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.

Nature-based Solutions (NbS) can help ensure the long-term reliability of water resources. Research has shown they can – depending on circumstance – be more cost-effective and longer-lasting than grey infrastructure, while generating multiple co-benefits for carbon, biodiversity and human health. Despite the promise of NbS, however, water sector actors and their financiers usually prioritize investments in traditional grey infrastructure because they are more familiar with its costs, benefits and returns. Most of them are unfamiliar with how to develop and assess the value of NbS projects, though research shows they’re interested in tapping into their multi-faceted benefits.

The Financing Nature for Water Security project of The Nature Conservancy (TNC) aims to produce and disseminate guidance that enables water sector actors (government agencies, water utilities, grass-root NGOs) and their funders (donors, development banks and private investors) to invest in NbS-WS, at scale, by mobilizing sustainable funding and repayable financing. The project comprises of technical modules, guidance documents, supporting databases and training materials.

FutureWater has been contracted by TNC to support the development of one of the content modules assembled under the project. The module “Technical Options” will help the reader understand the water security challenge(s) they are confronted with and identify the types of NbS that could help address those challenges. In particular, Futurewater works on the creation of 12 technical factsheets to be included in an annex to the main documentation, with each factsheet highlighting the key technical aspects, benefits and risks, and economic dimensions of an NbS. In addition, an inventory of relevant NbS databases, platforms, and references is delivered.

We are excited to announce that our high resolution dataset with global maps for Soil Hydraulic Properties HiHydroSoil v2.0 is now available on Google Earth Engine! 

It’s made available through the github page “Awesome GEE Community Datasets” by Samapriya Roy. A sample code on how to access the HiHydroSoil v2.0 dataset in Google Earth Engine can be found here (Google Earth Engine account required).

The HiHydroSoil v2.0 database can also be downloaded from the FutureWater website using the form below. Interested in HiHydroSoil v.20? Read more on the project page!

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)
Saturated Hydraulic Conductivity (m/d) of the Topsoil (0-30 cm).

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.

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.

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.

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.

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.