Spatiotemporal analysis of heavy rain-induced flood occurrences in Germany using a novel event database approach

Kaiser, Maria; Günnemann, Stephan; Disse, Markus

doi:10.1016/j.jhydrol.2021.125985

Cited by 23 publications

(9 citation statements)

References 59 publications

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“…In the case of BC-Stat, r remains below 0.6. The spatial pattern of RAD-BC design storms is much more in line with what is expected from the underlying processes, representing pure convection triggered, small-scale features for short-duration events and more organized largerscale frontal systems for longer-duration events Kaiser et al, 2021). Interestingly, the spatial pattern in the BW-Stat dataset follows this behavior in the case of a 1-year design storm (similar to RAD-BC, see Fig.…”

Section: Comparison Of Design Stormssupporting

confidence: 61%

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Haensler

Weiler²

2022

Hydrol. Earth Syst. Sci.

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Abstract. Spatially explicit quantification on design storms is essential for flood risk assessment and planning. Due to the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations only that have a substantially long time coverage. To achieve a regional picture, these station-based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates, based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random (short-duration events, 15 min) to a more structured, orographically influenced pattern (long-duration events, 24 h) seems to be much more realistic in the weather-radar-based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

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Section: Comparison Of Design Stormssupporting

confidence: 61%

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Haensler

Weiler²

2022

Hydrol. Earth Syst. Sci.

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“…In other words, hotspots occur where the drought index is high in the pixel and in the neighbourhood of that pixel. In this study a hotspot indicates accumulation of high SPEI values (or wet conditions) in time and space around a pixel, while a cold spot represents low SPEI values (or dry conditions) around a pixel in time and space 33 , 34 .…”

Section: Methodsmentioning

confidence: 99%

Development and application of high resolution SPEI drought dataset for Central Asia

et al. 2022

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Central Asia is a data scarce region, which makes it difficult to monitor and minimize the impacts of a drought. To address this challenge, in this study, a high-resolution (5 km) Standardized Precipitation Evaporation Index (SPEI-HR) drought dataset was developed for Central Asia with different time scales from 1981–2018, using Climate Hazards group InfraRed Precipitation with Station’s (CHIRPS) precipitation and Global Land Evaporation Amsterdam Model’s (GLEAM) potential evaporation (Ep) datasets. As indicated by the results, in general, over time and space, the SPEI-HR correlated well with SPEI values estimated from coarse-resolution Climate Research Unit (CRU) gridded time series dataset. The 6-month timescale SPEI-HR dataset displayed a good correlation of 0.66 with GLEAM root zone soil moisture (RSM) and a positive correlation of 0.26 with normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modelling System (GIMMS). After observing a clear agreement between SPEI-HR and drought indicators for the 2001 and 2008 drought events, an emerging hotspot analysis was conducted to identify drought prone districts and sub-basins.

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“…Actually the spatial pattern of RAD-BC design storms is much more in line with what is expected from the underlying processes (e.g. pure convection triggered, small scale and short duration event versus more organized larger scale frontal systems for longer duration events (Lengfeld et al, 2019;Kaiser et al, 2021). With respect to the absolute values, the direct comparison of BW-Stat and RAD-BC design storm intensities reveal that there are regions with substantially larger intensities in the RAD-BC dataset (e.g.…”

Section: Comparison Of Design Stormsmentioning

confidence: 58%

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Hänsler

Weiler

2021

Preprint

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Abstract. Spatially explicit quantification on design storms are essential for flood risk assessment and planning. Since the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations having a substantially long time coverage. To achieve a regional picture these station based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns from short duration (15 minute) to long duration (2 days) events seems to be much more realistic in the weather radar based design storm product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

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Spatiotemporal analysis of heavy rain-induced flood occurrences in Germany using a novel event database approach

Cited by 23 publications

References 59 publications

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Development and application of high resolution SPEI drought dataset for Central Asia

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

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