Modelling flood regulation ecosystem services dynamics based on climate and land use information

Thea Wübbelmann; Steffen Bender; Benjamin Burkhard

doi:10.3097/LO.202188

Authors

Thea Wübbelmann Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht (HZG), Fischertwiete 1; 20095 Hamburg, Germany
Steffen Bender Climate Service Center Germany (GERICS), Helmholtz-Zentrum Geesthacht (HZG), Hamburg, Germany
Benjamin Burkhard Leibniz University Hannover, Institute of Physical Geography and Landscape Ecology, Hannover, Germany; Leibniz Centre for Agricultural Landscape Research ZALF, Müncheberg, Germany https://orcid.org/0000-0001-8636-9009

DOI:

https://doi.org/10.3097/LO.202188

Keywords:

MAES, Indicator, HEC-RAS, Scenarios, Nature-based Solutions

Abstract

The concept of ecosystem service (ES) identifies benefits that people obtain from ecosystems with contributions to human well-being. One important ES under external pressure is “flood regulation” that describes an ecosystem’s capacity to reduce flood hazards.

Several related studies estimate current flood regulation ES. However, regional climate projections indicate a shift in precipitation patterns. Therefore, Climate and land use changes make it necessary to assess future supply in order to test functionality and adaptation measures. This study focuses on surface retention ES. We used two methods to show the relevance of different landscape scenarios and climate information for flood regulation ES supply: 1) hydraulic simulations with the model HEC-RAS 2) the flood retention capacity indicator suggested by the German MAES-Working group. We simulated two events: the historic flood of 2013 and future hypothetically 10% higher water levels. Furthermore, three land use change scenarios were evaluated.

The model results indicate water accumulation by vegetation. Higher water levels of future climate scenarios lead to an increase in flooded areas and higher water volumes. To evaluate flood regulation capacities, an approach solely based on 2D retention areas, such as the MAES-indicator, is not sufficient. Modelling approaches deliver the opportunity for future scenario simulations.

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