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.

Sustainable Development Goal (SDG) 6 seeks to ensure access to clean water and sanitation for all, focusing on the sustainable management of water resources, wastewater and ecosystems. The targets associated with SDG 6 are to be achieved by monitoring and improvement of several indicators. Assessment of these indicators requires a considerable amount of data, which are in many countries not readily available. Also in Myanmar, challenges are posed to the national statistical system to collect, manage and report the necessary input data. As the Myanmar branch of the lead UN development agency, UNDP Myanmar carries out activities to support implementation of the SDGs. Acknowledging the recent political developments in Myanmar, more than ever it is important to explore innovative sources of data to support monitoring and evaluation of progress towards the SDGs. FutureWater was contracted to produce an issue brief which explores the availability of geospatial data, in particular derived from Earth Observation (EO) from satellites, to monitor 4 water-related SDG indicators.

The objectives of this climate risk assessment for the Li River in China is to assess current flood risk and future flood risk in the Li river basin in China. With an average of 1800 mm annual total rainfall, floods are severe and frequent in the region. Additionally to rainfall, severe floods in are often related to discharges from upstream reservoirs

Given the fact that this area is data scarce, global datasets with climatic data (ERA5-Land), soil parameters (HiHydroSoil) and land cover (Copernicus) were used to feed a hydrological HEC-HMS model to calculate the discharge for the extreme event of June 2020. Based on measured water levels and discharge, it was possible to develop rating curves and with these rating curves, it was possible to estimate water levels in the river for current (validation) and future conditions. This analysis served as input for the full climate risk assessment,  in which possible interventions were proposed to reduce flood risk in the future.

The objectives of the Norfolk Water Fund is to secure good quality, long-term water resources for all water users, while protecting the environment and showcasing the county as an international exemplar for collaborative water management. The programme seeks to demonstrate how cross-sector, integrated water management and can deliver multiple benefits and help achieve the county’s net zero targets.

Water Funds are a well-established model for facilitating collective action to address water security challenges through the implementation of nature-based solutions (NBS) as a complement for more traditional so-called ‘grey’ infrastructure such as pipelines and treatment plants. Norfolk is one of two European pilots selected for Water Funds by The Nature Conservancy (TNC), to add to their global portfolio of Water Funds.

To deliver this programme, a variety of technical activities are required. These include assessing Water Security Challenges in the county, identifying the most relevant NBS to the context, and prioritising the most effective locations and strategies for their implementation. FutureWater will support these technical activities with NBS and water resources expertise alongside coordinating technical partners.

The MRCS regularly undertakes periodic regional and basin-wide studies on behalf of Member Countries to assess potential effects of increasing development, growing population and uncertainty in climate variability in the Lower Mekong Basin (LMB). Recent basin-wide assessment and reporting were found to be hampered by data limitations across a range of areas. With the basin undergoing rapid and extensive change, tracking changes in conditions, analyzing the potential implications, and working cooperatively to leverage the benefits and avoid the problems are seen as critical to achieving the objectives of the 1995 Mekong Agreement.

To provide a greater strategic direction to the monitoring and assessment effort, the Mekong River Basin Indicator Framework (MRB-IF) was developed and approved aiming at providing a consistent and streamlined approach to data collection, analysis, and reporting. Through the MRB-IF, the MRC Member Countries and stakeholders can be alerted to the key issues and trends across five core dimensions (environment, social, economic, climate change and cooperation). Included in the MRB-IF are (i) the extent of salinity intrusion in the Mekong Delta (MD) – Assessment Indicator 14 and (ii) the condition of riverine, estuarine, and coastal habitats – Assessment Indicator 16. A systematic process of collection and analysis of the data for status and trends evaluation regarding these indicators is currently missing.

The aim of this project is therefore to develop a basin-specific systematic approach to periodically assess the extent of salinity intrusion in the Mekong Delta and the conditions of the riverine, estuarine, and coastal habitats across the LMB. Methodologies to evaluate both indicators are developed relying on integration of satellite remote sensing data, GIS databases, and station data. The project involves an elaborate review of existing methodologies tested in the LMB and other river basins, an assessment of these methods regarding technical, economic and institutional aspects, and the development of a recommended methodology for adoption by MRCS, including guidance documentation for its stepwise implementation.

Nature-based Solutions (NbS) can help ensure the long-term reliability of water resources. Research has shown they can – depending on circumstance – be more cost-effective and longer-lasting than grey infrastructure, while generating multiple co-benefits for carbon, biodiversity and human health. Despite the promise of NbS, however, water sector actors and their financiers usually prioritize investments in traditional grey infrastructure because they are more familiar with its costs, benefits and returns. Most of them are unfamiliar with how to develop and assess the value of NbS projects, though research shows they’re interested in tapping into their multi-faceted benefits.

The Financing Nature for Water Security project of The Nature Conservancy (TNC) aims to produce and disseminate guidance that enables water sector actors (government agencies, water utilities, grass-root NGOs) and their funders (donors, development banks and private investors) to invest in NbS-WS, at scale, by mobilizing sustainable funding and repayable financing. The project comprises of technical modules, guidance documents, supporting databases and training materials.

FutureWater has been contracted by TNC to support the development of one of the content modules assembled under the project. The module “Technical Options” will help the reader understand the water security challenge(s) they are confronted with and identify the types of NbS that could help address those challenges. In particular, Futurewater works on the creation of 12 technical factsheets to be included in an annex to the main documentation, with each factsheet highlighting the key technical aspects, benefits and risks, and economic dimensions of an NbS. In addition, an inventory of relevant NbS databases, platforms, and references is delivered.