Over the last decades, efficient water resources management has been an important element of EU’s water policies, a topic that is addressed with renewed attention in the revised 2021 EU Adaptation Strategy, which lists the need for a knowledge-based approach towards water-saving technologies and instruments such as efficient water resources allocation. The IPCC special report on oceans and the cryosphere in a changing climate (2019) highlights the combination of water governance and climate risks as potential reasons for tension over scarce water resources within and across borders, notably competing demands between hydropower and irrigation, in transboundary glacier- and snow-fed river basins in Central Asia.

WE-ACT’s innovative approach consists of two complementary innovation actions: the first is the development of a data chain for a reliable water information system, which in turn enables the second, namely design and roll-out of a decision support system for water allocation. The data chain for the reliable water information system consists of real-time in-situ hydrometeorological and glaciological monitoring technology, modelling of the water system (including water supply and demand modelling and water footprint assessments) and glacier mass balance, data warehouse technology and machine learning. The roll-out of the DSS for climate-risk informed water allocation consists of stakeholder and institutional analyses, water valuation methods, the setup of the water information system to allow for a user-friendly interface, development of water allocation use cases, and feedback on water use through national policy dialogues.

The work of FutureWater within the WE-ACT study will focus on estimating the water demand and water footprints of the different users and activities within the Syr Darya river basin. Therefore, the effects of water allocation on water footprints, unmet water demand and environmental flow violations will be evaluated using a set of hydrological models such as SPHY and Water Allocation models (WEAP). This will be done for both the status quo and future scenarios.

Eswatini’s development is at risk by natural drought hazards. Persistent drought is exacerbating the country’s existing challenges of food security and the ability to attain sustainable development. Therefore, FutureWater, Hydrologic, and Emanti Management joined forces to bring together technologies and complementary expertise to implement the GLOW service which includes: short-term and seasonal forecasts of water availability and demand, an alerting service when forecasted water demand is higher than water availability, and water distribution advisories to reduce impact and maximise water security for all water users.

The GLOW service will be piloted in the Maputo River and Mbuluzi River Basins where three-quarters of the population of Eswatini lives, which includes the Hawane dam that supplies water to Mbabane (Capital City of Eswatini) and which is the major water supply source for Maputo, a Delta city (1 million inhabitants) which suffers from water shortages. The main beneficiaries of this project are the Joint River Basin Authority (JBRAS-PB) and the 5 River Basin authorities, AraSul (Mozambique) and the Department of Water and Sanitation (South Africa).

The innovation of GLOW is bringing together proven and award-winning technologies of advanced earth observation, open data, high-performance computing, data-driven modelling, data science, machine learning, operations research, and stakeholder interaction. These technologies require minimum ground truth information, which makes them very scalable and applicable in poorly monitored environments throughout the world. The coherent combination of the technologies into one decision support service ensures the optimum division of water, basically distributing every drop of water to meet the demands of all interests present in large river catchments.

Water resources around the globe are under increasing stress. Among other factors, climate change, rising food and energy demand, and improving living standards have led to a six-fold increase in global water withdrawals over the last century, with significant consequences for water quality and availability, ecosystem health, biodiversity, as well as social stability.

By advancing and linking water system models with models from sectors such as agriculture and energy, biodiversity, or sediment transport, the SOS-Water Project aims to lay the foundations for a holistic assessment framework of water resources across spatial scales. Based on five case studies of river basins in Europe and Vietnam – the Jucar River Basin in Spain, the Upper Danube region, the Danube and Rhine River deltas, and the Mekong River Basin – an interdisciplinary team of researchers from ten institutions across eight countries will develop a multidimensional SOS for water. The framework will enable the assessment of feedback loops and trade-offs between different dimensions of the water system and help address pressing global, regional, and local challenges.

In addition to going beyond state-of-the-art water systems modeling, the project will develop a comprehensive set of indicators to assess and monitor the environmental, social, and economic performance of water systems. The participating researchers will collaborate with regional and local authorities, water user representatives, non-governmental organizations, and citizens to co-create future scenarios and water management pathways. By streamlining water planning at different levels, it can be ensured that water allocation among societies, economies, and ecosystems will be economically efficient, socially fair, and resilient to shocks.

In partnership with project lead IIASA and partners such as Utrecht University and EAWAG, FutureWater is responsible for several tasks under the work package that looks to improve upon existing Earth Observation technologies for monitoring the performance of water systems. New applications will be developed and tested in the context of the SOS-Water case study basins of the Mekong and Jucar rivers.

The Mekong River and its tributaries are critical waterways that support the economy and food supply chains of both Cambodia and Lao PDR. However, these waterways also present a significant risk. Flooding and drought events are becoming increasingly erratic, longer, and more intense as a result of the compounding effects of anthropogenic climate change. In support of the Integrated Water Resource Management (IWRM) in the Mekong River basin, appropriate data collection, integrated management of data and proper analysis are crucial as a basis for decision-making and policy development. There have been numerous efforts in data collection, analysis, and monitoring by relevant key stakeholders such as MRC, MoWRAM, NCDM and other development partners. Those systems have been developed to track climate information, provide basic data for risk-informed technical planning at the national and local level. However, data availability and the ability to contextualize information at local level remain a big challenge for Cambodia, which leads to the persistence of misinformation and misunderstanding of risks associated with living near the Mekong River and its tributaries.

In 2021, UNDP received funding from Ministry of Environment the Government of Republic of Korea for the project “Enhancing Integrated Water Management and Climate Resilience in Vulnerable Urban Areas of the Mekong River Basin”. This project looks to address gaps in data collection management and analysis, enhance institutional and technical capacity at the subnational level for integrated climate and flood risk management, enhance availability of resources for investment in water-related risk reduction, and aid the flow of risk knowledge and coordination across the borders of Cambodia and Lao PDR. Key outputs include (i) technical studies on flood propagation models, hydrometeorological disasters forecasting models, capacity assessment, and improvement of risk monitoring systems and early warning system (EWS), (ii) capacity building design for climate change risk assessments, and hazard/vulnerability mapping using open source software, and (iii) design and piloting of disaster risk management initiatives at the community level.

FutureWater was hired by United Nations Development Programme (UNDP) Cambodia to provide strategic and technical support to the successful delivery of Mekong urban resilience project on its current and further initiatives to promote climate risk informed integrated water resource management in the target 3S and 4P river basins in Cambodia. This includes a close collaboration with the project team to design project intervention strategies through building alignment with existing efforts and ensuringe that the project is part of a bigger system of disaster risks informed water resources management in the target river basins.

Uzbekistan is highly sensitive to climate change which will cause changes in the water flows and distribution: water availability, use, reuse and return flows will be altered in many ways due to upstream changes in the high mountain regions, but also changes in water demand and use across the river basin. The resulting changes in intra-annual and seasonal variability will affect water security of Uzbekistan. Besides, climate change will increase extreme events which pose a risk to existing water resources infrastructure. An integrated climate adaptation approach is required to make the water resources system and the water users, including the environment, climate resilient.

This project will support the Ministry of Water Resources (MWR) of Uzbekistan in identifying key priorities for climate adaptation in the Amu Darya river basin and support the identification of investment areas within Amu Darya river basin. The work will be based on a basin-wide climate change risk assessment as well as on the government priorities with an explicit focus on reducing systemic vulnerability to climate change.

The project will undertake:

  • Climate change risk analysis and mapping on key water-related sectors, impacts on rural livelihoods, and critical water infrastructures.
  • Climate change adaptation strategic planning and identify barriers in scaling up adaptation measures at multiple scales with stakeholder consultation and capacity building approach.
  • Identification of priority measures and portfolios for integration into subproject development as well as for future adaptation investment in the Amu Darya river basin. The identification will cover shortlisting of potential investments, screening of economic feasibility, and potential funding opportunities.

FutureWater leads this assignment and develops the climate risk hotspot analysis, and coordinates the contribution of international and national experts, as well as the stakeholder consultation process.

The Mekong State Of the Basin Report (SOBR) is published by the Mekong River Commission (MRC) every five years, in advance of the cyclic updating of the Basin Development Strategy. The SOBR plays a key role in improving monitoring and communication of conditions in the Mekong Basin, and is MRC’s flagship knowledge and impact monitoring product. It provides information on the status and trends of water and related resources in the Mekong Basin. The 2023 SOBR is based on the MRC Indicator Framework of strategic and assessment indicators and supporting monitoring parameters, which facilitates tracking and analysis of economic, social, environmental, climate change and cooperation trends in the basin.

FutureWater was hired by MRC to perform the following tasks in support of the 2023 SOBR development:

  1. Data collection on the Extent of Salinity intrusion in the Mekong Delta and the conditions of the Mekong River’s riverine, estuarine, and coastal habitats
  2. Analyses of the extents of 2010, 2015, and 2020 LMB wetlands
  3. Analyses of the extents of key fisheries habitat areas in the LMB, and
  4. Data collection for all Assessment Indicators of MRB-IF for the Upper Mekong River Basin (UMB), including reporting and extracting key messages

Implementation of tasks 1 – 3 is achieved by using state-of-the-art remote sensing tools, such as the Google Earth Engine, building on the methods developed in the preceding project.

Task 4 builds on the findings of FutureWater’s contribution to the 2018 SOBR regarding the status of the UMB in China and Myanmar, more details can be found here.

The “Integrated Strategic Water Resources Planning and Management for Rwanda” consultancy project will assess and evaluate the availability and vulnerability of the country’s water resources up to around 2050 taking climate change into consideration.

Based on this, prioritization of investment options in grey and green infrastructure will take place, in order to formulate water resources investment plans. A revised water resources policy will be prepared that is in line with water security targets and SDG 6.

In more detail, the hydrological modelling assessment will result in update water accounts per sub-catchment up to 2050. Field work for assessing groundwater resources in key areas across the country is also performed. A detailed water allocation assessment will be performed using a water resources system model (WEAP), addressing water needs for the various users up to 2050. Water allocation plans will be developed from this modelling work, incorporating stakeholder inputs.

Then, a scenario analysis is performed to evaluate the potential of additional storage in the landscape: grey (reservoirs) and green (through Nature-based Solutions). This analysis will be complemented by field work and a pre-feasibility analysis will be performed on the prioritized options. A SWOT analysis will then lead to a number of possible flagship projects which of which a concept note is prepared.
Support to the revised national policy for water resources management will also be provided by defining new policy statements and actions informed by the results from the previous tasks and developing a new water resources policy that will guide the country towards achieving the NST1 and Vision 2050 targets.

The Swiss Agency for Development and Cooperation’s (SDCs) Global Programme Climate Change and Environment (GP CCE) India is supporting the operationalization of climate change adaptation actions in the mountain states of Uttarakhand, Sikkim and Himachal Pradesh through the phase two of the “Strengthening State Strategies for Climate Action” (3SCA) project that was launched in 2020. The second phase of 3SCA (2020-23), known as the Strengthening Climate Change Adaptation in Himalayas (SCA-Himalayas), while building on the experience and achievements of Phase 1, aims to showcase mountain ecosystem appropriate scalable approaches for climate resilience in water and disaster risk management sectors; using these efforts to enhance the capacities of the institutions across the Indian Himalayan Region (IHR) to plan, implement and mainstream adaptation actions into their programmes and policy frameworks; and disseminating the experiences and lessons at the regional and global level.

Within this programme, SDC has granted a project to FutureWater, together with Utrecht University, The Energy and Resources Institute (TERI), the University of Geneva and a few individual experts. The activities in this project focus on the development and application of climate responsive models and approaches for integrated water resources management (IWRM) for a selected glacier-fed sub-basin system in Uttarakhand and that at the same will find place in relevant policy frameworks paving way for their replication across IHR and other mountainous regions. This will allow the policy makers from the mountain states in India to manage the available water resources in an efficient and effective manner, benefiting the populations depending on these resources.

The combination of future climate change and socio-economic development poses great challenges for water security in areas depending on mountain water (Immerzeel et al., 2019). Climate change affects Asia’s high mountain water supply by its impact on the cryosphere. Changes in glacier ice storage, snow dynamics, evaporation rates lead to changes in runoff composition, overall water availability, seasonal shifts in hydrographs, and increases in extremely high and low flows (Huss and Hock, 2018; Lutz et al., 2014a). On the other and, downstream water demand in South Asia increases rapidly under population growth and increasing welfare boosting the demand for and electricity generation through hydropower. To address and adapt to these challenges integrated water resource management (IWRM) approaches and decision support systems (DSS) tailored to glacier- and snow-fed subbasins are required.

To fulfil the mandate outlined by SDC a framework is presented for IWRM and DSS for Himalayan subbasins consisting of three integrated platforms. (i) A modelling and decision support platform built around a multi-scale modelling framework for glacier and snow fed subbasins, based on state-of-the art and “easy to use” modelling technology. (ii) A stakeholder engagement platform to consult key stakeholders, identify key IWRM issues and co-design a new IWRM plan for Bhagirathi subbasin. (iii) A capacity building platform with on-site training and e-learning modules for the key project components: glacio-hydrological modelling, IWRM and DSS, to ensure the sustainability of the approach and pave the way for upscaling to other subbasins in the Indian Himalayan Region.

The three platforms are designed designed to be flexible, integrated and interactive. Moreover they align with the three outcomes of the project, thus contributing to: develop and validate an integrated climate resilient water resource management approach (Outcome 1); increase technical and institutional capacity in the fields of hydrological modelling, IWRM and DSS (Outcome 2); support the embedding of the IWRM approach tailored to glacier-fed Indian Himalayan subbasins in policies, and provide generic outputs and guidelines to facilitate upscaling to other subbasins in the Indian Himalayan Region (Outcome 3).

The modelling and decision support platform is designed for operation under the data scarce conditions faced in Himalayan catchments, and yields reliable outputs and projections. The modelling toolset covers the Bhagirathi watershed (Figure below) and consists of 3 hydrological models: (i) a high resolution glacio-hydrological model for the Dokriani glacier catchment (SPHY-Dokriani). Key parameters derived with this model are upscaled to (ii) a distributed glacio-hydrological model that covers the Bhagirathi subbasin (SPHYBhagirathi). Outputs of this model feed into (iii) a water allocation model that overlays the SPHY-Bhagirathi model in the downstream parts of the basin, where water demands are located (WEAPPODIUMSIM Bhagirathi). This modelling toolset is forced with downscaled climate change projections and socio-economic projections to simulate future changes in water supply and demand in the subbasin. On the basis of stakeholder inputs, adaptation options are identified and implemented in the water allocation model for scenario analysis. Thus, socio-economic projections and adaptation options are co-designed with the stakeholders to ensure maximum applicability, and are tailored to the requirements for formulation of the new IWRM plan. The outputs of the modelling toolset feed into the Decision Support System, where they are presented in such a way that they can truly support decision making in this subbasin. Results of the modelling, decision support and stakeholder engagement platforms jointly support the co-design of an IWRM plan for the subbasin. Capacity in glacio-hydrological modelling, IWRM and the use of DSS is built through a combination of on-site training and e-learning; replicable training modules are developed for glacio-hydrological modelling, IWRM and DSS in general and for this particular approach to support implementation and sustainability.

Overview of the Bhagirathi sub-basin. The inset on the right shows the Dokriani glacier watershed


Water and food security are at risk in many places in the world: now and most likely even more in the future, having large economic and humanitarian consequences. Risk managers and decision-makers, such as water management authorities and humanitarian-aid agencies/NGOs, can prevent harmful consequences more efficiently if information is available on-time on (1) the impact on the system, economy or society, and also (2) the probabilities for a failure in the system. EO information has proven to be extremely useful for (1). For looking into the future, considering the uncertainties, novel machine learning techniques are becoming available.

The proposed development is incorporated into an existing solution for providing Drought and Early Warning Systems (DEWS), called InfoSequia. InfoSequia is a modular and flexible toolbox for the operational assessment of drought patterns and drought severity. Currently, the InfoSequia toolbox provides a comprehensive picture of current drought status, based mainly on EO data, through its InfoSequia-MONITOR module. The proposed additional module, called InfoSequia-4CAST, is a major extension of current InfoSequia capabilities, responding to needs that have been assessed in several previous experiences.

InfoSequia-4CAST provides the user with timely, future outlooks of drought impacts on crop yield and water supply. These forecasts are provided on the seasonal scale, i.e. 3-6 months ahead. Seasonal outlooks are computed by a novel state-of-the-art Machine Learning technique. This technique has already been tested for applications related to crop production forecasting and agricultural drought risk financing. The FFTrees algorithm uses predictor datasets (in this case, a range of climate variability indices alongside other climatic and vegetative indices) to generate FFTs predicting a binary outcome – crop yields or water supply-demand balance above or below a given threshold (failure: yes/no).

The activity includes intensive collaboration with stakeholders in Spain, Colombia and Mozambique, in order to establish user requirements, inform system design, and achieve pilot implementation of the system in the second project year. Generic machine learning procedures for training the required FFTs will be developed, and configured for these pilot areas. An intuitive user interface is developed for disseminating the output information to the end users. In addition to development of the forecasting functionality, InfoSequia-MONITOR will be upgraded by integrating state-of-the art ESA satellite data and creating multi-sensor blended drought indices.

De Gezondheidsdienst voor Dieren (GD) heeft in de afgelopen jaren een analyse van leverbotbesmettingen uitgevoerd. Dit onderzoek heeft laten zien dat verschillende omgevingsfactoren een rol spelen zoals grondwaterstand, neerslag, temperatuur en bodemtype. Daarnaast is op basis van landsdekkende bodemvochtschattingen door FutureWater vastgesteld dat ook bodemvocht en leverbotbesmettingen gecorreleerd zijn, wat verklaard wordt doordat de leverbotslak in vochtige omstandigheden het beste gedijt. De GD geeft jaarlijks een leverbotprognose uit op basis van modelresultaten. Op basis van de eerdere onderzoeksresultaten heeft de GD sinds enkele jaren ook voorspellingen van bodemvocht in haar model opgenomen, welke jaarlijks door FutureWater worden geleverd voor elk postcodegebied in Nederland.

In het huidige project wordt een ontwikkelingsslag toegepast door het gebruik van de meest recent beschikbare datasets in het hydrologische model SPHY, om zo historische bodemvochtdata te leveren met een 100 x 100 m (1 ha) resolutie voor de periode van 2012 t/m 2021. Deze gegevens worden door de Gezondheidsdienst voor Dieren gebruikt om meer inzicht te verkrijgen in het risico op diergezondheidsproblemen die door klimaat worden beïnvloed.