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:


  • 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.

This project is part of the technical-innovation support provided by FutureWater to ECOPRADERAS, an EIP-AGRI Operational Group led by Ambienta Ing. and co-funded by the EU and the Spanish Ministry of Agriculture. As a general objective, ECOPRADERAS aims to improve the sustainable management of grasslands located at the Alagon Valley (Extremadura, Spain) through: (1) the transfer and implementation of innovative technologies, (2) the identification and strengthening of good cultural practices, and (3) the dissemination of the most relevant information and results among end users.

In the frame of ECOPRADERAS, FutureWater is tasked with the development of an operational monitoring tool able to inform, at the regional scale, on the health status of the grasslands by using satellite data of moderate spatial resolution. The ECOPRADERAS monitor includes the following innovative features:

  • Generation of a categorical index indicative of the health status of grasslands based on the combination of indices of spatial and temporal greenness anomalies.
  • Higher spatial details by using satellite images of moderate spatial resolution (collection of Landsat-8TM of 30 m resolution)
  • Large improvement for collecting and processing large satellite datasets by using the Google Earth Engine cloud-based geoprocessing platform (collection of Landsat-8TM from January 2014 onwards)
  • A user friendly web-mapping interface to visualize outputs

The methodology used by FutureWater uses massive data processing technologies in the cloud (Google Earth Engine) to compute a pixel-based categorical index that result of the combination of a spatial and a temporal anomaly of the greenness index (NDVI). After a local calibration that needs to be adopted, this qualitative index, called the Combined Index of Normalized Anomalies (ICAN) (figure), classifies the status of grasslands in the region of interest into different categories that informs on the health grasslands and how are they being managed. With the ICAN, land managers and local actors can early detect those portions in the landscape in which management practices may pose a risk for the sustainability of the agropastoral system and then would require special attention for improving them.

Logic diagram for computing the Combined Index of Normalized Anomalies (ICAN) in the ECOPRADERAS Monitor.The specific tasks developed by FutureWater included: the definition of a methodological framework for monitor the health of grasslands at the regional scale, the design of a processing and web-mapping platform and its practical implementation in the Alagon Valley (182 km2) from September 2019 to July 2020, and the calibration-validation of the results by comparing outputs with field observations collected in different pilot sites by other project partners.

An evaluation of the results points out to the strength of the methodology. The processing architecture is also easily scalable to other regions and rangeland landscapes. Further improvements have been also envisioned. The ECOPRADERAS Monitor stands as a very powerful tool to guide landscape managers local stakeholders on better decisions.

ECOPRADERAS Monitor at the Alagon Valley (Extremadura, Spain)

In 2017, AFD approved to finance the Water Resources Management and Agro-ecological Transition for Cambodia “WAT4CAM” Program Phase 1. This program will contribute to reduce poverty, develop the economy and reduce the vulnerability of rural populations to climate change by implementing a hydro-agricultural infrastructures rehabilitation program through an integrated approach, targeting the whole chain of water resources management, water services and agricultural production.

The strategy is to achieve intensification of cropping, modernization and climate smart practices to provide farmers with secure access to water. This is a challenging objective and a good understanding of the hydraulics of water flows in dry and wet season is needed. A consortium led by FutureWater was hired to perform WAT4CAM subcomponent 3.1, which concentrates on providing this understanding of both flood and dry season flows, demands and balance in the Preks intended for rehabilitation.

The initial stages of the project include the identification of current data, models and previous work, as well as a field survey with stakeholders. This information will be used to create an accurate and reliable modelling ensemble that makes maximum use of existing capacity in Cambodia. In addition, the consortium will use satellite-derived data products to (i) provide input to the simulation models, and (ii) calibrate and validate model results. Various sources of satellite imagery will be explored to map floods and irrigation practices, to implement an integrated “space hydrology” approach.

The modelling and knowledge generation from this study must support the other WAT4CAM components for the successful implementation of the Prek irrigation system improvements. The modelling itself is thus not the ultimate purpose, but rather the understanding and knowledge imparted to MoWRAM and the other components of the WAT4CAM program.

FutureWater’s role in the project is the overall project coordination and administration, as well as the implementation of satellite remote sensing and climate change analyses in support of the modelling components.

The Directorate of Water Resources and Improvement of River Systems (DWIR) is one of the key government agencies in the field of integrated water resources management in Myanmar. DWIR consists, next to its national head offices, of twelve regional offices. Regional DWIR offices concentrate on flood protection by maintenance of the river and its embankments.

National-level DWIR staff attended previous trainings on Google Earth Engine (GEE) organized by FutureWater and HKV in Myanmar, during which GEE was identified as a particularly relevant tool to support DWIR’s mission. FutureWater and HKV have also successfully collaborated in a Partners for Water project focusing on operational rainfall monitoring. In particular, regional-level DWIR staff can benefit from using GEE for successfully complying with their mandate concerning design and practical implementation of riverbank and flood protection measures. They need to work with geospatial data on historical river morphology, flood extent, as well as hydrological baseline data on e.g. rainfall and evapotranspiration. With the overall capacity of the regional-level staff somewhat lower than the national level staff, this TMT aims to achieve a great leap forward by acquainting regional staff with geodata access, analyses and interpretation using GEE, to benefit the quality of flood protection measures and overall water safety in Myanmar.

The training is implemented by a mix of Dutch and Burmese trainers, who provide a program consisting of a month on-distance support, a two-and-a-halve-week in-country training followed by a period of 6 months of regular on-distance support. Following the COVID-19 pandemic, in-country training components are converted to an eLearning approach.

Aim of the training

The training will enhance capacity of Egerton educational staff in accessing and using innovative data and tools in the public domain, to analyse crop performance and irrigation management. During the training, university participants will be specifically supported in developing course modules based on the skills gained. To maximize the impact in addressing the need for increased quality of higher education in the agricultural sector, representatives from other institutes, ministries and private sector companies will also be invited. The training will allow the staff to gain advanced skills in working with flying sensors (drones) and satellite-derived data to support agricultural and water-related challenges, such as pests and diseases, water efficiency in agriculture to enhance food security, and drought monitoring. They will acquire insight in and knowledge on analyzing the performance of crops, making the right intervention decisions and giving irrigation advice. For public sector representatives, the training objective is to obtain skills that can be directly and sustainably implemented in their respective organizations.

Overall, the Kenyan society at large will benefit from improved food security provided by well-educated agricultural researchers and professionals. This project forms an important step in the capacity building strategy as it focuses on strengthening the universities and preparing them to provide high quality education to the future generation agronomists and agricultural managers, as well as upgrading the knowledge of current professionals.

The training costs of four stages: an online training course, followed by an in-country training program, symposium and post-training support.

Stage 1: eTraining course

The first stage involved a weekly online training course that will start in January 2021, with a total of six sessions in six weeks. Participants will be consisting of University and TVET faculty members, university students, PhD candidates, researchers, Kenya Agricultural & Livestock Research Organization (KALRO) staff members, Agriculture Extension Staff from the County Government who are already involved in agricultural research and training and other private sector partners. Staff members from the university will be those that are involved in teaching agronomy, horticulture, agriculture engineering and agriculture extension courses and programs, i.e., soil, nutrient and water management, dryland farming, irrigated agriculture and crop protection. Non-university attendants will be technical staff who are close to the decision makers within their organizations. This will enhance the impact of the training by embedding the use of flying sensor and satellite-derived data for agriculture within these organizations and make sure that Kenya will pursue its activities in making use of this kind of information.

This first stage of the training course will be online and will focus on:

  1. Real Water Savings in Agricultural Systems including potential field interventions
  2. The use of WAPOR to access remotely sensed derived data
  3. The use of flying sensors (drones) in agriculture

The course will end with a test and evaluation and graduates will receive a certificate.

Stage 2: Targeted in-country training

After the first stage training a second in-country training will take place with a smaller group, focusing on the use of drones in agriculture. Here a selected group of 12 to 18 members will be trained. Focus will be on staff with lecturing responsibilities, to ensure impact on higher education provision and transfer of the new skills to students.

The in-depth training will consist of:

  1. Operating flying sensors manually and automatic, the processing of the collected data using open source software, interpretation and the subsequent decision making (recommendations to increase productivity) for (smallholder) farmers and actors
  2. Use satellite derived (precipitation) products to run crop growth models to provide advice on when and how much to irrigate in agricultural fields

Participants will work on hands-on exercises related to crop performance analyses, water demands and crop growth modelling. Application of the new skills will be further stimulated by assigning the participants clear, tailor-made goals at the end of the second training session, to be worked on during the distant-support period.

Stage 3: Symposium/knowledge sharing

Right after the second training session, a symposium will be organized for a larger audience including the superiors/managers (who most of the times are the final decision makers) of the training participants and representatives of similar organizations. During this knowledge sharing event, trainees and trainers will actively provide contributions to showcase the newly gained skills and their added value to the respective institutions and the Kenyan agricultural sector in general. By acquainting the responsible decision makers in these organizations with the potential applications of flying sensor and satellite-derived data relevant to them, this event will be crucial in ensuring a sustainable impact of the TMT.

Stage 4: Post-training support

In this period, progress will be actively monitored and the trainers will provide post-training support to the participants. The support will be both remotely (e.g. through Skype) by the Dutch training providers but also in-person by ThirdEye Kenya staff visiting the participants for Q&A sessions and to evaluate the implementation of the skills they obtained.

The Government of Angola is developing a policy to diversify the country’s economy, strongly dependent on the income of the oil sector. Agriculture is considered one of the priority sectors to be developed. A favorable climate and a relatively high availability of water resources and fertile soils, can lead Angola to become one of the leaders in agricultural production of the African continent.

With the objective of increasing agricultural production and productivity and favoring investment and innovation in related businesses, the present project arises: a nexus between policies, practice and knowledge. The project “Knowledge-to-Knowledge (K2K – Knowledge to Knowledge), funded by the Dutch government and managed by the University of Wageningen, aims to strengthen and enhance the capacity of the main Angolan knowledge institutions in agricultural sciences, to establish a strong relate between knowledge and practice. To do this, the development of skills in Geographic Information Technologies of the Faculty of Agricultural Sciences (FCA) of the José Eduardo dos Santos de Huambo University (UJES) is proposed.

Among other tasks, a program is established with an approach based on “Train the Trainers -TtT” (train the trainers), in which FutureWater has collaborated, with the aim of developing the knowledge and skills of the FCA staff. UJES in Geoinformatics and Remote Sensing. After the development of the TtT program, the staff of the University should be able to:

  1. Establish a University program of training in Remote Sensing
  2. Develop and maintain the necessary teaching material
  3. Initiate and carry out its own research program
  4. Develop small courses aimed at the agricultural sector