Space-time Variability of Area-Intensity-Duration-Frequency Curves from Radar Data
Abstract
Intensity-duration-frequency (IDF) curves are frequently used for the derivation of design storms. These curves are usually estimated from rain gauges and are valid for extreme rainfall at local observed points. Two common problems are involved: a) the regionalization of the rainfall statistics for unobserved locations and b) the indirect estimation of areal extremes using areal reduction factors (ARFs). Weather radar data are available with large spatial coverage and high resolution in space and could be used for a direct derivation of areal design storms avoiding ARFs for any location and catchment size. However, still one problem with radar data is the relatively short observation period for the estimation of extreme events. This study deals with the development of area-intensity-duration-frequency (AIDF) curves from weather radar data. One main objective is to analyze the space-time variability of these curves in order to assess the possibility to trade space for time in the estimation of extremes to compensate for the short radar observation periods. First, radar data are bias corrected with gauge data. Thereafter, AIDF curves are calculated for different spatial and temporal sample sizes. The AIDF curves are compared regarding their temporal and spatial variability. In order to reduce spatial variability, the extremes are normalized by an index value. Also, a grouping of locations according to their climatological and physiographical characteristics is carried out. The changing spatial resolution of the radar observations is also taken into account. The data used for this study cover about 20 years of observations from the three different radar devices in Germany and all non-recording rain gauges within the radar circles. AIDF curves are analyzed for rainfall durations from 5 minutes to 24 hours and return periods from 1 year to 30 years. It is assumed, that the spatial variability of AIDF curves decreases with increasing sample size, grouping and normalization and is finally comparable to temporal variability.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMH132...06G
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES;
- 1817 Extreme events;
- HYDROLOGY;
- 1854 Precipitation;
- HYDROLOGY;
- 4318 Statistical analysis;
- NATURAL HAZARDS