Search Results (337)

Accurate Land Use and Land Cover (LULC) mapping in high-resolution alpine environments is challenging due to complex terrain, heterogeneous vegetation, seasonal snow and ice cover, and the limited spectral information provided by conventional aerial imagery. Although foundation models such as the Segment Anything Model (SAM) effectively capture structural features, their class-agnostic design, limits fine-grained semantic discrimination and typically requires large annotated datasets. This study proposes a multi-modal deep learning framework for alpine LULC mapping using sparse annotations, which would fall under the category of weakly supervised learning. The framework employs a dual-encoder architecture that integrates RGB imagery, six-band multispectral imagery, and custom adapters for spectral indices, and Digital Surface Models (DSMs). A SAM-based encoder extracts geometric and contextual features from RGB imagery, while a dedicated encoder learns complementary spectral representations from multispectral data. To address the boundary uncertainty introduced by sparse supervision, we propose post inference hybrid refinement strategy that combines a Canopy Height Model (CHM) derived from the DSM to improve tree crown delineation with edge-based refinement for low vegetation classes, such as shrubs, and mathematical methods to refine road and building edge delineation with DSMs. Experimental results across fourteen alpine classes highlight that the framework achieves a validation mIoU of 0.777, macro-averaged over the fourteen classes. For the present sensor choice and classification scheme, no comparable multi-modal baseline exists. Thus, results are reported in absolute terms. The present multi-modal data fusion sets the baseline for future scalable alpine LULC mapping applications. Full article

Urban heat islands pose increasing risks to human settlements, yet the differential mechanisms by which grey–green spaces regulate diurnal and nocturnal land surface temperature across local climate zones remain insufficiently understood. This study addresses this gap through a Hangzhou case study, integrating a ten-indicator grey–green space system with two local spatial identification approaches—K-means clustering and an LCZ-inspired simplified scheme—and a Random Forest-SHAP framework. The LCZ-inspired scheme outperformed K-means clustering, with a mean diurnal–nocturnal Test R² of 0.4344 across twelve models, compared to 0.2977 for K-means. Diurnal and nocturnal LST were driven by systematically different factors: building density dominated daytime LST in most LCZ types (22.0% to 27.8%), while canopy height dominated nighttime LST (22.7% to 30.2%), revealing a systematic shift from building-dominated daytime to vegetation-dominated nighttime. This shift did not occur in compact built-up areas, suggesting that built-up density may be a threshold condition for the shift. Key variables exhibited nonlinear threshold effects with saturation points varying by LCZ type: canopy height cooling saturated at approximately 4 m in LCZ2 but required 17–21 m in LCZ3 and LCZA. These SHAP-based patterns and turning points should be regarded as exploratory, sample-dependent associations evaluated within the training data; their spatial stability across held-out regions was not assessed. Factor interactions were interval-dependent rather than globally fixed. Spatial cross-validation confirmed that random splitting substantially overestimated model performance, highlighting the necessity of spatially explicit validation. The methodological framework provides a replicable approach for urban thermal environment research and offers LCZ-specific threshold hypotheses for thermal regulation planning in subtropical megacities, subject to further spatial and cross-city validation. 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

Simulation of the surface energy balance (SEB) is essential for land–atmosphere coupling and weather–climate prediction, yet land surface models remain uncertain in turbulent and ground heat fluxes. We evaluated CoLM2024 with Land Cover Type (LCT) and Plant Community (PC) schemes and ECLand v1.0 against energy-balance-corrected observations from 80 PLUMBER2 towers spanning 11 land cover types. Observations and simulations were decomposed at 30 min, daily, and monthly scales. All experiments reproduced net radiation well, whereas ground heat flux was poorly simulated over forests and wetlands with excessive daytime amplitude. ECLand achieved the best latent heat flux performance through smaller systematic errors. PC reduced unsystematic errors, but this benefit was offset by a large negative growing-season bias. Sensible heat flux performance varied among land-cover classes: PC performed best over evergreen needleleaf and mixed forests, while ECLand was superior over broadleaf forests and had the lowest unsystematic errors, although with damped variability at all timescales. Performance was scale-dependent: ECLand was favored at 30 min, PC was competitive for daily forest sensible heat correlations, and no consistent ranking emerged monthly. These results indicate that complex canopy schemes require robust formulations, improved heat-storage, and careful parameter calibration to translate process detail into better flux simulations. Full article

Global Navigation Satellite Systems (GNSS) signals are delayed by the atmosphere, and the resulting zenith total delay (ZTD) provides valuable information on atmospheric water vapour for numerical weather prediction (NWP). Despite the demonstrated benefits of GNSS ZTD assimilation, the southern part of the Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp) domain remains sparsely observed, particularly along the main southwesterly moisture-transport pathway into Fennoscandia. This study investigates the impact of assimilating additional ZTD observations from northern Germany—provided by the Helmholtz Centre for Geosciences (GFZ)—into the MetCoOp system. By extending ZTD coverage upstream of the forecast domain, the study addresses a documented gap in previous MetCoOp assimilation research, which has largely focused on densely observed regions or event-specific cases. Since moisture transport into Fennoscandia is climatologically dominated by southwesterly flow from the North Sea, particularly during summer, strengthening ZTD coverage in the southern MetCoOp domain therefore provides critical upstream constraints on humidity and temperature advection. Using the convection-permitting High-Resolution Limited Area Model—Applications of Research to Operations at Mesoscale (HARMONIE–AROME) model coupled with the Surface Externalisée scheme (SURFEX), two experiments were run for June–August 2025: a baseline configuration and one including GFZ ZTDs. Assimilating GFZ data significantly refines the mid-to-lower-tropospheric moisture field (500–925 hPa). Although domain-averaged differences remain modest—specific humidity variations of ~1.3 g kg⁻¹ and temperature deviations near 1 K—spatial analyses reveal sharper moisture gradients and improved boundary-layer structure over land. These findings demonstrate that enhanced upstream ZTD density provides valuable constraints on moisture advection and latent-heat-related processes, thereby improving short-range humidity analyses in a high-resolution NWP system and complementing earlier studies conducted in Fennoscandia. Full article

The Lower Kura Basin is a tectonically active sedimentary region of Azerbaijan where geological and hydrogeological conditions may favor geothermal fluid circulation. This study develops a Geographic Information Systems (GIS)-based multi-criteria framework for regional geothermal favorability mapping by integrating five spatial criteria: land surface temperature (LST), fault density, slope, drainage density, and elevation. LST was derived from cloud-masked Landsat 9 Collection 2 Level-2 surface-temperature products acquired during summer 2025, while the topographic and hydrological criteria were prepared using a 30 m digital elevation model. All thematic layers were standardized to a common 30 m grid and reclassified into five favorability classes using explicitly defined classification rules. Criterion weights were determined using the Analytic Hierarchy Process (AHP), and the resulting pairwise comparison matrix yielded a consistency ratio of 0.024, indicating acceptable internal consistency. LST and fault density received the highest weights because they represent surface thermal expression and potential structural controls on subsurface fluid circulation, respectively, whereas slope, drainage density, and elevation were treated as secondary or indirect criteria. The baseline model identified 3681.9 km², corresponding to 27.71% of the study area, as having high or very high geothermal favorability. Two alternative weighting scenarios were subsequently evaluated to assess the sensitivity of the model to criterion weights. Despite substantial changes in the weighting scheme, 3624.0 km² (27.27% of the study area) consistently remained within the high or very high classes across all three scenarios and were therefore interpreted as robust geothermal favorability zones. Geological consistency was assessed through spatial comparison with regional geological evidence and mapped mud-volcano occurrences, while faults were not treated as an independent validation dataset because fault density was included as a model criterion. The proposed framework supports the identification of priority areas for further geothermal investigation and may contribute to the broader development of clean and renewable energy resources in Azerbaijan. The identified zones represent priority areas for further geological, hydrogeological, geophysical, geothermal-gradient, and exploratory investigations rather than confirmed geothermal resources. Full article

Arid mountain–oasis–desert regions exhibit strong terrain and surface-cover heterogeneity that is difficult to represent using conventional land-cover classification schemes. This study developed an ecosystem-oriented hierarchical framework for the Kashi region by combining a terrain-constrained Mountain extraction method with Automatic Deep Forest Shrinkage model (ADeFS). Nine ecosystem elements were defined: Mountain, Water, Forest, Cropland, Lake, Grassland, Desert, Ice, and Human. Mountain was first delineated as an independent physiographic element using a locally derived baseline surface, relative relief, slope, and topographic position, thereby reducing semantic overlap between terrain units and spectrally similar surface-cover classes. ADeFS was then adapted to classify the seven non-mountain classes, and Lake was subsequently separated from the unified Water class through visual interpretation. The results show that ADeFS achieved the highest accuracy, with an overall accuracy of 88.7% and a Kappa coefficient of 0.868. Independent field validation of the 2026 map yielded an overall accuracy of 86.2% and a Kappa coefficient of 0.825. From 2015 to 2026, the mountain-oasis-desert structure remained broadly stable, while Desert and Ice decreased and Forest, Grassland, and Cropland expanded. Ecosystem-element transitions were concentrated before 2021 and weakened thereafter. Landscape metrics showed that Desert remained the dominant matrix, Grassland had the highest patch density and edge density, and Cropland became increasingly aggregated within oasis agricultural areas. The framework provides an ecologically interpretable approach for ecosystem-element mapping and long-term monitoring in arid heterogeneous regions. 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

Soil moisture is a key forecast variable of land surface models. Direct assimilation of microwave brightness temperature data to optimize soil moisture initial fields is an effective approach to improve the simulation accuracy of soil moisture. However, most existing direct assimilation methods adopt physical radiative transfer models as observation operators, and their complex parametric errors greatly restrict the improvement in assimilation performance. This study introduces a high-precision MLP (Multilayer Perceptron)-based surrogate radiative transfer model as the observation operator. Combined with the Simplified Extended Kalman Filter (SEKF), it develops a direct radiance data assimilation system for the Common Land Model (CoLM). Assimilation experiments are conducted using brightness temperature data from the Microwave Radiation Imager (MWRI) onboard the FY-3D satellite. Their performance over China’s land areas is systematically assessed through comparison with the assimilation scheme based on the Community Microwave Emission Model (CMEM). The results show that the MLP-based assimilation scheme can effectively improve soil moisture simulation accuracy, yet the improvement varies across vegetation types: grassland areas achieve the largest error reduction (10.2%), while semidesert areas present the most prominent increase in the correlation coefficient (53.9%). Compared with the CMEM scheme, the MLP scheme exhibits better error stability and produces generally improved assimilation effects; specifically, in semidesert areas, the error decreases by 9.4%, and the correlation coefficient increases by 62.8%. This study demonstrates that deep learning-based observation operators have strong application potential for land surface data assimilation under complex physical mechanisms. Full article

Accurately simulating the spatiotemporal distribution of global atmospheric CO₂ remains challenging yet essential for reducing uncertainties in carbon source-sink inversions, quantifying the climate effects of heterogeneous CO₂ fields, and supporting the development of CO₂ observation networks. In this study, we simulated global atmospheric CO₂ concentrations (2010–2019) at a horizontal spatial resolution of 1° × 1° using the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) without data assimilation, with initial fields and flux data from the CarbonTracker CT2022 (CT2022) reanalysis product. The simulations were comprehensively evaluated against CT2022 and observations from ground-based (NOAA GML), airborne (ObsPack), and satellite (OCO-2) platforms. The results indicate that across all evaluated surface stations, CT2022 exhibits poorer overall statistical performance (R = 0.69, RMSE = 5.37 ppm, MB = 2.05 ppm) primarily due to noticeable overestimations at unassimilated ground stations, while IAP-AACM maintains robust performance across the surface network (R = 0.84, RMSE = 2.62 ppm, MB = 0.28 ppm). Vertically, airborne observations across eight global campaigns confirm that IAP-AACM accurately reproduces the vertical distribution of CO₂, maintaining strong correlations (R = 0.72–1.00) and performance comparable to the CT2022 reanalysis (R = 0.86–1.00). In terms of total column CO₂ concentrations (XCO₂), IAP-AACM exhibits strong agreement with satellite retrievals annually (R = 0.97, RMSE = 1.08 ppm, MB = 0.26 ppm), with seasonal metrics remaining consistently robust across all four seasons (R = 0.96–0.97, RMSE = 0.98–1.20 ppm, MB = 0.13–0.37 ppm), demonstrating large-scale transport fidelity on par with the CT2022 reanalysis. Finally, across representative ObsPack land sites, unassimilated IAP-AACM achieves a high median correlation (R = 0.97), low error (RMSE = 2.01 ppm), and low mean bias (MB = −0.45 ppm), closely approaching the assimilated CT2022 reanalysis product (R = 0.98, RMSE = 1.40 ppm, MB = −0.07 ppm). Further analysis indicates that the optimized IAP-AACM exhibits robust performance under stable boundary layer conditions, where the revised diffusion scheme produces higher vertical diffusion coefficients that help mitigate excessive near-surface CO₂ accumulation during nighttime. Overall, the optimized IAP-AACM effectively simulates the spatiotemporal distribution of global atmospheric CO₂, serving as a reliable tool to support advanced research. Full article

Traditional Instrument Landing Systems (ILSs) suffer from inherent limitations, including limited signal coverage and high maintenance costs. To address these issues, we propose a novel approach procedure design method based on the BeiDou Satellite-Based Augmentation System (BDSBAS), which is particularly suitable for airports with complex terrain conditions or no ground-based navigation infrastructure. In this method, a geometric constraint model of the approach trajectory is constructed in a local Cartesian coordinate system, and obstacle clearance performance is evaluated based on the Obstacle Assessment Surface (OAS) theory. Global Navigation Satellite System (GNSS) observations and BDSBAS augmentation data are processed collaboratively to calculate aircraft position solutions, as well as the corresponding horizontal and vertical protection levels, enabling comprehensive evaluation of the navigation system’s accuracy, integrity, continuity, and availability. The feasibility and performance of the proposed method are verified through practical airport deployment and dynamic flight tests. Experimental results show that the 95th-percentile horizontal and vertical position errors reach 1.31 m and 4.17 m, respectively, and all valid observation epochs fully comply with the protection level and alert limit specifications. Compared with the conventional ILS-based scheme, the proposed method reduces the total OAS area and the missed-approach Z-surface area by 44.25% and 67.90%, respectively. The findings demonstrate that BDSBAS can effectively support approach procedure design and guarantee high-precision navigation performance, while significantly reducing the reliance on airport-specific ground navigation infrastructure. Full article

Improving the flood adaptability of urban waterfront spaces is an essential entry point for enhancing regional stormwater regulation capacity, scientifically preventing flood disasters, and stabilizing urban water security. Existing flood adaptability assessments mostly rely on single weighting methods and individual evaluation models, inevitably causing systematic bias and low result robustness. Against this limitation, this study integrates remote sensing intelligent interpretation, spatiotemporal landscape pattern analysis, and multi-criteria decision theory to construct a comprehensive flood adaptability evaluation system under the pressure–state–response (PSR) framework. Innovatively, a game-theoretic combined weighting scheme integrating the entropy weight method, CRITIC method, and standard deviation method is proposed, and three complementary models including TOPSIS, VIKOR, and EDAS are coupled for cross-verification evaluation, which effectively improves the objectivity and robustness of spatial flood adaptability quantification. Taking Anqing City as a typical case, this study adopts Sentinel-2 time-series remote sensing images from 2016 to 2023 and applies an optimized random forest algorithm to automatically classify land cover. Five underlying surface types, including water bodies, vegetation, farmland, built-up areas, and bare land, are accurately extracted with an overall classification accuracy of around 90% for most years. Core landscape metrics such as Shannon’s diversity index and patch density are selected to systematically analyze the spatiotemporal differentiation characteristics of waterfront landscape patterns during the study period. The results indicate the obvious spatial heterogeneity of flood adaptability in Anqing City. Yingjiang District and Yuexi County present high comprehensive flood adaptability, while Wangjiang County and Huaining County show relatively low performance. Urban areas gain strong flood resistance from complete disaster prevention infrastructures and economic resilience; mountainous areas possess natural advantages in flood retention and drainage due to high vegetation coverage and topographic relief; by contrast, plain districts are severely restricted by low-lying terrain and insufficient drainage systems, resulting in prominent flood vulnerability. The proposed method is helpful for providing reliable scientific support for waterfront landscape optimization, zoned flood disaster management, and resilient water space planning in riverine cities. Full article

Urban heat and extreme-temperature risks increasingly constrain sustainable planning in semi-arid grassland towns. Taking Xi Ujimqin Banner as a case study, this research develops a remote-sensing-informed diagnosis-to-allocation framework for heat-resilient spatial planning. The framework first uses remote-sensing-derived land surface temperature (LST) and multi-source spatial indicators to identify model-explained associations between LST and ecological, morphological, land-use, accessibility, and socioeconomic variables. A CatBoost model with SHAP, PDP, and ICE interpretation was applied to explain dominant LST-related patterns. Spatial units were then classified by integrating LST intensity, sky view factor (SVF)-related spatial openness, land-use structure, ecological sensitivity, and economic output characteristics. Finally, a Quasi-oppositional Learning and Levy-flight enhanced Cheetah Optimization Algorithm (QLA-COA)-based constrained optimization model was used to generate alternative allocation schemes. In the framework, LST reduction and land-development economic benefit were treated as the two primary planning objectives, while SVF-related openness was incorporated as an auxiliary spatial form and radiative geometry indicator rather than as a direct temperature-control objective. Results show that CatBoost achieved the best LST prediction performance (R² = 0.834, RMSE = 1.762 °C, MAE = 1.371 °C). Cooling-priority, openness-priority, economic-priority, balanced-development, and ecological-restricted units accounted for 18.7%, 15.8%, 12.9%, 26.9%, and 25.7% of all units, respectively. The balanced-development scheme reduced mean LST by 1.30 °C while maintaining SVF-related spatial openness and improving economic benefits. This study provides a practical decision-support framework for climate-adaptive spatial governance in semi-arid grassland regions by linking LST diagnosis, spatial response translation, ecological constraints, auxiliary openness evaluation, and allocation-scheme comparison. The source code and processed spatial unit dataset used for model training, SHAP interpretation, and PDP/ICE visualization are publicly available. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article