To facilitate the needs of ZIPAK, this training aims to build data-driven capacities relevant to sustainable nature conservation practices and ecosystem-based natural resources management in Iran:
Leveraging the Climate Change Knowledge Portal (CCKP) for performing climate risk and vulnerability assessments
Leveraging the online dashboard Earth Map for environmental hazard mapping and socio-economic risk assessments
Applying the InVest model (Integrated Valuation of Ecosystem Services and Tradeoffs) for assessing ecosystem service provision
The training focuses on knowledge and skills development and how how to meaningfully integrate these capabilities into ZIPAK’s objectives on sustainable management of the environment and natural resources.
This project will support the government to enhance the river basin planning in the Amu Darya river basin. In addition to preparing an investment portfolio in representative irrigation and drainage subprojects, the project will undertake a knowledge-based approach to deliver climate adaptive solutions for water resources management.
The project will use state-of-the-art downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles, other relevant hazards, hydrological/crop/planning model outputs and local information for the CRA assessment. This project will help the Asian Development Bank (ADB) to identify investments in key sectors with an explicit focus on reducing vulnerability to climate change within the basin.
The project will undertake:
Climate change risk analysis and mapping on key water-related sectors, impacts on rural livelihoods, and critical water infrastructures.
Climate change adaptation strategic planning and identify barriers in scaling up adaptation measures at multiple scales with stakeholder consultation and capacity building approach.
Identification of priority measures and portfolios for integration into subproject development as well as for future adaptation investment in the Amu Darya river basin. The identification will cover shortlisting of potential investments, screening of economic feasibility, and potential funding opportunities.
Previous related FutureWater project can be found here.
Pakistan is ranked as the 8th most climate vulnerable country in the world as per the Global Climate Risk Index (2019) and in recent years has been facing the worst brunt of climate change. Irregular and intense precipitation, heatwaves, droughts, and floods have severely impacted the agriculture and water sector. Approximately, 90% of the country’s freshwater resources are utilized by the agricultural sector. However, lack of information services makes it a challenge to implement a water accounting system for improved water resources management.
The GCF funded project titled “Transforming the Indus Basin with Climate Resilient Agriculture and Water Management” aims to shift agriculture and water management to a new paradigm in which processes are effectively adapting to climate change and are able to sustain livelihoods. FAO Pakistan, as per the request of the Ministry of Climate Change, has designed the project to develop the country’s capacity to enhance the resilience of the agricultural and water sector. There are three major components:
1. Enhancing information services for climate change adaptation in the water and agriculture sectors
2. Building on-farm resilience to climate change
3. Creating an enabling environment for continued transformation
FutureWater will be actively involved in Component 1 which focuses on facilitating the development of a water accounting system and improving the availability and use of information services. Given the limited data availability in the region, FutureWater will integrate the use of remote sensing technologies within the existing Water Accounting methodology to address this gap. A capacity and needs assessment will be conducted and a series of tailor-made trainings will be designed subsequently to enable key government stakeholders to use open-source geospatial analysis tools as well as models to estimate real water savings, particularly in the context of agriculture. The trainings will help build the country’s capacity to implement water accounting at different spatiotemporal scales and cope with the worsening impacts of climate change.
Agriculture is a key sector of the Rwandan economy; it contributes approximately 33% to the gross domestic product and employs more than 70% of the entire labour force. Although some farmers are already using water-efficient irrigation infrastructure, too much of the available water is still lost due to unsustainable use of existing irrigation systems, and/or maximum crop yields are not achieved due to under-irrigation.
Hence, small to medium-sized food producers in Rwanda do not have sufficient access to information regarding optimal irrigation practices. To close this information gap, FutureWater has devised an innovation that can calculate a location-specific irrigation advice based on Virtual Weather Stations, expressed in an irrigation duration (“SOSIA”). The use of the outdated CROPWAT 8.0 method, and the lack of good coverage of real-time weather stations in Rwanda, means that current advice falls short. In addition, existing advisory services are often too expensive for the scale on which small to medium-sized farmers produce. There is a potential to increase the productivity of the irrigation water by up to 25%. Initially, the innovation will be disseminated via the Holland Greentech network, with a pilot in Rwanda consisting of 40 customers.
FutureWater has found with Holland Greentech an ideal partner to roll-out this innovation due to their presence in and outside of Rwanda, where they provide irrigation kits and advice. This offers the opportunity to quickly scale-up the proposed innovation. With their expertise in agro-hydrological modeling and the African agricultural sector, FutureWater and Holland Greentech respectively have acquired ample experience to make this innovation project and its knowledge development to a success.
The beneficiaries of this training, provided by FutureWater together with Solidaridad, belong to the Zambia Agricultural Research Institute (ZARI).
ZARI is a department within the Ministry of Agriculture of Zambia with the overall objective to provide a high quality, appropriate and cost-effective service to farmers, generating and adapting crop, soil and plant protection technologies. This department comprises a number of sections, one of which, for the purpose of this training request is the Soil and Water Management (SWM) division. ZARI and the SWM carry out demand-driven research, trying to find solutions to the problems faced by Zambian small-scale farmers, especially considering the near- and long-term impacts of climate change.
The training programme consists of a hybrid approach of e-learning and in-person training sessions and is structured around the following modules:
Remote sensing-based analysis using Google Earth Engine to assess trends in land use, management, degradation and hotspots for intervention.
Data collection and database management.
GIS and remote sensing to assess suitability for SWC.
Effectiveness and prioritization of SWC using open-source tools.
Independent working on case study.
At the end of the training, it is expected that participants have achieved several objectives such as acquisition of technical skills for extracting relevant data from open access remote sensing products and improved knowledge of data collection and database management.
UNCCD is the sole legally binding international agreement linking environment and development to sustainable land management. As some of the most vulnerable ecosystems and peoples can be found in arid, semi-arid and dry sub-humid areas, UNCCD especially addresses these drylands. Productive capacities in drylands are threatened by megatrends such as climate change and land degradation, where changing precipitation and temperature potentially exacerbate processes of degradation and where degraded lands make productive systems more vulnerable to impacts of climate change.
UNCCD therefore aims to support the reorientation of productive capacities towards sustainable and resilient patterns, in order to reverse the impact of land degradation and mitigate climate change impact. To this end, UNCCD is interested in the identification of regions and crops at a particularly high risk of land degradation and climate change impact. The outcomes of this activity should support informing of national governments of risk profiles of their main cash crops and, subsequently, support identification of alternatives for value chains that are projected to become insufficiently productive in the future.
Subsequent work will link towards opportunities around other megatrends such as population changes, consumption patterns, energy and shifting geopolitical patterns present in the identification of new value chains.
The Mekong State Of the Basin Report (SOBR) is published by the Mekong River Commission (MRC) every five years, in advance of the cyclic updating of the Basin Development Strategy. The SOBR plays a key role in improving monitoring and communication of conditions in the Mekong Basin, and is MRC’s flagship knowledge and impact monitoring product. It provides information on the status and trends of water and related resources in the Mekong Basin. The 2023 SOBR is based on the MRC Indicator Framework of strategic and assessment indicators and supporting monitoring parameters, which facilitates tracking and analysis of economic, social, environmental, climate change and cooperation trends in the basin.
FutureWater was hired by MRC to perform the following tasks in support of the 2023 SOBR development:
Data collection on the Extent of Salinity intrusion in the Mekong Delta and the conditions of the Mekong River’s riverine, estuarine, and coastal habitats
Analyses of the extents of 2010, 2015, and 2020 LMB wetlands
Analyses of the extents of key fisheries habitat areas in the LMB, and
Data collection for all Assessment Indicators of MRB-IF for the Upper Mekong River Basin (UMB), including reporting and extracting key messages
Implementation of tasks 1 – 3 is achieved by using state-of-the-art remote sensing tools, such as the Google Earth Engine, building on the methods developed in the preceding project.
Task 4 builds on the findings of FutureWater’s contribution to the 2018 SOBR regarding the status of the UMB in China and Myanmar, more details can be found here.
The practice of using remote sensing imagery is becoming more widespread. However, the suitability of satellite or flying sensor imagery needs to be evaluated by location. Satellite imagery is available at different price ranges and is fixed in terms of spatial and temporal resolution.
TerraFirma, an organization in Mozambique with the task to map and document land rights, hired FutureWater, HiView and ThirdEye Limitada (Chimoio, Mozambique) to acquire flying sensor imagery over a pilot area near Quelimane, Mozambique. The objective of this pilot project is to determine the suitability of using flying sensor imagery for cadastre mapping in an area of small-scale agriculture in Mozambique.
Flying sensor imagery is adaptable and can be deployed at any requested time. The suitability of these remote sensing approaches is piloted in this study for a small-scale agricultural area in Mozambique. A pilot area is used as case study with flights made during a period of a few days in December 2020, by local flying sensor (drone) operators in Mozambique (ThirdEye Limitada).
The flying sensor imagery was acquired over the period of a few days in December 2020, for a total area of 1,120 hectares. This imagery was used as input for various algorithms that can be suitable for classification and segmentation, namely R packages (kmeans, canny edges, superpixels, contours), QGIS GRASS segmentation package, and ilastik software. This study shows some initial results of using flying sensor imagery in combination with these algorithms. In addition, comparison is made with high resolution satellite imagery (commercial and publicly available) to indicate the differences in processing and results.
With the conclusions from this pilot project, next steps can be made in using flying sensor imagery or high resolution satellite imagery for small-scale agriculture in Mozambique. The time and effort needed for the delineation of field boundaries can be largely reduced by using remote sensing imagery and algorithms for automatic classification and segmentation.
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 (WEAP–PODIUMSIM 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.
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.