Earth

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

Climate change has accelerated the retreat of Andean glaciers, with significant recent losses in the tropical Andes. This study evaluates the extinction of the Carihuairazo volcano glacier (Ecuador), quantifying its area from 1312.5 m² in September 2023 to 101.2 m² in January 2024, its thickness (from 2.5 m to 0.71 m), and its volume (from 2638.85 m³ to 457.18 m³), before its complete deglaciation in February 2024; this rapid melting and its small size classify it as a glacierette. Multivariate analyses (PCA and biclustering) were performed to correlate climatic variables (temperature, solar radiation, precipitation, relative humidity, vapor pressure, and wind) with glacier surface and thickness. The PCA explained 70.26% of the total variance, with Axis 1 (28.01%) associated with extreme thermal conditions (temperatures up to 8.18 °C and radiation up to 16.14 kJ m⁻² day⁻¹), which probably drove its disappearance. Likewise, Axis 2 (21.56%) was related to favorable hydric conditions (precipitation between 39 and 94 mm) during the initial phase of glacier monitoring. Biclustering identified three groups of variables: Group 1 (temperature, solar radiation, and vapor pressure) contributed most to deglaciation; Group 2 (precipitation, humidity) apparently benefited initial stability; and Group 3 (wind) played a secondary role. These results, validated through in situ measurements, provide scientific evidence of the disappearance of the Carihuairazo volcano glacier by February 2024. They also corroborate earlier projections that anticipated its extinction by the middle of this decade. The early disappearance of this glacier highlights the vulnerability of small tropical Andean glaciers and underscores the urgent need for water security strategies focused on management, adaptation, and resilience. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Fresh water is vital for human activities; however, an increase in the contamination of water bodies has been observed, so it is necessary to monitor the degree of contamination and take measures to preserve it. In Altamira, Tamaulipas, the Guayalejo-Tamesí River basin has three estuaries and seven lagoons, including Laguna El Conejo, of which the National Water Commission only monitors one. The objective of this research is to determine water quality on the basis of the parameters COD, BOD₅, T, pH, and sediment characteristics. The open reflux method was used according to NMX-AA-030-SCFI-2012 for COD, BOD Track II, HACH equipment for BOD₅, and the granulometric characterization recommended by the Unified Soil Classification System ASTM D-2487-17. The water was found to be uniformly contaminated throughout its length in the range of 117.3–200 mg/L COD, and BOD₅ ranged from 15.8–112.75 mg/L throughout the study period, with sediments dominated by poorly graded soil and fine clay. Comprehensive management is needed because the BOD₅/COD ratio varies between 0.11and 0.56, indicating that it contains recalcitrant organic matter, which is difficult to biodegrade. Full article

Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how urbanisation, opportunity and orientation to engage with nature, and intergenerational transmission have shaped nature connectedness over time. The model simulates historical trends (1800–2020) against target data, with projections extending to 2125. The ABM revealed a significant nature connectedness decline with excellent fit to the target data, derived from nature word use in cultural products. Although a lifetime ‘extinction of experience’ mechanism refined the fit, intergenerational transmission emerged as the dominant driver—supporting a socio-ecological tipping point in human–nature disconnection. Even with transformative interventions like dramatic urban greening and enhanced nature engagement, projections suggest a persistent disconnection from nature through to 2050, highlighting locked-in risks to environmental stewardship. After 2050, the most transformative interventions trigger a self-sustaining recovery, highlighting the need for sustained, systemic policies that embed nature connectedness into urban planning and education. Full article

Dissolved oxygen (DO) is widely recognized as a fundamental parameter in assessing water quality, given its critical role in supporting aquatic ecosystems. Accurate estimation of DO levels is crucial for effective management of riverine environments, especially in anthropogenically stressed regions. In this study, a hybrid machine learning (ML) framework is introduced to predict DO concentrations, where optimization is performed through Genetic Algorithm Search with Cross-Validation (GASearchCV). The methodology was applied to a dataset collected from the Sitnica River in Kosovo, comprising more than 18,000 observations of temperature, conductivity, pH, and dissolved oxygen. The ML models Elastic Net (EN), Support Vector Regression (SVR), and Light Gradient Boosting Machine (LGBM) were fine-tuned using cross-validation and assessed using five performance metrics: coefficient of determination (${\mathrm{R}}^2$), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error MARE, and mean square error (MSE). Among them, the LGBM model yielded the best predictive results, achieving an ${\mathrm{R}}^2$ of 0.944 and RMSE of 8.430 mg/L on average. A Monte Carlo Simulation-based uncertainty analysis further confirmed the model’s robustness, enabling comparison of the trade-off between uncertainty and predictive precision. Comparison with recent studies confirms the proposed framework’s competitive performance, demonstrating the effectiveness of automated tuning and ensemble learning in achieving reliable and real-time water quality forecasting. The methodology offers a scalable and reliable solution for advancing data-driven water quality forecasting, with direct applicability to real-time environmental monitoring and sustainable resource management. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

In recent years, remote sensing and geographic information systems (GISs) have become essential tools for effective landscape management. This study utilizes these technologies to analyze land use and land cover (LULC) changes in Dibru-Saikhowa National Park, a riverine ecosystem in Assam, India, from its designation as a national park in 2000 through 2024. The satellite imagery was used to classify LULC types and track landscape changes over time. In 2000, grasslands were the dominant land cover (28.78%), followed by semi-evergreen forests (25.58%). By 2013, shrubland became the most prominent class (81.31 km²), and degraded forest expanded to 75.56 km². During this period, substantial areas of grassland (29.94 km²), degraded forest (10.87 km²), semi-evergreen forest (12.33 km²), and bareland (10.50 km²) were converted to shrubland. In 2024, degraded forest further increased, covering 80.52 km² (23.47%). This change resulted since numerous areas of shrubland (11.46 km²) and semi-evergreen forest (27.48 km²) were converted into degraded forest. Furthermore, significant shifts were observed in grassland, shrubland, and degraded forest, indicating a substantial and consistent decline in grassland. These changes are largely attributed to recurring Brahmaputra River floods and increasing anthropogenic pressures. This study recommends a targeted Grassland Recovery Project, control of invasive species, improved surveillance, increased staffing, and the relocation of forest villages to reduce human impact and support community-based conservation efforts. Hence, protecting the landscape through informed LULC-based management can help maintain critical habitat patches, mitigate anthropogenic degradation, and enhance the survival prospects of native floral and faunal assemblages in DSNP. Full article

In recent decades, the River Ganges in India has been heavily contaminated with domestic waste and industrial toxins because of cultural activities, a lack of community awareness, an absence of sewage disposal facilities, and rapid population growth. Previous studies have focused separately on either the physical or social factors associated with River Ganges pollution but have not combined these elements in a single study. To fill this research gap, our study assesses the bio-physical and social vulnerability of the River Ganges by using a holistic approach. The following four sampling stations were selected: Rishikesh, Haridwar, Kanpur, and Varanasi. These locations were chosen to test the water quality in bio-physical aspects and to assess the social perceptions of river vulnerability among the residents and visitors. Perceptions of river water quality and likely sources of pollution were gathered via the distribution of over 1000 questionnaires. Data collection took place in the winter and summer of 2022 and 2023. The results showed that river water quality is not suitable for drinking purposes at any of the four cities without conventional treatment, and that the river is unsuitable for bathing at all locations, except upstream of Rishikesh. Nearly 50% of those questioned agreed that the river is polluted, whilst 74% agreed that pollution has increased in recent decades, particularly in the last 10 years. These compelling results are critical for policymakers and decision makers. They highlight the urgent need for novel strategies that address Ganges pollution while fostering community health education and environmental management. By dispelling myths surrounding river quality, this study strengthens the ongoing efforts to restore the Ganges, ensuring that it remains a vital lifeline for present and future generations. Full article

Community-based systems present a key option for water services, especially in rural areas. Our goal is to achieve a state-of-the-art understanding of joint rural water supply development in Finland over 150 years. A mixed-methods approach was used: a literature survey and a questionnaire to selected experts. Based on the literature, a table including 23 decisions considered the most influential strategic events from 1872 to 2022 was produced. The table was sent to 10 selected experts known to be deeply familiar with the theme, all of whom replied. Joint rural water services in Finland have evolved based on demand through co-operative principles. The first documented scheme was constructed in 1872, while governmental financial support to rural water services started in 1951. It expanded in various forms until it dramatically declined in recent years. Multi-locality may increase the need for these services in the future. The expert survey revealed the following most influential long-term decisions: the first official water co-operative established in 1907, the land reform for immigrants and war veterans introduced in 1945, the Committee for Rationalisation of Households established in 1950, the start of domestic manufacturing of plastic pipes in 1954, and the Water Act enacted in 1962 to start water pollution control. This paper reminds us that urban and rural services are not contradictory but can supplement each other. Full article

This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogeneity. Utilizing advanced techniques like XGBoost, GBM, LightGBM, and CatBoost regressors, our research demonstrates the potential of these methods to enhance wildfire risk estimation. The Ensemble Mean model classified 50% of the land as low risk and 21% as high risk, while the Ensemble Max model identified 38% as low risk and 33% as high risk. Notably, 83% of wildfires between 2000 and 2024 occurred in areas marked as high-risk by both models. The findings reveal that a significant proportion of wildfires occurred in areas identified as high risk by both ensemble models, underscoring their effectiveness. This approach offers significant potential to mitigate wildfires’ environmental, economic, and social impacts, enhance climate resilience, and strengthen preparedness for future wildfire events. Full article

Increasing global population and threat from climate change are imposing economic, social, and ecological challenges to global food production. The demand for food is increasing, necessitating enhanced agricultural production with minimal environmental impacts. To meet this demand, sustainable intensification of both crops and livestock is necessary. This is more urgent in sub-Saharan Africa (SSA), a region characterized by low productivity and environmentally degrading agriculture. Integrated Agriculture-livestock-forestry (ALF) systems could be a key form of intensification needed for achieving food security and economic and environmental sustainability. The synergetic interactions between ALF nexus provide a mechanism to foster interconnectedness and resource circulation where practices of one system influence the outcomes in another. These systems enhance long-term farm sustainability while serving the farmers’ environmental and economic goals. It provides opportunities for improving food security, farmer incomes, soil health, climate resilience and the achievement of several UN Sustainable Development Goals. It is therefore crucial to strengthen the evidence supporting the contribution of these systems. On this basis, this paper reviews the potential pathways through which ALF nexus can enhance incomes, food security and climate change mitigation in SSA. The paper discusses the pathways through which the integration of crops, livestock and trees enhance (i) food security, (ii) incomes, (iii) soil health and (iv) mitigation of climate change in SSA. We argue that implementing ALF systems will be accompanied by an advancement of enhanced food security, farmer livelihoods and ecological conservation. It will foster a more balanced and sustainable sub-Saharan African agricultural systems. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

This study analyzes the evolution of hydrometeorological risks and severe weather events in Catalonia through an extensive review of 21,312 news reports aired by Televisió de Catalunya (TVC) between 1984 and 2019, 10,686 (50.1%) of which focused on events within Catalonia. The reports are categorized by the type of phenomenon, geographic location, and reported impact, enabling the identification of temporal trends. The results indicate a general increase in the frequency of news coverage of hydrometeorological and severe weather events—particularly floods and heavy rainfall—both in Catalonia and the broader Mediterranean region. This rise is attributed not only to a potential increase in such events, but also to the expansion and evolution of media coverage over time. In the Catalan context, the most frequently reported hazards are snowfalls and cold waves (3203 reports), followed by rainfall and flooding (3065), agrometeorological risks (2589), and wind or sea storms (1456). The study highlights that rainfall and flooding pose the most significant risks in Catalonia, as they account for the majority of the reports involving serious impacts—1273 cases of material damage and 150 involving fatalities. The normalized data reveal a growing proportion of reports on violent weather and floods, and a relative decline in snow-related events. Full article

Glaciers are significant sources of fresh water on planet Earth. The Hindukush–Karakoram–Himalayan (HKH) glaciers provide the water supply to more than half of the human population of the globe, for agricultural activities, biodiversity survival, and ecosystem services. In recent years, the loss of glacial ice has been forecasted to cause problems such as sea level rise, changes in water availability, and release of contaminants that reside in the surfaces of glaciers or within them. In this regard, mineralogical sediments play a significant role in the geochemistry of glaciers and element cycling. This study analyzed elemental pollutants found in the glaciers of Pakistan and investigated the diverse bacterial communities residing therein. Samples of ice and sediments were collected from the Gilgit, Hunza, and Swat glaciers in northern Pakistan. Nine elements, including co-factors, heavy metals, and nutrients, were assessed using atomic absorption spectrophotometry. The research findings indicate higher concentrations of the elements K, Fe, Cu, and Cr in Hunza glacier ice (Hgi) and Ni, Zn, As, and Cd in Gilgit glacier ice (Ggi). In terms of glacier sediments, Swat (Sgs), Gilgit (Ggs), and Hunza (Hgs) samples showed the highest concentrations of K, Cu, Ni, Zn, As, Pb, Cd, and, respectively, of Fe, and Cr. The amount of Cu and Cr is the same in Swat glacier ice and Swat glacier foot. However, the concentration of some elements (As, K, Pb, Zn) is higher in Swat glacier ice, while the amount of some elements (Cd, Ni) is greater in Swat glacier foot. Furthermore, microbial cultivation techniques revealed diverse bacterial communities inhabiting the sampled glaciers. Phylogenetic analysis of the bacterial isolates, based on 16S rRNA gene sequences, showed high homology (99–100%) with previously reported species. The resultant phylogenetic tree grouped the bacterial isolates, such as Serratia marcescens, Cupriavidus sp., and Bacillus cereus, with closely related species known for their roles in nutrient cycling, environmental resilience, and metal tolerance. These findings highlight the ecological significance and adaptive potential of microbial communities in glacier environments, emphasizing their role in elemental cycling and environmental resilience. Full article

Danube River represents a critical axis of ecological and economic importance for the countries along its course. From this perspective, this paper aims to assess the most significant characteristics of the river and of its main tributaries, as well as its impact on the environmental sustainability and socio-economic development. Navigation and the economic contribution of the Danube River are the key issues of this work, emphasizing its importance as an international transport artery that facilitates trade and tourism, and develops the energy industry through hydropower plants. The study includes an analysis of the volume of goods transported from 2019 to 2023, as well as an analysis of the goods traffic in the busiest port on the Danube. Furthermore, climate change affects the hydrological regime of the Danube, as well as the ecosystems, economy, and energy security of the riparian countries. Main impacts include changes in the hydrological regime, increased frequency of droughts and floods, reduced water quality, deterioration of biodiversity, and disruption of the economic activities dependent on the river, such as navigation, agriculture, and hydropower production. Thus, hydrological risks and challenges are investigated, focusing on the extreme events of the last two decades and the awareness of their repercussions. In this context, the national and international institutions responsible for monitoring and managing the Danube are presented, and their role in promoting a sustainable river policy is explored. Methods and technologies are shown to be essential tools for monitoring and prediction studies. The Danube includes an extensive network of hydrometric stations that help to prevent and manage the most significant risks. Finally, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of the development of the hydrological studies was conducted, highlighting the potential of the river. Full article

Flooding poses significant challenges in semi-arid regions, where irregular rainfall patterns increase environmental vulnerability. This study explicitly aims to improve flood susceptibility mapping by integrating advanced machine learning (ML) algorithms with geographic information systems (GIS) and remote-sensing data. Using data from the Upper Drâa Basin in southern Morocco, we applied boosting algorithms, including XGBoost, CatBoost, LightGBM, and Hist Gradient Boosting, to enhance the accuracy of flood risk assessment. Quantitative model evaluation shows that Hist Gradient Boosting achieved the best performance, with the lowest mean squared error (MSE = 0.06897) and root mean squared error (RMSE = 0.2626). It also attained the highest F1 score (0.8), overall accuracy (93.1%), and area under the curve (AUC = 0.833), indicating its superior predictive capability. These findings highlight the strong potential of novel boosting ensemble learning methods in flood susceptibility prediction and contribute valuable, data-driven insights for policymakers and urban planners to support effective flood mitigation strategies in southern Morocco. Full article