Research led by Energy and Environment Institute Research Fellow, Chris Skinner, has recently been published in the journal Earth Surface Processes and Landforms. The research, featuring an international team from Hull, Bristol, and Zurich, showed how different methods of measuring rainfall can lead to different predictions of landscape change in computer models.
Rainfall is a slippery thing. For those of us in the UK, we are very much aware that it can be raining one minute and then sunny the next, or it can be raining over your house but not over your friend’s house just a few streets away. This makes it a difficult thing to measure accurately and consequently meteorologists use several methods to try and do so.
The simplest way is to use a rain gauge. There are many different designs but, essentially, they are mostly all glorified buckets that fill with water as it rains, although some do take different approaches. They generally give us a good idea of how much rain has fallen at that spot between the times readings are taken. By automating the readings we can get a good idea of how the rainfall rate has changed over time.
Rain gauges cannot tell us how much rain has fallen outside the bucket. This is generally ok if you are still close to the bucket but the further away you get, the more of a problem this becomes. By using lots of rain gauges we can get a better idea of how the rainfall is varying across an area and we can use geostatistics to try and fill the gaps. However, the results will be different depending on the geostatistical method you decide to use.
Weather radar on the other hand is able to tell us the relative intensity of rainfall over an area. The radar sends out signals that are bounced back to it by rain and depending on the timing and strength of that signal we can tell where it is raining, and the areas it is heaviest. It does not directly measure the rain though and it needs calibrating against a reference point. This calibration may be less accurate as you move away from the reference point or if conditions change over time.
The consequence of this is the availability of different methods to measure rainfall, the results of which we call products. Each product will be different in its estimation of where, when, and how much rain has fallen and with many computer models of rivers relying on a measurement of rainfall as an input, the choice of product can have a big influence on the results from the model.
Landscape evolution models (LEMs) are designed to model changes to the Earth’s surface, usually over large areas and long time periods (at least one hundred years). Some of these models have become sophisticated and fast enough they can be used to explore more local and shorter-term changes. They need to use a rainfall product to run yet only rarely does a product exist that has a record long enough to cover the time scales simulated. Instead, we can use weather generators that take the characteristics of rain as recorded by a product to create long records of rainfall that are possible and likely based on the data.
In newly published led by EEI Research Fellow, Chris Skinner, a weather generator was used to produce long rainfall records based on different rainfall products, as well as a combination of information taken from each product. These synthetic records of rainfall were used to run a landscape evolution model for periods of 50 and 1500 years, finding that the patterns of erosion and deposition varied along a river depending on which product was initially used.
Due to the relationship between river flows and the movement of sediment in rivers, something known as the geomorphic multiplier, a small increase in river flow can result in a large increase in the amount of material eroded and transported by the river. This makes models of erosion and deposition extremely sensitive to changes in rainfall and consequently, the initial choice of rainfall product used can have a big influence on the model results.
As these modelling approaches are increasingly used to help understand the impacts of climate change or to help predict flood risk, understanding how the choice of rainfall product can impact results is crucial and needs to be properly managed by modellers.
You can read the full article here.