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.

Geodata tools have been developing rapidly in the past years and are vastly adopted by researchers and increasingly by policy-makers. However, the is still great potential to increase the practical application of these tools in the agricultural sector, which is currently applied by a limited number of ‘pioneering’ farmers. The information that can be gained from geodata tools on irrigation management, pest and nutrient management, and crop selection, is a valuable asset for farmers. Key players for providing such information to the farmers are the extensions officers. This project aims at training extensions officers in the use of these geodata tools. The beneficiaries in Egypt are: Tamkeen for Advanced Agriculture, FAODA, IDAM, Bio-Oasis, and LEPECHA. The selected participants will receive a training programme which consists firstly of several session on the background and theory of the geodata tools, provided through our online teaching platform (futurewater.moodle.school). Starting from May (2021) field schools will be set up to use the geodata tools for decision-making in these demonstration plots. In addition, modules are taught on the quality of the data, and profitability of such tools. Altogether, a group of carefully selected participants will receive training on these innovative tools and create a bridge to providing this information to farmers specifically the smallholder farmers.

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.

This glacio-hydrological assessment delivered river flow estimates for three intake locations of hydropower plants in Nakra, Georgia. The assessment included the calibration of a hydrological model, daily river discharge simulation for an extended period of record (1980-2015), and the derived flow duration curves and statistics to evaluate the flow operation of hydropower turbines. The daily flow calculations for the three sites (HPP1, HPP2 and HPP3) can be used in the hydropower calculations, and to assess the overall profitability of the planned investment, considering energy prices, demand, etc.

In the Nakra basin, glacier and snow model parameters were tuned to obtain accurate river flow predictions. Also, the latest technology of remote sensing data on precipitation and temperature (product ERA5) was used to reduce potential errors in flow estimates. Even though these flow estimates are useful for short-medium term evaluations on profitability of the planned investment, climate change pose a challenge for long-term evaluations. Glacier-fed and snow-fed systems, such as the Nakra basin, are driven by a complex combination of temperature and precipitation. Due to future increasing temperature, and changing rainfall patterns, glacier and snow cover dynamics change under climate warming. This can lead to shifts in the flows, like a reduction in lowest flows, and higher discharge peaks when the hydrological system shifts towards a more rainfall-runoff influenced system (Lutz et al. 2016). This can jeopardize the sustainability of the project on the long-term. To provide a better understanding of future river flows, it is recommended to develop a climate change impact assessment.

Several catchment plans have been already developed through the Dutch-funded Water for Growth programme. FutureWater played a paramount role in this programme by developing the water allocation models (WEAP) at national level and for several priority catchments. Moreover, FutureWater provided capacity building to local experts and staff on using and further developing and fine-tuning those WEAP models.

The current project aims at developing two catchment plans, for:

  1. Mukungwa catchment
  2. Akagera Lower catchment

These catchments were included in a previous national-level water resources allocation study performed by FutureWater. Four catchments were selected from this national level assessment to make catchment-level WEAP models to inform the catchment plans. A next step for the Rwanda Water Resources Board (RWB), is to prepare catchment plans for the above two catchments, for which this project will be instrumental.

For the two catchments, this study aims at (1) providing detailed information on available and renewable water resources, both surface and groundwater, and their spatial and temporal variations; and (2) to map and quantify water uses and water demands, to develop water allocation models that can be used as tools to manage operationally and plan the catchments in a sustainable way. The scenarios (options) assessed can also be essential input into the catchment management plan. This study will produce water allocation models based on current and potential uses in a time-horizon of 30 years.

The project is carried out in collaboration with a team of local experts and one of our partners Dr. Kaan Tuncok as a team leader.

Mukungwa and Akagera Lower catchments

This hydrological assessment delivered river flow estimates for an intake location of a potential hydropower plant in the Lukhra river, Georgia. The assessment included a tuning of a hydrological model based on knowledge of neighboring basins, daily river discharge simulation for an extended period of record (1989-2019), and the derived flow duration curves and statistics to evaluate the flow operation of hydropower turbines. The daily flow calculations for the site can be used in the hydropower calculations, and to assess the overall profitability of the planned investment, considering energy prices, demand, etc.

In the Lukhra basin, snow model parameters were tuned to obtain accurate river flow predictions. Also, the latest technology of remote sensing data on precipitation and temperature (product ERA5-Land) was used to reduce potential errors in flow estimates. Even though these flow estimates are useful for short-medium term evaluations on profitability of the planned investment, climate change pose a challenge for long-term evaluations. Snow-fed systems, such as the Lukhra basin, are driven by a complex combination of temperature and precipitation. Due to future increasing temperature, and changing rainfall patterns, snow cover dynamics change under climate warming. This can lead to shifts in the flows, like a reduction in lowest flows, and higher discharge peaks when the hydrological system shifts towards a more rainfall-runoff influenced system (Lutz et al. 2016). This can jeopardize the sustainability of the project on the long-term. To provide a better understanding of future river flows, it is recommended to develop a climate change impact assessment.

Kyrgyzstan is a highly mountainous country with relatively high precipitation in upslope areas. This, alongside the development and deforestation of basins to make way for industry and agriculture means that land has become increasingly degraded and vulnerable to erosion over recent decades. Reservoirs in the country provide access to water resources and energy in the form of hydropower, but are highly susceptible to sedimentation by eroded material. Sedimentation necessitates increased maintenance costs, reduces storage capacity and disrupts hydropower generation. It is therefore proposed that landscape scale restoration measures (e.g. tree planting) can provide key ecosystem services by reducing vulnerability to erosion and decreasing sediment delivery to reservoirs. This project therefore identifies highly degraded areas of land and determines in which of these interventions are possible. With the outcomes of this study, the World Bank – in partnership with the government of Kyrgyzstan – can prioritise investments in terms of landscape restoration efforts. The outcomes of this project will therefore reduce maintenance costs for reservoirs and contribute to the afforestation and restoration of multiple areas in Kyrgyzstan.

The training aimed at building and enhancing capabilities of the participants in environmental and hydrological monitoring and modeling and was funded by the Orange Knowledge Program of Nuffic. It gave the participants valuable and necessary knowledge on IWRM and it provided the participants with relevant hands-on experience and cutting-edge knowledge on innovative solutions in water allocation modeling and earth observation technologies.

Due to the ongoing COVID-19 situation, the training was held online using our eLearning platform FutureWater Moodle School. The beauty of this platform is that all online sessions can be recorded and they are still available for the participants to have another look at it. All material (exercises, manuals etc.) developed during the course is also still available on our FutureWater Moodle School. The Rwanda Water Resources Board is recruiting new staff in the future and this new staff will also have access to all material.

Topics covered in the training are:

WEAP:

  • Build a WEAP model from scratch
  • Work with WEAP’ Basic Tools
  • Create and run Scenarios in WEAP
  • Extract water balances from WEAP
  • Generate a hydrological model using WEAP’ Automatic Catchment Delineation Tool

Google Earth Engine:

  • First glance at JavaScript Syntax
  • Explore and visualize Landsat 8 Imagery
  • Create charts with Monthly NDVI Values
  • Use WaPOR for Water Productivity calculations
  • Work with CHIRPS Rainfall data
  • Evaluate the water balance of a catchment

 

A large Dutch consortium has joined in the project “Dutch network on small spaceborne radar instruments and applications (NL-RIA)”, led by TU Delft. The objective is to bundle the radar-related knowhow available in The Netherlands, and fill the knowledge gaps, in order to boost SmallSat radar-based Earth Observation technology. The focus of the project is on microwave remote sensing.

A key advantage of microwave remote sensing compared to optimal imagery is the all-weather/day and night observation capability, which greatly enhances the observation opportunities. This includes the ability to observe through clouds. Microwave remote sensing system include passive (radiometers) and active ones (radar altimeters, Synthetic Aperture Radars, precipitation radars, scatterometers, etc). This study will focus on altimeters and thus on active radar.

Continuous monitoring of fresh water bodies like rivers, lakes and artificial reservoirs, is important for water resources management, and thus for the principal water users in river basins, such as domestic, industrial and irrigation demands. Also, potentially there can be applications of this information for flood early warning, renewable energy (hydropower) and for the transport sector (shipping).

For the management of fresh water resources at the basin level, information on the status of surface water bodies is critical. In many areas in the world however, this information is scarce. Especially in developing countries, water level measurements of lakes and reservoirs are hardly available. In Europe, ground-based measurements are more common but sometimes performed by the entity operating the reservoir or river abstraction, and thus not available to water resources managers for the purpose of water resources planning. Also in transboundary (international) river basins, ground-based information is often not shared, so satellite-based information can be of high value for certain end-users (Zhang et al., 2014).

Altimeter measurements of rivers, lakes and artificial reservoirs and be used for two purposes:

  • Strategic planning of water resources, which requires water resources assessments to support for example river basin management plans
  • Operational management of water resources, for example for the hour-by-hour operational management of water release from reservoirs for hydropower.

The study performed by FutureWater focused on the first type of applications: strategic planning and decision making on the long-term. Especially for this purpose, satellite-based altimeter data has the potential to fill an important information gap. For the second type of applications: operational water management and short-term decision making, typically ground-level water level sensors are more cost-effective than satellite-based solutions.

Key results

From the analysis performed by FutureWater and based on literature review, the following key considerations are proposed for shaping a low-cost altimetry mission useful for assessing inland water bodies and water resources planning:

  • Altimetry information can be extremely useful for complex systems as for example swamps, where data on surface water levels and flows are scarce, as often the case in developing countries. Altimetry data can support the management and conservation of these systems that provide key ecosystem services for people and the environment.
  • To build hydrological models for water resources assessments, historic data is required to calibrate and validate the tools. To capture the variability in water resources systems and thus perform a successful validation, a period of around 10 years of altimetry data is recommendable.
  • A revisit frequency of 1 month is typically sufficient for water resources assessments. Higher frequencies are normally not necessary as they may only lead to minor improvements in the performance of the modeling tools. Lower frequencies (e.g. two months) are not sufficient to capture the seasonal pattern adequately.
  • The required accuracy is highly dependent on the characteristics of the water body and is a function principally of the annual dynamics in storage, and the depth-storage relationship. In case study I, with a very large but shallow water body, an accuracy of approx. 10 cm was considered necessary. For case study II, with a smaller and deeper water body, it was found that up to an error of 180 cm the performance of the model was not significantly affected.
  • The accuracy requirement can possibly also be expressed as a percentage of the annual variability in water levels, of a particular water body of interest. For example:
    • In case study I, annual increases of approximately 1 m are common. The accuracy requirement is approximately 10% of this (10 cm)
    • In case study II, water level increases or decreases within a year of around 15 m are possible. Also here, the accuracy requirement is in the order of 10-15% of this annual variability.
  • Finally it has to be noted, that the usefulness of the altimetry data is also dependent on the availability and quality of other datasets necessary for the hydrological modeling. These datasets are primarily the depth-volume relationship, ideally from in-situ measurements but possibly extracted from satellite data (Duan and Bastiaanssen, 2013b); as well as discharge data upstream or downstream of the water body. Without these data sources it is not possible to establish a reliable water balance of the water body.