Abstract
AbstractIn this work, we present a comprehensive evaluation of a stochastic multi‐site, multi‐variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km2. The regional weather generator, which is based on a first‐order multi‐variate auto‐regressive model, is setup using 53‐year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi‐day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma‐Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non‐precipitation variables. The weather generator can be used to generate synthetic event footprints for large‐scale trans‐basin flood risk assessment.
Highlights
Reliable flood risk assessments are challenging, in particular at large spatial scales (de Moel et al, 2015)
All figures below show the results for the RWG1-extended Generalized Pareto distribution (extGP) model version, whereas respective plots for the model version RWG0-mixed Gamma-GP distribution (mGGP) are provided in the Supporting Information
Graphical results for the RWG0-extGP are very similar to the RWG0-mGP version in most cases and are not shown here
Summary
Reliable flood risk assessments are challenging, in particular at large spatial scales (de Moel et al, 2015). The weather generator presented by Hundecha et al (2009) simulates continuous series of daily precipitation at the station locations using the spatial correlation structure of the observations. We evaluate the at-site performance of the RWG with regards to the daily mean temperature and solar radiation by comparing the mean and 99.9th percentile for each month and the nine selected stations.
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