Search Results (183)

Salinisation and metal contamination are among the leading causes of arable land loss worldwide, yet halophytes and metallophytes—the plants best adapted to these conditions—remain a considerably underexploited biotechnological resource. Using an eco-evo-devo perspective, in which environmental pressure, developmental plasticity and evolutionary outcome interact to shape adaptive traits, we examine evidence that salinity and metal tolerance arose independently and repeatedly across distant angiosperm lineages, converging on comparable structural and physiological solutions: succulence, anatomical transport barriers, root exudation, osmoprotection, and ion compartmentalisation. This convergence distinguishes stress-tolerance mechanisms that are general from those that are stressor-specific, informing efforts to transfer these traits into other extremophytes and conventional crops. In turn, we assess the ecological roles of halophytes and metallophytes in their natural habitats before evaluating their biotechnological applications, which range from halophyte-derived genes and promoters for crop improvement to halophyte biomass for bioenergy, biomaterials and remediation of saline, polluted soils and wastewaters, while metallophytes underpin phytoremediation, phytomining, and biomonitoring of metal-contaminated sites. Notwithstanding this progress, wider exploitation remains limited by the scarcity of crop-relevant gene-editing platforms for halophytes, low biomass yield in metal-hyperaccumulating species, and inconsistent phytochemical standardisation across growing conditions, as well as by three unresolved gaps: the lack of methods to partition host and microbiome contributions to tolerance, uncertainty over whether mechanisms shared between the two groups are convergent or evolutionarily conserved, and the undetermined contribution of epigenetic inheritance to stress adaptation. Translating this evolutionary and physiological knowledge into stress-adapted cultivars, optimised extraction systems, and field-ready phytoremediation and phytomining programmes is central to addressing land degradation, food security, and sustainable resource recovery. Full article

In crisis times, traditional models of water quality assessment and water resources management lose their effectiveness. In the current conditions of local climate change, accidental pollution, emergencies or military operations, there is an urgent need to transition from traditional descriptive hydrochemical monitoring toward intelligent analysis of spatial-temporal datasets. In this paper, the integrated approach combining spatial cluster analysis, GIS-based visualization, and machine learning is proposed for assessing the surface water quality under conditions of limited and incomplete hydrochemical monitoring data. A geospatial assessment of nutrient impacts on surface water quality was conducted using 192 hydrochemical observations collected during the 2024–2025 monitoring period at eight state monitoring stations located in the basins of the Teteriv, Uzh, Irsha, Ubort, Sluch, Hnylopiat, and Voznia rivers, Polissia, Ukraine. Permutation feature importance analysis based on the Random Forest model showed that nitrate concentration accounted for approximately 75% of the total relative importance, whereas phosphate concentration contributed approximately 14%, indicating that these variables were the most informative predictors among the investigated hydrochemical parameters. The latter parameters are associated with dissolved oxygen variability among the analyzed hydrochemical parameters. According to the results of this study, three interpretable groups of monitoring stations were formed: Cluster 1, representing moderate water quality with increased nutrient pressure, Cluster 2, representing comparatively favourable background conditions, and Cluster 3, representing a nitrate-dominated hydrochemical type. The Random Forest model demonstrated limited predictive performance (R² = 0.154), indicating that nutrient-related variables alone explain only a small proportion of dissolved oxygen variability. Hence, additional factors, including hydrological conditions, water temperature, organic matter decomposition, biological productivity, and catchment-specific characteristics, also play an important role in shaping oxygen dynamics. The spatial visualization of cluster membership showed that geographical location alone does not fully determine the surface water quality patterns in Ukrainian Polissia. Instead, the local catchment characteristics and land-use conditions appear to exert a stronger influence on the formation of nutrient-related water quality differences. The authors propose to employ the spatial cluster analysis and machine learning as a basic supporting tool for the transition from retrospective interpretation of hydrochemical monitoring data to predictive and adaptive water resources management. The integration of geospatial analysis and machine learning provides a practical decision-support framework for the early detection of anomalies, identification of potential pollution sources, and prioritization of river sub-basins for implementing nature-based solutions. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article

Droughts represent a major threat to water security and agricultural sustainability in the arid regions of Central Asia. The Balkhash–Alakol water management basin is particularly vulnerable due to its complex runoff formation, dependence on surface water resources, and the transboundary nature of the Ile River, the main inflow to Lake Balkhash. This study analyzes hydrometeorological data for 1950–2023 using the Standardized Precipitation Evapotranspiration Index (SPEI), Streamflow Drought Index (SDI), and Surface Water Supply Index (SWSI). Satellite-based indicators, including the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), were used to derive the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) for 2002–2023. Results indicate a significant increase in drought frequency and duration since the 1990s, with SPEI values reaching −2.6 during extreme events. Satellite observations revealed widespread vegetation degradation, with drought-affected areas (VHI < 30) exceeding 60% of the basin in severe years. Strong correlations between meteorological and hydrological drought indices (r up to 0.9) highlight the importance of cumulative moisture deficits. The results indicate an increase in the region’s aridity and the manifestation of drought in climatic, hydrological, and ecosystem changes. The practical significance of this study lies in the potential to use these results to improve drought monitoring and early warning systems, assess water risks, and develop measures for adapting to climate change and making decisions regarding water resource management in the Balkash–Alakol water management basin. Full article

Agricultural intensification in the steppe zone of Central Asia has increasingly exacerbated the tension between food security and the conservation of natural ecosystems. This study proposes an integrated landscape planning methodology for Kazakhstan by adapting the German Landschaftsplanung approach and the Chinese concept of “ecological red lines” to the conditions of post-Soviet land use. The focus is on the analysis of soil degradation and biodiversity loss. It is applied to the Ayyrtau district of the North Kazakhstan region, an area characterized by a heterogeneous landscape mosaic composed of arable land, a substantial share of degraded land, vulnerable steppe ecosystems, and woodlands. Using GIS analysis and remote sensing data, we identify nine types of landscape units, which are operational units for evaluating landscape functions and planning priorities. The result of this work is a map of the zoning of the planning area, which defines seven modes of eco-oriented management, ranging from strict protection to active agricultural production. The study demonstrates that the transition from an extensive monocultural system to a landscape-adaptive strategy can improve the spatial coordination between agricultural use and ecological protection, strengthen the regional ecological framework, and enhance ecotourism potential. For the first time, an integrated zoning system is presented, designed for use by local authorities as a decision support tool aimed at preventing land degradation. Full article

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article

The Agro-Pastoral Transitional Zone in northern China is a typical ecologically fragile area highly sensitive to climate change. Understanding how future climate change will affect the suitability of agricultural and pastoral land use in this region is a critical scientific issue for both climate change research and regional sustainable development. This study integrates multi-scenario climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with a land use suitability evaluation framework. Three Shared Socioeconomic Pathways (SSPs) are employed, driven by three Integrated Assessment Models (IAMs): IMAGE (SSP1-2.6, low emission), MESSAGE (SSP2-4.5, moderate emission), and REMIND-MAGPIE (SSP5-8.5, high emission). Future climate variables (annual precipitation and accumulated temperature ≥ 10 °C) are statistically downscaled to 1 km resolution for the years 2030, 2050, and 2100. Using a restrictive factor evaluation method that incorporates climatic, topographic, and edaphic indicators, we assess the evolution of land use suitability for both agriculture and livestock farming under each scenario. The results reveal that under moderate- and high-emission scenarios, thermal conditions gradually improve across the study area, particularly in the central and eastern parts, leading to enhanced natural suitability for agricultural and pastoral production. However, precipitation shows no consistent trend of increase or decrease. The moderate emission scenario (SSP2-4.5) yields the most balanced improvement in suitability, with the proportion of unsuitable agricultural land decreasing from 84.5% in 2030 to 37.4% in 2100, and unsuitable pastoral land decreasing from 53.1% to 13.6%. In contrast, the low-emission scenario (SSP1-2.6) results in a sharp contraction of suitable areas by 2100 due to concurrent warming and drying. These findings suggest that climate warming may benefit mid-to-high-latitude agro-pastoral transition zones under moderate emission pathways, but the benefits are spatially heterogeneous and contingent on precipitation stability. This study provides a scientific basis for regional land use planning and climate adaptation strategies. Full article

Freshwater resources constitute a fundamental component of coupled natural–human systems, underpinning ecosystem functioning, biogeochemical cycling, and socio-economic development. Anthropogenic climate change (i.e., climate change attributable to human activity, as distinct from natural climatic variability), driven primarily by greenhouse gas emissions and land-use change, is exerting significant pressure on the global hydrological cycle, resulting in increased hydroclimatic variability, intensification of extreme hydrometeorological events, and progressive degradation of freshwater quality and availability. This review synthesizes recent scientific evidence on climate-induced impacts on water systems together with emerging adaptation and resilience strategies. The analysis is based on a systematic assessment of 100 peer-reviewed studies indexed in the Web of Science Core Collection (2010–2025) and synthesized following a PRISMA-informed (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) narrative review protocol. Beyond confirming well-established trends in precipitation regimes, cryospheric decline, increasing evapotranspiration, and the growing frequency of droughts and floods, this review quantifies the magnitude of these changes across the reviewed literature, including an approximately 134% increase in flood-related disasters since 1980 and a 29% increase in drought duration since 2000. More importantly, it provides an integrated synthesis that links physical climate impacts with adaptation strategies and socio-ecological resilience frameworks within a unified analytical perspective, thereby complementing previous domain-specific reviews that have generally examined these dimensions separately. Prominent adaptation pathways identified across the reviewed literature include nature-based solutions, integrated water resources management frameworks, and the deployment of digital water technologies. In parallel, resilience is increasingly conceptualized as the adaptive capacity of socio-hydrological systems to absorb disturbances, reorganize, and transform under changing climatic conditions. The findings highlight the need to strengthen integrated and cross-sectoral water governance, enhance climate-informed decision-making, and expand monitoring and data infrastructures to improve long-term water security and socio-ecological resilience under accelerating climate change. Full article

Establishing an equitable, evidence-based mechanism for allocating flood prevention funding is critical to mitigating the risk of flash floods. However, existing research seldom accounts for the multi-dimensional nature of vulnerability or achieves an appropriate balance between efficiency and equity. To address this gap, we propose the Multi-dimensional Vulnerability-based Flood Disaster Fund Allocation Optimization Model (MD-FAOM), which integrates the coupling effects of exposure, sensitivity, adaptive capacity, and equity into allocation strategies using the NSGA-II algorithm, TOPSIS method, and geographical detectors. The model prioritizes funding for ecologically targeted flood prevention. We apply this framework to southern Tibet to derive optimal fund allocations and quantitatively assess the resulting benefits. Our results show that areas characterized by negative vulnerability account for 25.22% of the study region, mainly concentrated in Lhasa and Shannan. Under equivalent conditions, MD-FAOM delivers benefits across an area of 85,915 km², achieving an improvement rate of 30.11%. These findings demonstrate that integrating vulnerability science with distributive equity can optimize the allocation of limited resources, thereby enhancing both flood resilience and ecosystem conservation. This approach advances ecohydrological disaster management and supports the achievement of Sustainable Development Goals (SDGs) 13 (Climate Action) and 15 (Life on Land). 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

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

Small Mediterranean islands relying on the sun–sea–sand (3S) tourism model face growing climate risks that threaten their tourism-dependent economies. This study evaluates climate suitability for 3S tourism on the Island of Vis by integrating the Climate Index for Tourism (CIT) with land- use and land-cover (LU/LC) spatial analysis. The integration is operationalized by overlaying CIT-derived seasonal suitability windows with LU/LC-based spatial vulnerability maps, enabling identification of micro-zones where natural buffers (forest cover and elevation) can offset thermal discomfort during peak heat stress periods. Observed data reveals declining ideal 3S conditions from July to October, with the island already exceeding 50 days per year of Physiologically Equivalent Temperature (PET) above 35.1 °C, increasing by 0.7 days per year. Regional climate models tend to exhibit a cold bias over small Adriatic islands, largely related to their limited spatial horizontal resolution (12.5 km grid spacing). However, they robustly reproduce the direction of recent and projected warming trends. Future projections indicate that the annual number of strong heat stress days with PET above 35.1 °C increase from approximately one per year in the reference period to six under RCP4.5 and nine under RCP8.5, with both scenarios reducing ideal peak-summer conditions while extending favorable periods into transitional seasons. Spatial analysis shows that coastal zones have higher sealed surfaces and less forest cover, reducing natural shade and cooling capacity, while the island interior offers higher elevations, forest buffers, hiking trails, and a UNESCO Global Geopark. Drawing on social–ecological resilience theory, we conceptualize the island’s tourism system as an adaptive unit whose long-term viability depends on spatially diversified resource use and temporally extended seasonality. The integrated analytical framework identifies not only when conditions deteriorate but where alternative tourism resources exist, enabling more targeted adaptation planning and supporting diversification toward outdoor tourism forms. The novelty of this study lies in the systematic spatial integration of bioclimatic suitability assessments (CIT and PET) with LU/LC analysis at the micro-island scale. Such an approach moves beyond temporally focused climate–tourism indices to produce actionable, location-specific adaptation strategies. Full article

Land degradation and declining groundwater availability threaten the sustainability of rural livelihoods across semi-arid regions. This study evaluates the hydrological performance of contour-based trenches as a low-cost and replicable nature-based solution (Nbs) for soil restoration, runoff regulation, and potential distributed managed aquifer recharge (MAR) in Guerrero, Mexico. The structures were installed on 12% slopes and designed using a simplified water balance criterion based on trench storage capacity, runoff coefficient, and representative rainfall events. Each trench was constructed along contour lines with overflow notches and connecting micro-trenches to improve hydraulic continuity, reduce erosion, and enhance infiltration opportunities under degraded field conditions. After one year of field monitoring, the trenches reached an average filling efficiency of approximately 90% per effective rainfall event, with estimated infiltration rates ranging from 0.0069 to 0.011 L·s⁻¹. Soil moisture in the upper soil layer showed a relative increase of approximately 10–18% compared to adjacent untreated areas, while visible reductions in runoff velocity, sediment transport, and surface erosion were observed across the treated plot. Based on trench storage capacity, observed infiltration behavior, and assumed deep percolation fractions, the potential induced recharge was estimated between 216 and 360 m³·yr⁻¹ (43–72 mm·yr⁻¹). These values represent indicative plot-scale estimates rather than direct measurements of aquifer recharge, since no tracer studies or piezometric validation were performed. The results demonstrate that contour-based trenches contribute not only to infiltration enhancement and runoff control, but also to short-term soil restoration and improved water availability in rainfed agricultural systems. Their low-cost implementation, combined with community-based maintenance and adaptation to local environmental conditions, makes them a viable complementary strategy for strengthening decentralized water management, soil resilience, and climate adaptation in semi-arid rural landscapes. However, long-term effectiveness remains dependent on maintenance continuity, institutional support, and local governance conditions. Further multi-year monitoring and direct hydrogeological validation are recommended to improve the design and replicability of decentralized MAR systems. Full article

Human modification of tidal embayments, estuaries, and deltas through polders, dykes, and embankments has profoundly altered sediment dynamics and coastal morphology worldwide. Mont-Saint-Michel Bay (northwestern France) exemplifies a macrotidal system affected by large-scale land reclamation, accelerated infilling, rapid saltmarsh expansion, and progressive loss of the insular character of the World Heritage abbey. To restore its maritime setting, a large-scale restoration programme initiated in the 1990s combined engineering measures with nature-based management, including embankment removal, managed retreat, and controlled hydraulic flushing. Future morphodynamic evolution was initially assessed using a movable-bed physical model complemented by numerical simulations. Here, a 22-year LiDAR dataset is used to quantify post-restoration topographic changes and sediment budgets, and evaluate model performance. The results show enhanced erosion and deepening of tidal flats around Mont-Saint-Michel, indicating effective sediment export, together with spatial redistribution of salt marshes that maintained the overall ecological value of the bay. Discrepancies between model predictions and field observations reflect both the difficulty of reproducing long-term channel migration variability and evolving hydro-meteorological forcing conditions, as well as differences between the initially modelled restoration scheme and the engineering works ultimately implemented. This study provides a rare multi-decadal comparison between pre-project morphodynamic forecasts and post-restoration observations. The results highlight both the potential and the limitations of long-term morphodynamic forecasting in non-stationary tidal systems undergoing anthropogenic modifications and climate-driven environmental change, emphasising the importance of long-term monitoring and adaptive management strategies. Full article