Uzbekistan is highly sensitive to climate change which will cause changes in the water flows and distribution: water availability, use, reuse and return flows will be altered in many ways due to upstream changes in the high mountain regions, but also changes in water demand and use across the river basin. The resulting changes in intra-annual and seasonal variability will affect water security of Uzbekistan. Besides, climate change will increase extreme events which pose a risk to existing water resources infrastructure. An integrated climate adaptation approach is required to make the water resources system and the water users, including the environment, climate resilient.
This project will support the Ministry of Water Resources (MWR) of Uzbekistan in identifying key priorities for climate adaptation in the Amu Darya river basin and support the identification of investment areas within Amu Darya river basin. The work will be based on a basin-wide climate change risk assessment as well as on the government priorities with an explicit focus on reducing systemic vulnerability to climate change.
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.
FutureWater leads this assignment and develops the climate risk hotspot analysis, and coordinates the contribution of international and national experts, as well as the stakeholder consultation process.
The Asian Development Bank (ADB) identified the need for a detailed Climate Risk and Adaptation (CRA) assessment for the DKSHEP to understand the risk posed by the changing climate on hydropower and the environment. Therefore, the objective of this Climate Risk and Adaptation Assessment (CRA) is to assess the vulnerability of the project components to future climate change and recommend adaptation options for climate-proofing of the design. Therefore, this CRA covers both type 2 adaptation, related to system change and resilience building, as well as type 1 adaptation related to climate-proofing This CRA assesses historic trends in relevant climate-related variables and analyses climate projections for the DKSHEP. Based on these projections, an assessment of the current and future climate risks and vulnerabilities relating to the proposed project activities will be outlined. Finally, recommendations will be presented for climate adaptation measures.
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.
De Gezondheidsdienst voor Dieren (GD) heeft in de afgelopen jaren een analyse van leverbotbesmettingen uitgevoerd. Dit onderzoek heeft laten zien dat verschillende omgevingsfactoren een rol spelen zoals grondwaterstand, neerslag, temperatuur en bodemtype. Daarnaast is op basis van landsdekkende bodemvochtschattingen door FutureWater vastgesteld dat ook bodemvocht en leverbotbesmettingen gecorreleerd zijn, wat verklaard wordt doordat de leverbotslak in vochtige omstandigheden het beste gedijt. De GD geeft jaarlijks een leverbotprognose uit op basis van modelresultaten. Op basis van de eerdere onderzoeksresultaten heeft de GD sinds enkele jaren ook voorspellingen van bodemvocht in haar model opgenomen, welke jaarlijks door FutureWater worden geleverd voor elk postcodegebied in Nederland.
In het huidige project wordt een ontwikkelingsslag toegepast door het gebruik van de meest recent beschikbare datasets in het hydrologische model SPHY, om zo historische bodemvochtdata te leveren met een 100 x 100 m (1 ha) resolutie voor de periode van 2012 t/m 2021. Deze gegevens worden door de Gezondheidsdienst voor Dieren gebruikt om meer inzicht te verkrijgen in het risico op diergezondheidsproblemen die door klimaat worden beïnvloed.
High Mountain Asia (HMA) has the world’s largest ice and snow reserves outside the polar regions and is an important source of water for the major river systems in Asia, providing water for a population of more than a billion people. HMA has experienced many climatic changes which led to changes in the cryosphere and hydrological cycle. Past studies have focused on glaciers to derive the impact of either historical or future climate change on meltwater availability in basins in HMA. These basin-scale and regional studies use different data and approaches. Variability in approaches, data, and methods makes it difficult to align and compare the effects of climate change on future melt contribution and water availability across HMA.In this study, we bridge scale, variability in approaches, data, and methodological issues by implementing a consistent data and model. To robustly assess the 21st century climate change impact for hydrology in entire HMA at a wide range of scales, we use a high resolution cryospheric-hydrological model covering fifteen upstream HMA basins to quantify the compound effects of future changes in precipitation and temperature based on the range of climate change projections in the CMIP6 climate model ensemble.
Our analysis shows contrasting climate change responses across High Mountain Asia’s upstream river basins, dictated by the present-day variability in climate and hydrological regimes. At the large, river basin scale, the upstream basins of fifteen rivers in High Mountain Asia can be grouped into four hydrological regimes: glacial-nival, nival-pluvial, nival, and pluvial. Results show that an increased fraction of liquid precipitation due to climatic warming results in higher peak total runoff in all the basins. At the seasonal scale, the earlier onset of melting causes a shift in the magnitude and peak of water availability, to earlier in the year. At the decade to century scale, after an initial increase, the glacier melt declines by the mid or end of the century except for the Tarim river basin, where it continues to increase. Despite a large variability in hydrological regimes across HMA’s rivers, our results indicate relatively consistent climate change responses across HMA in terms of total water availability at decadal time scales.
Authors illustrates that the changes in total water availability are stronger in magnitude for the headwaters than at lower altitudes. It is the change in seasonality and changes in peak melt runoff that will pose the main challenge to be addressed in adapting to future changes in a region where food security, energy security as well as biodiversity, and the livelihoods of many depend on water from the mountains. These findings provide important information to support climate change adaptation policy planning in this climate change hotspot.
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.
The symposium focused on the application of open source Google Earth Engine platform for management of floods, drought, erosion and sedimentation issues in Myanmar. It started with a keynote speech from Mr. Johan Heymans, a Water attaché for the Dutch Embassy in Myanmar. He highlighted in his speech the importance of Google Earth Engine for the water professionals in Myanmar. The keynote speech was followed by presentations from FutureWater and HKV, where we highlighted the interesting application of GEE in some of our projects. Finally, participants presented their stimulating findings and skills obtained during their previous trainings. The symposium was followed by a brief discussion on the use of GEE institutionally by the attending government and private institution. All the training documents and presentations are published on the repository of Myanmar Water Portal.
Myanmar is a country with huge water and agriculture-related challenges. However, ground data on e.g. river flows, rainfall and crop growth are only very sparsely available. This training supported by Nuffic aimed to build capacity across the water sector in Myanmar in overcoming these limitations by using Google Earth Engine, a state-of-the art tool for accessing and processing a wealth of geographical datasets. Participants from academia, higher education, and govenment agencies, attended two training sessions hosted by YTU (the main requesting organization) and implemented by FutureWater and HKV. During the intermediate period, remote support was offered to the participants via Skype, email and the dedicated Facebook page. Results of the individual assignments, which were formulated by the participants based on their personal objectives, were presented in a final symposium.
Higher educational staff was trained to achieve sustainable impact by implementing Google Earth Engine in their curricula and train a new generation of modern and well-equipped water professionals. Public sector representatives participated to obtain skills that can be directly and sustainably implemented in their respective organizations, to benefit effective and equitable water management.
Flooding has always been a major cause of natural disasters in a mountainous country like Nepal. Among the many natural disasters that affect Nepal, the recurring floods during the monsoon season have catastrophic consequences every year. Nepal’s fragile geological conditions and complex topography combined with frequently occurring extreme rainfall during the monsoon poses risks to communities living along the flood plains. In order to ensure good flood management practices and the development of long-term water management strategies a good understanding of key hydrological processes and the ability to simulate future changes in streamflow is a prerequisite.
During recent years, FutureWater has done many projects in collaboration with NGO’s, INGO’s and academic institutions in Nepal. This is the first time FutureWater collaborated with the Institute of Forestry (IOF), Nepal to provide their teaching faculty and researchers a training on “Use of open source platform for hydrological modelling of data sparse regions in Nepal”. The Tailor Made Training (TMT) was fully funded by NUFFIC’s Orange Knowledge Programme (OKP) and took place from 8 April to 24 April 2019 in Pokhara, Nepal.
Essential skills, in particular modelling of hydrological processes are currently lacking at IOF, hampering the capacity to gain deep understanding of the present and future flood management situation in the region. Therewith IOF faces difficulties in developing long-term strategies to deal with climate change impacts for Nepal’s water resources. Further, the lack of ground-based measurements in the Himalayan region imposes an additional level of complexity while modelling the hydrological characteristics of this region. The use of readily available open source satellite-based data can augment the limited ground-based observation in the region.
Overall, the training fulfilled all the needs of the IOF, and was positively evaluated by the participants. This training program has encouraged the faculty members from IOF to use open source data and platforms in their future research and teaching.