate-informed PA strategies (23.4%), and soil degradation studies (3.1%). Remote sensing and integrated multi-technology systems were the most extensively investigated approaches, followed by ML-based models for yield prediction and crop monitoring. Overall, PA technologies enhanced maize productivity, nutrient-use efficiency, water-use efficiency, and yield prediction accuracy through site-specific management and data-driven decision support. Despite its considerable potential, the adoption of PA remains constrained by high implementation costs, technical complexity, and limited data availability. Collectively, these findings demonstrate that precision agriculture provides an effective framework for sustainable maize intensification by improving productivity, optimising resource-use efficiency, and strengthening resilience to climate variability. Full article

Freshwater scarcity can weaken water governance when hydrological pressure interacts with intensive agricultural demand, regulatory weakness, and political conflict. This research evaluates whether Irrigation District 005 (IR 005) in Chihuahua, northern Mexico, demonstrates local water-governance fragility across three domains: public security, the rule of law, and the ability to sustain water access and food production. A mixed-methods approach integrates legal and human rights documentation, institutional records, published studies, and a structured media review with hydrological, agricultural, climatic, and reservoir data. Water balances were analysed for 1998–2023, precipitation trends for 1980–2020, and crop water requirements were estimated using the Food and Agriculture Organization’s Irrigation and Drainage Paper No. 56 (FAO-56) Penman–Monteith framework, the crop coefficient (Kc), the water-stress coefficient (Ks), the United States Soil Conservation Service (SCS) Curve Number method, and application-efficiency assumptions. The 2020 water conflict resulted in fatalities, injuries, arrests, and documented human rights violations. Rule-of-law capacity was further diminished by unauthorised withdrawals, cultivation beyond authorised irrigation plans, and limited enforcement. The annual water balance shifted to persistent deficits after 2016, reaching an estimated deficit of 2268 cubic hectometres (hm³) in 2020. Annual precipitation did not exhibit a statistically significant monotonic decline during 1980–2020 (Mann–Kendall Z = −0.79, τ = −0.0878, p = 0.4251; Sen’s slope = −1.1628 mm yr⁻¹; Mann–Whitney p = 0.5313), indicating that recent stress is more closely linked to production scale, crop mix, governance conditions, and irrigation efficiency than to a long-term reduction in rainfall. Sensitivity analysis revealed that ±15% changes in Kc and Ks altered gross water requirements by approximately ±16–17%, while equivalent changes in effective precipitation produced changes of only 1–3%. These results demonstrate heightened water-governance fragility resulting from mutually reinforcing hydrological, institutional, and conflict-related pressures. Future research should refine locally calibrated water-demand parameters and develop reproducible monitoring systems that combine hydrological, institutional, satellite, and participatory data to support anticipatory, transparent, and rights-based water governance. Full article

This study investigates the influence of near-surface air temperature on the performance of internal combustion engines during mobile transects conducted in Mexicali, Baja California, Mexico, one of the hottest cities in North America. Field measurements were carried out along a 15 km urban transect on representative days in April, August, and February. Air temperature and relative humidity were recorded simultaneously at two engine air intake heights (0.66 m and 2.5 m), complemented by surface temperature data obtained from both in situ measurements and Landsat 8 thermal imagery. The results indicate that near-surface air temperature exhibits considerable spatial and temporal variability and is closely associated with land surface temperature (LST) patterns derived from satellite observations. The correction factor (Cf), used to quantify the combined effects of air temperature and atmospheric pressure on engine performance, showed that extremely high temperatures (approaching 50 °C) may reduce engine performance by up to 3.35% relative to standard test conditions. Conversely, cooler winter conditions may improve engine performance by approximately 4.6%. These results suggest that vehicle operation under extremely hot climatic conditions may deviate from the standardized assumptions adopted by the Intergovernmental Panel on Climate Change (IPCC) for emission factor estimation. This study contributes to a better understanding of the effects of extreme urban heat on vehicle performance and demonstrates that localized thermal conditions may influence the assumptions commonly used in vehicle emission assessments. The findings provide valuable information for improving greenhouse gas emission inventories and support evidence-based climate adaptation and urban planning strategies in arid cities. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article

Understanding habitat requirements across spatial scales is important for conserving declining migratory species such as the Cerulean warbler. This study integrates data from several forms of remote sensing platforms: automated recording units, airborne LiDAR, and satellite remote sensing, to characterize the habitat of Cerulean warbler detection sites across central Indiana. We also modeled the suitable habitat of the species across the contiguous United States. Automated recording units were deployed across four forest types, and automated classification was used to derive species detections, which were manually validated. Structural variables derived within 25 m and 50 m buffers included LiDAR-based canopy height metrics, vertical vegetation distribution, foliage height diversity, and satellite-derived Enhanced Vegetation Index (EVI). Cerulean warbler detections were identified at 14 of the 57 acoustic sensor locations. Habitat characteristics at detection and non-detection sites were compared using univariate statistical tests and logistic regression models. Habitat associations varied with spatial scale. At 25 m, detection sites had significantly lower vegetation cover within the 5–10 m height stratum, which was also the highest-ranked candidate predictor, whereas EVI was the highest-ranked predictor at 50 m. At the broad scale, occurrence records from the Global Biodiversity Information Facility (GBIF) were integrated with climatic, topographic, land-cover, and anthropogenic variables within a MaxEnt modeling framework. The model showed moderate predictive performance and identified land cover as a key predictor of habitat suitability, with deciduous forests showing the highest probability of occurrence. This study highlights how multi-modal sensor data can be integrated for biodiversity monitoring and habitat assessment. Full article

Effective conservation of the Near Threatened (NT) black-necked crane (Grus nigricollis) requires standardized frameworks for organizing multi-scale biodiversity data. This study proposes the Essential Biodiversity Variables (EBV) system as an optimal structure for archiving and sharing biodiversity data such as Habitat Suitability Index (HSI). Developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON), the EBV framework is an emerging system offering a robust solution for standardizing data exchange. Based on 483,592 valid location records of 106 black-necked cranes using satellite telemetry, we apply species distribution models and demonstrate how the multi-dimensional EBV architecture accommodates distinct life-stage preferences: breeding sites favor mid-elevations modulated by temperature; migration staging relies on precipitation regimes; and wintering grounds are driven by moisture availability and the avoidance of human-modified landscapes. The EBV NetCDF (Network Common Data Form) format functions as a self-describing hypercube that captures spatial, temporal, and life-stage dimensions while ensuring metadata transparency. This integration facilitates critical applications, including the identification of priority conservation areas and climate vulnerability assessments, thereby bridging the gap between species-specific modeling and global biodiversity monitoring standards. Full article

Urban tree mapping is necessary in environmental sustainability and climate change mitigation, and it depends heavily on the individual tree recognition and canopy segmentation to analyze city green cover. This systematic review discusses recent developments in the 2014–2026 mapping of these trees with the use of UAVs and high-resolution satellite imagery. Our preliminary selection of 4148 records reduced to 101 eligible publications following a systematic screening and synthesis of the records, assessed the efficiency of deep learning models such as Convolutional Neural Networks and Vision Transformers in processing various source images. We also compare object detection and semantic segmentation to see which one is more competent to deal with typical urban challenges, including overlapped canopies and building shadows. According to the reviewed studies, UAV-based models generally achieve higher spatial accuracy than satellite-based approaches for individual tree detection and crown delineation, with reported average Intersection over Union (IoU) values of approximately 70–75%, whereas satellite imagery provides superior spatial coverage for large-scale urban forest monitoring. Lastly, we present a research roadmap to address the existing weaknesses such as geographic bias, which propels the research direction towards multimodal data fusion and Foundation Models to sustain consistent, large-scale urban forest monitoring. Full article

Mediterranean coastal regions are increasingly affected by hydrometeorological hazards associated with high-impact weather events, including cyclones. Between 18 and 21 January 2026, the intense extratropical cyclone Harry affected Sicily, Sardinia, and Calabria, producing severe weather conditions including heavy precipitation, strong winds, and extreme wave activity. This study investigates both the meteo-marine characteristics of the event and its associated damage in Calabria, where the cyclone triggered multiple hazards (wave storms, landslides, flooding, and strong winds). Meteo-marine forcing was characterized using integrated rainfall data, wave parameters, and wind data. In situ observations, radar-derived precipitation estimates, satellite measurements, and model-based reanalysis products were combined to provide a comprehensive evaluation of the event. A georeferenced database of 195 damage records was compiled and classified according to the EU Floods Directive (2007/60/EC), allowing spatial analyses within a GIS framework. Although the cyclone produced exceptional rainfall totals, locally exceeding 580 mm in 90 h, the distribution of impacts reveals the predominance of coastal processes. Wave storm-related damage accounted for 68% of all recorded impacts, mainly affecting transportation and communication infrastructures, tourism facilities, and population. The prevalence of coastal damage appears to be linked not only to the intensity of marine forcing but also to its persistence which locally exceeded the maximum climatological persistence, suggesting that event duration plays a critical role in determining impact severity. Geomorphological analyses indicate that short-term coastal vulnerability is influenced not only by long-term shoreline evolution but also by local topographic characteristics and exposure to marine forcing. These findings contribute to improving risk assessment and mitigation strategies for Mediterranean coastal regions under a changing climate. Full article

Satellite-based remote sensing has become an essential tool for territorial monitoring and environmental analysis, particularly in the context of extreme events. The European Union’s Copernicus Programme provides open-access satellite data that support near-real-time environmental assessment. Among its missions, the Sentinel-2 satellites, equipped with the Multispectral Instrument (MSI), offer high spatial and spectral resolution through 13 spectral bands, enabling the detection and monitoring of water quality parameters in inland and coastal water bodies. In early May 2024, intense rainfall events in Rio Grande do Sul, southern Brazil, triggered one of the most severe flooding episodes recorded in the region over the past 40 years. The objective of this study is to assess the environmental impacts of this event by mapping the flooded areas and analysing changes in water quality using Sentinel-2 imagery. Sentinel-2 Level-1C (Top-of-Atmosphere) products were processed using the Copernicus Browser to evaluate variations in coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC). Temporal analysis based on true-colour composite images allowed the observation of a significant increase in water extent over a seven-day period. Temporal comparison of Sentinel-2 images acquired before, during, and after the flood consistently showed a progressive expansion of inundated areas, accompanied by spatially coherent increases in satellite-derived CDOM and DOC indicators during the flood peak, followed by a partial decrease after flood recession. Although no simultaneous field measurements were available for quantitative validation, the observed spatial–temporal patterns consistently indicate flood-induced deterioration of water quality. Elevated CDOM and DOC levels are commonly associated with increased organic matter inputs and may have implications for water availability and suitability for human and animal consumption. Given the expected increase in the frequency and intensity of extreme weather events driven by climate change, this study highlights the importance of satellite-based remote sensing for rapid environmental monitoring. The Sentinel satellite constellation demonstrates strong potential for near-real-time assessment, offering timely information and broad spatial coverage to support environmental management and decision-making. Full article

Reliable quantification of soil organic carbon (SOC) change is essential for evaluating long-term restoration trajectories and constraining the potential contribution of restored soils to atmospheric CO₂ mitigation. However, long-term SOC monitoring based exclusively on field measurements is often limited by data scarcity, spatial heterogeneity and the high cost of repeated sampling. This study assesses the potential of combining field observations, remote sensing-derived proxies and process-based modelling to simulate SOC dynamics in the Guadiamar Green Corridor, Seville, Spain, a restored area affected by the 1998 Aznalcóllar mining spill. SOC dynamics were simulated using the RothC model under a set of boundary condition scenarios, in which field-measured or locally observed input variables were progressively replaced by alternative data sources. The reference scenario was based mainly on field SOC and clay measurements and local meteorological records, while the remaining scenarios tested the use of NDVI-derived carbon inputs, satellite-derived and reanalysis-based climate variables, and SoilGrids-derived estimates of SOC and clay content. The simulations were evaluated against field observations collected over the restoration period. Overall, RothC reproduced the general direction of the surface-layer SOC decline observed in the monitoring plots, although most scenarios did not fully capture the magnitude of SOC loss measured in the field. Differences among boundary conditions highlighted the sensitivity of RothC to specific input variables, particularly initial SOC, clay content and evapotranspiration. Scenarios based on alternative soil datasets showed that global soil products can provide useful information where field data are limited, but their performance depends strongly on their agreement with local soil conditions. Similarly, the replacement of local climatic data with remote sensing or reanalysis products affected model outputs, especially when evapotranspiration was substituted. These results indicate that satellite-derived products, climate reanalysis and open-access environmental datasets can complement field measurements in SOC modelling, particularly in data-scarce restoration contexts. However, their use requires careful validation, as model accuracy depends on the variable being replaced, the quality of the proxy dataset and the calibration of RothC to plot-specific conditions. Full article