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 Lunyangwa Dam is the source of water supply for Mzuzu City, Ekwendi Town and surrounding areas. Currently, the yield of the dam is lower than the annual average daily water demand from the dam. A quick intervention for this problem is to raise the spillway of the Lunyangwe Dam.

In order to determine the height of the redesigned spillway, FutureWater conducted a hydrological study for the Lunyangwa Dam Catchment to determine flood extremes for several return periods. HEC-HMS was used for calculating the peak volumes and discharges. The input for the HEC-HMS model was retrieved using satellite-based datasets for rainfall and terrain. Furthermore, the flood routing was simulated with an elevation-storage curve. The output of this study will be used for the redesign of the spillway.

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.

In irrigated agriculture options to save water tend to focus on improved irrigation techniques such as drip and sprinkler irrigation. These irrigation techniques are promoted as legitimate means of increasing water efficiency and “saving water” for other uses (such as domestic use and the environment). However, a growing body of evidence, including a key report by FAO (Perry and Steduto, 2017) shows that in most cases, water “savings” at field scale translate into an increase in water consumption at system and basin scale. Yet despite the growing and irrefutable body of evidence, false “water savings” technologies continue to be promoted, subsidized and implemented as a solution to water scarcity in agriculture.

The goal is to stop false “water savings” technologies to be promoted, subsidized and implemented. To achieve this, it is important to quantify the hydrologic impacts of any new investment or policy in the water sector. Normally, irrigation engineers and planners are trained to look at field scale efficiencies or irrigation system efficiencies at the most. Also, many of the tools used by irrigation engineers are field scale oriented (e.g. FAO AquaCrop model). The serious consequences of these actions are to worsen water scarcity, increase vulnerability to drought, and threaten food security.

There is an urgent need to develop simple and pragmatic tools that can evaluate the impact of field scale crop-water interventions at larger scales (e.g. irrigation systems and basins). Although basin scale hydrological models exist, many of these are either overly complex and unable to be used by practitioners, or not specifically designed for the upscaling from field interventions to basin scale impacts. Moreover, achieving results from the widely-used FAO models such as AquaCrop into a basin-wide impact model is time-consuming, complex and expensive. Therefore, FutureWater developed a simple but robust tool to enhance usability and reach, transparency, transferability in data input and output. The tool is based on proven concepts of water productivity, water accounting and the appropriate water terminology, as promoted by FAO globally (FAO, 2013). Hence, the water use is separated in consumptive use, non-consumptive use, and change in storage.

A complete training package was developed which includes a training manual and an inventory of possible field level interventions. The training manual includes the following aspects:

  1. Introduce and present the real water savings tool
  2. Describe the theory underlying the tool and demonstrating some typical applications
  3. Learn how-to prepare the data required for the tool for your own area of interest
  4. Learn when real water savings occur at system and basin scale with field interventions

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.

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.