Radar rainfall estimates in an alpine environment using inverse hydrological modelling

Abstract

Abstract. The quality of hydrological modelling is limited due to the restricted availability of high resolution temporal and spatial input data such as temperature, global radiation, and precipitation. Radar-based rain measurements provide good spatial information. On the other hand, using radar data is accompanied by basic difficulties such as clutter, shielding, variations of Z/R-relationships, beam-resolution and attenuation. Instead of accounting for all errors involved separately, a robust Z/R-relationship is estimated in this study for the short range (up to 40 km distance) using inverse hydrological modelling for a continuous period of three months in summer 2001. River gauge measurements from catchment sizes around 100 km2 are used to estimate areal precipitation and finally Z/R-relationships using a calibrated hydrological model. The study is performed in the alpine Ammer catchment with very short reaction times of the river gauges to rainfall events.

Evaluation of Radar Quantitative Precipitation Estimates (QPEs) as an Input of Hydrological Models for Hydrometeorological Applications

“…In conjunction with the advances in radar systems, computer power, and hydrological models over the past decades, the application of radar QPEs as precipitation input for hydrological models has been increased since then (Beneti et al 2019;Khan et al 2019;Meischner 2005;Ran et al 2018). Different countries produce commercial weather radar QPEs providing a grid with precipitation accumulation over time [e.g., Next Generation Weather Radar (NEXRAD) in the United States, Nimrod in the United Kingdom, and Radar-Online-Aneichung (RADOLAN) in Germany] (Krajewski et al 2010a;Marx et al 2006;Moore et al 2004;Wijayarathne et al 2020). Also, the accuracy and reliability of weather radar QPEs have been significantly improved with the use of dual-polarized radar products such as specific differential phase K DP and differential reflectivity Z DR (Bringi et al 2011;Chandrasekar et al 2013;Hall et al 2015;Park et al 2005;Sugier et al 2006;Dufton 2016;Ryzhkov et al 2005).…”

High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

“…The flow regime of the Ammer is influenced by two factors: snowmelt in spring and a precipitation maximum in summer. As a result, maximum monthly discharge values occur in May in alpine subcatchments and in June and July for the downstream subcatchments (Marx et al 2006;Kunstmann et al 2005).…”