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 Sous-Massa basin is located in central Morocco. It represents an arid area that will likely face water resources challenges into the coming decades due to the influence of climate change and socioeconomic development. Indeed, increases in temperatures and decreases in precipitation are anticipated in the Sous-Massa region, alongside more extreme intense precipitation and drought events. It is therefore important the the impacts of climate change on water availability are better constrained to target resilience measures and better prepare for potential future water scarcity.

With the results of this project, IMWI will be able to apply the Water Accounting Plus framework to the Sous-Massa basin, helping to better constrain the likely impacts of climate change on future water availability. This project therefore helps support the targeting and prioritisation of climate resilient interventions which can be taken by the government and other members of the water sector in this area of Morocco.

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

 

Scientists from around the world have assessed the planet’s 78 mountain glacier–based water systems and, for the first time, ranked them in order of their importance to adjacent lowland communities, as well as their vulnerability to future environmental and socioeconomic changes. These systems, known as mountain water towers, store and transport water via glaciers, snow packs, lakes and streams, thereby supplying invaluable water resources to 1.9 billion people globally—roughly a quarter of the world’s population.

The research, published in the prestigious scientific journal Nature, provides evidence that global water towers are at risk, in many cases critically, due to the threats of climate change, growing populations, mismanagement of water resources, and other geopolitical factors. Further, the authors conclude that it is essential to develop international, mountain-specific conservation and climate change adaptation policies and strategies to safeguard both ecosystems and people downstream.

Globally, the most relied-upon mountain system is the Indus water tower in Asia, according to their research. The Indus water tower—made up of vast areas of the Himalayan mountain range and covering portions of Afghanistan, China, India and Pakistan—is also one of the most vulnerable. High-ranking water tower systems on other continents are the southern Andes, the Rocky Mountains and the European Alps.

To determine the importance of these 78 water towers, researchers analyzed the various factors that determine how reliant downstream communities are upon the supplies of water from these systems. They also assessed each water tower to determine the vulnerability of the water resources, as well as the people and ecosystems that depend on them, based on predictions of future climate and socioeconomic changes.

Of the 78 global water towers identified, the following are the five most relied-upon systems by continent:

  • Asia: Indus, Tarim, Amu Darya, Syr Darya, Ganges-Brahmaputra
  • Europe: Rhône, Po, Rhine, Black Sea North Coast, Caspian Sea Coast
  • North America: Fraser, Columbia and Northwest United States, Pacific and Arctic Coast, Saskatchewan-Nelson, North America-Colorado
  • South America: South Chile, South Argentina, Negro, La Puna region, North Chile

The study, which was authored by 32 scientists from around the world, was led by Prof. Walter Immerzeel (Utrecht University) and Dr. Arthur Lutz (Utrecht University and FutureWater), longtime researchers of water and climate change in high mountain Asia.

 

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 course on hydrology and water allocation modelling is organized for the Kenya Water Resources Authority (WRA) and funded by the Blue Deal program of the Netherlands. The first four-week course block introduces the participants to the main concepts in hydrology, hydrological modelling and data collection, including remote sensing. Exercises are provided on water balances, land use datasets, extraction of rainfall data from remote sensing datasets, among others.

The 5-week second block of the training is on the use of a water resources system model (WEAP) for water allocation. Participants will learn how to develop, run and evaluate a model, including scenario analysis, water balances, assess impact of changing priorities among users, and impacts on water shortage. The learned skills will be used afterwards for establishing a Water Allocation Plan for an important sub-basin of the Upper Tana river, providing water to many livelihoods in the catchment itself, but also to Nairobi city.

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.

In 2016, FutureWater released a new dataset: HiHydroSoil v1.2, containing global maps with a spatial resolution of 1 km of soil hydraulic properties to support hydrological modeling. Since then, the maps of the HiHydroSoil v1.2 database have been used a lot in hydrological modeling throughout the world in numerous (scientific) projects. A few examples of the use of HiHydoSoil v1.2 are shown in the report.

Important input of the HiHydroSoil database is ISRICS’ SoilGrids database: a high resolution dataset with soil properties and classes on a global scale. In May 2020, ISRIC has released the latest version (v2.0) of its Soilgrids250m product. This release has made it possible for FutureWater to update its HiHydroSoil v1.2 database with newer, more precise and with a higher resolution soil data, which resulted in the development and release of HiHydroSoil v2.0.

Soil information is the basis for all environmental studies. Since local soil maps of good quality are often not available, global soil maps with a low resolution are used. Furthermore, soil maps do not include information about soil hydraulic properties, which are of importance in, for example, hydrological modeling, erosion assessment and crop yield modelling. HiHydroSoil v2.0 can fill this data gap. HiHydroSoil v2.0 includes the following data:

  • Organic Matter Content
  • Soil Texture Class
  • Saturated Hydraulic Conductivity
  • Mualem van Genuchten parameters Alfa and N
  • Saturated Water Content
  • Residual Water Content
  • Water content at pF2, pF3 and pF4.2
  • Hydrologic Soil Group (USDA)

Download HiHydroSoil v2.0

The HiHydroSoil v2.0 database can be accessed after filling the brief request form below. A download link to the full dataset will then be provided. The HiHydroSoil v2.0 dataset is organized in two folders, one containing the original data for each of the six depths, and one with the aggregated subsoil and topsoil data. All data layers are delivered in geotiff raster format.

Important! To avoid lengthy download times, the data layers originally consisting of float data type were multiplied by a factor of 10,000, and subsequently converted to integer type. It is therefore required to translate the data to the proper units by multiplying with 0.0001. These steps are also described in the readme file delivered with the data.