TY - JOUR

T1 - Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany

AU - Nguyen, Viet Dung

AU - Merz, Bruno

AU - Hundecha, Yeshewatesfa

AU - Haberlandt, Uwe

AU - Vorogushyn, Sergiy

N1 - Funding Information: This research has been funded by the Federal Ministry of Education and Research of Germany in the framework of the project FLOOD (project number 01LP1903E) as a part of the ClimXtreme Research Network on Climate Change and Extreme Events within framework programme Research for Sustainable Development (FONA3). Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for the research group FOR 2416 ?Space-Time Dynamics of Extreme Floods (SPATE)? (project number 278017089) is gratefully acknowledged. We thank Dr. Francesco Serinaldi, Dr. Korbinian Breinl and an anonymous reviewer for constructive comments that helped to improve the manuscript. Open Access funding enabled and organized by ProjektDEAL.

PY - 2021/8/3

Y1 - 2021/8/3

N2 - In 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.

AB - In 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.

KW - correlation

KW - extreme

KW - flood

KW - large-scale

KW - multi-variate

KW - weather generator

UR - http://www.scopus.com/inward/record.url?scp=85103644214&partnerID=8YFLogxK

U2 - 10.1002/joc.7107

DO - 10.1002/joc.7107

M3 - Article

AN - SCOPUS:85103644214

VL - 41

SP - 4933

EP - 4956

JO - International Journal of Climatology

JF - International Journal of Climatology

SN - 0899-8418

IS - 10

ER -