Geographical information systems: We run into them almost unnoticed every day as the basis for weather maps or in car navigation systems. At the same time they are indispensable for us climate researchers when we want to fine-tune simulation models in order to examine something more closely, for example in at-risk regions of Africa where we are looking at future agriculture. However, with existing climate models we can only “zoom in” to 25 km, which isn’t accurate enough for a detailed analysis of individual farms’ fields. With the aid of geo software, at the KlimaCampus we have managed to map the agricultural conditions down to a scale of one kilometer.
In the project “The Future Okavango” we are investigating the land around the freshwater Okavango River, a vital lifeline flowing through Angola, Namibia and Botswana. To do so we are using SAGA, a geographical information system I developed. This software can link geometrical surface data like the location, form and size of mountains and rivers with information on population size or soil characteristics. Like a set of building blocks, this offers the basis for various three-dimensional maps. SAGA is freely available and has a global user community with up to 2,000 downloads per week.
To estimate the harvest along the Okavango, we are looking at the area’s natural resources potential, which includes ecosystem conditions such as temperature, precipitation and soil characteristics. This enables us to calculate how many people the area can potentially feed. To evaluate this valuable information locally, we need to convert existing large-scale climate models to smaller units.
For the project, our colleague Daniela Jacob first created a regional low-resolution climate model, which you can think of as a grid with 25-kilometer squares. Each of these squares provides a single value per characteristic, e.g. temperature. However, for our analysis we need smaller grid squares that are only one kilometer in size, i.e., in each large grid square there are 25 times 25 smaller squares. That makes 625 temperature values where before there was only one. Where do we get these values?
Here we used the well-known correlation between temperature and altitude: the higher the altitude, the colder it is. We know the altitude of the terrain, since there are already global high-resolution measurements of the earth. SAGA enables us to use these data sets. We then take several of the large grid squares and their temperature values and link them with the average altitude of that square. From this basic information we can calculate a curve that shows the temperature for any given altitude in that terrain. Using the high-resolution data for the site, the temperature can be found for each of the 625 small grid squares – simple but effective.
Using different methods we can calculate precipitation and wind to deliver an accurate picture of which plants will grow well where, where it would make sense to build reservoirs for rainwater and which areas are better left uninhabited. Working closely with officials and farmers in the area, we are now developing concrete recommendations for action.