Search Results (2,751)

Global hydrological models (GHMs) are essential for assessing water resources and flood risk, yet systematic evaluations of ISIMIP3a models remain limited. We evaluated eight standard ISIMIP3a GHM configurations against observed discharge at four principal Yangtze mainstem gauges using a multi-metric framework covering daily and monthly discharge, the seasonal cycle (defined as the mean annual cycle of monthly discharge), flow percentiles, and annual peak discharge. Metric-based model performance generally increased from daily to monthly and seasonal-cycle scales, partly reflecting the smoothing of short-term errors through temporal aggregation, whereas annual peak discharge remained more difficult to reproduce. WaterGAP2-2e achieved the strongest overall performance across the daily, monthly, and seasonal-cycle evaluations. However, this result was not fully independent of its inherited global discharge calibration, because three evaluation gauges (Yichang, Hankou, and Datong) spatially coincide with gauges in its calibration-station dataset. WaterGAP2-2e also systematically overestimated peak flows, with peak-flow Relative Bias (RBpeak) reaching 30.14%. Among the remaining configurations, WEB-DHM-SG showed the strongest overall performance and a relatively small station-averaged absolute peak-flow bias (|RB_peak| = 15.52%). However, this advantage did not extend to low-flow conditions. H08 and ORCHIDEE-MICT showed the weakest overall performance, with negative NSE values down to −3.61 and peak-flow biases reaching −99.10%, whereas MIROC-INTEG-LAND, CWatM, HydroPy, and JULES-W2-DDM30 showed intermediate performance. Overall, model suitability depended on temporal scale and flow regime, and apparent rankings should be interpreted in light of inherited calibration and configuration differences. These results provide a diagnostic basis for model selection and targeted improvement in regional discharge simulation and peak-flow-related applications. Full article

Riparian land use and land cover (LULC) exerts scale- and season-dependent controls on surface water quality, yet its influence in regulated agricultural–urban rivers is poorly characterized. We combined seasonal t-tests, one-way ANOVA, and redundancy analysis (RDA) at three riparian buffer scales (500, 1000, and 2000 m) to examine discharge, dissolved oxygen (DO), temperature, turbidity, and pH at four USGS stations along the Kansas River mainstem (2019–2026). DO and temperature showed the strongest seasonal contrasts: DO was 3.1–3.7 mg/L higher in the dry season and temperature 13–15 °C higher in the wet season, a coupling central to aquatic habitat suitability. Turbidity rose significantly in the wet season, consistent with agricultural runoff and sediment mobilization, whereas discharge showed no significant seasonal difference at three of four stations, reflecting upstream reservoir regulation. Spatial ANOVA detected station-level differences only for wet-season DO ($F_{3,28}=4.91$, $p=0.007$), which was lowest at the downstream urbanized station. RDA linked agricultural cover to turbidity and urban cover to reduced wet-season DO, although permutation tests were non-significant ($p\ge 0.42$) at $n=4$ replicates. Seasonality and riparian LULC jointly shape water quality along this regulated river, and the 500 m buffer is the most spatially discriminating scale for land-cover assessment. Full article

Climate and land-use change are shifting the spatial distribution and habitat suitability of many plant species, particularly those with narrow ecological niches and limited ranges. Dracaena cochinchinensis (Lour.) S.C.Chen is a medicinally important and nationally protected plant species distributed in tropical and subtropical southern China. Predicting its habitat suitability under current and future climate scenarios is essential for understanding its responses to environmental change and guiding conservation and resource management. In this study, an optimized maximum entropy model was used to predict the current habitat suitability of D. cochinchinensis in China and to assess changes in habitat suitability under future climate scenarios in the 2090s. The model showed reliable predictive performance for estimating species distribution. Under current conditions, suitable habitats were concentrated in southern China, particularly southern Yunnan, Guangxi, Guangdong, Hainan, Taiwan, and parts of Fujian, covering 23.93 × 10⁴ km² (8.31% of the study area). Mean temperature of the driest quarter (Bio9), temperature annual range (Bio7), and precipitation seasonality (Bio15) were the dominant predictors, highlighting the importance of thermal conditions in shaping species distribution, whereas lithology (Lith) and soil type (ST) were the major non-climatic contributors influencing habitat suitability. Under future scenarios, suitable habitats showed spatial redistribution rather than uniform expansion or contraction. The spatial comparison among future scenarios identified stable habitats in the central and southwestern parts of Hainan Island, south-central Yunnan, and western Guangxi, with habitat losses mainly occurring in coastal South China and limited expansion in southwestern regions. Habitat centroid shifts were scenario dependent, with an eastward shift under SSP1-2.6 and northwestward shifts under SSP3-7.0 and SSP5-8.5, with migration distances ranging from 60.09 to 165.77 km. These results indicate that climate change may substantially reorganize the distribution pattern of D. cochinchinensis in southern China. Therefore, future conservation planning should prioritize current high-suitability areas, potential macroclimatically stable areas, and emerging suitable habitats to support long-term preservation and sustainable utilization of nationally protected medicinal species. Full article

Stored-product insects threaten global food security, yet the environmental mechanisms governing their responses to climate change remain poorly understood. Existing pest distribution projections rarely integrate diurnal thermal variability with agricultural land use. Here, we show that diurnal thermal variability, together with agricultural land use, is a major determinant of habitat suitability for three globally important Callosobruchus pests across the Middle East. Using optimized species distribution models integrating climate, topography, and cropland under contrasting CMIP6 climate scenarios, we demonstrate that mean diurnal temperature range and cropland consistently emerge as the strongest predictors across all species, revealing the importance of daily thermal fluctuations beyond mean warming alone. Under the low-emission scenario (SSP1-2.6), suitable habitat by mid-century expands substantially for C. chinensis and C. phaseoli, while remaining little changed overall for C. maculatus, for which comparable local expansion and contraction largely offset one another; under the high-emission scenario (SSP5-8.5), gains are reduced, and localized contractions occur, particularly for C. chinensis and C. maculatus, the latter shifting to a slight net loss in total suitable area. Persistent climatic refugia remain along Mediterranean and Red Sea coastal regions, whereas habitat losses are concentrated in the northern Gulf lowlands and Zagros foothills. Our findings identify diurnal thermal variability as an overlooked dimension of stored-product pest ecology and show that integrating agricultural landscapes with climate projections can improve forecasts of future pest risk, providing a framework for climate-informed surveillance, biosecurity, and adaptation. Full article

Ensuring the long-term sustainable utilisation of supplemented cropland has become a critical challenge for implementing China’s requisition–compensation balance of farmland (RCBF) policy, particularly in the fragmented hilly and mountainous regions of Southwest China. Existing studies generally evaluate land suitability and utilisation performance separately, making it difficult to identify mismatches between theoretical suitability and actual utilisation and thereby limiting targeted regulation. To address this limitation, this study proposes a suitability–utilisation deviation identification (SUDI) framework, which integrates four sequential analytical components: three-dimensional suitability assessment, suitability–utilisation deviation identification, driving mechanism analysis, and sustainable regulation. Taking Beibei District of Chongqing as a case study, supplemented cropland parcels were identified using the 2020–2024 land change survey data. A three-dimensional suitability evaluation system incorporating production, ecological, and utilisation attributes was established to quantify theoretical land suitability. Actual utilisation performance was characterised using the land economic utilisation coefficient, and suitability–utilisation deviation was identified through residual analysis between theoretical suitability and utilisation intensity. A Bayesian-optimised Extreme Gradient Boosting-SHAP (XGBoost-SHAP) model was subsequently employed to reveal the nonlinear effects and interaction mechanisms of the driving factors. The results indicate the following: (1) supplemented cropland in Beibei District is predominantly characterised by medium-to-high suitability, with high-suitability patches exhibiting a mosaic spatial pattern of local aggregation and overall dispersion; (2) suitability–utilisation deviation is dominated by under-utilised plots, whereas well-matched and over-intensified plots account for substantially smaller proportions, indicating that insufficient realisation of land suitability is the prevailing utilisation pattern; and (3) the land economic utilisation coefficient is the dominant factor driving suitability–utilisation deviation, while high-standard farmland construction and plot area exhibit significant mitigating effects. Moreover, significant interaction effects between utilisation intensity and location-related variables reveal that unfavourable spatial conditions amplify deviation risk under intensive land use. The proposed SUDI framework extends conventional suitability assessment by explicitly linking suitability evaluation with utilisation performance, driving mechanism analysis, and differentiated regulation. It provides a transferable analytical framework for diagnosing suitability–utilisation mismatch and supports dynamic management and sustainable utilisation of supplemented cropland in fragmented hilly and mountainous regions. Full article

This study analyzes land use dynamics in Fălticeni Municipality over four decades (1985–2025), examining the influence of major political, socio-economic, and demographic changes on urban development. The transition from a centrally planned economy to a market economy, Romania’s democratic transformation and European integration, together with migration and population dynamics, have driven largely unregulated urban expansion at the expense of agricultural land and natural landscapes. Recent urban growth has also extended into areas with varying geomorphological vulnerability, increasing exposure to landslide hazards, while the city continues to face challenges related to abandoned industrial areas and insufficient forested land and green spaces. By integrating land use change analysis with physical vulnerability indicators, this study highlights the need for risk-informed and sustainable urban planning in medium-sized cities. It proposes a planning framework based on ecological zoning, controlled urban expansion on suitable terrain, brownfield redevelopment, the establishment of peri-urban forests, and the implementation of essential infrastructure supported by comprehensive geomorphological susceptibility assessments. The proposed approach provides practical guidance for enhancing urban resilience and can be replicated in other cities facing similar environmental and development challenges. Full article

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Climate change is expected to significantly alter surface air temperature and precipitation regimes across the Amazon Basin, with direct implications for agroforestry systems and climate-sensitive perennial crops. This study projects the impacts of anthropogenic global warming on the habitat suitability of Theobroma grandiflorum (cupuaçu), a keystone species for Amazonian agroforestry, local livelihoods, and the bioeconomy. Using an ensemble species distribution modeling (SDM) framework, we applied multiple algorithms and two CMIP6 global climate models (BCC-CSM2-MR and MIROC6) under the SSP3-7.0 and SSP5-8.5 scenarios for the near-future (2021–2040) and far-future (2061–2080). Contrary to the range contractions projected for many Amazonian endemics, our ensemble projections reveal a consistent trend of expanding climatically suitable area, primarily into adjacent ecological transition zones. This expansion is driven largely by basin-wide increases in mean annual temperature, the most influential predictor in our modeling study. Quantitatively, we project a net habitat expansion of 32.2% (415,114 km²) under high-concordance future scenarios. A core climate refuge of 955,570 km², representing 74.2% of the current suitable range, is identified as a high priority for in situ conservation. Conversely, approximately 25.8% of the current range (331,919 km²) may become climatically unsuitable, particularly in parts of the western Amazon. These findings suggest that reducing thermal constraints in currently marginal areas could open opportunities for integrating cupuaçu into climate-resilient agroforestry systems beyond its present distribution. Such heterogeneous responses of Amazonian agroforestry species to climate change highlight the importance of incorporating species-specific climate sensitivities into adaptation planning, land-use strategies, and climate-resilient bioeconomy policies under future warming scenarios. Full article

Climate change is expected to alter the climatic suitability of crops. This study evaluated the climatic suitability of maize cultivation in the TR21 Thrace Region of Türkiye using the EcoCrop model. Climatic suitability was assessed for a reference period (1950–2000) and projected for the 2050s using three CMIP5 global climate models (MPI_ESM_LR, HADGEM2_ES, and CNRM_CM5) under the RCP4.5 and RCP8.5 scenarios. The EcoCrop model implemented in DIVA-GIS was used to evaluate climatic suitability. All climate models projected increases in average annual temperature of 1.7–3.8 °C, whereas projected changes in average annual precipitation ranged from −92 to +46 mm. Despite variations in temperature and rainfall forecasts, the projections indicate that the area suitable for maize cultivation will increase; the HADGEM2_ES model produced the highest proportion of suitable areas. The suitability rate across all projections ranged from 54.7% to 96.8%. Projected climate change is likely to improve maize climatic suitability in TR21 by the 2050s. Because EcoCrop evaluates climatic suitability based solely on temperature and precipitation thresholds, the projected changes should be interpreted as a climatic envelope for maize rather than as a direct increase in future yield or productivity. These findings inform agricultural adaptation strategies and regional land-use planning under climate change. Full article

Geological conditions fundamentally constrain land-use suitability, terrain stability, and long-term sustainability, yet they remain underutilized in urban planning frameworks. This study presents a GIS-based geological suitability assessment model, tested on the City of Valjevo, Serbia, integrating lithological, structural, land use/land cover change and spatial data to guide urban development decisions. Seven lithological units—Quaternary deposits, lacustrine sediments, carbonate rocks, volcanic and pyroclastic rocks, ophiolites, ophiolitic mélanges, and Jadar Block sediments—were classified into four suitability classes based on lithological composition, structural characteristics, and rock mass behavior. An Integrated Geological Index (Igeo) was derived using an area-weighted approach across a regular hexagonal grid, enabling spatially explicit suitability mapping. Comparison with land use/land cover changes for 2012–2021 revealed a notable mismatch between geological suitability and observed development patterns: favorable and conditionally favorable terrains cover 72.5% of the study area but account for only 48.7% of recent urban expansion, while unfavorable terrains, occupying just 20.9% of the territory, absorbed 51.3% of new development. No expansion occurred within highly unfavorable terrains. These findings expose critical gaps in integrating geological criteria into planning practice and demonstrate a reproducible methodology for embedding geological suitability into sustainable urban development strategies across geologically heterogeneous regions. 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

Selecting suitable agricultural land is critical for food security and sustainable development, particularly in arid regions facing resource scarcity and environmental degradation. This study assesses agricultural land suitability in Sohag governorate, Egypt, using a hybrid Geographic Information System (GIS), Fuzzy Analytical Hierarchy Process (FAHP) and geostatistical approach. Thirty-four representative soil profiles were morphologically described and analyzed for ten physical (slope, depth, erosion, texture, stoniness, drainage) and chemical (EC, pH, CaCO₃, organic matter) criteria, weighted using both the Analytical Hierarchy Process (AHP) and FAHP. Geostatistical analysis characterized the spatial variability in soil properties across the study area. The two suitability maps were validated using Receiver Operating Characteristic (ROC) curves and Kappa statistics: the FAHP achieved higher prediction accuracy (AUC = 0.83, Kappa = 0.91) than the AHP (AUC = 0.81, Kappa = 0.88). The AHP-based map classified 40% (1154.92 km²) as moderately suitable (S2), 33% (952.81 km²) as marginally suitable (S3), and 27% (779.57 km²) as unsuitable (N); the FAHP-based map classified 42% (1212.67 km²) as S2, 30% (866.19 km²) as S3, and 28% (808.44 km²) as N. The proposed framework may serve as a reference for similar arid environments and support progress toward Sustainable Development Goals 2, 6, 8, 9, 11, 13 and 15, conditional on local soil, water, climate and management conditions. Full article

Transport decarbonization through electric mobility depends not only on how many charging stations are deployed but where, and whether expansion balances accessibility, grid readiness, land-use protection and regional equity. Türkiye, targeting net-zero by 2053 with electric car sales exceeding 10% of the market in 2024, shows a highly uneven charging network: provincial provision ranges from 9.0 to 155.6 points per 100,000 inhabitants, with the least-served half of the population holding only 22.3% of installed capacity (Gini = 0.311). This study develops a GIS-based multi-criteria framework treating charging expansion as a sustainability-constrained planning problem. Six criteria, namely population, GDP, transformer and transmission-line proximity, road-network proximity, and city-centre proximity, were harmonized to a 100-m grid via fuzzy membership functions, with an exclusion mask protecting sensitive land uses. Three weighting scenarios were compared: equal weights (EVCSI-A), Random Forest-derived weights (EVCSI-B), and expert AHP weights (EVCSI-C). Road accessibility (41.12%) and economic capacity (29.84%) dominated existing placement, explaining ~71% of feature importance, stable across algorithms and bootstrap replicates. National results reveal an efficiency–equity trade-off: EVCSI-B concentrates suitability in metropolitan corridors, EVCSI-A preserves broader coverage, and EVCSI-C reinforces metropolitan bias. Central and Eastern Anatolia remain underserved. We recommend sustainability-constrained screening followed by grid-capacity verification, positioning EVCSI as a transferable equity-monitoring tool supporting SDG 7, 9, 11 and 13. Full article

High-precision time-varying terrestrial absolute gravity observations provide a powerful tool for investigating mass redistribution processes within the Earth’s interior and at its surface. The southeastern margin of the Tibetan Plateau is a tectonically active region with frequent strong earthquakes, making accurate interpretation of local gravity observations critically important for earthquake prevention and disaster mitigation. Based on four epochs of absolute gravity measurements acquired using FG5(X) gravimeters at the Dongchuan station from 2017 to 2022, combined with co-located continuous GNSS vertical deformation data, a GLDAS Noah Land Surface Model, high-resolution remote sensing imagery, and forward numerical modeling, we performed quantitative corrections for crustal deformation and hydrological effects and analyzed the residual gravity variations. The results show that gravity change at the Dongchuan station exhibited an overall increasing trend with a maximum amplitude of 8.2 μGal during 2017–2022. While the 2017–2019 gravity changes were consistent with the combined effect of crustal vertical deformation and hydrological loading within uncertainty, a positive residual anomaly of several microGal remained after 2019, which was attributed to mass redistribution caused by nearby anthropogenic activities. This study confirms that the 2017–2022 Dongchuan observations meet the requirements for milliGal-level static gravity field research, with the 2017–2019 data suitable for time-varying gravity studies, while the post-2019 data require that gravity variations induced by anthropogenic activities in the vicinity of the station be accounted for when applied to such studies. Additionally, time-series high-resolution remote sensing can effectively identify hundred-meter-scale gravity disturbance sources near observatories, providing valuable support for gravity data interpretation and quality control. Full article