Surface Runoff, Percolation, Total Runoff and Evaporation from Precipitation 2017

Excursus – INKA BB

Calculation of the percolation water rates on an annual and monthly basis, and forecast changes due to climate change

Water balance data calculated with the ABIMO model provide a 30 year long-term mean value. However, in reality, the values fluctuate considerably on an annual basis, depending on the precipitation levels, and are also subject to fluctuations over the course of a year. In the present research project, percolation water rates were calculated with a considerably higher temporal resolution.


The results presented below were developed in the context of the Inka BB research project of the German Federal Ministry for Education and Research (Subsidy Code 01LR0803C), Subproject 23. INKA BB is the acronym for the Berlin-Brandenburg Climate Adaptation Innovation Network ( In this research project, which was broken down into 24 subprojects, subproject 23 addressed technologies for climate adapted water management in urban areas in the context of climate change. For this purpose, the climate scenarios developed by the project partner Potsdam Institute for Climate Impact Research (PIK) were incorporated into the various models in order to make statements on issues relevant for water management.

Statistical Base

The input data adopted comprised the land-use categories (cf. Maps 06.01 and 06.02, 2008 edition), the classified degree of impervious coverage (cf. Map 01.02, 2007 edition), the classified depth to groundwater (cf. Map 02.07, 2010 edition), and such soil parameters as usable field capacity and Kf values from the data base of the Berlin Environmental Atlas (cf. Map 01.06, 2009 edition), for approx. 25,000 polygons. Additional soil parameters, such as porosity, were substantiated by reference to the soil associations in the Environmental Atlas (cf. Map 01.01, 2009 edition), using values from the Soil Scientific Mapping Guideline (BGR, 2005). In case of incomplete data sets, plausible assumptions were made, or data was taken from already completed DHI-WASY projects. The result was that 78 different soil types, 156 soil textures, 12 depth-to-groundwater classes and 775 land-use classes were obtained.

The climate data used were the daily data for precipitation and potential evaporation from 11 precipitation stations in Berlin and the surrounding area. The climate data was gathered by the DWD and made available by the PIK in the context of the INKA BB research project. In order to be able to correctly represent the spatially differentiated distribution of precipitation in Berlin and the surrounding areas (cf. Map 04.08, 1994 edition), the climate data were extracted by means of inverse distance weighting (a geostatistical procedure) for 19 precipitation zones. The spatial distribution of the precipitation zones is shown in Figure 5.

Enlarge photo: Fig. 5: Distribution of the precipitation zones used in the ArcSIWA
Fig. 5: Distribution of the precipitation zones used in the ArcSIWA
Image: Umweltatlas Berlin

Model Description

The results presented below are based on an ArcSIWA model designed for the entire area of the state of Berlin, including the boundaries of the intake area of the Tegel waterworks. The ArcSIWA model (Monninkhoff, 2001) is a reduced precipitation-runoff model for a one-dimensional description of runoff formation and of the soil-water balance for quasi-homogeneous area segments, with a temporal resolution of one day. The ArcSIWA accounts for interception, trench storage, infiltration and vertical dampness flow to groundwater, including new formation of groundwater and capillary rise. Thepercolation water rates calculated by the ArcSIWA correspond to the quantity of water exiting vertically from the approx. 2 m thick soil zone. A detailed representation of the ArcSIWA model built is to be presented in Sklorz & Monninkhoff (2013) sometime in 2013.


Figure 6 shows the annual percolation water rates between 1961 and 1990 calculated by means of the ArcSIWA. It shows that the annual values often vary strongly, between 49 and 239 mm/a. The mean value for the 30 year period is 142 mm/a, and the median value, 156 mm/a. A significant trend, e.g. due to climate, cannot be ascertained during this time period. The results are generally quite comparable with the long-term mean of the percolation water rate (160 mm/a) from the ABIMO model (SenStadt, 2009c). By contrast with the values in Table 4, the stated values were ascertained by incorporating bodies of water into the mean at a percolation water rate of zero. Spatial differences in the model results were particularly evident in the central part of the city, where the ArcSIWA calculated significantly lower percolation water rates. This difference is essentially due to the different approaches for impervious coverage upon which the two models are based.

Enlarge photo: Fig. 6: Annual values of percolation water rates in Berlin for the period 1961 to 1990
Fig. 6: Annual values of percolation water rates in Berlin for the period 1961 to 1990
Image: Umweltatlas Berlin

Figure 7 shows the long-term monthly percolation water rates for the period 1961 to 1990. It shows that the percolation water rate could vary between 1.2 and 24.5 mm per month during the course of the year. The winter months show the highest percolation water rates, while in summer, the lowest percolation water rates occur.

Enlarge photo: Fig. 7: Long-term monthly percolation water rates for the period 1961 to 1990
Fig. 7: Long-term monthly percolation water rates for the period 1961 to 1990
Image: Umweltatlas Berlin

The period 1961 – 1990 was used for the model calibration of the INKA BB research project, subproject 23. Subsequently, changes of percolation water and groundwater new formation were calculated with the model based on the climate scenarios developed by the PIK. A comparison was then undertaken between the T-0 scenario (the reference scenario assuming no climate change) and the T-2 scenario (assuming a temperature increase of 2°C). The calculations show a clear reduction of groundwater new formation for the future, attributable to climate change (DHI, 2012).