Latest IWMI Publications

Citizen Service Center (CSC) Operators’ Training Manual for PM KUSUM Application Support in Uttar Pradesh, India

2026-10-22T10:06:54Z

dc.title: Citizen Service Center (CSC) Operators’ Training Manual for PM KUSUM Application Support in Uttar Pradesh, India dc.contributor.author: Khan, Ramsha; Banerjee, Anurag; Shukla, K. C.; Varshney, Deepak cg.contributor.programAccelerator: Scaling for Impact

Inclusive and Responsible Scaling in Zambia: How Enabling Environment Barriers Shape Enterprise Entry, Growth, Resilience, and Innovation Uptake Among Women and Youth

2026-10-22T07:37:43Z

dc.title: Inclusive and Responsible Scaling in Zambia: How Enabling Environment Barriers Shape Enterprise Entry, Growth, Resilience, and Innovation Uptake Among Women and Youth dc.contributor.author: Kasoma-Pele, Winnie; Enokenwa Baa, Ojongetakah; Nigussie, Likimyelesh; Wapamesa, Agrey; Nkosi, Mahlatse dcterms.abstract: This scoping review examines how Zambia’s enabling environment influences the inclusive and responsible scaling of agricultural innovations, particularly among women- and youth-owned enterprises. Conducted under the CGIAR Scaling for Impact (S4I) Program, the review assesses six interconnected domains: policy and regulation, financial market access, market coordination, institutional capacity, Gender Equality and Social Inclusion (GESI), and climate finance. It focuses on three Solution Tracks: inclusive mechanization, climate-smart agro-advisories, and forage-based livestock feed innovations. The findings reveal that regulatory complexity, limited access to affordable finance, fragmented markets, weak institutional coordination, discriminatory social norms, and restricted climate-finance opportunities collectively constrain enterprise entry, formalization, growth, resilience, and innovation uptake. These barriers disproportionately affect women and youth, particularly those operating informal enterprises with limited productive assets, market access, and decision-making power. The review highlights the importance of innovation delivery systems in connecting enterprises to technologies, finance, information, markets, and institutional support. It emphasizes that successful scaling requires more than expanding innovation reach; it must also ensure sustained adoption, equitable benefit sharing, and effective management of potential social and economic risks. The review recommends integrated interventions that simplify regulatory processes, expand inclusive and climate-resilient finance, strengthen market linkages and institutional capacity, and promote gender-responsive governance. It also proposes a preliminary AoW3 framework for estimating program reach and investment mobilization, supported by improved monitoring and evidence generation. Ultimately, responsible scaling requires coordinated systemic reforms that enable women and youth to participate meaningfully, sustain enterprise growth, and benefit equitably from agricultural transformation. cg.contributor.programAccelerator: Scaling for Impact

Knowledge Exchange Network (KEN) Innovation Showcase

2026-10-22T01:03:03Z

dc.title: Knowledge Exchange Network (KEN) Innovation Showcase dc.contributor.author: Langa, Nicole; Retief, Hugo; Garcia Andarcia, Mariangel; Roman, Henry dcterms.abstract: This report synthesises the proceedings and key findings of the Knowledge Exchange Network (KEN) Innovation Showcase, hosted by the International Water Management Institute (IWMI) in Pretoria, South Africa, from 2–4 June 2026, with support from Enabel’s Innovation Hub. The workshop brought together eight KEN member organisations and invited stakeholders to demonstrate digital innovations, exchange implementation experiences and explore pathways for collaboration and sustainability beyond grant funding. Organised around innovation, integration and impact, the three-day programme combined business-model development, demand-driven design, storytelling, technology demonstrations, digital inclusion assessments and policy advocacy. Demonstrations included the Limpopo River Basin Digital Twin, AI-assisted citizen-science monitoring and Water Copilot, alongside partner innovations in rural water services, community communication, landscape monitoring and digital learning. These exchanges highlighted how interoperable systems can connect community-generated observations, remote sensing, institutional records and modelling to support more accessible and informed decision-making. The workshop identified user-centred co-design, trusted partnerships, institutional ownership, responsible AI and sustainable financing as essential conditions for scaling. Discussions on the Multidimensional Digital Inclusiveness Index emphasised the need to address barriers associated with connectivity, affordability, language, disability, safety and digital literacy throughout development and implementation. A pre-mortem exercise identified shared risks, including weak data integrity, limited adoption, insufficient revenue, maintenance constraints and dependence on individual champions. Proposed responses included diversified financing, shared technical services, stronger partnerships, succession planning and clearer evidence-to-policy pathways. The report provides practical lessons and initial building blocks for continued KEN collaboration, demonstrating that lasting impact depends on the wider ecosystem supporting digital innovation. cg.contributor.programAccelerator: Digital Transformation

Integrating Multi-Sensor Satellite Data with Machine Learning for Irrigation Demand Forecasting and Climate-Resilient Agriculture in the Indus Basin

2026-10-21T05:42:26Z

dc.title: Integrating Multi-Sensor Satellite Data with Machine Learning for Irrigation Demand Forecasting and Climate-Resilient Agriculture in the Indus Basin dc.contributor.author: Hafeez, Mohsin; Aeman, Hafsa; Liaqat, Umar Waqas; Munir, Sarfraz; Bodla, Habibullah; Maqsood, Aarish dcterms.abstract: Pakistan's agricultural sector faces a growing water crisis, exacerbated by climate variability, over-extraction of groundwater, and misalignment between traditional irrigation scheduling and actual crop needs. Because 90% of the country's freshwater is used for irrigation, developing tools to improve water-use efficiency is essential to achieving food and water security. Early forecasting of crop-specific irrigation demand allows farmers, irrigation managers, and policymakers to align water supply with dynamic field requirements, optimize canal operations, and reduce water wastage. Early forecasting of crop irrigation demand enhances water efficiency and supports food security. Accurate irrigation forecasts enable farmers and decision-makers to schedule irrigation based on crop types and climate conditions, ensuring crops receive adequate water. Traditional biophysical models predict growth based on climate, soil, and irrigation factors, often assuming fixed water delivery schedules. These models focus on broad hydrological trends but lack tailored irrigation forecasts for specific crop stages. While machine learning (ML) has been applied to predict yield and biomass, forecasts of crop water requirements have been overlooked. The availability of cloud platforms such as Google Earth Engine (GEE) has revolutionized large-scale geospatial analysis, enabling timely, spatially resolved forecasting of agricultural irrigation demand. This is crucial for sustainable water use and agricultural resilience. GEE's cloud computing capabilities allow for parallel processing, reducing the need for local storage or high-performance computing infrastructure. Additionally, integrating remote sensing (RS) data archives with ML models and scalable application services creates a powerful environment for operational, data-driven irrigation applications. While many RS-based studies in GEE have focused on land use and crop classification, few have developed operational tools for irrigation forecasting using integrated climate and satellite information. This study addresses that gap by developing a cloud-based irrigation forecasting system within GEE, integrated with Google for training and testing geospatial remote sensing datasets to generate early irrigation forecasts. The system integrates multi-sensor satellite data, including Landsat-8, Sentinel-2, and PlanetScope, with archival datasets from 2015 to 2025 at 30 m, 10 m, and 3 m spatial resolutions, respectively, and combines them with climate information. A Random Forest (RF) machine learning model was implemented and trained using an 80-20% split to estimate crop-specific water requirements, measured as actual evapotranspiration (ETact), across spatial and temporal scales. The framework automates data retrieval and irrigation forecast generation, demonstrating GEE's potential as a cloud-native platform for real-time agricultural water management. Trained models demonstrated high validation accuracy for NDVI (R² = 0.9224), LST (R² = 0.8602), Rn (R² = 0.84), and SAVI (R² = 0.87). Models such as Convolutional Neural Network (CNN), XGBoost, and Random Forest were trained using an 80-20 train-test split. CNN achieved the highest R² of 0.89, followed by XGBoost (R² = 0.81) and Random Forest (R² = 0.76). These predicted variables served as inputs to an RF model that estimated ETact, the core metric used to calculate irrigation demand. The irrigation forecast was tested over two cropping seasons (Kharif and Rabi) using observed ET data from an Eddy Covariance Flux Tower. For rice in Kharif, CNN predicted 6.798 mm/day (flux tower: 6.99 mm/day), and for wheat in Rabi, CNN predicted 2.041 mm/day (observed: 1.86 mm/day). In wheat’s growth phase, CNN forecasted 29.87 mm/day, close to the flux tower’s 33 mm/day. Overall, CNN outperformed other models across various crops, showing strong spatial and temporal accuracy. The forecasting information was applied to wheat cropping systems during the Rabi season. Weekly ET maps generated by the system supported precise and timely irrigation scheduling. Integrating satellite-derived biophysical variables into the GEE cloud-based application system enables informed decision-making for farmers, extension agents, and water managers to advance sustainable water resource planning amid increasing climate variability. cg.contributor.programAccelerator: Policy Innovations

Scaling Water Bunds for Climate Resilience Management in Kenyan Rainfed Agri-Food Systems (SWaRM)

2026-10-21T04:06:08Z

dc.title: Scaling Water Bunds for Climate Resilience Management in Kenyan Rainfed Agri-Food Systems (SWaRM) dc.contributor.author: Nkosi, Mahlatse; Buisson, Marie-Charlotte; Owusu, Afua; Zane, Giulia dcterms.abstract: Kenya’s arid and semi-arid lands (ASALs) face compounding pressures from land degradation, erratic rainfall, and recurrent drought. Water bunds, simple semi-circular earthen structures dug along contour lines to capture rainwater, reduce runoff, and regenerate vegetation, offer a proven nature-based response. The policy challenge is no longer whether water bunds work, but how to scale adoption efficiently and equitably across Kenya’s diverse tenure contexts and governance settings. The Scaling Water bunds for climate Resilience Management in Kenyan rainfed agri-food systems (SWaRM) project addresses this through a four-year bilateral research program funded by the Netherlands-CGIAR Water-Food Nexus Research Program (NWO). Led by IWMI in consortium with Vrije Universiteit Amsterdam, Tegemeo Institute, and Justdiggit, SWaRM combines economic analysis of adoption determinants, impact evaluation of co-designed scaling tools, and multi-stakeholder platform facilitation for evidence-to-policy translation. Findings are expected to inform Kenya’s Land Reclamation Policy and generate transferable lessons for scaling nature-based water retention solutions across sub-Saharan Africa’s drylands. cg.contributor.programAccelerator: Sustainable Animal and Aquatic Foods

Early Forecasting of Crop Irrigation for Sustainable Water Use with Satellite Data and Machine Learning

2026-10-21T04:15:34Z

dc.title: Early Forecasting of Crop Irrigation for Sustainable Water Use with Satellite Data and Machine Learning dc.contributor.author: Aeman, Hafsa; Hafeez, Mohsin; Munir, Sarfraz dcterms.abstract: Early forecasting of crop irrigation demand improves water efficiency and supports food security. Accurate forecasts allow farmers and policymakers to schedule irrigation based on crop type and climate, ensuring timely water application throughout the growing season. Traditional biophysical models rely on fixed delivery schedules and broad hydrological trends, often lacking precision for specific crop stages. While machine learning (ML) has been widely used for yield and biomass prediction, its application in forecasting crop water requirements remains limited. This study bridges that gap by integrating remote sensing with ML to align water supply with crop demand under changing climate conditions, promoting sustainable irrigation. The study focuses on Pakistan’s Indus Basin, specifically Chaj Doab, located between the Jhelum and Chenab rivers. The region features flat terrain with coarse-textured alluvial soils and high evapotranspiration with wide variety of crops. The proposed methodology for estimating irrigation demand uses actual evapotranspiration (ETact) derived from satellite-based biophysical and climatic variables. Landsat imagery with a spatial resolution of 30 m resolution from 2015 to 2025 was used to calculate normalized difference vegetation Index (NDVI), soil adjusted vegetation index (SAVI), land surface temperature (LST), and net radiation (Rn). The dataset was split with 80% used for training and 20% for validation, to simulate a continuous forecasting scenario. The primary objective was to evaluate model performance in an unseen future period, reflecting irrigation forecasts in practice rather than re-learning from shuffled segments through temporal cross-validation. By contrast, the 80-20 split ensured a long historical record for vigorous training and a sufficiently large, continuous block of unseen data that spans entire season irrigation requirement which validation against observed evapotranspiration (ET) and local climate data from the Eddy Covariance Flux Tower, providing reliable ground-truth checks across entire cropping cycle. The machine learning models tested included CNN, XGBoost, and Random Forest. Among these, CNN achieved the highest performance with an R² of 0.89, followed by XGBoost (R² = 0.81) and Random Forest (R² = 0.76) on the testing samples. The short-term irrigation forecasting model was evaluated across two cropping seasons Kharif and Rabi, using observed ET values and local climate data from an Eddy Covariance Flux Tower. For rice during Kharif, CNN predicted 6.798 mm/day compared to the flux tower's 6.99 mm/day. During Rabi, the model predicted wheat ET at 2.041 mm/day, closely matching the observed 1.86 mm/day. During the growth phase of wheat in Rabi season, CNN forecasted ET at 29.87 mm/day, closely matching the flux tower measurement of 33 mm/day. Similarly, during early April, the model estimated 12.12 mm/day versus an observed 13.15 m/day. The lowest deviation occurred during the week of December, with both CNN and flux tower ET values closely aligned (6.99 and 6.89 mm/day, respectively). Overall, CNN showed the highest correlation than other models across multiple crops (maize, potato, guava, and orchards), showing strong spatial accuracy and temporal relevance. The outcomes support a wide range of users including farmers, local organizations, and decision-makers by enabling proactive irrigation planning. cg.contributor.programAccelerator: Policy Innovations

AI-Based Irrigation Demand Forecasting for Climate-Adaptive Crop Water Management in Irrigated Agriculture

2026-10-21T04:12:21Z

dc.title: AI-Based Irrigation Demand Forecasting for Climate-Adaptive Crop Water Management in Irrigated Agriculture dc.contributor.author: Aeman, Hafsa; Hafeez, Mohsin; Munir, Sarfraz dcterms.abstract: Accurate and timely forecasting of crop irrigation demand is a critical agrometeorological challenge, particularly in regions experiencing intensifying climate variability, water scarcity, and growing food insecurity. Weather conditions, including temperature extremes, precipitation deficits, and elevated evaporative demand, directly drive crop water requirements and can significantly reduce yields when irrigation scheduling is poorly timed. Existing studies on crop water demand have employed machine learning (ML) for actual evapotranspiration (ETa) prediction, incorporating feature selection to reduce data dimensionality and mitigate multicollinearity, thereby improving model performance. However, existing ETa models have limited spatial continuity and restricted applicability under changing climate conditions. This study addresses these limitations by developing a machine learning-based Irrigation Demand Forecasting Model (AI-IDFM) that integrates multi-sensor satellite data, spectral indices, and various climatic variables to determine crop water requirements under changing climate conditions. The actual crop evapotranspiration (ETact), a key agrometeorological variable linking atmospheric demand and crop water use is estimated through a surface energy balance framework using Landsat satellite data with 30 m spatial resolution, complemented by high-resolution (3 m) land cover information, capturing fine-scale microclimatic variability relevant for field-level irrigation decision-making. The key variables, including NDVI, SAVI, LST, and net radiation (Rn), along with climatic variables such as precipitation, temperature, and humidity, are incorporated into the model. Deep learning and machine learning approaches, including Convolutional Neural Network–Multilayer Perceptron (CNN-MLP), Random Forest (RF), and Gradient Boosting (GB), were used for training and validation. The CNN-MLP model performed best, achieving an R² of 0.90, followed by RF and GB with R² values of 0.87 and 0.80, respectively. From this study, the short-range irrigation advisories are generated by coupling AI-IDFM with daily meteorological outputs from the ICON ensemble model, providing 7-day forecasts of key atmospheric drivers, including temperature, total precipitation, and relative humidity. Forecasted inputs for April 2025 indicated temperatures ranging from 36.2-42.8°C, average humidity between 18.5-43.7%, reflecting intense thermal stress during the pre-Kharif transition period that directly amplify crop water demand. Model performance showed that CNN predictions closely matched observed ETact for rice (6.798 vs. 6.99 mm/day) and wheat (2.041 vs. 1.86 mm/day), validated against eddy covariance flux tower observations across Kharif and Rabi cropping seasons in Okara district, Punjab. The model consistently outperformed ensemble methods across diverse crop types. During the Rabi wheat growth phase, CNN forecasted ET at 29.87 mm/day compared to a flux tower measurement of 33 mm/day, with the lowest deviation recorded in December (6.99 vs. 6.89 mm/day). The results demonstrate that AI-IDFM provides a robust, weather-informed irrigation advisory framework capable of supporting farm-level decision-making across diverse cropping systems. The approach links weather data to actionable irrigation decisions, enabling scalable management from field to regional levels, improving water use efficiency and supporting climate-resilient agriculture in data-scarce systems. cg.contributor.programAccelerator: Policy Innovations

Synthesizing Flood Exposure Maps for Comprehensive Risk Assessment

2026-10-20T08:27:56Z

dc.title: Synthesizing Flood Exposure Maps for Comprehensive Risk Assessment dc.contributor.author: Gebre, Eden Seifu; Haile, Alemseged Tamiru; Bekele, Tilaye Worku; Asfaw, Wegayehu; Trigg, Mark A. dcterms.abstract: Flooding poses a major risk to rapidly growing cities like Addis Ababa, Ethiopia, where urban expansion, increasing impervious surfaces, and climate change continually intensify flood hazards. This study evaluates urban flood exposure by systematically comparing flood maps derived from fundamentally distinct methodologies. The primary objectives are to (i) delineate empirical flood exposure by integrating a multi-temporal Sentinel-1 SAR inundation map (2017–2023) with datasets on population, buildings, and road networks; and (ii) assess spatial agreement among exposure maps produced via hydrodynamic modeling, machine learning-based susceptibility analysis, and SAR mapping. The SAR-derived map was validated using a synthesized dataset of institutional records and citizen-science observations. Results reveal that 57 km² of buildings and 18 km of road networks are highly exposed to recurrent flooding. Furthermore, a critical spatial comparison using Jaccard (0.014–0.072) and Dice (0.027–0.143) coefficients, Moran’s I, and topographic correlation analysis revealed profound disagreement in flood risk extent and spatial distribution. These extreme discrepancies mathematically confirm the models capture distinct physical mechanisms rather than acting as redundant proxies. Consequently, relying on a single mapping framework inevitably biases urban flood risk estimation. This study provides baseline evidence advocating for a multi-criteria approach to support robust urban disaster risk management.

Enhancing Policy-Relevant Evidence for Improving Planning and Management of Smallholder Irrigation, Ethiopia

2026-10-20T01:09:22Z

dc.title: Enhancing Policy-Relevant Evidence for Improving Planning and Management of Smallholder Irrigation, Ethiopia dc.contributor.author: Tadesse, Mulugeta; Taye, Meron Teferi; Abdella, Mohammed; Gebreyesus, Kirubel; Ebrahim, Girma Yimer; Seid, Abdulkarim dcterms.abstract: Irrigation is expanding in Ethiopia, drawing on both surface water and groundwater resources. However, as irrigation expands, the sustainable use of these resources may become increasingly challenging without adequate monitoring and management. Reliable information on the extent of land under irrigation in a given season, as well as the areas likely to be irrigated in subsequent seasons, is essential for evidence-based irrigation planning and water resource management. Yet, such information is often unavailable or insufficiently accurate at the local level. This brief demonstrates an improved approach to mapping irrigated areas using Earth observation (EO) data. It highlights how accurate and timely information on irrigated areas can support more effective water resource management, inform irrigation planning, and contribute to Ethiopia’s long-term agricultural development. cg.contributor.programAccelerator: Policy Innovations

Local Knowledge and Institutions in Land Resources Management in Ethiopia

2026-10-20T01:04:39Z

dc.title: Local Knowledge and Institutions in Land Resources Management in Ethiopia dc.contributor.author: Mekuria, Wolde; Tegegne, Desalegn; Moges, Awdenegest; Tesfaye, Yitna; Teshale, Tirusew; Girma, Rediet dcterms.abstract: Local knowledge is a decisive yet under-recognized asset for sustainable land management (SLM) in Ethiopia’s Rift Valley landscapes. This brief synthesizes three complementary Halaba publications: a study of farmers’ perceived benefits and disbenefits of adopted SLM practices, a participatory resource-mapping study of gully rehabilitation, and a mixed-method assessment of inclusive land restoration. The evidence shows how knowledge held by farmers, elders, women, youth, watershed committees, and local practitioners improve decisions throughout the SLM cycle. Lessons from documented REDAA-SMILE project activities further demonstrate practical entry points: participatory resource mapping, validation of land and water management options, community-based restoration planning, field demonstrations, outreach, local rules and follow-up.

Recovering Daily Streamflow Data Gaps in the Lake Tana Sub-Basin: Leveraging Machine Learning and Remote Sensing Techniques in Ethiopia

2026-10-20T08:26:37Z

dc.title: Recovering Daily Streamflow Data Gaps in the Lake Tana Sub-Basin: Leveraging Machine Learning and Remote Sensing Techniques in Ethiopia dc.contributor.author: Lakew, Haileyesus Belay; Haile, Alemseged Tamiru; Taye, Meron Teferi; Asfaw, Wegayehu; Tedla, Hailay Zeray dcterms.abstract: Reliable daily streamflow observations are essential for sustainable water resource management, yet many developing countries face significant gaps in streamflow records due to conflict and financial constraints. This study evaluates machine learning (ML) and climatological mean (CM) approaches for filling long-term daily streamflow gaps in the Ribb and Gilgel Abbay watersheds of the Lake Tana sub-basin, upper Blue Nile basin. Specifically, decision tree regression (DTR), artificial neural network (ANN), linear regression (LR), seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), and CM methods were compared. Input variables for ML included remote sensing-based daily rainfall (with 0–4-day lags), season index, and date features. Model performance was assessed using Nash–Sutcliffe efficiency (NSE), Bias, coefficient of correlation (CC), and Kling–Gupta efficiency (KGE). DTR outperformed all other methods, achieving NSE scores of 0.81–0.82, Bias between 6.8 and 13.9%, CC of 0.90–0.93, and KGE of 0.82–0.85. It effectively preserved temporal variability and reproduced peak-flow events more accurately than both other ML methods and the CM approach. The results demonstrate that integrating DTR with rainfall and temporal features provides a reliable framework for estimating long-term streamflow data gaps, supporting effective water resource planning and management in regions with limited continuous streamflow observations.

Is Water User Association Governance a Reliable Measure of Irrigation Performance?

2026-10-16T10:08:51Z

dc.title: Is Water User Association Governance a Reliable Measure of Irrigation Performance? dc.contributor.author: Rajkhowa, Pallavi; Sikka, Alok; Sarangi, Arjamadatta cg.contributor.programAccelerator: Policy Innovations

What Keeps Proven Irrigation Innovations from Widespread Adoption in Nigeria?

2026-10-15T10:58:23Z

dc.title: What Keeps Proven Irrigation Innovations from Widespread Adoption in Nigeria? dc.contributor.author: Osei-Amponsah, Charity; Oke, Adebayo; Owolabi, Mubaraq Adetunji cg.contributor.programAccelerator: Scaling for Impact

Learnings from the Multidimensional Digital Inclusiveness Index: Centralizing Tool Recommendations for Inclusive Innovations

2026-10-16T01:06:58Z

dc.title: Learnings from the Multidimensional Digital Inclusiveness Index: Centralizing Tool Recommendations for Inclusive Innovations dc.contributor.author: Duarte, Ana Marta; Ziyard, Saliha; Martins, Carolina Iglesias; Garcia Andarcia, Mariangel dcterms.abstract: Digital tools are increasingly central to how CGIAR helps transform food, land and water systems, but they only deliver impact when the farmers, extension services and institutions they are built for can access, understand and trust them. The Multidimensional Digital Inclusiveness Index (MDII) evaluates how inclusive these agrisystem tools are across seven dimensions, from accessibility to ethics and co-creation. This project note, part of the MDII Learnings Series, presents the Recommendation Repository, which turns MDII evaluation results into feasible, prioritized improvements that development teams can act on without costly redesigns. Built from evaluations of eight IWMI WaPOR-based tools in five countries across Africa, the Middle East and South Asia, its findings show that inclusiveness gaps lie as much in governance, ethical safeguards and institutional support as in interface design. By centralizing recommendations, the repository lets development teams learn from patterns that recur across tools and countries rather than starting from zero, reduces duplicated effort, and makes improvement decisions transparent and evidence-based. In doing so, it strengthens responsible digital innovation across CGIAR’s portfolio and helps ensure that investments in digital agriculture reach the people they are meant to serve. cg.contributor.initiative: Digital Innovation cg.contributor.programAccelerator: Digital Transformation; Sustainable Farming

ආපදා අවස්ථාවකින් පසු දූෂිත පානීය ජලය කෙටිකාලීනව ආරක්ෂිතව භාවිතා කිරීම සහ දූෂිත පානීය ජල ළිං පිරිසිදු කිරීම සම්බන්ධයෙන් ශ්‍රී ලාංකික ජන සමාජය ස‍ඳහා ප්‍රජා මාර්ගෝපදේශය

2026-10-21T08:04:51Z

dc.title: ආපදා අවස්ථාවකින් පසු දූෂිත පානීය ජලය කෙටිකාලීනව ආරක්ෂිතව භාවිතා කිරීම සහ දූෂිත පානීය ජල ළිං පිරිසිදු කිරීම සම්බන්ධයෙන් ශ්‍රී ලාංකික ජන සමාජය ස‍ඳහා ප්‍රජා මාර්ගෝපදේශය dc.contributor.author: International Water Management Institute dcterms.abstract: This guide explains how communities can maintain safe drinking water during floods and other emergencies as an interim measure until regular water supply is reinstated. During such events, wells, taps, and storage systems may become contaminated by various pollutants. It advocates a multi-barrier approach encompassing source protection, hygienic collection and transport, household treatment, safe storage, and careful handling. Water sources should be fenced, protected from runoff, animals, latrine leakage, and waste, and supported by adequate drainage. The guide also outlines procedures for restoring flood-contaminated wells, which includes assessing contamination sources and structural damage, removing debris and sediment, flushing the well, and conducting chemical treatment. Furthermore, the guide emphasizes that regular inspection, user education, filter replacement, and ongoing maintenance are essential to prevent future contamination.

Development and Operationalization of a Citizen Science Framework for Hydrological Monitoring and Knowledge Co-Generation

2026-10-20T08:22:35Z

dc.title: Development and Operationalization of a Citizen Science Framework for Hydrological Monitoring and Knowledge Co-Generation dc.contributor.author: Haile, Alemseged Tamiru; Bekele, Tilaye Worku; Ferede, Mintamer; Demissie, Kirubel; Tedla, Hailay Zeray; Teshale, Yirgalem; Feyisa, Gudina Legesse; Haileslassie, Amare dcterms.abstract: Citizen science (CS) empowers communities in data collection and problem-solving, enhancing research inclusivity and local relevance. However, the absence of a comprehensive framework limits their development and potential contributions to sustainable practices. In this study, we developed and tested a CS framework for hydrological data collection and the co-creation of nature-based solutions (NbS). The framework has 3 major stages: establishment, operation and knowledge generation - each involving multiple activities. It was piloted in the Kelekindo watershed of Central Rift Valley Lakes Basin of Ethiopia, engaging the citizen scientists (CSts) in monitoring of rainfall, river water level, shallow groundwater (SGW) and water quality. The CS data allowed to investigate poorly understood hydrological behaviour of the watershed, including the fill-and-spill effect and characterization of an extreme runoff event. Co-interpretation of the data showed that SGW recharge could support thousands of farmers in practicing backyard irrigation. The river and well water in the study area were within safe limits for irrigation in terms of the commonly monitored water quality parameters. CSts were engaged in the selection and co-design of a priority NbS for watersheds. The study showed that a well-designed citizen science framework can generate reliable hydrological data and meaningfully engage communities. cg.contributor.programAccelerator: Scaling for Impact

Inadequate Waste Management Drives the Spread of Antimicrobial Resistance across the Watershed of a Large African City

2026-10-15T01:06:57Z

dc.title: Inadequate Waste Management Drives the Spread of Antimicrobial Resistance across the Watershed of a Large African City dc.contributor.author: Zhou, Yanyan; Haile, Alemseged Tamiru; Kibret, Mulugeta; Li, Hu; Haileselassie, Mihret Mersha; Bekele, Tilaye Worku; Kassa, Jemila Mohammed; Demissie, Kirubel; Wyrsch, Ethan R.; Djordjevic, Steven P.; Jobling, Kelly; Quintela-Baluja, Marcos; Su, Jian-Qiang; Mateo-Sagasta, Javier; Graham, David W. dcterms.abstract: Rapid urbanization in the Global South consistently outpaces sanitation infrastructure, transforming urban rivers into AMR reservoirs. Yet watershed-scale quantification of resistomes in such settings remains scarce, limiting exposure risk assessment and intervention planning. Here we present the most detailed spatial baseline for waterborne AMR in sub-Saharan Africa, using the Akaki River (Addis Ababa, Ethiopia) as a model system for rapidly urbanizing megacity watersheds with inadequate sanitation. We sampled 38 sites across an extreme pollution gradient during the dry season, when low flow produces maximum pollutant concentrations and the highest resident exposure risk. Water quality, microbiome marker data, 309 antibiotic resistance genes (ARGs), and 57 mobile genetic elements (MGEs) were quantified by high-throughput quantitative PCR. ARG and MGE abundances in central urban reaches exceeded 1010 gene copies L-1, indistinguishable from untreated sewage. Critically, clinically important ARGs co-occurred widely with broad-host-range MGEs, including the IncP-1α plasmid backbone, IS26, ISEcp1, IS6100, and class 1 integron integrase genes, indicating that ARGs are primed for potential horizontal gene transfer in waters where hundreds of thousands of Addis residents bathe, wash, and irrigate food crops. Dissolved oxygen depletion and elevated NH4-N correlated significantly with resistome expansion (p < 0.01), serving as robust chemical proxies for diffuse, system-wide raw sewage inputs that carry the actual mechanistic AMR drivers (e.g., heavy metals, antibiotic residues, and resistant hosts). The 38-site spatial design and dry-season worst-case conditions together provide a replicable model for AMR exposure assessment in comparable Global South cities. cg.contributor.initiative: One Health cg.contributor.programAccelerator: Sustainable Animal and Aquatic Foods

A Study on the Water-Energy-Food-Ecosystem (WEFE) Nexus in the Niger-Benue River System, Nigeria

2026-10-14T01:02:05Z

dc.title: A Study on the Water-Energy-Food-Ecosystem (WEFE) Nexus in the Niger-Benue River System, Nigeria dc.contributor.author: Zemadim, Birhanu; Mortey, Eric; Oke, Adebayo; Osei-Amponsah, Charity; Hafeez, Mohsin dcterms.abstract: The Niger–Benue River System is one of Nigeria’s most strategic hydrological networks, supporting irrigation, hydropower, fisheries, urban water supply, and ecosystem services. It faces growing pressure from unregulated abstraction, flooding, dry-season scarcity, pollution, land degradation, and fragmented institutions. This study applies the Water–Energy–Food–Ecosystem (WEFE) Nexus framework to assess spatial patterns and interdependencies across four hydrological areas: Niger North, Niger Central, Upper Benue, and Lower Benue. Using systems analysis, GIS, and open-source remote sensing datasets, the study maps key WEFE indicators, including land cover, runoff, groundwater storage, hydropower, solar and biomass potential, irrigated agricultural productivity, protected and wetland areas, evapotranspiration, and erosion risk. Resources are abundant but unevenly distributed; Upper Benue is the main runoff engine and has the strongest untapped solar and biomass potential, Niger North holds the largest groundwater storage, which supports dry-season agricultural resilience; Niger Central dominates existing hydropower and total agricultural production, and Lower Benue—despite its small area—has the highest erosion burden and the lowest protected-area coverage, indicating significant ecosystem vulnerability. The system cannot be managed through sector-specific or uniform interventions. Basin-specific strategies are needed to balance water potential, energy development, food production, and ecosystem protection. Priorities include coordinated governance, data-driven planning, and targeted investment in renewable energy, irrigation efficiency, wetland conservation, and erosion control. Strengthening ecosystem resilience is both an environmental priority and a foundation for Nigeria’s water, energy, and food security. cg.contributor.programAccelerator: Policy Innovations

What Nigeria Needs to Scale Solar Irrigation

2026-10-13T10:11:42Z

dc.title: What Nigeria Needs to Scale Solar Irrigation dc.contributor.author: Ojeleye, Oluwaseun A.; Oke, Adebayo; Tilahun, Seifu A.; Balana, Bedru; Kirui, Oliver K.; Abdullahi, Ibrahim cg.contributor.programAccelerator: Scaling for Impact

The Evolution of an Idea: 15 Years of the Water–Energy–Food–Environment Nexus

2026-10-13T04:44:15Z

dc.title: The Evolution of an Idea: 15 Years of the Water–Energy–Food–Environment Nexus dc.contributor.author: Junaid, Novaira; Hafeez, Mohsin; Asayama, Yumiko; Ringler, Claudia; Imran, Tania; Sinha, Vishwa Ranjan; Urfels, Anton; Ishii, Satoshi; Huizer, Tanya; Smith, Mark cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

Towards Sustaining Healthy Flows and Functions in the Omo-Gibe Basin: Second Stakeholder Workshop

2026-10-10T01:07:25Z

dc.title: Towards Sustaining Healthy Flows and Functions in the Omo-Gibe Basin: Second Stakeholder Workshop dc.contributor.author: Simaika, John; Haile, Alemseged Tamiru dcterms.abstract: On 25 September 2026, a second stakeholder workshop in Addis Ababa, building on the May 2026 consultation on environmental flows was held. Environmental flows (e-flows) are the amount, timing and quality of water a river needs to stay healthy and keep supporting people, farms and ecosystems. The event was part of IWMI's work under the CGIAR Multifunctional Landscapes Program. In the Omo-Gibe River Basin in Ethiopia a series of hydropower dams, expanding irrigation, limited monitoring data and variable rainfall all shape how water reaches the floodplains, communities and Lake Turkana downstream. Workshop participants came from government agencies, universities, research organizations, engineering institutions and civil-society groups. They reviewed new evidence on climate trends and field observations, the basin's ecosystem services, and the condition of the hydrological monitoring network. They also looked at a desktop-based e-flows model that estimates natural river flows where there are no gauges and at the results of a pre-workshop stakeholder questionnaire. Several messages came through consistently: (a) River health depends on seasonal floods, not just a minimum flow; (b) Water quantity and quality need to be assessed together; (c) Better monitoring is essential, and model results must be checked against field data; and, (d) Weak coordination between institutions remains a major barrier. The workshop set priorities for the next phase. These include tracking the strength of the evidence, assessing monitoring gaps, choosing test stretches of river, setting indicators, checking the models, clarifying institutional roles and running hands-on training. Together, these will feed into a workplan for assessing environmental flows in the Omo-Gibe. cg.contributor.programAccelerator: Multifunctional Landscapes

Why the next Agricultural Revolution Demands Policy Precision

2026-10-13T04:40:35Z

dc.title: Why the next Agricultural Revolution Demands Policy Precision dc.contributor.author: Urfels, Anton; Morovati, Khosro; Akram, Iqra; Holmatov, Bunyod cg.contributor.programAccelerator: Policy Innovations

The Silent Salinity Crisis Threatening Global Food Security

2026-10-13T04:14:26Z

dc.title: The Silent Salinity Crisis Threatening Global Food Security dc.contributor.author: Islam, Feroz; van Scheltinga, Catharien Terwisscha; Sikka, Alok; Hafeez, Mohsin cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

Transforming Agricultural Water Management for Healthy People and Planet: Thinking Beyond Water

2026-10-10T01:04:47Z

dc.title: Transforming Agricultural Water Management for Healthy People and Planet: Thinking Beyond Water dc.contributor.author: Mabhaudhi, Tafadzwanashe; Taguta, Cuthbert; Nhamo, Luxon; Mpandeli, Sylvester; Senzanje, Aidan; Madani, Kaveh; Dirwai, Tinashe Lindel dcterms.abstract: This paper develops the concept of Thinking Beyond Water, a transdisciplinary paradigm that reframes agricultural water management (AWM) as a systems catalyst for planetary health, equity, justice and resilience. Moving beyond conventional irrigation-centric models, we reimagine AWM through integrative frameworks such as the planetary boundaries, the doughnut economy and just transitions. We examine the limits of efficiency metrics and highlight trade-offs between equity and resource allocation and ecological externalities of infrastructure and technology-led approaches. We argue that AWM decisions can reinforce exclusion, ecological degradation and policy fragmentation when they are not assessed against planetary health criteria. Thinking beyond water does not replace nexus, systems or resilience approaches; it connects them to explicit planetary thresholds, justice criteria and governance. We set out three propositions: a systemic appraisal matrix that distinguishes threshold criteria that cannot be traded off against productivity from criteria that are scored and weighted; a mapping of financing instruments to the barriers they address and the safeguards they require; and a staged process for reorienting National irrigation plans. Ultimately, we propose that water must be seen not as an isolated input–output for agriculture but as a systems connector linking hydrological integrity, human and ecosystem health and human dignity across scales. cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Scaling for Impact; Sustainable Farming

Localized Nexus Solutions Could Offer a Global Blueprint for Agricultural Waste

2026-10-13T04:12:11Z

dc.title: Localized Nexus Solutions Could Offer a Global Blueprint for Agricultural Waste dc.contributor.author: Siddiqi, Afreen; Pasqual, Janaina; Marques, Felipe Souza; Hafeez, Mohsin; Somorin, Tosin cg.contributor.programAccelerator: Policy Innovations

Stakeholder engagement in food systems and nutrition research programs: Approach, insights, and lessons learned from the FRESH Initiative

2026-10-09T13:27:39Z

dc.title: Stakeholder engagement in food systems and nutrition research programs: Approach, insights, and lessons learned from the FRESH Initiative dc.contributor.author: Iruhiriye, Elyse; van den Bold, Mara; Aheeyar, Mohamed M. M.; Angeles-Agdeppa, Imelda; Mitchodigni, M., Irene; Mwombeki, Wiston; Olney, Deanna K.; Thow, Anne-Marie dcterms.abstract: Poor diets are a leading cause of malnutrition and diet-related diseases globally. Improving diets, including increasing fruit and vegetable (F&V) intake, is critical to address these challenges. Global F&V consumption, however, remains below the recommended 400 grams of fruit and vegetables per person per day. A complex set of factors related to the desirability, availability, accessibility, and affordability of F&V limits intake. This suggests that integrated, food-system-based solutions are needed. cg.contributor.initiative: Fruit and Vegetables for Sustainable Healthy Diets cg.contributor.programAccelerator: Better Diets and Nutrition

The Last Mile of Solar Irrigation in Bangladesh

2026-10-07T10:53:35Z

dc.title: The Last Mile of Solar Irrigation in Bangladesh dc.contributor.author: Bhaduri, Tanmoy; Bhattacharya, Jayanta

Toward Reliable Water Resource Assessment: Correcting Bias in Satellite-Based Rainfall and Model-Derived Discharge in Kenya’s Central Highlands

2026-10-08T01:00:35Z

dc.title: Toward Reliable Water Resource Assessment: Correcting Bias in Satellite-Based Rainfall and Model-Derived Discharge in Kenya’s Central Highlands dc.contributor.author: Yeboah, Felicia; Owusu, Afua; Leh, Mansoor; Akpoti, Komlavi; Mekonnen, Kirubel; Adamseged, Muluken Elias; Odera, Emma; Velpuri, Naga Manohar dcterms.abstract: Reliable precipitation and streamflow data are essential for hydrological modelling and water resources management, yet observational networks in many African regions remain sparse. This study evaluated five satellite precipitation products (SPPs) CHIRPS, PERSIANN-CCS, PERSIANN-PDIR, TAMSAT, and MSWEP and modelled stream-discharge data from VegET–mizuRoute model against in situ observations across the Central Highlands of Kenya for 2012–2021. Performance was assessed at eight precipitation and six discharge stations using pairwise statistics (RMSE, NSE, CC, RMB) and categorical metrics (POD, FAR) on a monthly scale. Four bias correction techniques (Least Squares, Quantile Mapping, Delta Method, Random Forest) were assessed to enhance the utility of the best-performing SPP and the modelled discharge data. CHIRPS and MSWEP consistently outperformed the other products: CHIRPS achieved RMSE of 34.85–134.26 mm, NSE of −0.94 to 0.44, and CC of 0.52–0.87, while MSWEP recorded RMSE of 24.17–125.66 mm, NSE of −0.91 to 0.65, and CC of 0.45–0.84. The VegET–mizuRoute model reproduced seasonal flow dynamics but showed station-dependent biases, with NSE from −2.28 to 0.23, RMB from −0.59 to 0.77, and CC from 0.46 to 0.71. Following bias correction, Random Forest yielded substantial improvements for both precipitation and discharge, underscoring the capacity of machine learning to address complex, nonlinear biases. It raised precipitation NSE to a consistently positive 0.85–0.95 across all eight stations while halving RMSE at most sites (9.05–46.23 mm). For discharge, Random Forest improved NSE at most stations to 0.80–0.88, with CC rising to 0.92–0.95, except at one station. Our findings confirm that systematic bias remains a dominant error source in global hydrological products in this region, and that machine learning-based bias correction is essential for the operational reliability and accuracy of these datasets for water management and flood applications. cg.contributor.programAccelerator: Sustainable Farming

Groundwater Occurrence and Quality in a Hyper-Arid Desert: Insights from High-Resolution Electrical Resistivity Surveys in Tharparkar, Pakistan

2026-10-20T13:58:31Z

dc.title: Groundwater Occurrence and Quality in a Hyper-Arid Desert: Insights from High-Resolution Electrical Resistivity Surveys in Tharparkar, Pakistan dc.contributor.author: Salam, Hafiz Abdul; Gul, Nazar; Ashraf, Muhammad; Iqbal, Naveed; Memon, Shahnawaz; Semiromi, Majid Taie dcterms.abstract: The Tharparkar District in Pakistan is among the world’s most water-scarce regions, characterized by its hyper-arid climatic conditions and the absence of perennial surface water resources. Understanding the occurrence of groundwater potential and its quality is therefore critical, particularly in remote regions where hydrogeological information remains limited. This study characterized groundwater resources using 576 high-resolution electrical resistivity survey (ERS) probes up to a depth of 300 m, conducted from 2017 to 2020. Complementary hydrochemical investigations included laboratory analysis of 168 groundwater samples collected from dug wells and 672 soil samples up to a depth of 0.60 m. Results demonstrated that the water table lies at depths of 2–15 m (26% of the area), 16–30 m (23%), and 31–90 m (51%). Interpretation of ERS data revealed that approximately 10% of the groundwater resources, covering the southern parts of Diplo, Islamkot, and Nagarparkar Talukas, are suitable for drinking at a depth of 25 m, with around 3% classified as fresh and 7% as slightly saline. Groundwater salinity increases with depth, as 51% of the area shows high salinity at depths of 26–50 m and very high salinity at depths of 51–200 m. Arsenic and fluoride concentrations exceed permissible limits in 10 and 28% of the groundwater samples, respectively. These findings provide robust geophysical and hydrochemical evidence to guide targeted groundwater development, water quality mitigation strategies and inform policy interventions, aimed at enhancing water security and safeguarding the health of communities living in severely water-stressed desert regions. cg.contributor.programAccelerator: Policy Innovations

Beyond the False Dichotomy

2026-10-07T03:35:23Z

dc.title: Beyond the False Dichotomy dc.contributor.author: Jirasinha, Radheeka; McCartney, Matthew P.; Wickramaratne, Chaturangi

Groundwater Solutions for Water Security in the Lake Victoria Basin of Kenya

2026-10-07T03:32:42Z

dc.title: Groundwater Solutions for Water Security in the Lake Victoria Basin of Kenya dc.contributor.author: Zemadim, Birhanu; Mwangi, Obadiah; Gebrezgabher, Solomie A. dcterms.abstract: The Lake Victoria Basin (LVB) is East Africa's most productive water region, generating about 59% of Kenya's surface water and supporting more than 42 million people across five countries. It sustains dense rural populations and smallholder agriculture but faces mounting pressure from land degradation, rainfall variability, pollution, and unplanned groundwater abstraction. This report synthesizes published literature on the Kenyan LVB and combines it with a groundwater assessment at Jimo Aggregated Farm, Kisumu County. From March to June 2026, IWMI and ASAL-Green consulted over 80 community members; leaders chose groundwater because the nearby Sao River flows into Lake Victoria, and authorities prohibit abstraction from it. The regional review shows that most of the Kenyan LVB lies on the Precambrian Basement Complex, where groundwater depends on weathered and fractured zones rather than primary porosity. At Jimo, a Very Low Frequency Electromagnetic Resistivity survey of 14 sounding points recommended a borehole of 120–150 m, with an expected yield of 3.0–7.0 m³/hr. Three priorities emerge: (i) pair community demand with geophysically verified siting; (ii) embed groundwater development in catchment-scale soil and water conservation, as about 45% of basin land is erosion-prone; and (iii) strengthen data-sharing through the Lake Victoria Basin Commission. Replicated community-scale assessments can improve water security and fill aquifer data gaps. cg.contributor.programAccelerator: Multifunctional Landscapes

Guidelines for the Community on the Safe Interim Use of Contaminated Drinking Water and Cleaning of Contaminated Drinking Water Wells in the Post-Disaster Sri Lankan Context

2026-10-15T01:02:51Z

dc.title: Guidelines for the Community on the Safe Interim Use of Contaminated Drinking Water and Cleaning of Contaminated Drinking Water Wells in the Post-Disaster Sri Lankan Context dc.contributor.author: International Water Management Institute dcterms.abstract: This guide explains how communities can maintain safe drinking water during floods and other emergencies as an interim measure until regular water supply is reinstated. During such events, wells, taps, and storage systems may become contaminated by various pollutants. It advocates a multi-barrier approach encompassing source protection, hygienic collection and transport, household treatment, safe storage, and careful handling. Water sources should be fenced, protected from runoff, animals, latrine leakage, and waste, and supported by adequate drainage. The guide also outlines procedures for restoring flood-contaminated wells, which includes assessing contamination sources and structural damage, removing debris and sediment, flushing the well, and conducting chemical treatment. Furthermore, the guide emphasizes that regular inspection, user education, filter replacement, and ongoing maintenance are essential to prevent future contamination.

Rights of Nature or Human Rights?

2026-10-05T10:58:05Z

dc.title: Rights of Nature or Human Rights? dc.contributor.author: Wickramaratne, Chaturangi

Benchmarking the Malawi Water Sector Joint Sector Review System: A Diagnostic Assessment and Reform Pathways for SPR–JSR Governance

2026-10-06T01:02:37Z

dc.title: Benchmarking the Malawi Water Sector Joint Sector Review System: A Diagnostic Assessment and Reform Pathways for SPR–JSR Governance dc.contributor.author: Tasowana, Akuzike; Matchaya, Greenwell C.; Ndalowa, Dan; Khosa, Sangwani; Ravindranath, Darshini; Tharmendra, Pulendran; Mailosi, Martin dcterms.abstract: Malawi has an established water-sector Joint Sector Review (JSR) that provides an important government-led platform for assessing sector performance, convening stakeholders, promoting mutual accountability and agreeing priority actions. This report examines how that foundation can be strengthened so that the JSR evolves from an annual review event into a continuous system for learning, accountability, resilience and performance improvement. Drawing on 126 stakeholder interviews, analysis of three JSR cycles (2023–2025), quantitative governance assessment and multi-stakeholder validation, the study identifies opportunities to strengthen follow-up of undertakings, ownership of agreed actions, sector data systems, financing accountability, stakeholder participation, and links between review findings and national planning and budgeting. The assessment also highlights the need to broaden the JSR’s performance lens beyond conventional WASH service indicators by more systematically incorporating water security and climate resilience, including climate risks, resilience indicators, climate expenditure, infrastructure sustainability and operations and maintenance. This would enable the JSR to better reflect Malawi’s growing exposure to droughts, floods and other water-related climate risks and connect sector performance with wider national resilience objectives. The report proposes strengthening existing government systems rather than creating parallel mechanisms. Undertaken through the Water Resilience Tracker within the FCDO-supported Just Transitions for Water Security programme, it provides an evidence base for continued government-led reform of Malawi’s water-sector accountability system.

Human Rights or Nature’s Rights

2026-10-06T07:46:06Z

dc.title: Human Rights or Nature’s Rights dc.contributor.author: McCartney, Matthew P.

How to Value Pakistan’s Water in a Changing Climate

2026-10-13T17:59:36Z

dc.title: How to Value Pakistan’s Water in a Changing Climate dc.contributor.author: Davies, Stephen; Ali, Muhammad Tahir; Ali, Mubarik; Akram, Iqra; Hafeez, Mohsin; Ali, Muhammad Faisal; Farooq, Shujaat; Ringler, Claudia cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

User Needs for AI-Supported Water Management in the Limpopo River Basin

2026-10-06T01:03:35Z

dc.title: User Needs for AI-Supported Water Management in the Limpopo River Basin dc.contributor.author: Clifford-Holmes, Jai; Retief, Hugo; Langa, Nkateko; Garcia Andarcia, Mariangel dcterms.abstract: This report presents a synthesis of user needs identified through a workshop hosted by the Limpopo Watercourse Commission (LIMCOM) in Maputo, Mozambique, on April 16, 2026. The workshop formed part of a two-day capacity building, testing, and scenario co-development event focused on the LIMCOM Digital Twin ecosystem and the AI Water Project. It brought together technical representatives from Botswana, Mozambique, South Africa, and Zimbabwe to identify requirements for dashboard design, AI-enabled modelling workflows, and conversational decision-support interfaces. The AI Water Project is being developed under the CGIAR AI Hub for Water, in partnership with IWMI, AI71, and other collaborators, with support from the UAE-Gates AI for Agriculture Ecosystem. The report provides a qualitative synthesis of expert and user input (rather than a statistically representative survey). Participants identified a need for decision support tools grounded in management questions, including water allocation, environmental flows, flood and drought management, water quality, land degradation, sedimentation, infrastructure planning, and transboundary coordination. They also highlighted data gaps, uneven monitoring coverage, limited telemetry, fragmented information systems, and varying institutional capacity. The findings indicate a demand for tools that support current status visualization, scenario comparison, natural language querying, uncertainty communication, and accessible reporting. Technical users require transparent data, assumptions, diagnostics, and exportable outputs, while policy and leadership audiences need maps, dashboards, concise summaries, and report-ready products. The report concludes that the project’s value lies in creating a usable pathway from data and models to decisions, supported by transparency, explainability, data governance, human oversight, and sustained Member State participation. cg.contributor.programAccelerator: Digital Transformation

Aligned Policies and Science Are Pathways for Ethiopia’s Smallholder Irrigation

2026-10-05T09:03:25Z

dc.title: Aligned Policies and Science Are Pathways for Ethiopia’s Smallholder Irrigation dc.contributor.author: Taye, Meron Teferi; Ebrahim, Girma Yimer cg.contributor.programAccelerator: Policy Innovations

A Roadmap and Tools for Water-Energy-Food Nexus Security in South Asia

2026-10-13T18:03:56Z

dc.title: A Roadmap and Tools for Water-Energy-Food Nexus Security in South Asia dc.contributor.author: Sikka, Alok; Holmatov, Bunyod; Ringler, Claudia; Hafeez, Mohsin cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations

The Growing Importance of Hydro-Diplomacy in Delivering Cross-Border Water Solutions

2026-10-05T08:59:55Z

dc.title: The Growing Importance of Hydro-Diplomacy in Delivering Cross-Border Water Solutions dc.contributor.author: Gafurov, Zafar; Nasser, Hazem Al cg.contributor.initiative: NEXUS Gains cg.contributor.programAccelerator: Policy Innovations; Scaling for Impact