Details
| Original language | English |
|---|---|
| Article number | 1424 |
| Number of pages | 19 |
| Journal | Water |
| Volume | 12 |
| Issue number | 5 |
| Publication status | Published - 16 May 2020 |
Abstract
Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km 2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.
Keywords
- Modeling, R-factor, Radar climatology, RADKLIM, Rain gauge, Rainfall intensity, Soil erosion, USLE
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
- Social Sciences(all)
- Geography, Planning and Development
- Agricultural and Biological Sciences(all)
- Aquatic Science
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Water, Vol. 12, No. 5, 1424, 16.05.2020.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Comparing rainfall erosivity estimation methods using weather radar data for the state of Hesse (Germany)
AU - Kreklow, Jennifer
AU - Steinhoff-Knopp, Bastian
AU - Friedrich, Klaus
AU - Tetzlaff, Björn
N1 - Funding information: This research was funded by the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG) within the project “KLIMPRAX–Starkregen,” working package 1.4.
PY - 2020/5/16
Y1 - 2020/5/16
N2 - Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km 2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.
AB - Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km 2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.
KW - Modeling
KW - R-factor
KW - Radar climatology
KW - RADKLIM
KW - Rain gauge
KW - Rainfall intensity
KW - Soil erosion
KW - USLE
UR - http://www.scopus.com/inward/record.url?scp=85085865893&partnerID=8YFLogxK
U2 - 10.3390/w12051424
DO - 10.3390/w12051424
M3 - Article
VL - 12
JO - Water
JF - Water
SN - 2073-4441
IS - 5
M1 - 1424
ER -