This course on hydrology and water allocation modelling is organized for the Kenya Water Resources Authority (WRA) and funded by the Blue Deal program of the Netherlands. The first four-week course block introduces the participants to the main concepts in hydrology, hydrological modelling and data collection, including remote sensing. Exercises are provided on water balances, land use datasets, extraction of rainfall data from remote sensing datasets, among others.
The 5-week second block of the training is on the use of a water resources system model (WEAP) for water allocation. Participants will learn how to develop, run and evaluate a model, including scenario analysis, water balances, assess impact of changing priorities among users, and impacts on water shortage. The learned skills will be used afterwards for establishing a Water Allocation Plan for an important sub-basin of the Upper Tana river, providing water to many livelihoods in the catchment itself, but also to Nairobi city.
Aim of the training
The training will enhance capacity of Egerton educational staff in accessing and using innovative data and tools in the public domain, to analyse crop performance and irrigation management. During the training, university participants will be specifically supported in developing course modules based on the skills gained. To maximize the impact in addressing the need for increased quality of higher education in the agricultural sector, representatives from other institutes, ministries and private sector companies will also be invited. The training will allow the staff to gain advanced skills in working with flying sensors (drones) and satellite-derived data to support agricultural and water-related challenges, such as pests and diseases, water efficiency in agriculture to enhance food security, and drought monitoring. They will acquire insight in and knowledge on analyzing the performance of crops, making the right intervention decisions and giving irrigation advice. For public sector representatives, the training objective is to obtain skills that can be directly and sustainably implemented in their respective organizations.
Overall, the Kenyan society at large will benefit from improved food security provided by well-educated agricultural researchers and professionals. This project forms an important step in the capacity building strategy as it focuses on strengthening the universities and preparing them to provide high quality education to the future generation agronomists and agricultural managers, as well as upgrading the knowledge of current professionals.
The training costs of four stages: an online training course, followed by an in-country training program, symposium and post-training support.
Stage 1: eTraining course
The first stage involved a weekly online training course that will start in January 2021, with a total of six sessions in six weeks. Participants will be consisting of University and TVET faculty members, university students, PhD candidates, researchers, Kenya Agricultural & Livestock Research Organization (KALRO) staff members, Agriculture Extension Staff from the County Government who are already involved in agricultural research and training and other private sector partners. Staff members from the university will be those that are involved in teaching agronomy, horticulture, agriculture engineering and agriculture extension courses and programs, i.e., soil, nutrient and water management, dryland farming, irrigated agriculture and crop protection. Non-university attendants will be technical staff who are close to the decision makers within their organizations. This will enhance the impact of the training by embedding the use of flying sensor and satellite-derived data for agriculture within these organizations and make sure that Kenya will pursue its activities in making use of this kind of information.
This first stage of the training course will be online and will focus on:
Real Water Savings in Agricultural Systems including potential field interventions
The use of WAPOR to access remotely sensed derived data
The course will end with a test and evaluation and graduates will receive a certificate.
Stage 2: Targeted in-country training
After the first stage training a second in-country training will take place with a smaller group, focusing on the use of drones in agriculture. Here a selected group of 12 to 18 members will be trained. Focus will be on staff with lecturing responsibilities, to ensure impact on higher education provision and transfer of the new skills to students.
The in-depth training will consist of:
Operating flying sensors manually and automatic, the processing of the collected data using open source software, interpretation and the subsequent decision making (recommendations to increase productivity) for (smallholder) farmers and actors
Use satellite derived (precipitation) products to run crop growth models to provide advice on when and how much to irrigate in agricultural fields
Participants will work on hands-on exercises related to crop performance analyses, water demands and crop growth modelling. Application of the new skills will be further stimulated by assigning the participants clear, tailor-made goals at the end of the second training session, to be worked on during the distant-support period.
Stage 3: Symposium/knowledge sharing
Right after the second training session, a symposium will be organized for a larger audience including the superiors/managers (who most of the times are the final decision makers) of the training participants and representatives of similar organizations. During this knowledge sharing event, trainees and trainers will actively provide contributions to showcase the newly gained skills and their added value to the respective institutions and the Kenyan agricultural sector in general. By acquainting the responsible decision makers in these organizations with the potential applications of flying sensor and satellite-derived data relevant to them, this event will be crucial in ensuring a sustainable impact of the TMT.
Stage 4: Post-training support
In this period, progress will be actively monitored and the trainers will provide post-training support to the participants. The support will be both remotely (e.g. through Skype) by the Dutch training providers but also in-person by ThirdEye Kenya staff visiting the participants for Q&A sessions and to evaluate the implementation of the skills they obtained.
For the two study catchments, satellite imagery and field observations were combined to perform a land degradation assessment and to identify trends. Secondly, baseline hydrological conditions were assessed using a hydrological simulation model. Future changes in hydrology and hydropower generation were evaluated by running the biophysical model for a Business-as-Usual scenario, accounting for land degradation trends, changes in water use, and climate change.
Subsequently, the impacts of three catchment investment portfolios (low, medium, high) containing different catchment activities were quantified with respect to the BaU scenario. Benefits and costs were analysed for the hydropower developers to evaluate whether it makes sense for them to invest in improved catchment activities. For one of the catchments this is clearly the case (Kiwira, Tanzania).
The analysis shows that the impacts of climate change on revenue from hydropower are in the same order of magnitude as the other negative anthropogenic factors: increased domestic water use demand in the catchment and land degradation due to poor conservation of natural areas and poor agricultural practices.
The framework used for this evaluation has been the IFC Performance Standards 3 (Resource Efficiency and Pollution Prevention) and 6 (Biodiversity Conservation and Sustainable Management of Living Natural Resources), complemented with what is considered good practice.
Hasta el momento no existe una metodología ampliamente aceptada para cuantificar el impacto del riesgo climático en proyectos de recursos hídricos que son apoyados y financiados por el Grupo del Banco Mundial. El Grupo de Evaluación Independiente (IEG) en su informe de 2012 titulado «Adaptación al clima Cambio: Evaluar la experiencia del Grupo del Banco Mundial», reconocía que «los modelos climáticos han sido más útiles para establecer el contexto que para informar de las mejores opciones de decisión política y de inversión” y que «a menudo tienen un valor agregado relativamente bajo para muchas de las aplicaciones descritas». En el informe se reconoce que «aunque el sector hidroeléctrico tiene una larga tradición para gestionar la variabilidad climática, el Grupo del Banco carece de herramientas de orientación específica y de metodologías apropiadas para incorporar las consideraciones del cambio climático en el diseño y la evaluación de los proyectos hidroeléctricos».
Tras su publicación en 2015 («Confrontando la incertidumbre climática en la planificación de recursos hídricos y el diseño de proyectos: El marco del árbol de decisiones»), el DTF se ha aplicado a diferentes proyectos del Banco en seis casos piloto de diferente índole (generación hidroeléctrica, suministro de agua, y riego) y financiado con fondos del Water Partnership Program. Este esfuerzo continúa en el marco de este análisis para dos proyectos adicionales que reciben financiación del Fondo Fiduciario para el Crecimiento Verde de Corea (KGGTF) y que se centran en aumentar la resiliencia y seguridad hídrica frente a inundaciones y el aumento del riego en la cuenca del río Nzoia en Kenia, y en la aplicación de la Guía de Resiliencia Climática del Sector Hidroeléctrico, basada en el DTF, para la central hidroeléctrica de pasada de Kabeli-A en Nepal.
FutureWater contribuye al proyecto mediante la ejecución de tareas específicas encaminadas a evaluar el riesgo de ambos proyectos mediante la modelización de cultivos y de asignación de agua en el caso de estudio de Nzoia, y la modelización hidrológica para cuencas de alta montañas en el caso de estudio de Nepal.
Twiga’ is the Swahili word for ‘giraffe’, a keen observer of the African landscape. TWIGA aims to provide actionable geo-information on weather, water, and climate in Africa through innovative combinations of new in situ sensors and satellite-based geo-data. With the foreseen new services, TWIGA expects to reach twelve million people within the four years of the project, based on sustainable business models.
Africa needs reliable geo-information to develop its human and natural resources. Sixty percent of all uncultivated arable land lies in Africa. At the same time Africa is extremely vulnerable to climate change. Unfortunately, the in situ observation networks for weather, water, and climate have been declining since the 1970s. As a result, rainfall predictions in Africa for tomorrow have the same accuracy as predictions in Europe, ten days ahead. To realize the tremendous potential of Africa while safeguarding the population against impacts of climate change, Earth observation must be enhanced and actionable geoinformation services must be developed for policy makers, businesses, and citizens. New in situ observations need to be developed that leverage the satellite information provided through GEOSS and Copernicus (Open data/information systems).
TWIGA covers the complete value chain, from sensor observation, to GEOSS data and actionable geoinformation services for the African market. The logic followed throughout is that in situ observation, combined with satellite observations and mathematical models, will result in products consisting of maps and time series of basic variables, such as atmospheric water vapour, soil moisture, or crop stage. These products are either produced within TWIGA, or are already available with the GEOSS and Copernicus information systems. These products of basic variables are then combined and processed to derive actionable geo-information, such as flash flood warnings, sowing dates, or infra-structural maintenance scheduling.
The TWIGA consortium comprises seven research organisations, nine SMEs and two government organisations. In addition it uses a network of 500 ground weather stations in Africa, providing ready-to-use technical infrastructure.
FutureWater’s main role in TWIGA is centered around the use of flying sensors to map crop conditons, flood extent, and energy fluxes, complementing and improving data from in situ sensors and satellites. Furthermore, FutureWater is involved in innovative app development.
A key factor in enabling an increase and efficiency in food production is providing farmers with relevant information. Such information is needed as farmers have limited resources (seed, water, fertilizer, pesticides, human power) and are always in doubt in which location and when they should supply these resources. Interesting is that especially smallholders, with their limited resources, are in need of this kind of information. Spatial information from flying sensors (drones) can be used for this. Flying sensors offer also the opportunity to obtain information outside the visible range and can therefore detect information hidden for the human eye (Third Eye). Nowadays, low-cost sensors in the infra-red spectrum can detect crop stress about two weeks before the human eye can see this.
The ThirdEye project supports farmers in Mozambique and Kenya by setting up a network of flying sensors operators. These operators are equipped with flying sensors and tools to analyse the obtained imagery. Our innovation is a major transformation in farmers’ decision making regarding the application of limited resources such as water, seeds, fertilizer and labor. Instead of relying on common-sense management, farmers are now able to take decisions based on facts, resulting in an increase in water productivity. The flying sensor information helps farmers to see when and where they should apply their limited resources. We are convinced that this innovation is a real game-changing comparable with the introduction of mobile phones that empowered farmers with instantaneous information regarding markets and market prices. With information from flying sensors they can also manage their inputs to maximize yields, and simultaneously reduce unnecessary waste of resources. In summary, the missing information on markets has been solved by the mobile phone introduction, the flying sensors close the missing link to agronomic information on where to do what and when.
Thanks to our innovation, farmers’ demand for key agricultural information will be satisfied by means of an extension service based on flying sensor (drone) information. The deployment of flying sensors is unique in its ability to provide farmers with real-time, high-resolution, and on-demand information. We provide essential agricultural information:
At an ultra-high spatial resolution (NDVI)
With unprecedented flexibility in location and timing
Based on wavelengths not observable by the human eye
With a country-specific business oriented approach.
To this end, we use low-cost high-resolution flying sensors (drones) in a development context to ensure that farmers will get information at their specific level of understanding, and simultaneously develop a network of service providers in Mozambique and Kenya.
A flying sensor is a combination of a flying platform and camera. Reliable flying sensors are on the market in a wide-range of categories each with its specific characteristics. Based on the consortium’s experiences over the last years low-cost flying sensors have been identified that are excellent equipped for our innovation. Typically, a flying sensor flies at a height of 100 meter and overlapping images are taken about every 5 seconds. This results in individual images covering about 50 x 50 meter and an overlap of 5 images for each point on earth. So, in order to cover 100 ha 500 images are taken during a flight.
The use of Flying Sensor is unique and no comparative techniques exist that provide farmers with real-time high-resolution information. The use of satellites to provide farmers with spatial information has been promoted but has three main limitations: they have fixed overpass times, the spatial resolution is low, and the presence of clouds halters the information. It is unlikely that, within the coming decades, progress in satellites will be real competitors of flying sensors. Another category of comparable techniques to provide farmers with information is the use of ground sensors. Typical examples of these sensors are soil moisture devices, soil sampling and laboratory analysis, crop sampling and laboratory analysis. However, all those sensor techniques have the common limitation that information is only local point representative, while the main question farmers have is regarding to spatial differences. Moreover, these ground sensors are in all cases too expensive to be used by small-scale farmers.
Our flying sensors have cameras which can measure the reflection of near-infrared light, as well as visible red light. These two parameters are combined with a formula, giving the Normalized Difference Vegetation Index (NDVI). This information is delivered at a resolution of 2×2 cm in the infra-red spectrum. Infra-red is not visible to the human eye, but provides information on the status of the crop about two weeks earlier than what can be seen by the red-green-blue spectrum that is visible to the human eye. NDVI is the most important ratio vegetation index and says something about the photosynthesis activity of the vegetation. Moreover, NDVI is an indicator for the amount of leaf mass, and therefore, ultimately biomass. In general, open fields have a NDVI value of around 0.2 and healthy vegetation of around 0.8. NDVI values give an indication of crop stress. This can be caused by a lack of water, lack of fertilizer, pests or abundancy of weeds.
When light falls on a leaf, reflection occurs. The amount of reflection of green light (0.54 µm) is very high, making plants green to the human eye. Healthy vegetation does not reflect much red light (0.7 µm), since it is absorbed by chlorophyll abundant in leaves. In the near-infrared spectrum (0,8 µm) the amount of reflection increases rapidly to 80% of the incoming light. This increase is caused by the transition of air between cell kernels. This is characteristic for healthy vegetation.
Damaged plant material does not show this increase in reflected near-infrared light. Moreover, the reflection of red light is much higher than in healthy plant material. By measuring the reflection in these spectra, damaged plant material can be distinguished from healthy plant material (Schans et al., 2011).
From 2014 to 2017, FutureWater has been granted support from the Securing Water for Food program, funded by USAID, Sida and the Dutch Government of Foreign Affairs, for piloting the use of flying sensors to support farmers in Mozambique with their decision making in farm and crop management. In Mozambique, we have transferred our technical skills to local ThirdEye operators over the past 3 years. We currently have 6 active local operators providing service to more than 3,500 farmers over more than 1,600 ha. These operators are able to support over 400 small-scale farmers, by collecting information and sharing it with farmers on weekly basis. Based on the information, farmers take decisions on where to do what in terms of irrigation, fertilizer application and pesticides, helping them improve their water productivity. Furthermore, they now have the capacity to deal with technical issues and are very skilled in providing advice to farmers. As a result, the farmer’s water productivity was increased by 55%, meaning less water is used to achieve the same crop yield as without ThirdEye services. ThirdEye has evolved since 2014 from a start-up to becoming the leading company in Mozambique in the field of mapping and monitoring services for farmers based on aerial images, which will continue to expand its activities over the coming years.
Since last year, the ThirdEye service is also implemented in Kenya as part of the Smart Water for Agriculture program implemented by SNV. Last month the first round of training was given to 5 operators, who will be serving at least 2,000 smallholder farmers the coming months. Training consists of flying sensor use, technical skills, safety and protocols, imagery processing and consultancy. After this, the operators will start working regularly in the regions of Meru and Nakuru. Here they will go the farmer’s fields, conduct flying sensors flights, process the images and give advice on improving their agricultural practices. Next to the service for smallholder farmers, ThirdEye delivers various services to medium and big sized farmers.
“WISE-UP to Climate” is a project launched by the IUCN Global Water Programme that will demonstrate natural infrastructure as a ‘nature-based solution’ for climate change adaptation and sustainable development. The project’s name stands for ‘Water Infrastructure Solutions from Ecosystem Services Underpinning Climate Resilient Policies and Programmes’.
WISE-UP runs over a four -year period and link ecosystem services more directly into water infrastructure development, starting with work in the Tana (Kenya) and Volta (Ghana-Burkina Faso) river basins.
The project is coordinated by a global partnership that brings together the International Water Management Institute (IWMI), the Overseas Development Institute (ODI), the Council for Scientific and Industrial Research in Ghana (CSIR), the University of Nairobi, the University of Manchester, the Basque Centre for Climate Change (BC3), and the International Union for Conservation of Nature (IUCN).
IWMI is leading the work in the Tana basin, Kenya, and aims to build on the previous work done in this basin, mainly:
Green Water Credits project – Financial mechanism for connecting downstream water users with upstream land and water managers (farmers), impacts on flows and sediments of a set of sustainable land management options.
Nairobi Water Fund – led by The Nature Conservancy. Focus on 3 priority watersheds upstream of Masinga and implementation of Water Fund focusing on hydropower and Nairobi Water Supply.
The objective is to evaluate the impacts of climate change on investments in sustainable water and land management in the Upper Tana. More specifically, the analysis should provide insight in how climate change can influence the biophysical effectiveness of different land management options in the Upper Tana, focusing on flows and sediments that influence downstream relying services, mainly hydropower.
For this study, FutureWater will focus on the Thika/Chania watershed, containing the important Mwagu intake for Nairobi Water Supply. The previously built SWAT model for this area will be used to assess the impact of land management interventions under six different climate scenarios. Streamflow dynamics, sediment concentration at specific points of interest and total sediment loads in the watershed will be assessed to evaluate the sustainability of land and water management, by taking business-as-usual practices as a reference. Next to baseline conditions, the study will focus on three future periods: foreseeable future: (2030s), long-term future: (2050s), and far horizon (2080s).
‘WISE-UP to climate’ is a project that demonstrates natural infrastructure as a ‘nature-based solution’ for climate change adaptation and sustainable development. The project will develop knowledge on how to use portfolios of built water infrastructure (eg. dams, levees, irrigation channels) and natural infrastructure (eg. wetlands, floodplains, watersheds) for poverty reduction, water-energy-food security, biodiversity conservation, and climate resilience.
The International Water Management Institute (IWMI) is carrying out hydrological modeling of the Tana Basin. It needs improved rainfall datasets for this modeling exercise. FutureWater supports IWMI in preparing improved rainfall forcing based on the CHIRPS dataset. This dataset has daily rainfall data starting in 1981 to near-present, based on high-resolution satellite imagery and in-situ station data, and consists of gridded rainfall time series for trend analysis and seasonal drought monitoring.
This project will undertake an assessment of the soil erosion and sediment loads in Upper Tana Catchment areas and estimate the impact of sediment deposition on the reservoir capacities of the principal dams. A physiographical baseline will be established through an intensive monitoring campaign of flow and sediment loads throughout the basin, bathymetric surveys of the reservoirs and soil erosion modeling to assess the current situation. FutureWater will be responsible for the modeling assessment.
An extensive monitoring campaign is conducted measuring sediment load through water sampling at key stations throughout the basin on a biweekly basis as well as major flood events. Besides, long-term siltation data will be obtained from the bathymetric surveys of the principle reservoirs. These project datasets will be used to simulate the current level of soil erosion for each sub catchment based on slope, rainfall intensity, soil type, land use, crop patterns and land use practices. For the calibration of the sediment flows in the basin, the strategy is to calibrate first with the long-term siltation rates calculated from the bathymetric surveys and secondly with the flow and sediment data from the current measurement campaign.