Distributed Hydrological Modelling- and Stochastic Weather Generator-based Combined Estimation Technique for Future Flood Frequency Using Regional Climate Model Simulation

Regional impacts of global warming are evidently very likely to affect hydrological processes, especially, the characteristics of flood events. In order to provide reasonable estimation for the future trends of flood event frequency, we have developed a combined modelling approach for the target watershed as follows:

(1) First, our 3D, spatially distributed, physically based, distributed parameter hydrological model DIWA (DIstributed WAtershed) has been adapted.

(2) Then, we calibrated and validated the GIS-based DIWA model, based on historical meteorological and run-off data.

(3) We characterized our stochastic weather generator DIWA-SWG (DIWA Stochastic Weather Generator) model, based on 30 years historical meteorological data.

(4) Then, the climate change has been estimated in order to analyze the effects on floods. For this purpose, simulation of regional climate model PRECIS has been used. This hydrostatic model has been developed by the Hadley Centre of the UK MetOffice, and adapted for the Central/Eastern European region by the Department of Meteorology Eotvos Lorand University. The applied horizontal resolution is 0.22° (~25 km), and the model contains 19 atmospheric vertical levels with sigma coordinates. The necessary initial and lateral boundary conditions are provided by the global climate model HadCM3. In order to eliminate the bias of the climate model experiment, the raw daily output data (i.e., precipitation, minimum and mean temperature) has been corrected using the quantile matching technique for each grid point located within the target area. In this process, the monthly empirical distribution functions of simulated raw data in each grid point are fitted to the observed monthly distributions using the calibration period of 1983-2010. Then, the correction factors are applied to the entire gridded daily time series (e.g., 1961-2050). The corrected outputs enable us to build a stochastic weather generator for each grid point using DIWA-SWG model embedded into Monte Carlo simulations, and result in a large ensemble of possible future hydrometeorological conditions on daily scale.

(5) Finally, we ran the calibrated DIWA hydrologic model using stochastically generated daily sequences of weather conditions on the basis of the estimated climatic conditions.

As a result of this approach, the statistical analysis of simulated run-off data leads us to estimate the change in flood frequency, which is key information for the local governments and water management to develop efficient flood protection strategy.