Understanding the potential of hydrological forecasting for improving decision making in water resources management and agricultural practice in developing regions

Hydrological forecasting has tremendous potential for helping to better manage water resources systems, water-dependent systems such as hydropower and agriculture, as well as to provide early warning of extreme events such as floods and droughts to help mitigate impacts. For example, forecasts of river flows over the next few months can help managers optimize dam operation and water allocations; forecasts of the start of the growing season based on soil moisture can help farmers choose what type of crops to plant and when to plant them; forecasts of the likelihood of drought can help communities better prepare to ensure water and food security.

The skill of hydrological forecasts is highly dependent on the predictability of climate which varies depending on the location and time scale of the forecasts and on the lead time (how far in advance the forecast is made). The skill is also dependent on the states of the hydrological system, such as soil moisture, groundwater or snowpack, that are used to initialize hydrological model forecasts, and how these initial conditions persist and propagate through the hydrological system. Much recent research has focused on the relative contribution of initial conditions versus climate predictability, and whether improvements in skill can be derived from better estimation of hydrological states. A range of sources of data for estimation include traditional gauge measurements of precipitation and streamflow, satellite remote sensing of water levels, river flows, precipitation, soil moisture, and snow, and emerging sources of data from low-cost environmental sensors, non-traditional sensor networks, and crowd-sourcing. However, little work has been done to understand how these can be best utilized to improve forecasts, and how forecasts can be translated into actionable and practical information for a range of end users.

The overall aim of the proposed research is to understand how hydrological forecasts can be improved in the context of better decision making. Specifically, the research will aim to 1) understand the potential for traditional and new sources of data for characterizing hydrological states; 2) develop assimilation methods to integrate different sources of data into hydrological models to improve hydrological predictability; 3) understand how forecasts can be translated into actionable information. There will be opportunities for the research to contribute to and draw from projects focused on water and food security in sub-Saharan Africa, where forecasts have the potential to aid in decision making at all levels from smallholder farmers to international river basin authorities.

Candidates must have or expect to gain a first or strong upper second class degree, in a numerate discipline that may include: geography, engineering, computer science, mathematics, and physics. Experience of computer programming, environmental modelling, statistics, and remote sensing is desirable but not essential as training can be provided. Details on how to apply are available from Julie Drewitt, email [Email Address Removed]. Informal enquiries may be made to the project supervisors. For the latest information on postgraduate opportunities within Geography and Environment, please visit our website at: http://www.southampton.ac.uk/geography/postgraduate/research_degrees/studentships.page?

The Research Group details can be found at: https://www.southampton.ac.uk/geography/research/groups.page