Modelling Rain to River over Africa - A Paper Review

By Chris Skinner @cloudskinner

I recently had my first research article published - "Hydrological modelling using satellite rainfall estimates in a sparsely gauged river basin: The need for whole-ensemble calibration". It has been accepted by the Journal of Hydrology, with which I am very pleased, and it is available to view for free until the 27th February 2015 here. If you are reading this after that date, I'm afraid you will need a subscription to the Journal to view it.

The problem the project was hoping to address is the issue of a lack of equipment available in many parts of the world which records rainfall. There are several methods of doing this, which I explain in an older post, but the most common ways are to use a network of rain-gauges or radar, both of which are expensive to install and maintain. For many nations, the measurement of rainfall is not a priority enough to invest in these networks but they would benefit greatly from having reasonable estimations of how much rain has fallen - it allows them to monitor water resources, forecast floods and droughts, and even predict how many crops will grow.

A Map of the Senegal River Basin. The rain-gauge network used for the study covered this wide area, yet the hydrological modelling focused on the Bakoye catchment (area in the south-east, containing both the Bakoye and Baoule rivers). (Image by Kmusser) 

"Senegalrivermap" by Kmusser - Own work, Elevation data from SRTM, drainage basin from GTOPO [1], all other features from Vector Map.. Licensed under CC BY-SA 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Senegalrivermap.png#mediaviewer/File:Senegalrivermap.png

My research focused on a section of the Senegal River Basin, a large area with only 81 rain-gauges across the wider region with which to estimate rainfall, a density of 1 gauge per 7,000km². The density of the network is the equivalent of covering the UK with only 27 rain-gauges. There would be no way we could capture the complexity of our rainfall with so few gauges, and in reality the UK maintain a network of over 4,000 gauges with a density of 1 gauge per 76km². In addition to this, the UK also maintain 15 rain recording radar stations, yet none are available to the Senegal Basin region.

Currently, the best way around this and to fill in the gap is to use information from satellites. For continuous observation of the same area it is not yet possible to directly observe rainfall, but it is possible to monitor other factors that indicate rainfall, particularly the temperature of clouds. Unlike the UK, this area of Africa generally only receives one type of rainfall, so there is a strong relationship between the temperature of the tops of clouds and whether they are raining - if the cloud is below a specified temperature it is assumed to be raining - the longer it remains below that threshold, the more intense the rain is thought to be. This is all calibrated against the very little rain gauge data that is available.

Example of a processed satellite image showing the number of hours each pixel of the image is below the specified rainfall (here it is -20c). The number of hours is known as the Cold Cloud Duration (CCD) and this is related to rainfall. The rain gauges used for the project are represented by the white circles.

Unfortunately, as this is not directly observing rainfall it can be wrong on occasion, and especially so when a user tries to make use of it at a smaller scale than the density of the rain gauge network. When the estimates are used as information for models, such as a hydrological model to estimate river flows, the errors in the rainfall estimates are passed on to that model. What we do know, however, is just how wrong the rainfall estimate could possibly be and this allows us to try and represent this.

The increasingly common way of representing this, what scientists call uncertainty, in the rainfall estimate is to take the value of how wrong it might be and to randomly produce several hundred different versions of the possible rainfall - each different but equally possible based on the information available to us. The users takes this ensemble rainfall estimate and feeds each member individually into the hydrological model and produces an ensemble of river flow estimates. Statistics can be used to suggest the probability of river flows for each step in the record.

View of the across the River Senegal in the Kayes region, downriver and north of the study site. (Image by Bourrichon)

This is where my research came into the process. Hydrological models need setting up before they are used and this is done via a process called calibration. You need a period of the record with rainfall data and recorded river flow data, and you vary adjustable values within the model (these are called parameters) until you get the closest match between the recorded river flows and what the model estimates. You then test this against another period of recorded river flow that was not used in the calibration to test its performance (known as validation or verification).

It is good practice to calibrate a model using the same data you intend to drive the model with. For example, the model will not perform as well if you calibrate it using rainfall data as estimated by rain gauges, but subsequently run the model using satellite rainfall estimates. This poses a particular problem for when you intend to run a hydrological model using ensemble rainfall estimates. This has previously been performed using either using the original satellite estimate (disregarding its uncertainty), or an average derived from the ensemble members. However, I tried the calibration using all the ensemble members individually, but assessing the model performance with them as a whole - this is called whole-ensemble calibration and named EnsAll in the paper.

Graph showing the mean error from the Pitman model, run separately with each ensemble member using data for the period 1997-2005. The model was calibrated using data for 1986-1996 using the whole-ensemble method (EnsAll), the daily mean rainfall estimate from all the ensemble members (EnsMean), and the theoretical mean of the ensembles (EnsExp). EnsAll clearly produces less error than the other two calibration methods on this measure.

The parameter values produced using the whole-ensemble calibration produced more accurate river flow estimates from the ensemble rainfall estimates than those produced by the other methods. In fact, the whole-ensemble calibration was the only method to produce 'reliable' estimates during validation, with the other methods proving no better than making an educated guess based on the rainfall record for the period.

I do hope that this provides you with a better understanding of the paper. If you want to know more of the technical details it is all in there, along with some nice statistics and some graphs to make Dave Gorman weep with joy. The research represents only a very small facet of the problem, which will require many more small facets to solve rather than one big one. I hope to produce a few more.

The paper can be viewed for free until 27/02/2015 using this link. The reference for the paper is - 

Skinner, C. J., Bellerby, T. J., Greatrex, H., and Grimes, D. I. F., 2015. Hydrological modelling using satellite rainfall estimates in a sparsely gauged river basin: The need for whole-ensemble calibration. Journal of Hydrology, 522, 110-122

Measuring Rainfall

Measuring Rainfall by Chris Skinner (@cloudskinner)

Before I embarked upon my PhD research I had not paid much attention to how we recorded rainfall. My previous experience, probably like many people, came from my Primary School that had a small weather station in the grounds, that consisted of a weather vane for measuring wind direction, and a raingauge for recording the rainfall. It was nothing more than a small bucket, which collected the rainfall and you recorded the level from the side each day. If it was up to 4mm, you would record it as 4mm of rain having fallen in the last day.

That was it, as far as my knowledge went, and as far as I assumed it went in regards to recording rainfall for the weather forecasts. I wasn’t wrong, the Met Office here in the UK do still make extensive use of raingauges to observe rainfall. I will let Ralph James explain them to you –

However, as I soon learnt, raingauges only measure rainfall at one stationary point. The little bucket I used at my Primary School could tell me how much rain fell at the school, but it could not tell me how much rain fell at my house, or how extensive that rainfall was. To fill in the gaps, meteorologists use weather radars. Over to Biz Kyte –

Brilliant! There we have it then, measuring rainfall, easy peasy. You just need a network of thousands of raingauges, enough radar stations to cover your country and enough highly qualified engineers and scientists to operate and maintain it all.

You won’t be surprised to hear that these conditions do not extend to many areas of the world. Sub-Saharan Africa for example has not had the resources and/or the political will to establish the infrastructure required for timely, accurate rainfall observations, and this has implications when trying to forecast floods, crop yields, droughts or water resources. Obviously, being able to observe rainfall in realtime in this region would be greatly beneficial, but the installation and maintenance of raingauge and radar networks is just not currently feasible.

One way is to turn to satellite observations. Satellite platforms carrying Passive Microwave (PM) sensors are the most accurate for this role, with the instruments measuring the amount of microwave backscatter from the Earth’s surface. As droplets of water scatter the microwave signal in a distinctive way it is possible to directly observe where it is raining and its relative intensity. But (there’s always a but), PM sensors have to be placed in Low Earth Orbits (LEO) to operate, and therefore travel over the planet’s surface, recording snap shots of the rainfall as it goes. To add to problem, sandy ground scatters microwaves in a similar way to water, making observations by PM satellites more difficult in arid regions, such as much of sub-Saharan Africa.

Another way, such as that adopted by the TAMSAT team at the University of Reading, is to use Thermal InfraRed (TIR) instruments mounted on geostationary platforms. These satellites orbit at a distance that allows them to orbit at the same speed as the Earth’s rotation, meaning they always observe the same area of the planet’s surface - this is known as a geo-stationary orbit. TAMSAT use a relationship called Cold Cloud Duration (CCD), where it is assumed that if a cloud is cold enough, it will be raining, and the amount of time a cloud is below that temperature will let the team calculate the rate of rainfall. It is an indirect relationship, so it does not directly record the rainfall, but it does provide an estimate that is accurate enough, and timely enough, to be useful in forecasting seasonal crop yields or droughts.

Again, there is a but. TAMSAT produces ten-day observations, useful for the above applications, but not very useful for flood forecasting, for example, that requires realtime observations at atleast a daily timestep. It is possible to use the CCD method for this, but the observations are highly uncertain so require some complex statistics to be properly used. This has led meteorologists to get creative.

Telecommunications are taking off in sub-Saharan Africa, with mobile phones spreading fast. Professor Hagit Messer, of the University of Tel Aviv, suggested that interference of signals sent between antennas by rainfall could be used to measure the rainfall rate between the antennas. Over a whole network of telecommunication antennae the spatial spread of rainfall and its intensity could be built up, evolving over time. This form of rainfall observation could be used to dramatically improve the spatial and temporal coverage over sub-Saharan Africa, with little need for additional investment.

And again, there is a but. Whether it is observation by radar, satellite or telecommunication networks, the instruments can only record where it is raining, when it is raining, and the relative intensity of the rainfall. That relative intensity needs calibrating, bringing scientists full circle back to the humble raingauge. There are raingauges in sub-Saharan Africa, but not a lot of them. The study area I researched had one gauge per 7,000km^2, enough to cover the whole of the UK with just 27 raingauges, and of course these weren’t evenly spread, concentrated along rivers and in towns, leaving large areas relatively uncovered.  They can also be poorly maintained and not all raingauges record all of the time.

There are some good stories about raingauges in Africa. A couple I have heard from the TAMSAT team are of one gauge that recorded no rainfall at night, even during the wet season. When investigated it was found the locals looking after the gauge were storing inside so it wouldn’t be stolen. Another gauge was consistently recording a light drizzle – this was caused by people hanging wet clothes on it to dry. We have similar issues in the UK, with one organisation who should know better placing a gauge on their roof next to an air conditioning vent that blew rainfall away from it.

One project that I am excited about is TAHMO. The project team have the highly ambitious objective of dramatically increasing the raingauge coverage (as well as coverage of other meteorological instruments), for sub-Saharan Africa by mass producing a cheap, self-contained weather station and distributing them to schools. One of the most significant outputs to date was the creation of a low cost acoustic disdrometer, that uses the vibrations of falling raindrops to measure rainfall rates and reports the readings automatically using mobile phone technology. For me, this is the great hope of rainfall observation for poorly gauged regions and really hope they can pull it off. For now, I’ll leave you with Rolf Hut discussing TAHMO, acoustic disdrometers and tinkering.