Floods and River Flooding 2024

Methodology

For areas where a flood risk has been identified, flood hazard maps and flood risk maps must be developed or updated, and flood areas must be designated. The flood hazard maps show which areas may be affected by flooding with varying probabilities of occurrence. In Berlin, the following three flood scenarios are considered based on the FRMD:

  1. Flood with a low probability or extreme event (rare event, HQselten/ HQextrem)
  2. Flood with a medium probability (medium event, HQmittel)
  3. Flood with a high probability (frequent event, HQhäufig).

Table 1 outlines the flood events and flood scenarios as defined by the River Basin Community Elbe.

Tab. 1: Flood event/ flood scenario definitions

  • Event

    Description

  • Low-probability or extreme event
    (HQselten / HQextrem)

    HQselten or HQextrem events are statistically very rare. Extreme events occur due to the failure of flood control structures, an unfavourable combination of rare flood events, or an unfavourable combination of rare flood events with discharge impairments caused by structural or other factors. For areas along the Elbe River with flood control measures in place, the default scenario shown is an extreme event combined with the failure of flood control structures. To depict these flood areas on the map, HQ200 is used, representing a flood that statistically occurs every 200 years. In areas without flood control structures, HQ200 is also applied. HQ200 is also applied.

  • Medium-probability event
    (HQmittel)

    As defined by the European FRMD, HQmittel represents a flood event that statistically occurs once every 100 years (HQ100). This does not rule out, however, the possibility that such an event may occur multiple times within a hundred-year period. According to German water law, HQ100 is used to define designated flood areas.

  • High-probability event
    (HQhäufig)

    HQhäufig is a flood event that statistically occurs much more frequently than once every 100 years. Along the bodies of water of the River Basin Community Elbe, the probability of an HQhäufig event recurring is typically estimated to be every 5, 10 or 20 years.

  • HQ: Abbreviation or flood discharge.

In Berlin, flood hazard maps were created for various flood scenarios and their probabilities, using different methods. First, it was essential to coordinate with the State of Brandenburg, as the bodies of water from Brandenburg flow into Berlin, and as the Havel River exits Berlin back into Brandenburg. Second, the medthods needed to be adapted to the local natural conditions and the availability of data. A summary of the methodological approaches is provided below. Table 2 gives an overview of the methods used and related studies. For a more detailed description of the methods, please refer to the mentioned studies.

The flood hazard maps for floods with a medium probability serve as the basis for defining flood areas, which are established by the relevant authorities. Public participation is encouraged to involve affected citizens and stakeholders. This allows for local knowledge and concerns to be taken into account and helps promote acceptance for the measures taken.

Tab. 2: Flood hazard maps and methods employed

  • Flood hazard map

    Method and Study

  • Müggelspree and Gosener Wiesen

    hydrodynamic model; IWU 2015

  • Lower Havel / Lower Spree

    gauge statistics; IWU 2014

  • Erpe

    precipitation-runoff model combined with hydraulic model; IPS 2013

  • Panke

    precipitation-runoff model combined with hydraulic model; IPS 2009

  • Tegeler Fließ

    precipitation-runoff model combined with hydraulic model; Koenzen et al. 2011

  • Wuhle

    precipitation-runoff model combined with hydraulic model; ProAqua 2021

Müggelspree and Gosener Wiesen

The Berlin Müggelspree and Gosener Kanal are located in the backwater area of the Mühlendamm reservoir. Water levels are primarily regulated by the weirs and locks at the Mühlendamm lock, the Kleinmachnow lock and the Oberschleuse (upper lock). Due to this regulation and the reservoir’s large retention capacity, annual flow rates and water levels are not always directly correlated. Flood damage events do not necessarily have to be linked to exceptionally high inflows from the Spree River. In the past, regulation was based on situational factors and other criteria.

In preparation for the development of flood hazard maps, comprehensive studies were conducted to explore the potential influence of targeted weir management, aiming at minimising the risk of negative impacts from flood events. Using the hydro-numerical model GERRIS/HYDRAX developed by the German Federal Institute of Hydrology, the effects of water level management during floods in 1975, 1994, and 2011 were analysed through non-stationary, one-dimensional calculations. Drawing on past events, management strategies were developed in collaboration with the Wasserstraßen- und Schifffahrtsamt Berlin (Waterways and Navigation Authority), considering existing objectives and constraints. These included minimising damage in residential areas during floods, preventing damage to timber pilings due to the required lowering of water levels at the Mühlendamm lock weir, and maintaining navigation for as long as possible.

The results suggest that the impact of flood discharges from the Spree River can be mitigated through effective control of the weirs. An agreement was reached between the Directorate General for Waterways and Shipping (GDWS), which oversees the Waterways and Shipping Authority (WSA, responsible for operating the weirs and locks), and the Senate Department responsible for water management. This agreement aims to reduce the negative impacts of floods by proactively managing water levels in the Berlin impoundment.

The findings from the State of Brandenburg were adopted for the Gosener Wiesen region, as floodwaters cross the Berlin-Brandenburg State border during flood events, and as the developed method does not cover this area (IWU 2015).

Lower Havel/ Lower Spree

The Berlin region of the Lower Havel/ Lower Spree is part of the Brandenburg impoundment. The approach used here was adjusted to that of the State of Brandenburg to ensure a methodologically consistent strategy across all areas of the Brandenburg impoundment. An extreme value statistical analysis of water levels was conducted for seven gauges (Charlottenburg Unterpegel, Sophienwerder, Spandau Unterpegel, Freybrücke/Tiefwerder, Pfaueninsel, Potsdam Abz., and Potsdam Lange Brücke) for the period from 1964 to 2013. These flood levels serve as supporting points for the water level gradient for a 100-year flood event. The water surface elevation was derived through linear interpolation of the supporting points, taking into account changes in gradients due to varying flow rates and cross-sections. For the flood areas, a distinction was made between flow-through areas (Lower Havel I flood area) and impounded flood areas (Lower Havel II flood area) to standardise specific exceptions to land use restrictions based on hydraulic conditions (IWU 2014).

Erpe, Panke, Tegeler Fließ, and Wuhle

The methodology used to develop the flood hazard maps for the Erpe, Panke, Tegeler Fließ, and Wuhle rivers are comparable in essence. To estimate flood event flow rates, a hydrological precipitation-runoff model was created for each catchment area. This model accounts for important runoff-producing factors, such as land use, topography, soil conditions, impervious soil coverage, and the effects of management practices and rainwater discharges. The model was calibrated and validated using precipitation and climate data, along with runoff measurements. The design runoff calculated from the hydrological model was used as input for hydraulic modeling to determine water levels and flow conditions. One-dimensional (non-)stationary models were employed for this purpose. The hydraulic models primarily rely on geometric data regarding flow cross-sections as well as information on flow conditions and roughness. The cross-sections determined during the survey were expanded to include the foreland areas using the digital terrain model (DTM). The hydraulic model was also calibrated and validated using water level time series and the water levels recorded during the surveys. Based on this model, water surface elevations were calculated both for flood events and the areas affected by flooding (IPS 2009, IPS 2013, Koenzen et al. 2011, and ProAqua 2021).