This project will support the government to enhance the river basin planning in the Amu Darya river basin. In addition to preparing an investment portfolio in representative irrigation and drainage subprojects, the project will undertake a knowledge-based approach to deliver climate adaptive solutions for water resources management.

The project will use state-of-the-art downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles, other relevant hazards, hydrological/crop/planning model outputs and local information for the CRA assessment. This project will help the Asian Development Bank (ADB) to identify investments in key sectors with an explicit focus on reducing vulnerability to climate change within the basin.

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.

Previous related FutureWater project can be found here.

Agriculture is the most water demanding and consuming sector, globally responsible for most of the human induced water withdrawals. This abstraction of water is a critical input for agricultural production and plays an important role in food security as irrigated agriculture represents about 20 percent of the total cultivated land while contributing by 40 percent of the total food produced worldwide.

The FAO Regional Office for Asia and the Pacific (FAO-RAP) is concerned about this increase in water use over the last decades that has led to water scarcity in many countries. This trend will continue as the gap between water demand and supply is projected to widen due to factors such as population growth and economic development, and environmental factors such as land degradation and climate change.

Unfortunately, solutions to overcome the current and future water crisis by looking at the agricultural sector are not simple and have often led to unrealistic expectations. Misconceptions and overly simplistic (and often erroneous) views have been flagged and described over the last recent decades. However, uptake of those new insights by decision makers and the irrigation sector itself has been limited.

The “Follow the Water” project will develop a Guidance Document that summarizes those aspects and, more importantly, quantifies the return flows that occurs in irrigated systems. Those return flows are collected from a wide range of experiments and are collected in a database to be used as reference for new and/or rehabilitation irrigation projects.

The FAO/FutureWater project will also develop a simple-to-use tool to track water in irrigated systems using so-called “virtual tracers”. The tool will respond to the demand for a better understanding the role of reuse of water in irrigated agriculture systems. An extensive training package, based on the Guidance and the Tool, is developed as well.

FAO plays an essential role in backstopping the development of the Guidance and the Tool and promoting. FutureWater takes the lead in development of the Guidance, the Tool and the training package. With this, FAO and FutureWater will contribute to a sustainable future of our water resources.

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

El proyecto de consultoría “Planificación y gestión estratégica integrada de los recursos hídricos para Ruanda” evaluará la disponibilidad de los recursos hídricos del país para el horizonte 2050 y su vulnerabilidad frente al cambio climático. En base a las previsiones de disponibilidad y riesgo, se realizará una priorización de las posibles opciones de inversión en infraestructura gris y verde que podrían ser integrados en la planificación hídrica nacional para alcanzara los objetivos de seguridad hídrica y de desarrollo sostenible (ODS 6).

La evaluación de recursos hídricos en un contexto de cambio climático se apoyará en herramientas de modelización hidrológica y asignación de recursos entre diferentes usos (modelo WEAP), y de contabilidad del agua a nivel de subcuenca. Paralelamente a la modelización se realizarán trabajos de campo orientados a la evaluación de los recursos hídricos subterráneos. Los mecanismos de asignación de recursos se cuantificarán bajo diferentes escenarios de uso incorporando las visiones y demandas de las partes interesadas.

Tras la evaluación de recursos y demandas-asignación, se cuantificará el potencial existente para incrementar la capacidad de almacenamiento y regulación de agua mediante la inclusión de infraestructura gris (embalses) y verde (Soluciones basadas en la Naturaleza). La evaluación del potencial y priorización de las soluciones planteadas se apoyará en visitas de campo y un análisis de viabilidad y DAFO las opciones candidatas. Para las opciones finalmente seleccionadas se desarrollarán fichas descriptivas de carácter conceptual para su integración en los instrumentos de planificación.

Por último, y en base a los resultados obtenidos en las tareas interiores, el trabajo de consultoría apoyará la revisión de la política nacional de gestión y planificación de recursos hídricos mediante la definición de nuevas declaraciones y políticas que ayuden a alcanzar los objetivos NST1 y Visión 2050.

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.