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The Decision Tree Framework is a decision support tool that aims to help project managers and development practitioners to pragmatically assess potential climate risks. This document, developed by the Water Global Practice with the support of our Water Partnership Program (WPP), helps practitioners navigate the maze of existing climate assessment methods and models. The tool first screens for climate vulnerabilities, and a \"decision tree\" subsequently helps project teams assess and then develop plans to manage climate and other risks. It uses a step-by-step design--similar to a tree on which each \"branch\" builds off the previous one. [Foreword]

Scale choices influence the content of a study (the problems on the agenda, the options found and the impacts addressed) and the process (actors involved, their dedication and criticalness). This work synthesizes different perspectives on scale choices (spatial boundary setting and temporal boundary setting) in policy analysis.

The Decision Tree Framework described in this book provides resource-limited project planners and program managers with a cost-effective and effort-efficient, scientifically defensible, repeatable, and clear method for demonstrating the robustness of a project to climate change. At the conclusion of this process, the project planner will be empowered to confidently communicate the method by which the vulnerabilities of the project have been assessed, and how the adjustments that were made (if any were necessary) improved the project's feasibility and profitability. The framework adopts a \"bottom-up\" approach to risk assessment that aims at a thorough understanding of a project's vulnerabilities to climate change in the context of other nonclimate uncertainties (for example, economic, environmental, demographic, or political). It helps to identify projects that perform well across a wide range of potential future climate conditions, as opposed to seeking solutions that are optimal in expected conditions but fragile to conditions deviating from the expected.

Bridging Boundaries: Making Scale Choices in Multi-Actor Policy Analysis on Water Management synthesizes different perspectives on scale choices (spatial boundary setting, temporal boundary setting and selection of level of aggregation) in policy analysis. Scale choices influence the content of a study (the problems on the agenda, the options found and the impacts addressed) and the process (actors involved, their dedication and criticalness). Scale choices are not politically neutral: they may have advantages or disadvantages for certain actors by putting their urgent problems and their preferred options on the agenda, while they may hide or stress positive or negative impacts of options. Yet, little is known about the specific effects of scale choices and how they are made in practice. In this research, the making of scale choices is studied in two cases in practice: the Long Term Vision Study of the Scheldt Estuary and the Water Shortage Study of the Netherlands. Scale choices appear to be an important framing instrument that can be used by the policy analyst. Therefore, framing guidelines and recommendations are provided that help policy analysts and other actors involved to make scale choices in multi-actor policy analysis processes on water management.

The deficiency of freshwater has become a global issue in the recent era, especially in water-scarce hard rock region including India. Groundwater (GW) as a natural resource is decreasing at an alarming rate in West Bengal, India. Sustainable use and planning for better management of groundwater resources are essential; thus, spatial modelling of GW distribution requires proper assessment to conserve and manage the groundwater resource. Machine learning algorithms in RS-GIS environment plays a crucial role in exploration, assessing, monitoring and conserving groundwater resource in this regard. Logistic regression (LR), support vector machine (SVM) and random forest (RF) were used to develop groundwater potential zone (GWPZ) of water-stressed district Purulia with the help of 10 GW controlling factors including geology, geomorphology, lineament density, slope, soil texture, drainage density, GW level, rainfall, NDVI and NDWI. Multi-collinearity analysis was also used to eliminate collinearity issues among all controlling factors. In this study, the total area has been divided into five groups under the very low, low, moderate, high and very high groundwater potential zone categories. It has been calculated that most of the area has moderate groundwater potentiality, which is 29% of the total regions. About 8%, 19%, 25% and 18% of the study area fall under the very low, low, high and very high zones. Finally, all the adopted models were validated through ROC–AUC using GW depth data from CGGB and 484 validating point datasets in this area. The AUC values of adopted LR, SVM and RF models are 0.801, 0.849 and 0.878, respectively; implies that RF is a more reliable algorithm with better predictive ability than other models in the study area. This study's findings will help decision-makers take proper strategies and planning of groundwater resource management for this particular water-scare hard rock region.

Kolkata city is the capital of West Bengal, India. Due to improper administration, this city has long been plagued by a drinking water shortage. The rapid urbanisation and population growth, the rising daily water demand, and the steadily falling availability of drinking water per capita are the main contributors to the city’s drinking water shortage. The sustained development of drinking water supplies in Kolkata city depends on effective water resource management. With decision-making approaches, we can easily handle the drinking water scarcity situation. Therefore, we have developed a novel multi-criteria decision-making (MCDM) technique for the sustainable development of the drinking water crisis in Kolkata city. First, we introduce a fraction ranking (Rλ+,Rλ-) method of single-valued triangular bipolar neutrosophic (SVTrBN) number based on grades (Vλ+,Vλ-) and illegibilities (Aλ+,Aλ-). Here, we have invented the λ-weighted positive and negative fraction index for SVTrBN-numbers. Using the fraction ranking method, we formulate a novel MCDM technique. This decision-making technique is applicable for accurate decisions, primarily when human choices depend on positive and negative effects. In addition, we give some fundamental definitions and important ethos of bipolar neutrosophic numbers. By the proposed MCDM technique, we have exercised a water resources management (WRM) problem in Kolkata under a bipolar neutrosophic environment. To check the applicability and feasibility of the proposed MCDM method, we illustrated numerically and graphically of proposed WRM problem under SVTrBN-environment.

Global urbanization and increasing water demand make efficient water resource management crucial. This study employs Multi-Criteria Decision Making (MCDM) to evaluate smart city water management strategies. We use representative criteria, employ objective judgment, assign weights through the Analytic Hierarchy Process (AHP), and score strategies based on meeting these criteria. We find that the “Effectiveness and Risk Management” criterion carries the highest weight (15.28%), underscoring its pivotal role in strategy evaluation and robustness. Medium-weight criteria include “Resource Efficiency, Equity, and Social Considerations” (10.44%), “Integration with Existing Systems, Technological Feasibility, and Ease of Implementation” (10.10%), and “Environmental Impact” (9.84%) for ecological mitigation. “Community Engagement and Public Acceptance” (9.79%) recognizes involvement, while “Scalability and Adaptability” (9.35%) addresses changing conditions. “Return on Investment” (9.07%) and “Regulatory and Policy Alignment” (8.8%) balance financial and governance concerns. Two low-weight criteria, “Data Reliability” (8.78%) and “Long-Term Sustainability” (8.55%), stress data accuracy and sustainability. Highly weighted strategies like “Smart Metering and Monitoring, Demand Management, Behavior Change” and “Smart Irrigation Systems” are particularly effective in improving water management in smart cities. However, medium-weighted (e.g., “Educational Campaigns and Public Awareness”, “Policy and Regulation”, “Rainwater Harvesting”, “Offshore Floating Photovoltaic Systems”, “Collaboration and Partnerships”, “Graywater Recycling and Reuse”, and “Distributed Water Infrastructure”) and low-weighted (e.g., “Water Desalination”) strategies also contribute and can be combined with higher-ranked ones to create customized water management approaches for each smart city’s unique context. This research is significant because it addresses urban water resource management complexity, offers a multi-criteria approach to enhance traditional single-focused methods, evaluates water strategies in smart cities comprehensively, and provides a criteria-weight-based resource allocation framework for sustainable decisions, boosting smart city resilience. Note that results may vary based on specific smart city needs and constraints. Future studies could explore factors like climate change on water management in smart cities and consider alternative MCDM methods like TOPSIS or ELECTRE for strategy evaluation.

At present, inland fisheries are not often a national or regional governance priority and as a result, inland capture fisheries are undervalued and largely overlooked. As such they are threatened in both developing and developed countries. Indeed, due to lack of reliable data, inland fisheries have never been part of any high profile global fisheries assessment and are notably absent from the Sustainable Development Goals. The general public and policy makers are largely ignorant of the plight of freshwater ecosystems and the fish they support, as well as the ecosystem services generated by inland fisheries. This ignorance is particularly salient given that the current emphasis on the food-water-energy nexus often fails to include the important role that inland fish and fisheries play in food security and supporting livelihoods in low-income food deficit countries. Developing countries in Africa and Asia produce about 11 million tonnes of inland fish annually, 90 % of the global total. The role of inland fisheries goes beyond just kilocalories; fish provide important micronutrients and essentially fatty acids. In some regions, inland recreational fisheries are important, generating much wealth and supporting livelihoods. The following three key recommendations are necessary for action if inland fisheries are to become a part of the foodwater-energy discussion: invest in improved valuation and assessment methods, build better methods to effectively govern inland fisheries (requires capacity building and incentives), and develop approaches to managing waters across sectors and scales. Moreover, if inland fisheries are recognized as important to food security, livelihoods, and human well-being, they can be more easily incorporated in regional, national, and global policies and agreements on water issues. Through these approaches, inland fisheries can be better evaluated and be more fully recognized in broader water resource and aquatic ecosystem planning and decision-making frameworks, enhancing their value and sustainability for the future.

Monitoring the qualitative status of freshwaters is an important goal of the international community, as stated in the Sustainable Development Goal (SDGs) indicator 6.3.2 on good ambient water quality. Monitoring data are, however, lacking in many countries, allegedly because of capacity challenges of less-developed countries. So far, however, the relationship between human development and capacity challenges for water quality monitoring have not been analysed systematically. This hinders the implementation of fine-tuned capacity development programmes for water quality monitoring. Against this background, this study takes a global perspective in analysing the link between human development and the capacity challenges countries face in their national water quality monitoring programmes. The analysis is based on the latest data on the human development index and an international online survey amongst experts from science and practice. Results provide evidence of a negative relationship between human development and the capacity challenges to meet SDG 6.3.2 monitoring requirements. This negative relationship increases along the course of the monitoring process, from defining the enabling environment, choosing parameters for the collection of field data, to the analytics and analysis of five commonly used parameters (DO, EC, pH, TP and TN). Our assessment can be used to help practitioners improve technical capacity development activities and to identify and target investment in capacity development for monitoring.

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.