💧 WASH Investment Research Portal

arXiv:2603.00538v2 Announce Type: replace Abstract: This paper introduces a new method for performing field transfer operations in black-box coupling, when source discretization information is not available. This approach uses a stochastic approximation of the Galerkin projection which leads to a method that asymptotically provides conservation. Error in the accuracy and conservation has been compared to the mesh intersection method and radial basis functions on a simple domain, as well as on meshes of the LTX fusion reactor. For all cases tested, our new method provides higher accuracy and less conservation error than radial basis functions and can be used for black-box coupling, unlike the mesh-intersection method. Additionally, we demonstrate the implementation and performance of our method on an NVIDIA GeForce RTX 4060 GPU, showing that the cost is competitive with the mesh intersection method.

dc.title: Revitalizing Ethiopia’s Amibara Irrigation Scheme: Governance, Institutions, and Stakeholder Engagement dc.contributor.author: Mekuria, Wolde; Dessalegn, Mengistu; Abdella, Mohammed; Haile, Alemseged Tamiru dcterms.abstract: This study aimed to assess irrigation practices, institutional arrangements, and stakeholder dynamics to inform the revitalization of irrigation schemes, using the Amibara irrigation scheme in Ethiopia as a case study. Specifically the study (i) examined water use and management practices, associated challenges, and their implications for revitalization, (ii) assessed institutional structures and key governance gaps in water access, use and management, (iii) identified and mapped stakeholders involved in revitalization based on their roles, interests and influence, (iv) analyzed the motivations of different stakeholder groups to engage in the revitalization process, (v) assessed the needs and capacities of stakeholders to influence revitalization; and (vi) proposed engagement strategies to enhance participation, collaboration and partnerships. The study used key informant interviews, group discussions and observations, with multi-stage stakeholder analysis. Qualitative data were analyzed using deductive content analysis, while stakeholder mapping tools assessed legitimate power and power resources, interests, and influence. Results showed that the use of irrigation water is dominated by individualized practices, causing water shortages, conflicts, and governance gaps threatening sustainability. The stakeholder analysis identified diverse groups interested in revitalizing the scheme, indicating the need for multisectoral collaboration. Most stakeholders had perceived high interest but low influence, while some had high interest and influence or low interest but high influence, requiring tailored engagement strategies. Stakeholder motivation stems from expected benefits and alignment with their goals. Participation can be increased through better access to data, technical and financial support, capacity building, and clear roles. Stakeholders could contribute expertise and resources. Barriers included unclear mandates, funding gaps, limited technical capacity, peace and security issues, and environmental risks like soil salinity and water scarcity. The findings emphasize that without strengthening institutional frameworks, promoting collective action, and establishing irrigation water user associations, infrastructure rehabilitation alone will not yield lasting benefits. It is also suggested that engagement approaches focusing on trust-building and collaboration, as well as integrated social and institutional reforms aligned with national and sub-regional policies, are crucial. Effective revitalization benefits from public-private partnerships and community-government collaboration. We recommend clarifying roles, formalizing partnerships, ensuring transparent communication, and establishing coordination mechanisms.

dc.title: A Computational Framework for Basin-Scale Pond Recharge Estimation Using Multi-Source Geospatial Data dc.contributor.author: Sharma, Yaggesh Kumar; Alam, Mohammad Faiz; Sharma, Navneet; Pavelic, Paul; Kim, Seokhyeon; Raj, Ravi dcterms.abstract: Water scarcity and groundwater depletion are increasing due to anthropogenic and climatic pressures. This study presents a computational framework for pond recharge estimation in the Ramganga Basin (RGB), India. At present, most ponds in the area are not utilized as Managed Aquifer Recharge (MAR) systems; however, increasing interest necessitates data-driven approaches, and the proposed framework enables systematic evaluation of MAR potential at the basin scale. A large-scale geospatial dataset of 7443 ponds was compiled by integrating field surveys, government records, and remote sensing data. Model validation was conducted using recharge observations from 23 monitoring stations in the Moradabad zone. The framework incorporates ten hydro-environmental predictors within a spatial modeling pipeline to identify high-potential recharge zones. Among the three machine learning models evaluated, Gradient Boosting showed the best predictive performance, with an Area Under the Curve (AUC) of 0.92. It also achieved consistent performance in pond recharge rate prediction, with R of 0.83 and lower RMSE and MAE compared to other models. Overall, machine learning approaches performed better than conventional statistical methods across the selected evaluation metrics. Spatially explicit recharge potential zones were delineated for the entire RGB, revealing clear variability across different geomorphological and hydrological settings. Because validation sites are concentrated within alluvial plain regions, the framework is considered most reliable under similar hydrogeological conditions, while additional validation is required for hilly regions. Overall, the proposed framework provides a reproducible and scalable approach for identifying recharge zones and assessing pond-based groundwater replenishment, supporting data-driven MAR planning in water-stressed basins. cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

dc.title: Societal Challenges and Suitability Mapping of Resilient Nature-Based Water Solutions: Evidence from Case Studies in White Nile, El Gedaref, and Kassala, Sudan dc.contributor.author: Mekuria, Wolde; Girma, Rediet; Moges, Awdenegest; Balana, Bedru; Kirui, Oliver K.; Khalifa, Muhammad; Ruckstuhl, Sandra dcterms.abstract: This study focuses on three refugee-hosting areas in Sudan—Kassala, El Gedaref, and White Nile—with three main aims: to assess key societal and environmental challenges in each state; to identify Resilient Nature-based Water Solution (RNBWS) suited to local socio-ecological conditions; and to map areas most suitable for implementing these solutions to enhance environmental sustainability and livelihood resilience. The study combined a review of published and grey literature with multi-temporal satellite imagery and geographic information system (GIS)-based analysis. These methods were used to assess societal challenges, land-use and land-cover change, land degradation neutrality, and site characteristics such as slope and soil type. Spatial analysis, guided by the International Union for Conservation of Nature (IUCN) Global Standards for Nature-based Solutions and the Restoration Opportunities Assessment Methodology (ROAM), was used to identify and prioritize suitable areas for RNBWS. Results show that economic hardship, conflict, weak governance, natural disasters, and climate change are key drivers of displacement and migration across the three states. Over the last two decades, forestlands, shrublands, grasslands, and water bodies have declined, while agricultural land, settlements, and degraded areas have expanded. Socio-economic and governance constraints—including insecure land tenure, dependence on rainfed agriculture, food insecurity, competition over land, water, grazing, and fuelwood, and weak institutional coordination—shape both vulnerability and implementation feasibility. At the same time, the analysis identified extensive areas suitable for multiple RNBWS, including tree-based solutions, rainwater harvesting, soil and water conservation, and ecological restoration. The findings provide spatially explicit evidence to guide humanitarian programming, state-level planning, climate adaptation, and investment decisions in Sudan’s displacement-affected landscapes. cg.contributor.programAccelerator: Food Frontiers and Security

dc.title: Water Storage Gap in the Tana-Beles Sub-Basin, Upper Blue Nile, Ethiopia dc.contributor.author: Assefa, Tewodros T.; Taye, Meron Teferi; Ebrahim, Girma Yimer; Seid, Abdulkarim dcterms.abstract: Water demand is rising globally due to population growth, rapid urbanization, climate change, and the need for economic development. The need for additional water storage is therefore increasing to meet these demands. Water storage, both natural and built infrastructure, offers a way to manage the availability of water resources. Properly planning and optimizing diverse water storage options for increasing water demand requires an assessment framework. We developed a framework for assessing water storage gaps under current and future scenarios. It is applied in an African setting in the Tana-Beles sub-basin of the Blue Nile basin, one of Ethiopia’s economic growth corridors, where irrigation and hydropower development are planned. The volume of usable water storage in Lake Tana, groundwater, and built reservoirs was estimated and compared with irrigation, energy, domestic, livestock, and industrial water demands for the current and future periods. Results showed that the current annual water storage gap is 613 MCM. The storage gap increases to 3663 MCM by 2040s, about five times higher than in the current period. Besides, spatial disparity exists between where most water storage is available and where the water demand occurs. Particularly, demands for irrigation, domestic use, and livestock are spread throughout the sub-basin while there is a strong reliance on surface water sources, which are located in the upstream part of the basin. Given that these sources are subject to spatial and temporal limitations, diversifying natural and built water storage sources is recommended to address the increasing gap between supply and demand. cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

dc.title: Unlocking Scale: Strategic Challenges for Solar Energy Systems for Agriculture in Ethiopia and Kenya dc.contributor.author: Adamseged, Muluken Elias; Kamanda, Josey; Haileslassie, Amare; Ravindranath, Darshini; Schmitter, Petra S.; Zewde, Yidnekachew dcterms.abstract: Despite more than a decade of investment, Solar Energy Systems for Agriculture (SESA) in Ethiopia and Kenya has yet to move beyond pilots toward commercial scale. The picture is uneven: solar water pumps are commercially mature — Kenya alone accounts for roughly 65 percent of sub-Saharan Africa’s solar water pump market — while post-harvest technologies such as cold storage, drying and milling remain at pilot stage. This SoLAR 2 brief identifies four interlocking barriers: a “pilot trap” in which technically successful demonstrations fail to become commercially viable markets, as the distribution channels, financing partnerships and support services built around them dissolve once donor funding ends; a; fragmented policy and institutional architecture, with no dedicated national SESA strategy in either country and inconsistently applied fiscal incentives; a chronic financing gap, as lenders continue to perceive agro-solar as high-risk and loan products remain poorly matched to seasonal farm cash flows; and hardware-only business models that concentrate risk on the customer and lack after-sales support. Blended finance — combining concessional donor capital with commercial lending and government risk-sharing facilities — is highlighted as the most credible path to crowding in private investment. The brief closes with coordinated recommendations for donors, governments, the private sector, and financial institutions.

dc.title: Advances in Remote Sensing Techniques for Surface Soil Moisture Estimation: A Systematic Review of Recent Developments (2019–2024) dc.contributor.author: Rawat, Monika; Nguyen-Huy, Thong; Sena, Dipaka Ranjan; Ali, Aram dcterms.abstract: Surface soil moisture (SSM) is a vital variable for irrigation management, estimation of crop water stress, and agricultural drought management. This systematic review integrates recent progress (2019–2024) in remote sensing-based SSM estimation, based on 116 peer-reviewed articles selected using PRISMA guidelines. The review indicates that multi-sensor techniques, integrating optical, radar, and climate information coupled with machine learning (ML) and data assimilation methods, have immensely enhanced the spatial and temporal resolution of SSM products. These developments have brought SSM retrieval within the realm of useful, field-scale application for agricultural water management. Hybrid models and AI-downscaled approaches, in particular, have a very high potential for operational decision-making across varying agro-ecologies. Trends in performance, regional research gaps, and areas for improvement in terms of data coverage, especially for semi-arid and smallholder-dominated landscapes, are also addressed in this review. SWOT analysis of prominent retrieval algorithms identifies their advantages and limitations in application, revealing the compromises between complexity, scalability, and accuracy. Although there has been increasing technical development, with few exceptions, there is no large-scale application in actual irrigation systems. The article ends by placing greater emphasis on enhancing stronger validation protocols, improved application within crop and hydrological models, and region-tailored modifications of retrieval workflows. In the future, new satellite missions and enhanced ground data infrastructure offer opportunities to enhance the contribution of SSM to climate-resilient agriculture. This review offers a timely basis to advance SM monitoring systems that are not only scientifically valid but operationally pertinent to sustainable water management in agriculture.

dc.title: Ethiopia Plans to Scale Solar Irrigation with Public and Private Sector Momentum dc.contributor.author: Tafesse, Yonas; Admasu, Zeleke Belay; Wamba, Elizabeth cg.contributor.programAccelerator: Scaling for Impact

dc.title: Governance Structure, Adaptation Options, and Climate Resilience of the Agricultural and Water Sectors of Morocco: Pathway Analysis in the Context of the Oum Er Rbia Basin dc.contributor.author: Saleth, Rathinasamy Maria; Ait El Mekki, Abdelkader; Amarasinghe, Upali A.; Amarnath, Giriraj; Brouziyne, Youssef dcterms.abstract: This chapter evaluates a novel methodology in the empirical context of the Oum Er Rbia Basin, Morocco. The methodology uses an econometric model, which captures the intricate interactions among climate change (CC), transformative adaptation options (TAOs), multiscale polycentric governance (MPG), and rural welfare (RW) as sequentially linked equations, each characterizing different impact pathways underlying the CC-TAO-MPG-RW interaction process. Selected TAOs are: (a) climate-resilient crop patterns, (b) drip system and irrigation modernization, and (c) contract farming and public–private partnership. MPG elements cover: (a) institutions, (b) infrastructures, and (c) private players in the water, agriculture, and livestock sectors. Estimating the econometric model using data from 176 stakeholders, pathway analysis is performed to provide insights into the policy roles of different TAOs, MPG elements, and impact transmission variables in enhancing climate resilience and rural welfare. The chapter concludes by highlighting implications for theory, methodology, and policy; limitations and caveats for obtained results; and directions for future research.

dc.title: Earth Observation Technologies for Agricultural Risk Management in Fragmented Croplands of India dc.contributor.author: Bandopadhyay, Subhajit; Dey, Sourav; Grover, Latika; Ghosh, Subhasis; Kour, Sneha; Das, Barnali; Ghosh, Surajit dcterms.abstract: A significant issue in Indian agriculture is the fragmentation of croplands into small landholdings, which results in the division of agricultural land into smaller and often uneconomical parcels. Fragmentation, or the breakdown of landholdings into smaller parcels, has an adverse impact on crop yields and productivity due to its uneconomic operational sizes. Therefore, accurate mapping of small landholdings (SLs) is necessary for precise monitoring of crop health, soil conditions, water usage, and many other factors, which can significantly improve the productivity of fragmented land parcels and sustain the country's’s food security. This comprehensive review provides insights into the complex dynamics of SLs in India by leveraging Earth Observation (EO) based remote sensing data and technology, synthesizing the existing literature, methodologies, and outcomes, as well as technological advancements, their challenges and limitations. This study aims to synthesize the current challenges, management practices, and applications of Earth Observation (EO) technologies for mapping, monitoring, and parametric assessment of small-scale agricultural landholdings in India. The review also discussed different remote sensing platforms and how to utilize their varied spectrums for identifying and characterizing SLs at different geographies in India. By incorporating EO approaches into Disaster Risk Reduction (DRR) frameworks, the study highlights how fragmented croplands can be better identified, monitored, and safeguarded against disasters and climate-induced agricultural risks. This study will support the decision-making process and policy formulation in the Indian agricultural system by providing comprehensive insights from EO-based sensing perspectives. Finally, this will help to plan more productive and sustainable farming methods, which will be advantageous to both farmers and the national economy.

dc.title: Filling the Gaps in Carbon Credits from Alternate Wetting and Drying (AWD) in Bangladesh dc.contributor.author: Bhattacharya, Jayanta; Tripathi, Gaurav; Ravindranath, Darshini dcterms.abstract: This fact sheet outlines how Bangladesh can harness carbon markets to accelerate the adoption of Alternate Wetting and Drying (AWD) in rice cultivation, particularly in Solar Irrigation Pump (SIP) command areas. Developed under the Swiss Agency for Development and Cooperation (SDC)-supported Solar Energy for Agricultural Resilience (SoLAR) project, implemented by IWMI and partners, the fact sheet demonstrates how AWD can reduce greenhouse gas emissions, conserve water, and generate additional income for farmers through carbon credits. Field pilots involving 600 farmers across 26 Bangladesh Agricultural Development Corporation (BADC) SIP sites are building the scientific evidence, digital monitoring, reporting and verification (MRV) systems, and policy frameworks needed to scale carbon finance. The publication highlights key challenges, including limited farmer awareness, weak MRV infrastructure, and policy gaps, while proposing practical recommendations to operationalize Bangladesh's agricultural carbon market. By linking climate-smart irrigation with carbon finance, the initiative aims to strengthen water resilience, improve farmer livelihoods, and support the country's national climate commitments.

dc.title: Database of Remote Sensing and Machine Learning-Based Key Performance Indicators for Monitoring Agricultural Water Management Efficiency in Climate Change Adaptation Across Africa dc.contributor.author: Obahoundje, Salomon; Zwart, Sander J.; Schmitter, Petra S. dcterms.abstract: Database of Key Performance Indicators for Monitoring Agricultural Water Management Efficiency in Climate Change Adaptation Across Africa. This database aims to support the assessment of Agricultural Water Management (AWM) interventions for climate adaptation across multiple spatial scales in Africa. Specifically, it seeks to: 1. Assess the application of remote sensing technologies, multi-source geospatial datasets, and machine-learning approaches in quantifying intervention performance, monitoring adaptation outcomes, and supporting evidence-based decision-making across farm, watershed, regional, and continental scales.

dc.title: Database of Key Performance Indicators for Monitoring Agricultural Water Management Efficiency in Climate Change Adaptation Across Africa dc.contributor.author: Obahoundje, Salomon; Zwart, Sander J.; Schmitter, Petra S. dcterms.abstract: Database of Key Performance Indicators for Monitoring Agricultural Water Management Efficiency in Climate Change Adaptation Across Africa. This database aims to support the assessment of Agricultural Water Management (AWM) interventions for climate adaptation across multiple spatial scales in Africa. Specifically, it seeks to: 1. Identify and classify AWM technologies and practices implemented to strengthen agricultural resilience to climate variability and change; 2. Compile and synthesize the indicators and key performance metrics employed by development partners, research institutions, and national programs to evaluate the effectiveness of AWM interventions and adaptation outcomes.

dc.title: Drought Characterisation across Cocoa Farming Zone in Ghana and Côte d’Ivoire dc.contributor.author: Obahoundje, Salomon; Tilahun, Seifu A. dcterms.abstract: The analysis uses the The analysis uses the CHIRPS precipitation dataset at 5 km spatial resolution to compute the Standardized Precipitation Index (SPI) over cocoa production zones in Côte d’Ivoire and Ghana for the period 1981–2023. CHIRPS integrates satellite-based rainfall estimates with in-situ station observations, providing a long-term, high-resolution gridded precipitation product suitable for drought monitoring in data-scarce regions. SPI was calculated by standardizing monthly accumulated precipitation relative to a long-term climatological baseline. To capture hydroclimatic variability across different temporal scales relevant to agriculture, SPI was computed at 1-, 3-, 6-, and 12-month timescales (SPI-1, SPI-3, SPI-6, SPI-12). SPI-1 reflects short-term moisture conditions influencing crop establishment, SPI-3 captures seasonal rainfall variability affecting crop growth and yield development, SPI-6 represents medium-term soil moisture conditions relevant to seasonal agricultural performance, while SPI-12 characterizes long-term hydrological anomalies influencing overall water availability. The resulting SPI time series enables classification of hydroclimatic conditions into seven standardized categories ranging from extreme dryness to extreme wetness.CHIRPS precipitation.

dc.title: Drought Characterisation across Agro-Climatic Zones in Ghana dc.contributor.author: Obahoundje, Salomon; Tilahun, Seifu A. dcterms.abstract: The analysis uses the CHIRPS precipitation dataset at 5 km spatial resolution to compute the Standardized Precipitation Index (SPI) across agro-climatic zones in Ghana for the period 1981–2023. CHIRPS combines satellite-based rainfall estimates with in-situ station data, providing a long-term, high-resolution gridded precipitation product well suited for drought monitoring in data-sparse regions. SPI was derived by standardizing monthly accumulated precipitation against a long-term climatological baseline. SPI was computed at multiple time scales (SPI-1, SPI-3, SPI-6, and SPI-12) to capture short-, medium-, and long-term moisture conditions relevant to agricultural systems. SPI-1 reflects immediate meteorological conditions affecting crop emergence and early growth, SPI-3 captures seasonal rainfall anomalies influencing crop development and yield formation, SPI-6 represents medium-term moisture deficits relevant to soil water availability and cropping season performance, while SPI-12 characterizes long-term hydrological drought conditions affecting groundwater, reservoir storage, and overall agricultural water security. The resulting time series captures interannual to multi-decadal rainfall variability and supports the classification of hydroclimatic conditions into seven categories, ranging from extreme dryness to extreme wetness.

dc.title: Brief: Waterproofing Prosperity: Water-Cycle Intelligence and a Green-to-Blue Investment Logic dc.contributor.author: Dupont, Anna; Adoflsson, Elin dcterms.abstract: Human activity is destabilizing the water cycle, turning hydrological disruption into a growing headwind on economic resilience, prosperity, and macro-financial stability. Yet, current investment portfolios systematically undervalue the functioning, geographical extent, and economic contribution of the ecohydrological systems that regenerate water flows, rainfall, and storage across landscapes and economies. The result is a widening gap between what finance is designed to do and what hydrological stability actually requires. This brief argues that closing this gap requires a new investment logic that treats eco-hydrological systems as core economic infrastructure and integrates hydrological risk into capital allocation decisions. Governments, public and development banks, companies, and multilateral environmental institutions must increasingly direct capital toward the ecosystems, landscapes, and communities that reduce systemic water-related risks, sustaining both green and blue water systems across scales. This brief should be read alongside “Macro-financial stability in a changing water system: evolving policy and mandates,” which addresses mandates and prudential architecture and provides an upgraded playbook placing water cycle stability at the core of macro financial governance, while this brief presents partnership-based, whole-of-water-cycle frameworks as mechanisms that can translate hydrological integrity into risk-informed, long-term, and place-based investment pathways.

Large-scale hydraulic projects (LHPs) can avoid substantial direct socio-economic and environmental losses from flood events, while these avoided flood-related disruptions are transmitted through supply chains, and accumulate across the regional economy under flood-risk conditions. Therefore, a water conservancy general equilibrium model (WCGE) is developed in this study to quantify the indirect flood-control benefits under flood-risk conditions. The proposed WCGE model is applied to the Three Gorges Project (TGP), the world’s largest hydropower project, to evaluate its indirect flood-control benefits in the Yangtze River Economic Belt (YREB). The results indicate that the TGP avoids indirect GDP losses in the YREB under different flood-risk scenarios, with increasingly pronounced indirect flood-control benefits as flood risk rises from low to high and stronger absolute effects under the 2022 economic development level. Under the high-risk scenario, the avoided indirect GDP losses reach 323.667 billion yuan and 483.039 billion yuan under the 2016 and 2022 baselines respectively, while the avoided GDP loss in the 2022 scenario reaches 0.86%. Additionally, the TGP provides significant indirect benefits for sectoral output, commodity prices, local commodity demand, and trade activities, particularly in agriculture, food and tobacco processing, and transportation. The developed WCGE approach is expected to highlight the economy-wide, sectorally differentiated, and risk-dependent indirect flood-control benefits of large hydropower projects and contribute to evaluating the indirect benefits of other large-scale hydraulic projects worldwide.

Bank collapse is a natural phenomenon that is widespread along both sides of alluvial plain rivers, leading to instability in river courses and frequent changes in erosion and sedimentation, and causing a series of economic, environmental, and social issues, which have adverse effects on sustainable development. Traditional methods of bank collapse patrol are largely limited in scope and efficiency, making it difficult to detect hazards promptly. Utilizing satellite remote sensing imagery, particularly Chinese GAOFEN and Sentinel-1 data, can provide valuable insights for monitoring and early warning systems. To address these challenges, this study proposes a cloud-collaborative deep learning framework, termed Bank Collapse Deep Learning Cloud-Collaborative Network (BCDL-C2Net), which integrates multi-source satellite remote sensing data with dual cloud platforms, namely, Google Earth Engine (GEE) and Pixel Information Expert Engine (PIE-Engine), to enable efficient and scalable bank collapse detection. Within the BCDL-C2Net framework, GEE is employed for rapid, long-term (2004–2024) and large-scale bankline change screening using the Modified Normalized Difference Water Index (MNDWI) and Otsu threshold segmentation algorithm, while PIE-Engine is utilized to implement deep learning-based refined identification and verification of typical bank-collapse events. The main findings can be summarized as follows. (1) Based on Landsat-derived bankline changes, continuous bank collapse zones longer than approximately 2 km were identified through visual interpretation. The number of continuous bank-collapse zones increased from 15 on both the left and right banks during 2004–2010 to 37 on the left bank and 34 on the right bank during 2010–2015, and then decreased markedly after 2015, with collapse-prone reaches mainly distributed around bends, branching channels, and mid-channel bars. (2) The refined identification results show that the proposed framework can identify representative collapse events with scales of approximately 100 m × 60 m at Xiaopan and 80 m × 20 m at Tianzi No.1, and has practical potential for detecting typical bank collapses larger than approximately 60 m × 20 m. (3) By coupling GEE-based large-scale screening with PIE-Engine-based refined identification using GAOFEN and Sentinel-1 imagery, BCDL-C2Net provides a hierarchical technical pathway for operational bank collapse monitoring.

Compounding crises increasingly expose hidden fragilities in socio-technical systems. At the same time, they generate political momentum and practical urgency for institutional and governance innovation, as emergency measures often become prototypes for routine practice. This paper develops a national-scale, multi-sector water-energy-emissions (WEE) scenario model for Rwanda and uses it to test how demand growth, hydrological stress, and supply-side choices jointly shape future energy security and emissions trajectories through 2050. Water enters the framework in two directions: as a determinant of energy supply, through the translation of river-discharge seasonality and drought stress into monthly hydropower availability, and as a consumer of energy, through the electricity required to supply, distribute, and treat water and wastewater. The analysis shows that, under SSP2 and especially SSP5 growth conditions, emissions remain strongly demand-driven; therefore, even ambitious demand-side and supply-side measures are best interpreted as pathways that moderate, rather than fully reverse, emissions growth. A scenario decomposition further indicates that demand-side energy efficiency is the single most influential lever, while supply-side measures primarily reshape the electricity mix and system resilience rather than aggregate emissions. The contribution of the study is to identify which combinations of efficiency, electrification, renewable deployment, thermal retirement, water-service efficiency, and hydrological risk management most effectively reduce system stress and improve resilience under compound crises.

Wastewater treatment infrastructure has not kept pace with rapid urbanization in many large cities across the tropics. This has resulted in severe organic pollution affecting urban lakes and reservoirs, with lakeside communities demanding action to protect and restore the water resources. This study compares two restoration interventions aimed at addressing the quality and quantity of inflows of untreated wastewater: diversion channels and sewage treatment and compares these with no intervention measures. The study examines how choice of intervention impacts water quality and the delivery of ecosystem services across eight urban lakes in Bengaluru. The results indicate that, while diversion channels can significantly improve water quality in the target lake, they also present trade-offs that may affect other ecosystem services such as commercial fisheries, groundwater recharge and flood regulation, as well as pushing the wastewater problem downstream. Interventions need to be aligned with the intended uses and management priorities across the city scale. Effective wastewater treatment is clearly the ideal solution for cities, but when wastewater infrastructure is lacking, diversion channels provide some localized benefits. Even with sewage treatment, our study shows that a mix of other local interventions, including constructed wetlands, may be necessary, particularly in tropical monsoon climates, to achieve water quality to sustain biodiversity, fisheries and other provisioning, regulatory and cultural services.

BackgroundEffective and efficient wastewater treatment plant is fundamental to public health and environmental protection. In South Africa, persistent failure of municipal wastewater treatment plants represents a critical challenge to water security.ProblemThe Lebowakgomo Wastewater Treatment Plant (WWTP) in Limpopo Province of South Africa is an example of this national crisis and disaster to the environment, with chronic and deep-rooted malfunctions leading to systematic discharge of untreated effluent into the Chueniespoort and Olifants River systems.ObjectiveThis study aims to identify the underlying systemic causes of the Lebowakgomo WWTP failure and propose integrated, systems-level interventions for sustainable rehabilitation.MethodologyA qualitative case study design was employed, framed by systems theory and the socio-ecological paradigm. A documentary analysis of 23 official documents (2010–2024) was conducted, including municipal Integrated Development Plans (n = 4), Green Drop audit reports (n = 3), infrastructure assessments (n = 6), and relevant academic literature (n = 10). Thematic analysis followed Braun and Clarke’s six-phase framework, using deductive coding based on pre-defined system components termed technical, operational, governance, financial, environmental.ResultsThe analysis identified chronic hydraulic overload operating at 173% of design capacity of 3.0 ML/d, these demonstrate a severe operational deficiency as a result of the preventative maintenance, skilled personnel, and entrenched institutional failures in governance and funding. Empirical evidence shows biological oxygen demand (BOD) levels exceeding 50 mg/L (standard <20 mg/L), faecal coliform counts >10,000 CFU/100 mL (standard <1,000 CFU/100 mL), and ammonia concentrations >15 mg/L (standard <3 mg/L). These factors form interconnected feedback loops driving systemic collapse of the plant.ContributionThis study develops a novel systems-based diagnostic framework that explains wastewater treatment failure as the emergent product of interacting technical, governance, financial, and environmental subsystems, it is the first to operationalize feedback loops in South Africa’s municipal wastewater infrastructure, offering a replicable model for the Global South.

Accurate calibration of urban drainage models is critical for reliable stormwater management. This study applies the OSTRICH-SWMM framework, which integrates the Storm Water Management Model (SWMM) with multiple parallel optimization algorithms, to systematically evaluate calibration performance in a large-scale urban drainage system with a total area of 25.6 km2 located in the southern part of Jiujiang, China. Three algorithms were tested: Asynchronous Parallel Dynamically Dimensioned Search (ParaDDS), Real-coded Genetic Algorithm (RGA), and Simulated Annealing (SA). Results show that ParaDDS delivered the most robust performance, achieving Nash–Sutcliffe efficiency (NSE) values of 0.864 for calibration rainfall event and 0.757 for validation event, with minimal variability among top 25-percentile solutions. RGA also performed well with a NSE value of 0.861 for calibration and 0.601 for validation whereas SA had a NSE value of 0.778 for calibration and 0.482 for validation. These findings demonstrate the effectiveness of the OSTRICH-SWMM framework for automatic calibration of complex urban drainage systems and underscore the capability of parallel optimization algorithms, particularly ParaDDS, in achieving stable and reliable parameter sets.

Human activities have significantly altered Earth’s systems, necessitating a better understanding of human-water interactions within models that simulate Earth system processes. One of the major challenges in water resource management is identifying system elements and analyzing the interconnectedness in social-ecological systems and their adaptive feedbacks across multiple spatial and temporal scales. This study applies the Pluralistic Water Research (PWR) approach to analyse emergent human–water interactions in Garajonay National Park, La Gomera, Canary Islands, Spain, in relation to the period before and after the major wildfire in 2012. The PWR approach was chosen to translate plural stakeholder knowledge into a system-level diagnosis in a structured way that can support context-sensitive land and water management in coupled human–water systems. Focusing on the interactions among agriculture, tourism, and the protected cloud-forest system, the study combines document analysis, stakeholder interviews, and an eDPSIR-based causal mapping approach to identify key pressures, feedbacks, and response options. The analysis reveals five main feedback structures linking land abandonment, wildfire risk, water scarcity, pollution, ecosystem degradation, and stewardship, as well as five clusters of stakeholder responses related to education and awareness, ecotourism development, mobility and access regulation, landscape and fire-prevention management, and water governance and agricultural adaptation. The results show that dense environmental connectivity supports fog-dependent recharge, forest functioning, and water regulation under normal conditions. Under disturbance conditions, the same connectivity can amplify disturbance propagation when drought, fire, land abandonment, and localized human pressures interact. At the same time, social and institutional connections appear comparatively weak and fragmented, limiting coordinated responses across sectors. Resilience in Garajonay depends on the interplay between beneficial ecohydrological linkages, disturbance-sensitive feedbacks and adaptive governance capacity.

This study investigated the long-term socio-hydrological resilience of the Chao Phraya River Basin in Thailand by utilizing 76 historical Buddhist temples as materialized archives of ancestral flood awareness. By integrating extensive field surveys which measured architectural parameters, such as total height increase (H1), structural foundation elevation (H2), and overall ground elevation (H3), with Rainfall-Runoff-Inundation (RRI) model simulations, this study established a quantitative baseline for evaluating human-flood interactions across diverse hydrological return periods. The results demonstrated that most historical temples successfully maintained functional resilience during a 20-year return period flood, as their structural elevations effectively prevented water ingress into sacred interior spaces. A significant positive correlation (r ≈ 0.686) was identified between the Exposure Indicator (Indicator E) and the architectural elevation (H1), substantiating that temple designs were deliberately calibrated to local flood characteristics rather than being governed solely by religious or aesthetic traditions. Furthermore, this study identified distinct regional heterogeneity in resilience strategies: downstream cities (Ayutthaya and Chainat) relied on localized structural elevation (H2) to mitigate the prohibitive costs of deep inundation, whereas midstream cities (Sukhothai and Nakhon Sawan) utilized site-wide ground elevation (H3) due to the shallower, widespread flooding characteristic of the region. In the upstream region of Chiang Mai, temples exhibited extreme safety margins in their foundation heights to counteract the high uncertainty and rapid velocity of mountainous flash floods. Ultimately, this study underscored the value of faith-based structures as physical proxies for reconstructing historical human-flood interactions and provided a comprehensive basin-scale perspective on traditional disaster resilience.

Excess phosphorus in aquatic ecosystems promotes eutrophication, leading to ecological alterations that modify the abundance of phytoplankton and aquatic macrophytes. This study evaluated chemical precipitation with aluminum sulfate as a management strategy for Lake Busa by assessing phosphorus removal and its effects on the aquatic macrophyte Schoenoplectus californicus (C.A.Mey.) Soják and the fish Cyprinus carpio Linnaeus, 1758. Jar tests were performed to determine the optimal aluminum sulfate dose for phosphorus removal. The effects of treated water were subsequently evaluated biologically in a 35-day microcosm experiment using 54 S. californicus individuals distributed among 12 experimental containers. Acute toxicity was assessed in newly hatched C. carpio fry through 96-h laboratory exposure tests using untreated water, treated water, and potable-water controls. Because the experimental design did not include replicated containers for each treatment, the biological responses are presented as observed patterns under the tested conditions rather than statistically validated treatment effects. Under the experimental conditions evaluated, aluminum sulfate doses of 34−40 mg/L removed 88.9−90.9% of phosphorus (from 0.09−0.11 mg/L to 0.01 mg/L), while residual aluminum concentrations remained between 0.061 and 0.071 mg/L. Under the experimental conditions evaluated, S. californicus grown in treated water showed a mean height increase of 5.28 cm and a mean stem diameter increase of 0.12 cm, whereas plants maintained in untreated lake water showed mean increases of 12.5 cm and 0.27 cm, respectively. A mortality rate of 93.33% was observed in newly hatched C. carpio fry after 96 h of exposure to treated water containing residual aluminum. These findings indicate that although aluminum sulfate effectively reduces phosphorus and limits macrophyte growth, the associated residual aluminum may adversely affect aquatic biodiversity. Consequently, chemical precipitation should be evaluated using both water-quality and biological indicators before large-scale application in eutrophic lakes.

What drives, or prevents, monopolistic providers in regulated sectors from merging into larger entities? While typically studied within public service regulation, this question carries profound socio-hydrological relevance for urban water services, as their organization directly affects key societal outcomes such as water security. Within the literature, aggregation is often approached through economies of scale. This paper argues that such outcomes cannot be explained by efficiency considerations alone. Instead, it requires a socio-hydrological perspective centered on the distribution of rents and the heterogeneity of stakeholders’ incentives—dynamics involving complex power relations and institutional lock-ins that a simple economic analysis cannot fully capture. Adopting a game theory approach, we investigate the existing rents and aggregation payoffs for the actors involved and assess whether mutually beneficial cooperation arrangements, corresponding to a non-empty core, can emerge. We develop an integrated framework combining a simplified financial model with a constrained transferable utility cooperative game involving municipalities, an incumbent utility, and a potential “Big Player,” each evaluating aggregation based on status-quo rents and aggregation payoffs. The analysis is grounded in a synthetic case study representing a medium-sized Italian water service area, capturing institutional and economic features applicable to similar contexts: mature infrastructural settings, fragmented governance, and regulated natural monopolies where local authorities retain significant decision-making power over service organization. Tested against an extensive robustness assessment, results show that aggregation is not systematically prevented by the absence of efficiency gains, but by misaligned incentives. Municipalities may support aggregation due to political payoffs, while the operator requires financial viability, and the incumbent utility resists due to the loss of control-related rents. This leads to a divergence between financially viable and politically acceptable coalitions, implying that a stable grand coalition is structurally difficult to achieve within such institutional settings. The framework serves as a diagnostic tool to identify feasible coalitions and potential blocking actors. By demonstrating the persistence of these dynamics across a wide parameter space, the findings highlight a structural tension between industrial efficiency and political feasibility, providing a formal explanation of why efficient outcomes remain institutionally difficult to achieve even when economically justified.

Community-scale sponge city retention tanks need accurate short-term inflow forecasts. The forecasts support automated pump scheduling and overflow prevention. Most existing data-driven models were built for natural catchments or city-scale combined sewers. Community-scale sites differ. They show persistent dry-weather baseflow and compound detention from distributed low-impact development (LID) facilities. These features limit how well existing frameworks transfer. This study builds a lightweight, lag-aware framework for 5 min-ahead inflow prediction. A cross-correlation function (CCF) analysis extracts one physically interpretable rain-fall–inflow lag. The lag is combined with autoregressive inflow terms, recent rainfall, and multi-scale cumulative rainfall to form a 12-feature input. The framework was tested on 100 days of 5-min rainfall and inflow records from a 51484.6 m2 LID-equipped residential community in City P, China. The site includes permeable pavements, depressed green spaces, and bioretention cells. The CCF identified a representative rainfall–inflow response timescale of about 70 min, consistent with the compound detention behavior of the LID facilities. Five models were compared under the same features. A dual-layer LSTM reached the highest test-set NSE of 0.7436. A single-layer LSTM (0.7403), SVR (0.7398), and dual-layer GRU (0.7348) fell within a 1% NSE band. A Random Forest baseline reached only 0.6724. The NSE ceiling reflects the baseflow-dominated variance of the inflow series, not a weakness of the models. The ablation study shows that the CCF-derived lag feature gives the largest single rainfall-related accuracy gain. Forecasts are near-unbiased at the 5 min step. The compact feature space lets operators swap the model to match on-site computing limits, which makes the framework easy to deploy at other sponge city communities.