Search Results (2,231)

Climate change poses increasing challenges to Reclamation Consortia, which must ensure equitable and sustainable water distribution under conditions of growing scarcity. This study evaluates supplemental irrigation management strategies adopted by three Reclamation Consortia in the Venetian Plain (Northeast Italy): Piave, Veneto Orientale and Acque Risorgive. The Consortia were selected based on their territorial and structural characteristics, as well as their different approach to managing water resources. This study fills a critical gap by integrating FAO AquaCrop-based estimates of irrigation needs for the 2022 and 2023 irrigation seasons in maize, grapevine and radicchio with an institutional analysis of Reclamation Consortia, offering an innovative framework that links technical and governance aspects of sustainable water management. Results reveal considerable variability among Consortia in terms of organizational structure, technological adoption, and resilience to drought. The 2022 season, characterized by extreme drought, required substantially higher irrigation volumes across all crops and soil types with significant differences compared to 2023 (p < 0.001), particularly for maize and grapevine (73% more irrigation water in maize). Well-drained soils and sprinkler irrigated crops showed the highest water demand (+45 mm compared to drip irrigation, p = 0.058), while loamy soils and drip systems proved more efficient. The Piave Consortium demonstrated the most advanced management system, supported by digital tools and structured rotation schedules. Nevertheless, structural factors, such as geographic location and infrastructure capacity, play a critical role in shaping resilience, leading to higher vulnerability in Consortia like Veneto Orientale and robustness in Acque Risorgive during drought conditions (i.e., 2022). Overall, the findings highlight the need to strengthen the main pillars of adaptation in irrigated agriculture, i.e., technology (decision support systems), governance (inter-Consortium coordination), and infrastructure (storage facilities), to promote flexible irrigation planning, enhance adaptive capacity, and ensure long-term sustainability under changing climatic conditions. These strategies also contribute directly to the achievement of Sustainable Development Goals 2, 6, and 13 (Zero Hunger, Clean Water and Sanitation, and Climate Action) by improving water use efficiency, securing crop production, and enhancing resilience to climate change. Full article

Identification of contamination sources is critical for effective remediation planning in contaminated aquifers. This study presents a simulation–optimisation framework that was developed to reconstruct the release history and identify the potential source location after tetrachloroethene (PCE) concentrations that exceeded regulatory limits were detected in production and monitoring wells at the Hrastje well field. The approach integrates a physically based groundwater flow and solute transport model with an evolutionary algorithm to estimate unknown source parameters. The method was tested under realistic field conditions, accounting for the complexity and uncertainty of the subsurface environment. In the optimisation procedure, parameter values converged towards optimal estimates, and the simulated PCE concentrations in monitored wells showed good agreement with the observed values. The delineated source location and the reconstructed temporal and spatial dynamics of PCE contamination in the aquifer provide essential guidance for decision makers in designing and prioritising remediation strategies. By narrowing the potential source area, more targeted and cost-effective field investigations can be planned. The developed model offers a practical tool for evaluating alternative remediation scenarios, supporting adaptive water resource management and safeguarding the drinking water supply. Full article

Ethiopia is among the most climate-vulnerable countries in Africa, with agriculture, water resources, health, and disaster risk management highly exposed to climate variability and change. This study examines the role of climate services in supporting climate change adaptation in Ethiopia by combining analyses of historical climate trends, future projections, national policy frameworks, and survey data from both users and providers of climate information. Results show that rainfall and temperature time-series exhibit significant variability, with increasing frequency of droughts and rising temperatures already threatening livelihoods and food security. Climate projections indicate continued warming and uncertain but increasingly extreme rainfall patterns, underscoring the urgency of adaptation. National strategies—including the Climate Resilient Green Economy (CRGE) Strategy, Growth and Transformation Plans (GTP I and II), and the National Adaptation Plan (NAP-ETH)—highlight the centrality of climate services in guiding adaptation across sectors. Survey findings reveal that climate services provided by the Ethiopian Meteorological Institute (EMI) are widely valued, particularly seasonal climate predictions, but challenges persist in accessibility, capacity, infrastructure, and alignment with user needs. Despite high satisfaction levels among users and providers, gaps remain in technical expertise, dissemination mechanisms, and service co-production. Strengthening climate services—through improved technical capacity, institutional coordination, and user-driven design—will be critical for enhancing Ethiopia’s resilience. The lessons drawn are also relevant to other African countries where climate services can play a critical role in bridging the gap between climate science and climate-resilient development. Full article

Mountain glaciers are sensitive indicators of climate variability, and their retreat since the end of the Little Ice Age (LIA) has strongly reshaped alpine environments worldwide. In the Greater Caucasus, glacier shrinkage has accelerated over the past century, yet detailed multi-temporal reconstructions remain limited for many glaciers. Here, we reconstruct the post-LIA evolution of Tsaneri–Nageba Glacier, one of largest ice bodies in the Georgian Caucasus, and document the development of its newly formed proglacial lake. Using a combination of geomorphological mapping, historical maps, multi-temporal satellite imagery, Uncrewed Aerial Vehicle (UAV) photogrammetry, and sonar bathymetry, we quantify glacier change from ~1820 to 2025 and provide the first direct measurements of a proglacial lake in the Tsaneri–Nageba system—and indeed in the Georgian Caucasus as a whole. Our results reveal that Tsaneri–Nageba Glacier has shrunk from ~48 km² at its LIA maximum to ~30.6 km² in 2025, a loss of −43.5% (or −0.21% yr⁻¹). The pace of shrinkage intensified after 2000, with the steepest losses recorded between 2014 and 2025. Terminus positions shifted up-valley by nearly 3.9 km (Tsaneri) and 4.3 km (Nageba), accompanied by fragmentation of the former compound valley glacier into smaller ice bodies. Long-term meteorological records confirm strong climatic forcing, with pronounced summer warming since the 1990s and declining winter precipitation. A proglacial lake started to form in mid-summer 2015, which by 03/09/15 had a surface area of ~14,366 m², expanding to ~106,945 m² by 10/07/2025. The lake is in contact with glacier ice and is thus prone to calving. It is dammed by unconsolidated moraines and bounded by steep, active slopes, making it susceptible to generating a glacial lake outburst flood (GLOF). By providing the first quantitative measurements of a proglacial lake in the region, this study establishes a baseline for future monitoring and risk assessment. The findings highlight the urgency of integrating glaciological, geomorphological, and hazard studies to support community safety and water resource planning in the Caucasus. Full article

Recent advancements in remote sensing and geospatial processing tools have ushered in a new era of mapping and monitoring landscape changes across various scales. This progress is critical for understanding and anticipating the underlying drivers of environmental change. In particular, large-scale Land Use and Land Cover (LULC) mapping has become an indispensable tool for territorial planning and monitoring. This study aims to map and evaluate LULC changes in the El Jadida region of Morocco between 1985 and 2020. Utilizing multispectral Landsat imagery, we applied and compared three supervised machine learning classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NNET). Model performance was assessed using statistical metrics, including overall accuracy, the Kappa coefficient, and the F1 score. The results indicate that the RF algorithm was the most effective, achieving an overall accuracy of 90.3% and a Kappa coefficient of 0.859, outperforming both NNET (81.3%; Kappa = 0.722) and SVM (80.2%; Kappa = 0.703). Analysis of explanatory variables underscored the decisive contribution of the NDWI, NDBI, and SWIR and thermal bands in discriminating land cover classes. The spatio-temporal analysis reveals significant urban expansion, primarily at the expense of agricultural land, while forested areas and water bodies remained relatively stable. This trend highlights the growing influence of anthropogenic pressure on landscape structure and underscores its implications for sustainable resource management and land use planning. The findings demonstrate the high efficacy of machine learning, particularly the RF algorithm, for accurate LULC mapping and change detection in the El Jadida region. This study provides a critical evidence base for regional planners to address the ongoing loss of agricultural land to urban expansion. Full article

Round scad (Decapterus maruadsi) and jack mackerel (Trachurus japonicus) are economically significant pelagic species widely distributed in the northern South China Sea (SCS), with overlapping habitats and life history stages. To examine the distribution patterns of round scad and jack mackerel and their responses to environmental variables, we conducted a preliminary analysis using catch and environmental data from four seasonal surveys around Hainan Island. Three species distribution models—generalized linear models (GLM), generalized additive models (GAM), and random forests (RF)—were applied to quantify species–environment relationships. Explanatory variables included both biotic and abiotic factors: temperature, salinity, water depth, sea surface chlorophyll a concentration (SSC), phytoplankton abundance, and zooplankton abundance. The results revealed pronounced spatial heterogeneity in the high-density areas of both species. Among the models, GAM consistently explained a higher proportion of deviance in the observed distributions. Further analysis showed that round scad and jack mackerel responded differently to environmental gradients such as water depth and temperature, although their responses to varying plankton concentrations were largely consistent. Specifically, round scad are typically found in waters at depths ranging from 0 to 50 m, whereas jack mackerel tend to inhabit depths exceeding 100 m. In response to high plankton abundance, both species exhibit a notable increase in resource availability when plankton levels surpass 3. These findings indicate distinct spatial niches and suggest potential competition in feeding ecology between the two species. Overall, the study enhances understanding of the spatial dynamics of key commercial species in the northern SCS and provides valuable insights for sustainable fisheries management and conservation planning. Full article

Spatial mismatches between ecosystem services and human demands pose critical challenges for sustainable land use in ecologically fragile regions. Rapid urbanization intensifies land-use conflicts in ecologically fragile regions, threatening ecosystem services and habitat sustainability. This study addresses this challenge by quantifying spatial mismatches between landscape resource functions (LRFs: natural, traditional, and humanistic) and service demands (LSFs, e.g., catering and public facilities) in Xinxian County, in China’s Dabie Mountains, using multi-source data (DEM, POI big data, and remote sensing) and spatial analysis (nearest neighbor indices, kernel density, and multi-ring buffers). The results reveal that concentrated natural LRFs in high-elevation single-core clusters exhibit low dispersion, thus increasing vulnerability to land conversion, while agglomerated LSFs in urban cores exacerbate ecosystem service inequalities. Crucially, service deficits beyond 3 km buffers and the fragmentation of traditional agricultural zones indicate potential erosion of regulating services, as inferred from spatial mismatches (e.g., soil retention and water regulation), and cultural resilience. These spatial mismatches act as proxies for habitat risks, in which humanistic landscape expansion competes with ecological corridors, amplifying fragmentation. To mitigate risks, we propose (1) enhancing connectivity for natural resource corridors to stabilize regulating services, (2) reallocating LSFs to peri-urban buffers to reduce pressure on critical habitats, and (3) integrating ecosystem service trade-offs into landscape planning. This framework provides an actionable pathway for balancing development and habitat conservation in mountainous regions undergoing land-use transitions. Full article

The Island of Unguja in Zanzibar (Tanzania) has experienced an accelerated urban development growth since the 1990s due to a rapidly increasing population. These rapid land demands put additional stress on the country’s ability to plan urban centers, cities, and the management of natural resources. The study aimed to determine the impact of urbanization on groundwater availability in the catchment area of the Masingini–Mwanyanya forest reserves from 1992 to 2022. The study used a detection approach to determine the Land Use Land Cover (LULC) changes for three decades, starting from 1992 to 2022. Landsat remote sensed images of 1992, 2002, 2012, and 2022 were used. Additionally, a paired t-test was conducted to determine the significant changes in mean population growth, urbanization, and humidity. The aquifer recharge evolution analysis was conducted using the QGIS software (3.34.8 released version). Obtained results revealed that for these three decades, the forest areas decreased by 14.5% (i.e., from 8.3 km² in 1992 to 7.1 km² in 2022), while built-up area increased from 0 km² in 1992 to 1.7 km² in 2022. Moreover, the evolution of undesirable Land Use Land Cover (LULC) changes, particularly the persistent conversion of forested areas into built-up zones, has been detected. This trend poses a significant threat to the sustainable management of water resources and catchment forest reserves. The study also indicated a decline in the recharge of the coastal aquifer supplying Zanzibar City, which decreased from 15.5 Mm³ to 11.1 Mm³. These findings highlight that the Masingini Forest Reserve is increasingly encroached by rapid urbanization, which is a phenomenon that may jeopardize the availability and sustainability of groundwater resources in the catchment without proper urban planning. Based on these results, the study recommends further research and upscaling of the existing findings, as well as collaboration with relevant authorities to redefine the Masingini–Mwanyanya forest catchment area to ensure the sustainable use of groundwater resources. Full article

Geological models form the basis for scientific investigations of both the surface and subsurface of urban environments. Urban cover, however, usually prohibits the collection of new subsurface data. Therefore, models depend on existing subsurface datasets that are often of poor quality and have an uneven spatial and temporal distribution, introducing significant uncertainty. This research proposes a novel method to mitigate uncertainty caused by clusters of uncertain data points in kriging-based geological modelling. This method estimates orientations from clusters of uncertain data and randomly selects points for geological interpolation. Unlike other approaches, it relies on the spatial distribution of the data and translating geological information from points to geological orientations. This research also compares the proposed approach to locally changing the accuracy of the interpolator through data-informed local smoothing. Using the Ouseburn catchment, Newcastle upon Tyne, UK, as a case study, results indicate good correlation between both approaches and known conditions, as well as improved performance of the proposed methodology in model validation. Findings highlight a trade-off between model uncertainty and model precision when using highly uncertain datasets. As urban planning, water resources, and energy analyses rely on a robust geological interpretation, the modelling objective ultimately guides the best modelling approach. Full article

Marine spatial planning (MSP) has emerged as a fundamental process for achieving the balanced development of marine ecology, economy, and society. However, increasing conflicts among multiple marine uses, particularly between port development, industrial activities, fisheries, recreation, and ecological protection, highlight the pressing demand for robust and science-based planning tools. In this study, we propose an integrated analytical framework for multi-objective spatial suitability evaluation to optimize MSP. Using the coastal waters of Dalian, China, as a case study, we evaluated the spatial suitability of five key marine activities (ecological protection, mariculture, port construction, wind energy farm development, and coastal tourism) and applied a multi-criteria decision-making approach to inform spatial zoning. The results emphasize the region’s ecological significance as providing critical habitats and migratory corridors for protected and threatened species as well as fishery resources, while also revealing substantial spatial overlaps between conservation priorities and human activities, particularly in nearshore zones. The optimized zoning scheme classifies 22.0% of the coastal waters as Ecological Redline Zones, 32.4% as Ecological Control Zones, and 45.6% as Marine Exploitation Zones. This science-based spatial classification effectively reconciles ecological priorities with development needs, providing a spatially explicit and policy-relevant decision support tool for MSP. Full article

This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management. Full article

Access to clean, hygienic, and sufficient potable water is a concern in many countries. To ensure this, asset management, planning, and structured pipe renewal are crucial in providing an adequate level of service. However, there is a significant backlog in municipal pipe renewal, which needs to be addressed to raise the standard of potable water supply to an acceptable level in countries across most continents. Therefore, the objective of this research was to improve decision-making to reduce this backlog. Competent personnel are a scarce resource and not easily replaced. Standardized decision-making is considered an efficient approach to addressing the shortage of skilled personnel in pipe renewal. However, its effectiveness depends on its adaptability to the varying complexity and scale of such projects during implementation. This research is based on a literature review that explores decision theories, project definitions, and project models, and compares the typical characteristics of pipe renewal projects with those of other infrastructure projects. The research highlights that structured and standardized decision-making processes are essential to ensure appropriate asset management of the pipe network and sufficient pipe renewal. The main outcome of this research is a tailored project model that supports better front-end decision-making in pipe renewal projects through improved information flow. Full article

Zambia continues to experience increasingly frequent and intense droughts, with the 2023/2024 season among the most severe in recent history. These events have threatened livelihoods, strained water and food systems, and placed immense pressure on already limited national and local resources. Given the limited knowledge in the literature on drought management in Zambia, this study investigated the state of localized district efforts across the country. By using mixed methods with a total of 161 interviews, it assessed the participation of district governments and sector players across key components of drought governance, including early warning, monitoring, vulnerability and impact assessment, mitigation, and response. Although Zambia has made notable progress in establishing national institutional frameworks and climate policies, key findings reveal a pattern of limited proactive engagement, with most participation occurring only in response to extreme events like the 2023/2024 drought. This reactive posture at the district level is further compounded by inadequate resources, limited coordination, a lack of localized drought planning, and systemic bureaucratic constraints that undermine a timely and effective response. Nonetheless, numerous opportunities exist to strengthen drought management by localizing decision-making, integrating indigenous knowledge into existing early warning systems, and leveraging community-based infrastructures to maximize scarce resources and build long-term resilience. The paper concludes with recommendations for enhancing Zambia’s drought preparedness and response capacity through inclusive, risk-based, and proactive strategies; insights that can be adapted to other developing country contexts. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Accurate prediction of extreme precipitation events remains a critical challenge in hydrological forecasting due to their rare occurrence and complex statistical behavior. These extreme events are becoming more frequent and intense under the influence of climate change. Their unpredictability not only hampers water resource management and disaster preparedness but also leads to disproportionate impacts on vulnerable communities and critical infrastructure. Therefore, in this article, we introduce a hybrid modeling framework that combines Generalized Extreme Value (GEV) distribution fitting with deep learning models to forecast monthly maximum precipitation extremes. Long Short-term Memory models (LSTMs) are proposed to predict the cumulative distribution (CDF) values of the GEV-fitted remainder series. This approach transforms the forecasting problem into a bounded probabilistic learning task, improving model stability and interpretability. Crucially, a tail-weighted loss function is designed to emphasize rare but high-impact events in the training process, addressing the inherent class imbalance in extreme precipitation predictions. Results demonstrate strong predictive performance in both the CDF and residual domains, with the proposed model accurately identifying anomalously high precipitation months. This hybrid GEV–deep learning approach offers a promising solution for early warning systems and long-term climate resilience planning in hydrologically sensitive regions. Full article