A strategic framework for sustainable water resource management in small island nations: the case of Barbados

Integrated water resource management has been discussed since at least the Civil War; yet, there is still no integrated framework for sustainably managing water. Recognizing this need, the Water Environment Research Foundation (WERF) funded a research project to develop an integrated, conceptual framework for sustainable water resources management. Through WERF funding, this framework was developed over the past four years. Development of the framework was guided by the U.N. Agenda 21, Global Water Partnership, the Enlibra Principles, and Panarchy Theory. The conceptual framework for Sustainable Water Resources Management considers water as a renewable, but finite resource with global and regional constraints. It integrates ecological, economic, and social considerations through institutional and legal/regulatory constructs to move toward sustainable water resources. Implementation of the framework is guided by a process flow?chart that considers both crisis management and proactive management activities. We believe that sustainability is as much an outcome as a goal. If water resources are viewed within a total systems context and monitored, assessed and adaptively managed through time, sustainable water resources are the outcome. This title belongs to WERF Research Report Series ISBN: 9781843397564 (Print) ISBN: 9781780403984 (eBook)

Assessment and implementation of a methodological framework for sustainable management: Lake Kinneret as a case study

Sustainable water resource management has become a critical issue for the development of cities that suffer scarce water resources. Tianjin City, located in China's Huaihe basin, one of the most polluted and water‐scarce river basins in the country, is a typical example in which water is posing a major constraint to the development. This paper examines the current status of the use of water resources, and the current practices and policy measures taken for water resource management in Tianjin, with a view to drawing lessons through an evaluation of these measures. The study illustrates the role of cities and their complex interaction with their peripheries for the allocation of scarce water resources, and it suggests that a systems approach should be adopted in order to analyse and understand the complexity of the entire picture. Based on this review and evaluation of Tianjin's experience, the authors propose a framework for sustainable water resource management in cities, emphasizing the importance of taking full consideration of resource/environmental capacity and an integrated systems approach for problem solving. Copyright © 2001 John Wiley & Sons, Ltd. and ERP Environment

Groundwater is a critical resource in arid regions such as Khamis Mushayt, located in southwestern Saudi Arabia, where surface water availability is limited. This study integrates various geospatial and environmental datasets to delineate groundwater potential zones (GWPZs) using Geographic Information Systems (GISs) and remote sensing (RS) techniques. Key parameters considered include lithology, slope, drainage density, precipitation, soil type, and vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous studies and field data. The results reveal three distinct groundwater potential zones: poor, moderate, and good. Areas with good groundwater potential account for 8.2% of the study area (16.3 km2) and are predominantly located in the eastern and central parts of the study area, in valleys and low-lying regions with permeable geological formations such as alluvial deposits, supported by higher drainage density and favorable precipitation. Conversely, poor-potential zones represent 27.6% (54.50 km2), corresponding to areas with steep slopes and impermeable rock formations. Moderate-potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and account for 64.2% (127.0 km2). Validation using existing well data demonstrates strong agreement between the identified potential zones and actual groundwater availability. These findings provide a strong framework for sustainable water resource management, urban planning, and agricultural development in Khamis Mushayt and similar arid regions.

Sustainable urban water management is increasingly challenged by uncertainty, imprecision, and hesitancy in evaluating alternative water sources. This study proposes a novel multi-criteria decision-making (MCDM) framework based on fractional orthopair fuzzy (FOF) sets, designed to model partial hesitancy and fractional expert judgments more effectively than traditional fuzzy methods. Integrating an entropy-based weighting scheme and the technique for order preference by similarity to ideal solution (TOPSIS), the framework is applied to assess water resource alternatives in Lahore, Pakistan a city facing rapid groundwater depletion, urban expansion, and declining surface water quality. The evaluation considers three key criteria: water quality, availability, and affordability across the alternatives of surface water, groundwater, and rainwater. Results show that rainwater harvesting is the most sustainable option, with a closeness coefficient of :0.8396, outperforming alternatives in terms of both cost-effectiveness and safety. Sensitivity analysis on parameters (:p, :q) confirms the model’s robustness. The findings offer actionable guidance for water authorities, emphasizing the importance of rainwater harvesting and reduced reliance on depleting groundwater. The proposed model is adaptable to other urban regions, provided expert input and contextual data are available.

Conceptually, water resources management means optimization of a goal function which integrates requirements and, and constraints, of, interconnected hydrological, ecological and economic aspects of the water resource management. Establishment of the goal function should allow combining of the economic activities, hydroecological studies and economic valuation within a holistic methodological framework. The set of the management measures allowing the optimization of the goal function under a pre-condition of conservation of the ecosystem services in some predefined reference/desirable state defines sustainable management policy.The examples of the natural waterbodies for which such a goal function has been established are extremely rare if at all they exist (unknown to us). In this presentation, we outlined a methodological framework for sustainable water resource management comprising of ecological monitoring, quantified water quality and an ecosystem model. We tested the proposed framework on the subtropical Lake Kinneret (Israel), a major national water resource. Methodologically, this study linked the economic activities in Lake Kinneret and its watershed (i.e. nutrient loads and water supply regimes) with lake water quality, sustaining of which was considered the management objective. Based on analysis of the monitoring data and model scenario simulations we established quantitative relationships between changes to lake water level and nutrient loading and water quality. We assessed a set of values of nutrient loads from the watershed and water levels that will allow conservation of the lake water quality within predefined limits thereby defining limits for a sustainable management policy for the lake water resources. The defined sustainable management policy is in good correspondence with the loads and permissible water level ranges estimated from lake-based monitoring . Our approach to assessment of the sustainable management policy was based on a single, hydroecological criterion: the necessity to sustain lake water quality within a desirable, reference state. However, in reality, the sustainable management policy should be focused on a social-ecological system and not an aquatic ecosystem per se. Therefore, water resources management should be based on multi-criteria; it should also account for the economic aspects (costs and benefits for society) of the problem. Establishment of the quantitative relationships between economic activities, water quality and total economic value of water resources is a challenging scientific problem. Its solution will be a pivotal step towards adaptive water resources management.

Flooding is a major concern for the scientific community, and it has been exacerbated by climate change. Accurate prediction of these extreme events is crucial for adequate preparedness. This study investigates the potential of advanced artificial intelligence (AI) techniques to enhance the accuracy of flood prediction and provide actionable insights for flood management. This study focuses on the African context, where data are scarce and the weak capacity of governments to react to floods makes populations vulnerable. It adopted advanced recurrent neural network architectures such as the long short-term memory (LSTM) and the convolutional long short-term memory (ConvLSTM) models, focusing on hydrological modeling innovation. The results indicated a high performance of these models in simulated runoff. The coefficient of determination (R2) and Nash–Sutcliffe efficiency between observed and simulated runoff are approximately 0.96 and 0.95, respectively, for the ConvLSTM model and 0.95 and 0.95 for the LSTM model. This study generated the flooding risk area maps. These maps represent a significant decision-making tool for flood management. This research confirms the effectiveness of deep learning in hydrology and proposes an innovative methodological framework for sustainable water resource management in the African context.

Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential of treated wastewater for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic-Mamdani (FLM), were applied to three years of daily inlet and outlet water quality data. Fuzzy Logic was employed to predict the usability potential of treated wastewater, with ANFIS categorizing quality parameters and ANN-based high-performance models (low MSE, 74–99% R2) applied in the fuzzy inference system. The qualitative reuse potential of treated wastewater for agricultural irrigation ranged from 69% to 72% based on the best-performing model. It was estimated that treated wastewater could irrigate approximately 35% of a 20,000-hectare agricultural area. By integrating machine learning models, this research enhances the accuracy and interpretability of wastewater quality predictions, providing a reliable framework for sustainable water resource management. The findings support the optimization of wastewater treatment processes and highlight AI’s role in advancing water reuse strategies in agriculture, ultimately contributing to improved irrigation efficiency and environmental conservation.

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

This study addresses the information and software tools used for modeling hydrological basins in the Ukrainian Carpathians, a region distinguished by its complex terrain and diverse ecosystems. The research focuses on a variety of hydrological modeling software, including HEC-HMS, SWAT, and GIS applications, which facilitate the analysis of hydrological processes and the assessment of river runoff dynamics. Furthermore, the integration of meteorological data and land use information is discussed to enhance the accuracy of hydrological models. The results highlight the significance of using advanced software for understanding the impacts of climate change, land use changes, and human activities on water resources in the region. This research aims to provide a framework for sustainable water resource management and contribute to the conservation of aquatic ecosystems in the Ukrainian Carpathians.

This study addresses the information and software tools used for modeling hydrological basins in the Ukrainian Carpathians, a region distinguished by its complex terrain and diverse ecosystems. The research focuses on a variety of hydrological modeling software, including HEC-HMS, SWAT, and GIS applications, which facilitate the analysis of hydrological processes and the assessment of river runoff dynamics. Furthermore, the integration of meteorological data and land use information is discussed to enhance the accuracy of hydrological models. The results highlight the significance of using advanced software for understanding the impacts of climate change, land use changes, and human activities on water resources in the region. This research aims to provide a framework for sustainable water resource management and contribute to the conservation of aquatic ecosystems in the Ukrainian Carpathians.

This groundbreaking study addresses the imperative to comprehend gravity shifts resulting from Groundwater Storage (GWS) variations in the Arabian Peninsula. Despite the critical importance of water resource sustainability and its relationship with gravity, limited research emphasizes the need for expanded exploration. The investigation explores the impact of GWS extraction on the gravity field, utilizing Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data in addition to validation using WaterGAP Global Hydrology Model (WGHM). Spanning April 2002 to June 2023, the study predicts GWS trends over the next decade using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model.  The comprehensive time-series Analysis reveals a huge GRACE-derived GWS trend about -4.90±0.32 mm/year during the period of study. This significantly influences the gravity anomaly GA values, demonstrating a corresponding fluctuation in GWS time series. The projected GWS indicates a depletion rate of 14.51 km³/year over the next decade. The correlation between GWS and GA is substantial at 0.80, while GA and rainfall correlation is negligible due to low precipitation rates. Employing multiple linear regression explains 80.61% of the variance in gravity anomaly due to GWS, precipitation, and evapotranspiration. The study investigates climate change factors—precipitation, temperature, and evapotranspiration—providing a holistic understanding of forces shaping GWS variations. Precipitation and evapotranspiration exhibit nearly equal values, limiting GWS replenishment opportunities. This research holds significance in studying extensive GWS withdrawal in the Arabian Peninsula, particularly concerning crust mass stability. Integrating GRACE and hydrological models’ datasets furnishes a comprehensive understanding, contributing valuable foresight into the future trajectory of GWS. The results illuminate intricate relationships between GWS, gravity anomalies, and climate factors, presenting a robust framework for sustainable water resource management. 

Due to anthropogenic activities, the earth's surface is consistently being altered. These alterations take the form of Land-use/cover change (LULCC), which is a fundamental driver of global, regional and local environmental change. LULCC studies have become pivotal in supplementing our understanding and observations of environmental change. However, understanding the past and present spatial-temporal variability of LULCC characteristics and their link to future land-use/cover trajectories at a catchment scale is limited, particularly in Southern Africa. To address this limitation, this study simulated future land-use change utilising a spatially distributed, empirical land-use modelling approach for the uThukela and uMngeni catchments in the KwaZulu-Natal province of South Africa. The CA-Markov model, a popular and frequently utilised model employed in land-use and land-cover (LULC) predictive modelling, was selected to simulate LULCC conjointly with Geographic Information Systems (GIS) techniques. The obtained kappa values (Kstandard, Klocation and Kno) achieved during the validation were all above 80%, thus indicating the model's reliability and capability to successfully predict future LULC in the study sites. Future projections indicated that both study areas are anticipated to experience anthropogenic induced LULCC, which further fragments the landscape configuration, functionality and ecological stability. Historical analysis of LULCC between 1990 and 2018, in the study catchments revealed considerable declines in the areas under grassland and indigenous forest, while the areas under cultivated land, commercial forestry and urban LULC classes increased. Future LULC projections showed that urban and agriculture land-uses increased significantly, with natural land-use classes such as grassland, other vegetation and indigenous forest declining in spatial extent across both. With an understanding of the extent of projected LULCC by 2030 within both catchments, proactive planning and management within the framework of sustainable land-use planning and water resource management in the respective catchments can be undertaken. Moreover, the results of the land-use modelling study can be used to infill historical data gaps, support effective land-use planning and provide a means to evaluate the impacts of different future development pathways.

Redefining virtual water allocation in China based on economic welfare gains from environmental externalities

Traditional water quality classification methods rely on fixed threshold values, which limits their ability to reflect the degree of deviation from these boundaries. This rigid approach often results in uncertainties when assessing the ecological status of rivers. Fuzzy logic, in contrast, provides a more flexible framework by incorporating gradual transitions between classes and accounting for the relative importance of parameters. In this study, a fuzzy logic-based classification system was developed to evaluate the water quality of the Gökırmak River (Türkiye) and was compared with the conventional water quality index defined by national standards. Ten physicochemical parameters (temperature, pH, dissolved oxygen, electrical conductivity, nitrate, nitrite, ammonium, phosphate, biochemical oxygen demand, and chemical oxygen demand) were monitored monthly at six stations for one year. The fuzzy logic model was constructed using triangular membership functions and a Mamdani inference system. Model performance was assessed by comparing fuzzy classification results with expert evaluations based on the Surface Water Regulation. The system achieved 90% agreement, calculated as the ratio of consistent classifications to the total number of cases, demonstrating that fuzzy logic can serve as a reliable tool in water quality assessment. The findings highlight that fuzzy logic-based approaches not only reduce classification uncertainties but also provide a decision support framework for sustainable water resource management. Further research should expand the dataset across longer time periods and incorporate retrospective records to improve generalizability.