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.

Today, FutureWater has released a new high resolution dataset containing global maps for Soil Hydraulic Properties: HiHydroSoil v2.0! It is the second generation HiHydroSoil dataset, after the first release in 2016. HiHydroSoil v2.0 is a definite upgrade of the first version, since it is build with higher quality input layers and with a higher spatial resolution of 250 m.

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

The HiHydroSoil v2.0 database can be accessed through the FutureWater website (also attached to this news article): https://www.futurewater.eu/projects/hihydrosoil/. After filling the brief request from, a download link to the full dataset will 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.

Does drip irrigation lead to real water savings? What is the impact of changing the irrigation efficiency on basin scale water flows? How can water managers implement water savings technologies that lead to real water savings? Several of such questions were discussed during a recent webinar.

FutureWater in collaboration with the FAO Near East and North Africa Regional Office provided this webinar on Real Water Savings. The various tools developed under the on-going FAO project (https://www.futurewater.eu/projects/training-package-for-water-productivity-and-real-water-savings/) were presented. Its relevance for the Near East and North Africa region was demonstrated by showcasing several case studies of water savings technologies (such as drip irrigation) and using the newly developed Excel-based REWAS tool to provide an assessment of field and basin scale water saved. The webinar ended with a lively discussion on the applicability of these tools for the Near East and North Africa region context. We look forward to following up on several of the opportunities and issues raised during this discussion.

Screenshot of the webinar, with our colleagues Jonna van Opstal and Peter Droogers

FutureWater in Mozambique had yet another successful partnership added to its already extensive collaborative activities with this Southern African country. ARA-Sul, the waterboard responsible for water management in the region around Maputo, received an eLearning course on supporting water allocation permit decisions using the WEAP tool. This training was a continuation of collaboration with the same waterboard in 2015, with the main difference that because of the COVID-19 situation, all training was developed and delivered as eLearning.

The intensive training took place within a time frame of 3 weeks with collective video meetings twice a week for one to two hours using Zoom. Participants were enthusiastic that they could do the training assignments at their own premises and in their own pace. “Training material was really of good quality and with a wide variation of training manuals, YouTube instruction videos, set of reference documents, and PowerPoints recordings”, said one of the trainees.

The training was setup in the context of the Blue-Deal initiative, a collaboration between Water Boards in Mozambique and in the Netherlands. At the end of the training participants were able to run the WEAP model and to evaluate inputs and explore outputs of various water allocation scenarios.

Printscreen of the WEAP model used for the training

 

FutureWater completed two eLearning courses on hydrology and on climate change for staff of PLN. PLN (Perusahaan Listrik Negar) is the biggest electricity company in Indonesia. PLN is increasingly expanding its activities to hydropower and therefore additional training needs on hydrology and climate change was needed. FutureWater developed and delivered those trainings to PLN in an eLearning setting using tailored-made training material.

“I’d never imagined that eLearning could be so effective and enjoyable”, was the response of one of the 45 trainees. Training material was developed with specific focus on studying by participants individually and at their own pace. Each study session of eight hours was supported by one to two hours of collective video meetings using Zoom. “Preparing training material took much more effort compared to a traditional (live) training”, said Peter Droogers of FutureWater. “But the benefit is that training material could be reused by other groups”, he added.

The overall feedback of the 45 participants was overwhelmingly positive. FutureWater will continue developing and delivering eLearning courses as long as covid-19 travel restrictions are in place, and most likely even beyond.

Printscreen of one of the eLearning training lessons provided by our colleague Peter Droogers (top left).

 

The overall aim of the Guidance is to supporting adaptation decision making for climate-resilient investments with the main objective to scale-up ADB’s investments in climate change adaptation in Asia and the Pacific. The Good Practice Guidance on climate-resilient infrastructure design and associated training modules will help project teams to incorporate climate projections information into project design. The guideline is based both on insights gained by experts in supporting climate-resilient project development, and on state-of-the-art reviews of emerging engineering design and decision-making protocols that reflect the impacts of climate change. Sector guidance will be provided for agriculture and food security, energy, transport, urban development, and water. FutureWater takes the lead in the water sector guidance.

Training modules targeting member countries officials and ADB operational staff involved in the design of resilient infrastructure projects will be developed to facilitate the wider dissemination of, and capacity building around, the good practice guidance and enhanced availability of climate projections data. Training modules will be developed for both in person delivery at training sessions and distance learning to enable on-demand technical capacity building. The format of the in-person training sessions will be determined in consultation with the operational teams and could take a “training of trainers” approach.

Indonesia is endowed with a full range of both renewable and fossil resources of energy, actively exploited to feed its growing economy. Emphasis has been on fossil, hydroelectric and geothermal resources rather than wind and solar. PLN (Perusahaan Listrik Negara) is the Indonesian State Electricity Company. It is an Indonesian government-owned corporation which has a monopoly on electricity distribution in Indonesia and generates the majority of the country’s electrical power, producing about 175 TWh annually. Only a small fraction of this originates from hydropower.

Indonesia has five large hydropower plants with a capacity over 250 MW: Cirata on Java (1008 MW), Saguling on Java (701 MW), Tangga on Sumatra (317 MW), Sigura-gura on Sumatra (286 MW), and Pamona on Sulawesi (260 MW). The Indonesian government aims to develop more hydropower with quite a strong focus on small and micro hydropower plants.

Capacity of PLN staff to understand the hydrology related to hydropower electricity generation needs to be enhanced. Also, the knowledge of the potential impact of climate change on hydropower requires additional capacity of PLN’s staff. Especially their ability to understand and judge feasibility studies undertaken by external consultants requires upgrading their level of knowledge. Also staffs’ capacity to understand climate risk assessment studies, as today required by most investors, should be further developed.

FutureWater was asked to develop and provide training on those two aspects (hydrology and climate change). Given the huge area of the country and PLN staff working in large distances from each other, it was decided to provide training in a eLearning setting. Initially about 25 staff will be trained and based on lessons learnt the training package will be adjusted to staff needs and further training will be undertaken.

In irrigated agriculture options to save water tend to focus on improved irrigation techniques such as drip and sprinkler irrigation. These irrigation techniques are promoted as legitimate means of increasing water efficiency and “saving water” for other uses (such as domestic use and the environment). However, a growing body of evidence, including a key report by FAO (Perry and Steduto, 2017) shows that in most cases, water “savings” at field scale translate into an increase in water consumption at system and basin scale. Yet despite the growing and irrefutable body of evidence, false “water savings” technologies continue to be promoted, subsidized and implemented as a solution to water scarcity in agriculture.

The goal is to stop false “water savings” technologies to be promoted, subsidized and implemented. To achieve this, it is important to quantify the hydrologic impacts of any new investment or policy in the water sector. Normally, irrigation engineers and planners are trained to look at field scale efficiencies or irrigation system efficiencies at the most. Also, many of the tools used by irrigation engineers are field scale oriented (e.g. FAO AquaCrop model). The serious consequences of these actions are to worsen water scarcity, increase vulnerability to drought, and threaten food security.

There is an urgent need to develop simple and pragmatic tools that can evaluate the impact of field scale crop-water interventions at larger scales (e.g. irrigation systems and basins). Although basin scale hydrological models exist, many of these are either overly complex and unable to be used by practitioners, or not specifically designed for the upscaling from field interventions to basin scale impacts. Moreover, achieving results from the widely-used FAO models such as AquaCrop into a basin-wide impact model is time-consuming, complex and expensive. Therefore, FutureWater is developing a simple but robust tool to enhance usability and reach, transparency, transferability in data input and output. The tool is based on proven concepts of water productivity, water accounting and the appropriate water terminology, as promoted by FAO globally (FAO, 2013). Hence, the water use is separated in consumptive use, non-consumptive use, and change in storage (see Figure).

Separation of water use according to the FAO terminology.

A complete training package is developed which includes a training manual and an inventory of possible field level interventions. The training manual includes the following aspects: 1) introduce and present the real water savings tool, 2) Describe the theory underlying the tool and demonstrating some typical applications, 3) Learn how-to prepare the data required for the tool for your own area of interest, 4) Learn when real water savings occur at system and basin scale with field interventions.

Methodology

  • Development of adaptation benefit-cost framework: The framework was developed in a manner to make it possible to isolate development- and climate-related benefits and costs of individual projects and to assess the sensitivity of adaptation benefits and costs to the uncertainty inherent in regional climate change scenarios.
  • Development of analytical tools and procedures: The project developed general procedures and specific analytical tools for consistently measuring the costs and benefits of adaptation projects in the agriculture sector in Africa. These procedures and tools allow multi- and by-lateral development institutions to evaluate the benefits and costs specifically related to climate adaptation “add-ons” to sustainable development projects.
  • Application of analytical tools and procedures: The project applied these procedures and analytical tools to estimate the benefits and costs of a well-defined adaptation project in the agricultural sector, particularly on the predominant crop in The Gambia: millet.
  • Water-crop model:
    A detailed water-crop model has been setup and applied for a reference period and for future projected climates. Adaptation strategies have been defined and explored with the model developed and an economic analysis have been applied on the results.

Overview of The Gambia. Landsat composite from 1990.

The major steps taken were:

  • collection of base data and information
  • extraction of IPCC projections for The Gambia
  • downscaling of these projections to the local conditions for The Gambia
  • setup of a crop-water model
  • evaluation of the impact of climate change on yields
  • definition of adaptation strategies
  • evaluation of the impact of these adaptation strategies
  • evaluation of the economics of these adaptation strategies

Result and conclusions

For the development and application of the adaptation benefit-cost framework data from two GCMs were used while concentrating on the most common grain crop in The Gambia: millet. The most relevant adaptation strategies were selected: crop variety improvements, fertilizer applications and irrigation. However, the modeling framework as it is setup can be easily applied to other GCMs, SRES scenarios, crops, soils, or adaptation strategies.

From the analysis it is clear that the impact of climate change on millet yields depends highly on the GCM selected. The HADCM3 projections indicate a much drier future, while the ECHAM4 ones indicate somewhat more rainfall in the future. Considering the “no-regret” principle, we decided to explore the adaptation strategies for the HADCM3 projections only.

Model mean anomaly for A2 Maximum Temperature °C.

Emphasize was put on the annual variation, and more specifically on the successive years of low yields. Introduction of irrigation appears to be the most successful adaptation strategy, yields will increase and, moreover, year-to-year variation decreases substantially.

Variation in annual precipitation over the entire country (Based on: CRU dataset).

A rough estimate of the benefits in terms of gross return was carried out by multiplying the yield by the price of millet (about $ 0.15 kg-1). For the irrigation adaptation strategy this means that the gross return per hectare will increase from $170 to $235. As mentioned before, the reduction in year-to-year variation by the adaptation strategies will be even more important and should be analyzed in detail.

Finally, the most promising adaptations has to be implemented and successive studies should look into whether these adaptation strategies can be adopted through market forces, whether the government should impose these by subsidizes or tax regulations, or whether bi-lateral aid should focus on this in an effort to minimize risks of food shortages.

Jilin Yanji Low-Carbon Climate-Resilient Urban Development in China is included in the Asian Development Bank’s (ADB) Country Operations Business Plan (2017–2019). The project is expected to have four outputs that are linked and integrated, and expected to generate co-benefits and higher efficiencies, the outputs as described in ADB’s project concept paper are as follows:

  • Output 1: Sustainable, low-carbon, and intelligent urban transport system implemented.
  • Output 2: Sponge city and climate-resilience plan completed, and infrastructure constructed.
  • Output 3: Water supply and wastewater management systems improved.
  • Output 4: Capacity inflow-carbon and climate-resilient urban infrastructure planning developed.

Yanji City is located in the Yanbian Korean Autonomous Prefecture (YKAP) in the east of Jilin Province in the economically challenged north east of the People’s Republic of China (PRC), bordering the Democratic People’s Republic of Korea (DPRK) to the southeast and the Russian Federation to the northeast. Yanji is an ancient city on the Bur-Hatong River, surrounded by hills, and its easternmost border is about 15 kilometers from the Sea of Japan.

In the context of the project a Climate Risk Assessment (CRA) will be undertaken. The CRA will follow the so-called bottom-up approach where the driving force of risks are not climate projections (GCMs), but the overall risk taking into consideration uncertainties in climate projections. This bottom-up approach is developed by increasing recognition of the fundamental uncertainty of future climate discourages the overinterpretation of model generated climate projections. In other words the main difference between the top-down and the bottom-up approach are in the use of GCM projections. The top-down approach is constraint (limited) to the GCM projections, while the bottom-up approach considers a range of potential changes in climate.

The CRA will be undertaken in four steps:

  1. Analysis of historic climate events
  2. Projections of future climates
  3. Impact and vulnerability of climate change
  4. Adaptation options and recommendations for design

Analysis will be based on a mixture of data and tools such as NASA-NEX-GDDP, WEAP, local data sources, Google Earth Engine, amongst others.