Water Data Science for Action (WDSA)

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Co-design and implementation of Index Based Flood Insurance (IBFI) in flood proofing communities of Mazabuka District, southern Zambia
(Report, 2024-12-30) Umer, Yakob; Makungwe, Mirriam; Amarnath, Giriraj; Jacobs-Mata, Inga; Banda, N.; Njoroge, M.
The Magoye River, a vital resource for communities in Zambia's Southern Province, has a history of seasonal flooding with severe consequences. During the rainy season, the river often overflows, inundating surrounding areas and causing widespread crop and infrastructure damages. Factors such as deforestation and climate change exacerbate this natural phenomenon, leading to loss of lives, displacement, and destruction of infrastructure, farmlands, and livelihoods. An innovative insurance solution has been developed to address these challenges, compensating affected communities for losses incurred during flood events. The CGIAR Initiative on Diversification in East and Southern Africa, Ukama-Ustawi (UU), identified the bundling of climate insurance as an innovative climate risk management solution for smallholder farmers in Southern and Eastern Africa. This innovation involved mapping flood-prone agricultural areas near the river, ensuring tailored support for those most vulnerable. The innovation was aimed to mitigate the financial risks associated with recurrent flooding by providing timely insurance payouts to farmers while promoting climate resilience. The International Water Management Institute (IWMI) in collaboration with Agriculture and Climate Risk Enterprise (ACRE Africa) and Professional Insurance Company piloted the Index Based Flood Insurance (IBFI) among smallholder farmers residing along the Magoye River, Mazabuka District, Southern Province, Zambia. IWMI has developed the IBFI concept in South Asia covering India and Bangladesh using an integrated approach of combining flood model and earth observation data to flood proofing among vulnerable populations (Amarnath et al. 2024). A similar concept was scaled out for IBFI product covering three villages at the lower Magoye Catchment and first of its kind in Zambia. To build trust and enhance resilience within affected communities, a robust flood index insurance product that is flexible, transparent, and closely aligned with ground-level realities was developed. It minimizes spatial, temporal as well as design-related basis risks, ensuring payouts accurately reflect actual losses. During the development of the IBFI product, the experts from the Water Resources Management Authority (WARMA), Ministry of Agriculture, Department of Community Development, Disaster Management and Mitigation Unit (DMMU) in co-designing and implementing with the pilot villages. The IBFI pilot was conducted for the 2024-2025 rainy season in the Mogoye Catchment, Southern Province, Zambia. The product targets 250 households in the Magoye Catchment of Zambia's Southern Province. Coverage focuses on flood risk, measured via in-situ gauges and satellitemonitored flood depth along the Magoye River. Insurance applies to individual pixels along the river, with each participating farmer receiving about K 1786 equivalent to about USD 64 as the sum insured. The 150-day cover period (November 1, 2024 – March 30, 2025) uses a 10-day observation period for flood events. Payouts are tiered based on flood depth percentiles: minor flooding (80th percentile trigger, 25% coverage), moderate (85th percentile, 50% coverage), major (95th percentile, 75% coverage), and catastrophic (99th percentile, 100% coverage). In summary, the IBFI pilot represents a vital step forward in addressing the twin challenges of climate change and poverty. Its success could herald a transformative approach to flood risk management, offering a lifeline to vulnerable farmers and contributing to sustainable development in the region. Through innovation and collaboration, this initiative underscores the potential of insurance-based solutions in building climate resilience and securing livelihoods.
Using machine learning tools for salinity forecasting to support irrigation management and decision-making in a polder of coastal Bangladesh
(Brief, 2024-12-30) Behera, Abhijit; Sena, Dipaka Ranjan; Matheswaran, Karthikeyan; Jampani, Mahesh; Hasib, Md. R.; Mondal, M. K.
Water-salinity dynamics and stakeholder perceptions of a polder in coastal Bangladesh: a socio-hydrological perspective
(Brief, 2024-12-30) Jampani, Mahesh; Hasib, Md. Raqubul; Mizan, Syed Adil; Md, Nesaruddin; Mondal, M. K.; Sena, Dipaka Ranjan; Alam, Rubayat; Joshi, Deepa; Matheswaran, Karthikeyan
Leveraging satellite-based evapotranspiration monitoring to unlock agricultural water use insights in the Ganges and Mekong deltas
(Brief, 2024-12-30) Puvanenthirarajah, Suvasthiga; Matheswaran, Karthikeyan; Jampani, Mahesh
Nature-based solutions for river restoration and flow management: the case of Kitwe City, Zambia
(Book Chapter, 2025-01-30) Umer, Yakob; Debele, S. E.; Mvula, C.; Amarnath, Giriraj; Chisola, M. N.; Marti-Cardona, B.
River systems worldwide are under significant anthropogenic pressures and climate-related challenges, leading to ecosystem degradation and increased flood risk. This chapter demonstrates how Nature Based Solutions (NbS) can contribute to river restoration while reducing flood risk, supporting wider sustainable goals. To this end, this chapter evaluates the effectiveness of NbS interventions in river restoration and flood risk management in the Kitwe City, Zambia. The methodology involves using a hydraulic model to simulate river flow under different NbS scenarios (retention ponds and woodland reforestation), and to compare the simulated flood depth and flow velocity in pre- and post-intervention conditions. The findings indicate that the presence of NbS significantly reduces flood risks, with retention ponds and woodlands leading to flood depth reductions ranging from 0.09 m to 0.18 m and 0.06 m to 0.11 m, respectively. Regarding flow velocities, retention ponds reduced them by an average of 0.11m/s, and woodlands, by 0.07 m/s. These results indicate that both NbS types reduce flood depth and velocity, with ponds being slightly more effective than woodland in the particular setting of the Kitwe District. The findings suggest that integrating NbS into river restoration can mitigate flood risks, improve ecosystem resilience, and contribute to long-term sustainability. These results inform risk management policies and emphasise the need for interdisciplinary collaboration to upscale NbS for maximum ecological and societal benefits.
Sri Lanka's Climate Smart Governance Dashboard: training manual
(Training Material, 2024-12-30) Alahacoon, Niranga; Amarnath, Giriraj; Sivananthan, Piriyanka
Sri Lanka’s geographical and climatic conditions expose it to significant natural disaster risks, including floods, droughts, landslides, and cyclones. These recurring hazards disrupt socio-economic stability, causing loss of life, displacement, and environmental degradation. To combat these challenges, Sri Lanka emphasizes sustainable development and disaster preparedness. The Climate-Smart Governance (CSG) Dashboard enhances Sri Lanka’s capacity to address climate challenges effectively by streamlining data, fostering collaboration, and supporting evidence-based strategies. Its user-friendly interface, robust functionalities, and global adaptability make it vital to building climate resilience and advancing national and international sustainable development. The Climate-Smart Governance (CSG) Dashboard is an innovative platform providing data on climate-related hazards, vulnerability, scenarios, and sector-specific information. Developed as part of the CGIAR initiative on Climate Resilience (ClimBeR), the CSG Dashboard is crucial in supporting nations undertaking the UNFCCC National Adaptation Plan (NAP) process. Aligned with the iterative nature of the NAP process, the CSG Dashboard enhances adaptive capacity and resilience, minimizing vulnerability to climate change impacts. The training manual aims to guide users through the platform, ensuring ease of use and a clear understanding of its functionalities.
Strengthening anticipatory action in Zambia: a practical workshop on utilizing CGIAR and Zambia Red Cross Society solutions
(Report, 2024-12-30) Jeya Raj, Renuka; Alahacoon, Niranga; Amarnath, Giriraj; Wina, W.
Zambia, a country rich in natural resources and biodiversity, is increasingly vulnerable to the escalating threat of climate-related shocks, particularly droughts and floods. These extreme weather events have far-reaching consequences, disrupting livelihoods and severely undermining food security nationwide. In this context, traditional reactive disaster response methods are inadequate, often arriving too late to prevent significant humanitarian crises. To address this, Anticipatory Action (AA) has emerged as a forward-thinking strategy that harnesses the power of scientific forecasts and early warning systems. By triggering pre-agreed, evidence-based interventions before a climate disaster unfolds, AA aims to minimize the destructive impacts of such events, protecting both lives and livelihoods. This proactive approach represents a paradigm shift in disaster management, moving from a reactive to a preventive model. Central to the success of AA in Zambia is the integration of cutting-edge tools and platforms. The CGIAR AWARE (Early Warning, Early Action, Early Finance) platform is a crucial resource, offering advanced predictive analytics and climate modeling. The Zambia Red Cross Society (ZRCS) has also developed tools designed to operationalize AA on the ground. Leveraging these resources, Zambia can significantly bolster its resilience against extreme weather events. By embedding AA into national and local disaster risk management frameworks, the country can both reduce the humanitarian toll of disasters as well as safeguard its developmental gains. This approach, however, requires concerted efforts and collaboration among the various stakeholders, including government agencies, international partners, and local communities, to ensure that the benefits of AA are fully realized.
Dynamic and scalable framework for flood early warning: Zambia case study
(Brief, 2024-01-25) Padhee, Suman Kumar; Alahacoon, Niranga; Amarnath, Giriraj
Flood forecasting and early warning systems (FFEWS) are vital for safeguarding vulnerable communities, enabling anticipatory action to mitigate livelihood and income losses before the disaster strikes. This study introduces a dynamic, and scalable framework for flood indicators in Zambia using Global Precipitation Measurement GPM-IMERG (Integrated Multi-satellitE Retrievals for GPM) satellite rainfall data, Global Forecast SystemGFS global weather forecast data, and a GIS-based basin network database (BND). The framework is designed to enhance FFEWS through the CGIAR AWARE platform, strengthening anticipatory action mechanisms. Evaluation of the GPM-IMERG data reveals a strong correlation with ground-based station observations and reliable performance metrics, confirming its suitability for real-time rainfall monitoring. To address limitations in ground data, the framework incorporates intensity duration frequency (IDF) curves across various return periods to generate flood early warning indicators. The BND ensures that flood warnings work for both individual sub-basins and their combined runoff responses. A Comparison of three historical flooding events in Zambia as documented by EM-DAT, demonstrates the reliable performance of GPM-IMERG in identifying floods. A recent flooding event in the Southern province was analysed using GPM-IMERG and GFS data, showing reasonable forecasting of sub-basin level rainfall intensity and accumulation up to 10 days in advance, with accuracy improving closer to the event. This framework aims to bolster resilience and promote proactive disaster management through the AWARE platform, with the potential scalability across Africa and Asia.
Identifying aquaculture potential in northern Ghana: assessing small reservoirs using surface area dynamics, hydrological regimes, and socio-economic indicators
(Poster, 2024-12-09) Akpoti, Komlavi; Zwart, Sander; Marie-Charlotte, Buisson
This study presents a comprehensive assessment of small reservoir suitability for aquaculture development in Northern Ghana, focusing on approximately 2000 small reservoirs. By mapping surface area dynamics during the dry season (November to April) from 2018 to 2024, we identified significant variations in water availability. Among these reservoirs, 450 were surveyed to gather detailed information on multiple uses, including irrigation and aquaculture. To enhance our understanding, we organized a workshop to gather expert opinions on critical biophysical and socio-economic indicators for aquaculture development. Using this expert input, coupled with the survey data and surface area dynamics, we evaluated the potential of each reservoir for aquaculture. Additionally, we assessed discharge variability and its contribution to water availability in the reservoirs using the VegDischarge v2 data for the upstream catchment of each reservoir. Our findings highlight the reservoirs most suitable for aquaculture, providing a robust framework for sustainable aquaculture development in the region. This analysis supports water resource management and promotes resilient aquatic food systems, addressing food security and economic development in Northern Ghana.
Leveraging multiscale polycentric governance (MPG) and climate-smart governance (CSG) tools for climate change adaptation in Kenya
(Brief, 2024-12-30) Wakhungu, H.; Kaloi, F.; Ali, J.; Laichena, J.
Strengthening the implementation of the Climate Smart Governance (CSG) Dashboard and the provincial-level consultations to gather insights for enhancing the CSG Dashboard
(Report, 2024-12-30) Alahacoon, Niranga; Amarnath, Giriraj; Sivananthan, Piriyanka
Climate change-induced extreme weather events have significantly impacted smallholder farmers in low and middle-income countries, jeopardizing their livelihoods and food security. With more frequent and intense erratic rainfall patterns, droughts, and storms, farmers face heightened risks of crop failure, livestock loss, and property damage. These events also disrupt agricultural cycles and threaten already scarce water resources. The cumulative effects of climate-related disasters perpetuate cycles of poverty and ultimately affect global food security. Transformative climate adaptation solutions coupled with equitable policies are critical to bolster the resilience of these vulnerable communities and safeguard global food security. The Climate-Smart Governance (CSG) Dashboard supports countries in coordinated mid- to long-term adaptation planning. It supplies accurate data and analytics on climate vulnerabilities, governance capacities, and potential climate-smart interventions to strengthen national adaptation planning (NAP) at the national and local scales and achieve the progress of the Sustainable Development Goals. Organizations can utilize this up-to-date data to develop effective, well-coordinated adaptation policies; monitor and evaluate progress towards meeting adaptation goals; collaborate for effective use of resources; and share information on best practices and investment opportunities. The CSG Dashboard was developed as part of the CGIAR initiative on Climate Resilience (ClimBeR). ClimBeR aims to transform the climate adaptation capacity of food and agricultural systems in low- and middle-income countries by tackling vulnerability to climate change at its roots and supporting countries as they adapt and improve resilience, and build equitable and sustainable futures. This workshop, held in Anuradhapura, focused on introducing and strengthening the implementation of the Climate Smart Governance Dashboard through provincial-level consultations to gather insights for enhancing the CSG Dashboard. Commencing in 2023, Sri Lanka’s National Planning Department and its Climate Change Secretariat collaborated with the CGIAR ClimBeR team to initiate the development of the Climate Smart Governance dashboard for Sri Lanka. The CDG Dashboard addresses the lack of timely, context-specific, actionable information necessary for local communities and planners to make informed decisions on climate adaptation interventions. The CGIAR Climate Smart Governance (CSG) Dashboard was developed by the International Water Management Institute (IWMI) in partnership with Sri Lanka's National Planning Department and the Climate Change Secretariat of the Ministry of Environment. The platform is the first of its kind and serves as a facilitation planning tool for national adaptation planning, to bolster Sri Lanka's resilience to climate change impacts and aims to foster better, climatesmart, and integrated decision-making processes. It has also been launched in Zambia and Senegal, and is accessible to governments and communities worldwide.
CGIAR’s AWARE platform: promoting early warning of and effective response to climate hazards
(Video, 2025-01-07) Amarnath, Giriraj
As the climate crises worsens, extreme weather events are becoming increasingly more frequent and variable. This exacerbates the risks facing people in climate-vulnerable low- and middle-income countries. The Early Warning, Early Action, Early Finance (AWARE) platform, developed by the CGIAR Research Initiative on Climate Resilience (ClimBeR) helps countries overcome such challenges and protect communities ahead of impending climate-related hazards, rather than afterwards. The platform is based on the principle of ‘early warning to early action and early finance,’ which emphasizes the importance of anticipatory action measures to mitigate the impacts of hazards and reduce human and economic losses.
Unlocking community resilience in the face of climate uncertainties.
(Video, 2025-01-07) Mascarenhas, Martina
Building resilience at the community level requires an integrated approach – one that brings together Climate Change Adaptation and Disaster Risk Reduction strategies. This approach is now more urgent than ever as we see the ever-increasing rise of climate hazards and disasters. We need to implement locally led climate action at multiple levels and by leveraging multiple stakeholders. The CGIAR Research Initiative on Climate Resilience (ClimBeR’s) LocAlly led Climate adapTation ChampION (ACTION) Grant Program is a good example of this. ClimBeR is catalyzing community-led climate adaptation in Monze’s Hanzila community, through a community-led solar-powered borehole activity. This is just one example of how an integrated approach can build resilient communities in the face of climate uncertainties.
Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations
(Abstract, 2024-12-11) Behera, Abhijit; Sena, Dipaka Ranjan; Hasib, Md. R.; Matheswaran, Karthikeyan; Jampani, Mahesh; Mizan, Syed Adil; Islam, Md. J.; Alam, R.; Mondal, M. K.; Sikka, Alok Kumar
The coastal zone of Bangladesh comprises several polders, which are low-lying tracts of land surrounded by embankments to protect against tidal floods and saline water intrusion. They also enhance freshwater availability and aid in improving land productivity. These polders are equipped with sluice gates for water to drain out and intake into the polders. Each sluice has its own catchment area, defined by the elevation and connectivity with canal systems that carry fresh or saline water from surrounding rivers or streams. The sluice gates operation is influenced by in-polder water management for crop cultivation, diurnal tidal dynamics, and the seasonal variations of saline and fresh water in the peripheral river networks. During the dry season, limited flows in the lower Ganges River allow seawater to push inland, causing saltwater intrusion in the peripheral rivers until the rainy season. Community-coordinated sluice gate operations can improve water management, facilitating timely drainage and irrigation, which is essential for high-yielding rice and subsequent dry-season crops. To address these challenges, a multi-variate LSTM (Long Short-Term Memory) model was employed to forecast salinity levels in rivers near 29 sluice gates in a polder near Khulna City in southwest Bangladesh. Utilizing salinity data from July 2011 to December 2022, the models were trained (2011-18) and validated (2018-20) with covariates of discharge, water level, and an upstream reference station. A hierarchical variable additive approach was used to sequentially estimate salinity from upstream to downstream. The NSE was over 0.90 and PBIAS under 5% for all sluice gate locations, confirming accuracy in reconstructing the time series. For forecast testing, the 2020-22 dataset also showed significant confirmation with NSE values over 0.90 and PBIAS under 10%. With readily available input data, the developed salinity forecast model can effectively capture annual and seasonal salinity fluctuations along all sluice gate locations. These forecasting capabilities can potentially identify critical seasonal windows for sluice gate operations, giving the farmers in the polder a 30-day lead time for freshwater intake for irrigation and starting agricultural operations in the aman season.
Crop evapotranspiration dynamics in the Ganges and Mekong deltas
(Abstract, 2024-12-09) Matheswaran, Karthikeyan; Puvanenthirarajah, Suvasthigha; Jampani, Mahesh; Sena, Dipaka Ranjan
Asian Mega Deltas (AMD) are food baskets providing livelihoods for farmers and food security for millions. In recent years, many countries have been facing significant pressures to balance sustainable resource use and confront climate change impacts and increasing demands from population growth. In the Ganges and Mekong Deltas, these imminent challenges and anthropogenic pressures are more severe due to increasing pressure to enhance the productive use of land and water resources. The growing variability in water availability is caused by climate change and decreased freshwater flows from upstream. Continuous estimates of evapotranspiration (ET) are essential for evaluating the productivity of agriculture areas in these deltas, but these estimates are often not readily available. In this study, we derived monthly ET in the Ganges and Mekong deltas using remote sensing-based data and an energy balance model to assess its trends. We used the Google Earth Engine (GEE) version of the Surface Energy Balance Algorithm for Land (SEBAL) model with inputs from ERA5 Land meteorological data and LANDSAT 8 and 9 satellite images covering the period from 2014 to 2024. Due to heavy cloud cover during the monsoon season, a gap-filling process was used to produce seamless monthly ET estimates for these two deltas. The ET estimates were aggregated based on land cover types to determine trends in agricultural water use during both the monsoon and non-monsoon seasons. In areas where information about the crop types is available, like in the Mekong Delta, the ET estimates were further aggregated for specific main crop types. Initial results showed an increasing trend in ET at the regional level, indicating intensified agricultural activities within the two assessed deltas. The ET estimates produced from our study will serve as a basis for evaluating the land and water productivity of the deltas, demonstrating the scalability of remote sensing data and energy-based models in estimating ET fluxes at various scales and assessing their trends in different land cover areas.
Evaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signals
(Abstract, 2024-12-13) Panjwani, Shweta; Jampani, Mahesh; Amarnath, Giriraj; Sambou, Mame Henriette Astou
Food security has become a critical issue in Senegal due to agricultural losses from climate-related risks and the growing population. In recent years, several studies have reported crop yield losses as a result of seasonal climate variability and extreme events, but crop-wise in-depth analysis is lacking. In this context, we performed district-wise statistical and spatial extent analysis for major growing crops using earth observation and agronomic data from the government to estimate crop-wise correlation. Further, regression analysis was performed for major crops, such as maize and groundnut, using satellite-based climate and vegetation data and observed yield data over a 12-year period. Our results suggested that maize and groundnut crops are mainly distributed in all agroecological zones except the Niayes zone and Senegal River valley in terms of cultivated area and harvested crop yield. We found that seasonal rainfall, particularly from May to September, is highly correlated with the yield, and a 10-20% decrease in seasonal rainfall can lead to crop losses. Additionally, the impact of seasonal rainfall may differ across districts due to climate variability, the onset of monsoon, and cropping seasons. We used the best-fit combinations of rainfall and NDVI and machine-learning models to predict crop yield for the upcoming season for major crop growing districts, with an accuracy (R2) ranging from 0.6 to 0.8 and a one-month lag to the harvest period. The overall goal is to integrate the predictive modeling results into early warning systems such as CGIAR AWARE, which could enhance Senegal's agricultural resilience to climate change and inform decision-makers to take early action.
Evaluating the impact of climate variability and water hazards on vector-borne disease patterns to develop early warning signals
(Abstract, 2024-12-12) Jampani, Mahesh; Amarnath, Giriraj
In recent decades, the effects of climate change have been profound, affecting precipitation, temperature trends, and hydrological cycles, thereby influencing the prevalence of water and vector-borne diseases. Specifically, it is becoming more evident that mosquito-borne diseases like malaria and dengue are prevalent with seasonal dynamics. Understanding the complex dynamics to develop effective measures and interventions and to mitigate health risks associated with water hazards and climate variability is crucial. The current research highlights the impacts of climate change with case studies from Senegal in West Africa on malaria prevalence and Sri Lanka in South Asia on dengue prevalence. These two case studies utilized earth observation and recorded case data to evaluate the intrinsic links between water, climate, disease prevalence, and health risks using statistical and spatial analysis and predictive modeling. Both case studies demonstrate the interplay of water-climate-health nexus, emphasizing the importance of climate and seasonal patterns in spreading vector-borne diseases. Changes in precipitation, temperature patterns, alternate wetting and drying conditions, and extreme events like floods show visible patterns of disease prevalence, which can create favorable environments for the breeding and proliferation of disease-carrying mosquitoes. In Senegal, changes in rainfall patterns and seasonality have a strong influence on the distribution of malaria, potentially exposing new populations in specific seasons. Similarly, the prevalence of dengue fever is higher in Sri Lanka in wet regions, and flooding can also create suitable habitats for the Aedes mosquitoes that are responsible for transmitting the virus. The research findings underscore the importance of seasonal trends and predictive analytics in developing early warning systems that can alert health authorities to early action and minimize health risks. Overall, this research sheds light on the influence of climate change on vector-borne diseases and contributes to a comprehensive understanding of the interconnectedness between water, climate, and human health for developing early warning signals.
From drought to abundance
(Website, 2024-08-19) Amarnath, Giriraj; van Koppen, Barbara
Cultivating change: Sri Lanka’s smallholder farmers explore climate-resilient solutions
(News Item, 2024-03-18) Knapp, Juliana
The best time to plan for drought is when it’s raining: disaster response needs to move from reactive to proactive if we are going to adapt to climate change
(News Item, 2024-02-27) McDonnell, Rachael

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