- 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.
Recently, The Nature Conservancy (TNC) has published a How-to Guide to Develop Watershed Investment Programs, accessible here. This important resource was designed to meet the needs of practitioners and investors across a variety of sectors who may wish to improve biodiversity, climate and water security outcomes by employing nature-based solutions. It comprises several components, including a set of Deep Dives which offer detailed guidance on key subject matter areas for WIP preparation.
FutureWater has contributed to the How-to Guide by providing input to several sections, most notably by developing the Deep Dive containing eleven NbS Options Factsheets. These factsheets outline the typical properties of each NbS option including the water security challenges addressed, additional co-benefits, typical cost profiles, and risks. The factsheets were compiled by exploring a range of information sources, including technical reports, policy documents, operational protocols and guidelines for NbS implementation, and academic literature.
More information about the overall project can be found here.
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.
32/40 hours per week, location Wageningen, The Netherlands, starting data TBD
FutureWater is looking for an experienced agricultural water expert for our expanding international activities, particularly in Asia and Africa, who has affinity with technical tasks but also appetite for leading and coordinating some of the projects. At FutureWater you work in a dynamic organization, independently or in a team, on both organizational and technical aspects of challenging projects at the interface of water management, agriculture, nature-based solutions, and climate change. The following tasks and responsibilities are associated with the position:
- Lead analytical work in projects on the interface of water resources and agriculture
- Provide technical support and guidance within the team around these topics
- Work directly with clients to ensure project scope and expectations are well-aligned, and deliverables fall within the applicable scope and budget
- Project planning and coordination of budget and resources, and communication and cooperation with partner organizations (private sector, NGO, universities)
- Collaborate on or lead project proposals and have affinity with project acquisition
It is expected that about half of the work will consist of technical contribution to projects (data analysis, modeling, interpretation of results, reporting), and half of the time to the other tasks and responsibilities listed here.
The ideal candidate has an academic background in agro-hydrology, irrigation, or agricultural water management, followed by at least 5 years of progressively autonomous, professional experience.
Further requirements include:
- Proven planning and organizational skills
- Demonstrable experience with contact with clients, ideally demonstrated by successful completion of multiple international projects
- An existing network of relevant public and private organizations, in The Netherlands and/or abroad
- Experience with crop water modeling or similar (e.g. AquaCrop or CROPWAT)
- Affinity with programming (e.g. Python, R) is appreciated
- Affinity with climate smart or precision agriculture
- Fluent in English (required) and Dutch (preferred)
- Valid permission to work in The Netherlands (already obtained)
In the event of proven suitability, there is the possibility of a permanent employment contract.
FutureWater is a research and consultancy company with the objective of contributing to the sustainable management of water, worldwide. FutureWater focuses on the application and development of scientific methods and concepts to provide advice and solutions in the field of water management. Simulation models, Geographic Information Systems, remote sensing, innovative data processing techniques and training play a key role in this. Activities take place in the Netherlands and abroad. Typical clients include World Bank, Asian Development Bank, NGOs, river basin organizations, national and regional governments, and research organization.
More information or apply?
Interested? Send your application letter and CV to firstname.lastname@example.org.
This vacancy is open until 20 May 2022.
The “Integrated Strategic Water Resources Planning and Management for Rwanda” consultancy project will assess and evaluate the availability and vulnerability of the country’s water resources up to around 2050 taking climate change into consideration.
Based on this, prioritization of investment options in grey and green infrastructure will take place, in order to formulate water resources investment plans. A revised water resources policy will be prepared that is in line with water security targets and SDG 6.
In more detail, the hydrological modelling assessment will result in update water accounts per sub-catchment up to 2050. Field work for assessing groundwater resources in key areas across the country is also performed. A detailed water allocation assessment will be performed using a water resources system model (WEAP), addressing water needs for the various users up to 2050. Water allocation plans will be developed from this modelling work, incorporating stakeholder inputs.
Then, a scenario analysis is performed to evaluate the potential of additional storage in the landscape: grey (reservoirs) and green (through Nature-based Solutions). This analysis will be complemented by field work and a pre-feasibility analysis will be performed on the prioritized options. A SWOT analysis will then lead to a number of possible flagship projects which of which a concept note is prepared.
Support to the revised national policy for water resources management will also be provided by defining new policy statements and actions informed by the results from the previous tasks and developing a new water resources policy that will guide the country towards achieving the NST1 and Vision 2050 targets.
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.
Sustainable Development Goal (SDG) 6 seeks to ensure access to clean water and sanitation for all, focusing on the sustainable management of water resources, wastewater and ecosystems. The targets associated with SDG 6 are to be achieved by monitoring and improvement of several indicators. Assessment of these indicators requires a considerable amount of data, which are in many countries not readily available. Also in Myanmar, challenges are posed to the national statistical system to collect, manage and report the necessary input data. As the Myanmar branch of the lead UN development agency, UNDP Myanmar carries out activities to support implementation of the SDGs. Acknowledging the recent political developments in Myanmar, more than ever it is important to explore innovative sources of data to support monitoring and evaluation of progress towards the SDGs. FutureWater was contracted to produce an issue brief which explores the availability of geospatial data, in particular derived from Earth Observation (EO) from satellites, to monitor 4 water-related SDG indicators.
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.