The importance of hydrological services and forecasting for decision -making

The Arid and Semi-Arid Lands (ASALs) in East Africa comprise 70% of the region and are characterized by high temperatures, arid and semi-arid environments, and low rainfall of about 600 millimetres annually. This makes it necessary to develop hydrological models and services which predict future water availability and distribution.

The second Down2Earth monthly seminar series ‘Can We Talk?’ themed ‘The Importance of Hydrological Services and Forecasting for Decision Making in the Eastern Africa Region’ was held on 9th May 2023. A recent blog by Khalid Hasaballah underscores the vital role of hydrological forecasts as an essential tool for water management, informing water management decisions, including water allocation, irrigation scheduling and emergency response planning.

Hydrological forecasts translate climate information into useful water and land information that can support local communities to reduce climate risks. The importance of hydrological forecasting for decision-making includes predicting water availability, informing and planning long-term strategies, water allocation and management, determining the optimal allocation of water resources, and environmental impact assessment activities on the water cycle, pollution, and climate change. The Down2Earth project is translating climate information into valuable information regarding water availability, in the dryland areas of Somalia, Kenya and Ethiopia using Climate into Useful Water and Land Information in Drylands (CUWALID). However, it is essential to partition the different components of the water fluxes using hydrological models to provide more useful information to local communities. Simply providing information on rainfall may not be sufficient as communities need to know the expected amount of water during a season to plan their crop selection. Risk management can be provided through early warning systems and informing emergency response plans. Decision makers, policymakers, organizations and institutions working on disaster risk management can act on risks that may come up due to the hazards predicted.

Uncertainty of climate data can influence the accuracy of hydrological forecasts. Various methods are used to deal with climate data uncertainty, such as bias correction and calibration of hydrological models. Types of hydrological models include; Rainfall Runoff Models, which use rainfall and potential evapotranspiration to predict river discharge, soil moisture, and related fluxes. Another type of hydrological model is the Water Resources Model, which deals with issues related to reservoir management, lake management, and water allocation. To improve hydrological forecasting, there is a critical need to enhance climate data products, hydrological measurements and hydrological modelling skills to add value to data, and hence improve the accuracy of hydrological forecasts to support countries to manage water resources effectively.

Matters arising

Hydrological forecasts help communities to have information about forecasting. For example, communities in some parts of Ethiopia and Somalia are being evacuated to higher places due to flooding and hydrological forecasts can help provide information about potential flooding events, giving communities time to evacuate and prevent loss of life and property. By enhancing the climate services capacity and supporting adaptation policies and communication in the regions, projects like D2E will be able to support communities in climate adaptation and resilience through the development of the (CUWALID) hydrological model.

Hydrological forecasts can also support initiatives such as those by the Kenya government which encourage transition from rain-fed agriculture to more irrigation agriculture. However, smallholder farmers do not have the adequate infrastructure for small-scale irrigation. Therefore, the Kenya government can provide training and extension services on sustainable irrigation practices. This will help smallholder farmers optimize their water use and minimize negative environmental impacts and transition to more irrigation agriculture. It will also build resilience to climate change, and increase their productivity and income through the development of water catchment systems and other irrigation infrastructure (e.g., dams, canals, and pumps) that will help them to cope with droughts and other extreme weather events.

Water scarcity challenges are due to climate change, population growth, poor governance, and inadequate infrastructure which pose challenges in hydrological forecasting. These challenges are induced by increased trends of extreme events, changing basin climate and hydrology, and demands for a unified and versatile hydrological forecasting system operating at local and continental scales. Despite having water policies, there are enforcement gaps that need improvement in the policy content and practice.

One of the challenges of hydrological forecasting is estimating the parameters as they are difficult to measure, and some of them have a high range. Additionally, rural communities often rely on their own traditional forecast methods, for instance using the intestines of goats to predict upcoming seasons, which have become less reliable due to climate change. To mitigate these challenges, there is a pressing need to enhance the accuracy of hydrological models and provide reliable information to communities.

It is important to note that hydrological models deal with both surface water and groundwater. However, there is a lack of periodic data sharing in hydrological observed data, as data is not collected daily and needs to be converted from water level to river discharge, which may take time. To improve the accuracy of hydrological forecasting, hydrological services need to improve their data collection methods, share data regularly, and develop efficient models that can accurately predict water availability and potential risks associated with climate change.

In hydrological forecasts and services, one of the biggest challenges faced is infrastructure management for flood and water allocations. A prime example of this is the case of Lake Victoria between 2018 and 2020, where there was a rise in the water levels which resulted in extensive flooding causing damage to infrastructure, displacement of communities and loss of livelihoods. On the other hand, the subsequent decline in water levels posed challenges for water allocation, particularly for sectors such as agriculture, hydropower generation, and domestic water supply. Although the forecast was accurate and countries acted on it, the issue was the long period it took, which was two years, for the water to move towards South Sudan. This problem can be mitigated by using water resources models to understand the expected inflow for the next seasons. By using this information, the infrastructure can be operated efficiently, taking into consideration the expected inflow, and this will ultimately enhance the management of water resources.

Conclusion

Hydrological services and forecasting are crucial for managing water resources and adapting to the risks of climate change, especially in ASALs where water resources are scarce. However, the accuracy of climate input into hydrological systems needs to be ascertained by minimizing error cascading from climate to hydrological forecast. Therefore, it is crucial to improve the climate data, the hydrological observations and the models' skill. Additionally, infrastructure management for flood and water allocations remains a significant challenge. By addressing these challenges and improving hydrological services and forecasting, we can ensure the sustainable management of our precious water resources for future generations.

Based on the success of the initial seminar CWT 1 and the enthusiastic response from the audience, it has been agreed that the seminars will now take place on the first Tuesday of each month.