SYNOPSE
Project start: 01.05.2013
Duration: 3 Jahre
Funding: BMBF
Project lead: Harald Kunstmann
Involved scientists: Manuel Lorenz, Andreas Wagner, Sven Wagner
Project partners:
Dr.-Ing. Pecher & Partner Ingenieurgesellschaft mbH
Hamburger Stadtentwässerung
Institut für technisch-wissenschaftliche Hydrologie GmbH
Leibniz Universität Hannover
Stadtentwässerung Braunschweig
Universität Stuttgart
Summary
In order to increase the planning reliability of urban drainage systems, the longest possible time series of meteorological input data in fine spatial and temporal resolution are indispensable for hydraulic modeling. Precipitation is the most important factor here, as extreme events lead to peaks in the load on the canals. However, as rainfall patterns are expected to change as a result of climate change, major uncertainties arise in terms of sizing of the technical facilities.
The Chair of Regional Climate and Hydrology participates in SYNOPSE - in addition to the stochastic methods of the University of Stuttgart and the University of Hannover - with the subproject Dynamic-Stochastic Methods for the generation of spatiotemporally coherent and high-resolution meteorological fields.
The aim of this subproject is to simulate time series of current and future precipitation with the help of the regional climate model WRF. To validate the model, soil measurements and radar data are used to study the realistic mapping of total and precipitation patterns. This also provides new insights into the scale-dependent performance of regional climate models.
In addition, geostatistical procedures are used and developed to correct the model and to transfer the meteorological fields to the desired resolution (1x1 km, 5 minutes). The main focus is on Copula-based methods, as they can be flexibly adapted to the relationships of spatially and temporally distributed variables. Once again, the procedures are developed on the basis of the control period, in order to finally transfer them into the future in the appropriate manner.
The combination and comparison of physically-based models with stochastic methods seeks to better understand the bandwidth and errors of the two approaches, ultimately leading to a more secure estimate of future meteorological fields.
The project is funded under the BMBF funding measure INIS - Intelligent and Multifunctional Infrastructure Systems for Sustainable Water Supply and Sanitation.
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