ZDIN-Zahlen im Detail

Allgemeiner Test des SARIMAX Auto-Arima Modells zur Vorhersage von univariaten Zeitreihen aus der Wasserbedarfserfassung.

Intense rainfall events with high intensities over short durations frequently result in substantial runoff and increased potential for flooding in affected catchments. The accurate assessment of flood hazards remains challenging due to the high variability of rainfall dynamics and their spatial distributions. Rain gauge stations provide precise point measurements; however, they lack information on the spatial distribution of rainfall. Conversely, weather radar offers high-resolution spatial and temporal rainfall data but is subject to biases and uncertainties that require correction. Previous studies have predominantly focused on pointwise comparisons of rainfall data products. In contrast, this study utilizes data from 109 catchments in Lower Saxony, Germany, to evaluate the ability of station-based and radar-derived rainfall data (using the corrected Radklim product from the German Weather Service) to explain and classify observed runoff events. These events are categorized as Flash Floods, Short-Rain Floods or Long-Rain Floods and the quality of the rainfall data is analyzed in relation to these classifications. Furthermore, the study investigates whether significant runoff events can be exclusively explained by one rainfall data source. By comparing catchment-averaged rainfall from stations and radar, this research highlights the strengths and limitations of both data types in representing rainfall-runoff relationships. The findings will contribute to improved flood hazard assessment and emphasize the importance of selecting appropriate rainfall datasets for hydrological analyses and early warning systems.