Parameter regionalization for hydrological models using Context Free Grammar
Abstract
Hydrological models are important tools to help determining effective water resources management practices. For example, they can be applied to estimate runoff, optimize flood protection measures, or to decide the most effective strategies for electric power production. Hydrological models are also used in climate change impact assessments, for example, when future drinking water or irrigation water amounts have to be estimated. Hydrological and especially runoff generation processes are very complex and are dependent on a number of factors. Next to the precipitation amounts and intensities, the generation of runoff is largely controlled by the physio-geographic conditions of the landscape/catchment. Most important are the topography of the area, the land use and land cover (including impermeable surfaces and vegetation), the soil (depth and texture) and the prevailing soil moisture conditions. Hydrological models need to incorporate the most dominant hydrological processes as well as the physio-geographical characteristics of the catchment into a mathematical formulation; the catchment characteristics then “enter” the model in the form of so-called process or model parameters. One of the major current challenges in hydrology is that these catchment characteristics cannot be observed or determined catchment-wide in a way that is required as input by the models. In this project, we will develop a methodology that will be able to estimate so-called transfer-functions directly from runoff data measured at several gauges within the catchment. These transfer-functions mathematically relate available landscape characteristics (e.g. digital elevation models/topography from laser scans; land use land cover from satellite images) to the necessary model parameters at each pixel/location in the catchment. The new method will not only estimate the mathematical structure of these transfer functions, it will also derive its coefficients. Thereby using novel methods from Applied Informatics and in particular from the area of “Context Free Grammar”. The new method to derive the necessary transfer functions will be developed and implemented for three hydrological models differing in complexity (among which the spatially distributed conceptual rainfall-runoff model COSERO, used by the VERBUND trading GmbH, Austria for its operations) and tested for a number of different catchments in Austria and in Europe. In this way, we anticipate developing a methodology, which will allow for highly improved runoff prediction in catchments by incorporating an improved estimation process for model parameters via transfer-functions that are based on the available physical characteristics of a catchment.
Hydrology Rainfall-Runoff-modelling parameter regionalization context free grammar Optimization
Publikationen
New symbolic regression methods for estimating parameter transfer functions for hydrological models
Autoren: Feigl, M; Klotz, D; Herrnegger, M; Schulz, K Jahr: 2019
Conference & Workshop proceedings, paper, abstract
Project staff
Karsten Schulz
Univ.Prof. Dipl.Geoökol. Dr.rer.nat. Karsten Schulz
karsten.schulz@boku.ac.at
Tel: +43 1 47654-81699
Project Leader
01.07.2018 - 30.06.2022
Mathew Herrnegger
Dipl.-Ing. Dr.nat.techn. Mathew Herrnegger
mathew.herrnegger@boku.ac.at
Tel: +43 1 47654-81618
Sub Projectleader
01.07.2018 - 30.06.2022
Claire Brenner
Dipl.-Ing. Dr. Claire Brenner
claire.brenner@boku.ac.at
Project Staff
01.07.2018 - 30.06.2022
Moritz Feigl
Dipl.-Ing. Dr. Moritz Feigl
moritz.feigl@boku.ac.at
Tel: +43 1 47654-81610
Project Staff
01.07.2018 - 30.06.2022