Our Research in Focus 1 – Hydrological modelling in data-sparse areas

Hydrological modelling using ensemble satellite rainfall estimates in a sparsely gauged river basin: The need for whole-ensemble calibration

Chris Skinner, Tim Bellerby, Helen Greatrex, and David Grimes.

Published in Journal of Hydrology, Volume 522, March 2015, 110-122

Read here (paywall) or free, non-formatted version here


This research highlighted some of the issues with using information from satellites to estimate how much rain has fallen over an area. Many satellite rainfall estimation methods cannot directly observe the rainfall itself, instead inferring the amount from other information, in this case the temperature of clouds – clouds cool whilst raining so if they are below a certain temperature it is likely they are raining. This method was pioneered by University of Reading’s TAMSAT team.

A map showing satellite derived rainfall estimates for Africa
Example of rainfall estimates derived from satellite information using the TAMSAT cold cloud duration method. Image from the TAMSAT website.

The relationship between cloud temperature and rainfall is not perfect and changes with distance and time, causing the rainfall estimates to be wrong in places. As we increase the detail we want to use the chances of it being wrong increase so to overcome this we can use lots and lots of repeat estimates – each using different relationships between cloud temperature and rain in different places and at different times – to create a set of results, each unique but with an equal likelihood of being correct.

Map of Senegal Basin area from the paper. Black dots show the location of the sparse network of rain gauges available for the research.

This set of estimates can be used to run different types of models – for example, to run crop yield models to help us predict how well crops will have grown. For this research they were used to run a hydrological model to predict how much water will have flowed through a river basin. These models rely on us tuning values in them so that the outputs closely match observed values, a process called calibration, but when you have a set of different rainfall estimates for the same period which do you use to perform the calibration? The method that produced the best results was to use all of them together.

Why does it matter?

In the UK, our rainfall is monitored by a long-established and well-funded network of rain gauges and rain radar, providing us with a high detailed view of rainfall across the whole country in near real-time. This wealth of data helps us operate detailed forecasting systems to predict rainfall in the future and inform warning systems for drought and flooding. However, in many parts of the globe these networks to observe rainfall do not exist.

Across sub-Saharan Africa, including in the Senegal River Basin that provided the case study for this work, the lack of ground instrumentation is particularly acute. To fill the gaps in the data satellite information is used to estimate rainfall but this is not as accurate or as detailed as networks of ground instruments. These estimates are used to determine drought and flood warnings and by insurance companies that pay out to farmers who have lost crops, and consequently income, due to a lack of rain. It is important we understand fully what satellite estimates are telling us so we can make the correct decisions.

Why should you read it?

If you are interested in learning more about rainfall estimation is data-sparse regions and/or hydrological modelling using uncertain probabilistic input data.