Search Results (156)

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies. Full article

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its uneven uptake raises important questions about the territorial factors that influence certification. This study examines the spatial determinants of Blue School certification in Mallorca, Spain, where a bottom-up pilot initiative successfully certified 100 schools. Using Maximum Entropy (MaxEnt) modeling, we estimated the spatial probability of certification based on 16 geospatial variables, including proximity to Blue Economy actors, hydrological networks, transport accessibility, and socio-economic indicators. The model achieved strong predictive performance (AUC = 0.84) and revealed that features such as freshwater ecosystems, traditional economic structures, and sustainable public transport play a greater role in school engagement than coastal proximity alone. The resulting suitability map identifies over 30 high-potential, non-certified schools, offering actionable insights for targeted outreach and educational policy. This research highlights the potential of presence-only modeling to guide the strategic expansion of Blue Schools networks. Full article

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability. Full article

The Cauto River Basin (CRB), the heartland of Cuban agriculture, has been hit hard by drought and water shortages. In response to this pressing issue, this study provides a comprehensive assessment of the water supply, demand and balance within the Cauto River Basin, considering the baseline and projected socio-economic and climatic conditions by coupling SWAT and WEAP models. The obtained results revealed that the annual flow in the CRB is projected to slightly decrease (2.5%), in which, the reduction in the rainy season (3.1%) will be higher than that in the dry season (1.3%). The total water demand in the baseline scenario is around 1.194 billion m³, dominated by agriculture (96%), with rice crops requiring nearly half. For the future scenario of 2050, the study showed a 16.6% surge in demand to 1.394 billion m³, driven by climate change and agricultural expansion. However, domestic use will decrease by 10% due to population reduction. The water deficit in the future is projected to increase by 52% from 172.4 to 262.7 million m³ due to a rising water demand and declining water supply. This study shows that integrating a hydrological model into a water allocation model is a promising approach to estimate the water supply, demand and balance, which is a crucial component of water resources management. Full article

This study aims to analyze the relationships between concerns and sentiments of stakeholders and the drought stage in a semi-arid region of Ceará from Language Technologies based on Artificial Intelligence. The dataset comprises 36 meeting minutes of water management bodies (2007–2024), of which 17 correspond to dry periods and 19 to normal periods (reservoir volume > 50%). Natural Language Processing (NLP) techniques were applied to generate word clouds, and sentiment analysis was performed using a Large Language Model (Llama 3.2, 3B). Sentiment scores were compared with reservoir volume data. Results show that both perceptions and themes differed between drought and normal phases, with higher water availability coinciding with more positive sentiments. A moderate positive correlation was found between sentiment and reservoir volume (r = 0.53, p = 0.00095, 95% CI [0.24, 0.73]). Statistical tests confirmed differences between periods (Welch’s t-test, p = 0.0018; Mann-Whitney, p = 0.0039). Box-plot analyses indicated that over 75% of sentiments were positive in normal phases, while about 65% were negative in drought phases. These findings highlight the sensitivity of human perceptions to hydrological conditions and point to the potential of LLMs as innovative instruments for integrating qualitative data into complex socio-environmental analyses. Full article

This review examines South Africa’s escalating flood vulnerability through a synthesis of over 80 peer-reviewed articles, historical records, policy reports, and case studies. Using a PRISMA-guided analysis, the study identifies key climatic drivers, including extreme rainfall from tropical–temperate interactions, cut-off lows, and La Niña conditions that interact with structural weaknesses such as inadequate drainage, poorly maintained stormwater systems, and rapid urban expansion. Apartheid-era spatial planning has further entrenched risk by locating marginalised communities in floodplains. Governance failures like weak disaster risk reduction (DRR) policies, fragmented institutional coordination, and insufficient early warning systems intensify flood vulnerabilities. Catastrophic events in KwaZulu-Natal (KZN) and the Western Cape (WC) illustrate the consequences exemplified by the April 2022 KZN floods alone, which caused over 450 deaths, displaced more than 40,000 people, and generated damages exceeding ZAR 17 billion. Nationally, more than 1500 flood-related fatalities have been documented in the past two decades. Emerging resilience pathways include ecosystem-based adaptation, green infrastructure, participatory governance, integration of Indigenous knowledge, improved hydrological forecasting, and stricter land-use enforcement. These approaches can simultaneously reduce physical risks and address entrenched socio-economic inequalities. However, significant gaps remain in spatial flood modelling, gender-sensitive responses, urban–rural disparities, and policy implementation. The review concludes that South Africa urgently requires integrated, multi-scalar strategies that combine scientific innovation, policy reform, and community-based action. Embedding these insights into disaster management policy and planning is essential to curb escalating losses and build long-term resilience in the face of climate change. Full article

Urban flooding presents increasingly complex challenges exacerbated by climate change, rapid urbanization, and aging infrastructure. This investigation combines planning theories and socio-hydrological modelling to create a planning-adaptable urban flood management strategy. The case study of Chiba Prefecture, Japan, demonstrates this approach in depth. By applying the Social-Ecological-Technological Systems (SETS) framework in combination with planning theories, the study has identified the relationship between the conventional engineered methods and the newly introduced environmentally friendly (nature-based) solutions. Our findings, which are based on content analysis of 23 official statutory planning documents, indicate that there is a significant focus on the conservation of ecosystems and green infrastructure balanced with issues of emergency planning and community engagement. One of the points that the results highlight is integrating the ecological, social and technological aspects in order to create flood management policies that are both robust and fair. This integrated approach offers a robust framework for mitigating flood risks while promoting sustainable urban development and long-term community resilience. Full article

Groundwater resource management involves complex socio-hydrological systems characterized by dynamic feedback, uncertainty, and common misconceptions among decision-makers. While deterministic models like MODFLOW simulate physical hydrology effectively, they fall short in capturing the social, legal, and behavioral dynamics shaping groundwater use. System dynamics (SD) modeling offers a robust alternative by incorporating feedback loops, delays, and nonlinearities. Yet, model conceptualization remains one of the most challenging steps in SD practice. Experienced modelers often apply a “Lego-like” approach—assembling new models from pre-validated sub-models. However, this strategy depends on access to well-documented sub-model libraries, which are typically unavailable to newcomers. To address this barrier, we systematically review and classify socio-economic sub-models from existing groundwater SD literature, organizing them by system archetypes and generic structures. The resulting modular library offers a practical resource that supports newcomers in building structured, scalable models. This approach improves conceptual clarity, enhances model reusability, and facilitates faster development of SD models tailored to groundwater systems. The study concludes by identifying directions for future research, including expanding the sub-model library, clarifying criteria for base-model selection, improving integration methods, and applying these approaches through diverse case studies to further strengthen the robustness and utility of groundwater SD modeling. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

This study presents a comprehensive assessment and strategic framework for implementing Nature-Based Solutions (NBSs) to mitigate flooding in Lethem and Tabatinga, Region 9 of Guyana. The communities are increasingly vulnerable to flooding due to climate variability, hydrological dynamics, and socio-economic factors. A mixed-methods approach, comprising hydrological modelling and observation, a questionnaire survey with a sample of households in both communities, and interviews with municipal administrators, was utilised to acquire data for the study. The study utilised the Statistical Package for Social Sciences (SPSS) to analyse the socio-economic impacts of flooding in the two communities. The results revealed that recent events, such as the significant floods of 2022, have prompted an urgent need for sustainable management strategies. Community engagement efforts, supported by data analysis through remote sensing technology, identified flood-prone areas and vulnerable populations, including women, the elderly, and persons with disabilities. Chi-Square testing was conducted to determine mutual dependence between the communities’ livelihood activities and disruptions to income and working days, and their ability to deal with flooding. Based on the results, the farmers were the group that the highest inability to deal with flooding. Existing infrastructure, including drainage systems and emergency response initiatives led by the Civil Defence Commission, has contributed to improved flood management; however, limitations persist, particularly in urban planning and land use practices. This study underscores the detailed process of implementing and adopting NBS approaches, such as flood conveyance solutions and water storage and bio-retention solutions. These solutions can improve water quality, preserve ecosystems, and enhance community well-being while reducing flood risks. Applying these solutions in the targeted communities promises to bolster ecological resilience, support climate adaptation, and reduce the incidence and the impact of floods in the sampled communities. Full article

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article

Urban growth has intensified the generation of solid waste, particularly in densely populated and vulnerable neighborhoods, leading to environmental degradation and public health risks. This study presents a multidisciplinary methodology to estimate the mass of macroplastic litter mobilized from urban surfaces into nearby watercourses during storm events. Focusing on the Villa Páez neighborhood in Cordoba, Argentina—a data-scarce and flood-prone urban basin—the approach integrates socio-environmental surveys, field observations, Google Street View analysis, and hydrologic modeling using EPA SWMM 5.2. Macroplastic accumulation on streets was estimated based on observed waste density, and its transport under varying garbage collection intervals and rainfall intensities was simulated using a conceptual pollutant model. Results indicate that plastic mobilization increases substantially with storm intensity and accumulation duration, with the majority of macroplastic mass transported during high-return-period rainfall events. The study highlights the need for frequent waste collection, improved monitoring in vulnerable urban areas, and scenario-based modeling tools to support more effective waste and stormwater management. Full article

The study presents a scenario analysis of the long-term dynamics of the water level of Lake Balkhash, one of the largest closed lakes in Central Asia, taking into account climate change according to CMIP6 scenarios (SSP2-4.5 and SSP5-8.5) and socio-economic factors of water use. Based on historical data (1947–2021) and a water balance model, the contribution of surface runoff, precipitation and evaporation to the formation of the lake’s hydrological regime was assessed. It was established that the main source of water resources for the lake is the flow of the Ile River, which feeds the western part of the reservoir. The eastern part is characterized by extremely limited water inflow, while evaporation remains the main element of water consumption, having increased significantly in recent decades due to rising air temperatures. Increasing intra-seasonal and interannual fluctuations in water levels have been recorded: The amplitude of short-term fluctuations reached 0.7–0.8 m, which exceeds previously characteristic values. The results of water balance modeling up to 2050 show a trend towards a 30% reduction in surface inflow and an increase in evaporation by 25% compared to the 1981–2010 climate norm, which highlights the high sensitivity of the lake’s hydrological regime to climatic and anthropogenic influences. The results obtained justify the need for the comprehensive and adaptive management of water resources in the Balkhash Lake basin, taking into account the transboundary nature of water use and changing climatic conditions. Full article

For a long time, drought disasters have brought about a wide range of negative impacts on human socio-economics. Especially in large basins with many tributaries, once hydrological drought occurs synchronously in several tributaries, the hydrological drought condition in the mainstream will be aggravated, which will lead to more serious losses. However, there is still a lack of research on the probabilistic risk of simultaneous hydrologic droughts in various areas of large watersheds. In this study, the Standardized Runoff Index was used to characterize hydrological drought, and the Standardized Runoff Index (SRI) sequence characteristics of each region were analyzed. Subsequently, a multiregional hazard encounter probability distribution model with an R-vine structure was constructed with the help of the vine copula function to study the risk pattern of simultaneous hydrological drought in multiple tributaries under environmental changes. The model results showed that the probability of the four basins gradually decreased from 7.5% to 0.16% when the SRI changed from ≤−0.5 to ≤−2.0, indicating that the likelihood of the joint distribution of the compound disaster decreases with increase in the drought extremes. Meanwhile, the probability of hydrological drought in the three major basins showed significant spatial differences, and the risk ranking was Dongting Lake Basin > Poyang Lake Basin > Han River Basin. The model constructed in this study reveals the disaster risk law, provides theoretical support for the measurement of hydrological drought risk in multiple regions at the same time, and is of great significance for the prediction of compound drought disaster risk. Full article

The failure of traditional drought indices to capture the dynamic supply–demand imbalance in socio-hydrological systems hinders proactive water management and necessitates novel assessment frameworks. The reservoir drought warning water level, serving as a dynamic threshold indicating supply–demand imbalance, provides a critical basis for drought early warning. From a socio-hydrological drought perspective, this study develops a framework for determining staged and graded soft partition thresholds for reservoir drought warning water levels, encompassing three key stages: water stress analysis, phase classification, and threshold determination. First, water demands for the ecological, agricultural, and domestic sectors were quantified based on hydrological analysis and official operational rules. Second, an optimized KPCA-Fisher model delineated the intra-annual supply–demand dynamics into distinct periods. Thirdly, the soft partition thresholds were formulated by coupling these multi-sectoral demands with water deficit rates using a triangular membership function. Applied to the Xianan Reservoir, the framework yielded distinct drought warning thresholds for the identified main flood, critical demand, and dry seasons. Validation against historical droughts (2019 and 2022) confirmed that these soft thresholds more accurately tracked the drought evolution process compared to traditional hard partitions. Furthermore, a sensitivity analysis identified the ecological water demand methodology as a key factor influencing the thresholds, particularly during the critical demand period. The proposed framework for determining staged and graded reservoir drought warning water levels better reflects the complexity of socio-hydrological systems and provides a scientific basis for refined reservoir drought early warnings and management under changing environments. Full article