Articles | Volume 26, issue 7
https://doi.org/10.5194/hess-26-1907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2022
Research article |
| 14 Apr 2022
| 14 Apr 2022
Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity
University of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
Pasquale Borrelli
Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy
Department of Biological Environment, Kangwon National University,
Chuncheon 24341, Republic of Korea
Panos Panagos
European Commission, Joint Research Centre (JRC), Ispra, Italy
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Revised manuscript accepted for HESS
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Rainfall erosivity is the main driver of water-induced soil erosion. A ground radar-based data was used to prepare a rainfall erosivity map of Europe. This study shows that the radar-based data products are a valuable solution for estimating large-scale rainfall erosivity, especially in regions with limited station-based precipitation data. A rainfall erosivity ensemble was derived to give first insights into a future avenue for updatable pan-European rainfall erosivity predictions.
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Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.
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For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Francis Matthews, Pasquale Borrelli, Panos Panagos, and Nejc Bezak
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-402, https://doi.org/10.5194/hess-2024-402, 2025
Revised manuscript accepted for HESS
Short summary
Short summary
Rainfall erosivity is the main driver of water-induced soil erosion. A ground radar-based data was used to prepare a rainfall erosivity map of Europe. This study shows that the radar-based data products are a valuable solution for estimating large-scale rainfall erosivity, especially in regions with limited station-based precipitation data. A rainfall erosivity ensemble was derived to give first insights into a future avenue for updatable pan-European rainfall erosivity predictions.
Nejc Bezak, Panos Panagos, Leonidas Liakos, and Matjaž Mikoš
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Short summary
Short summary
Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.
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Preprint withdrawn
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We assess if a simplistic model can simulate the timing of soil erosion and sediment transport (delivery) in several small agricultural catchments in North-West Europe. The findings show that the loss of soil in fields and the delivery of sediment to streams are related in complex (non-linear) ways through time which impact our knowledge of soil redistribution. Furthermore, we show how adaptations of simplistic models can be used to reveal the missing processes which require future developments.
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Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
Short summary
Short summary
Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Cited articles
Aghakouchak, A., Mehran, A., Norouzi, H., and Behrangi, A.: Systematic and
random error components in satellite precipitation data sets, Geophys. Res.
Lett., 39, L09406, https://doi.org/10.1029/2012GL051592, 2012.
An, Y., Zhao, W., Li, C., and Liu, Y.: Evaluation of Six Satellite and
Reanalysis Precipitation Products Using Gauge Observations over the Yellow
River Basin, China, Atmosphere, 11, 1–20, https://doi.org/10.3390/atmos11111223,
2020.
Angulo-Martínez, M. and Beguería, S.: Trends in rainfall erosivity in NE Spain at annual, seasonal and daily scales, 1955–2006, Hydrol. Earth Syst. Sci., 16, 3551–3559, https://doi.org/10.5194/hess-16-3551-2012, 2012.
Ballabio, C., Borrelli, P., Spinoni, J., Meusburger, K., Michaelides, S.,
Beguería, S., Klik, A., Petan, S., Janeček, M., Olsen, P., Aalto,
J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Tadić, M. P., Diodato,
N., Kostalova, J., Rousseva, S., Banasik, K., Alewell, C., and Panagos, P.:
Mapping monthly rainfall erosivity in Europe, Sci. Total Environ., 579,
1298–1315, https://doi.org/10.1016/J.SCITOTENV.2016.11.123, 2017.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019a.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk,
A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWep v2 Global 3-hourly
0.1∘ precipitation: Methodology and quantitative assessment, B.
Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019b.
Beck, H. E., Wood, E. F., McVicar, T. R., Zambrano-Bigiarini, M.,
Alvarez-Garreton, C., Baez-Villanueva, O. M., Sheffield, J., and Karger, D.
N.: Bias correction of global high-resolution precipitation climatologies
using streamflow observations from 9372 catchments, J. Climate, 33,
1299–1315, https://doi.org/10.1175/JCLI-D-19-0332.1, 2020.
Bezak, N., Ballabio, C., Mikoš, M., Petan, S., Borrelli, P., and Panagos,
P.: Reconstruction of past rainfall erosivity and trend detection based on
the REDES database and reanalysis rainfall, J. Hydrol., 590, 125372,
https://doi.org/10.1016/j.jhydrol.2020.125372, 2020.
Bezak, N., Borrelli, P., and Panagos, P.: A first assessment of rainfall
erosivity synchrony scale at pan-European scale, Catena, 198, 105060,
https://doi.org/10.1016/j.catena.2020.105060, 2021.
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V.,
Montanarella, L., and Panagos, P.: An assessment of the global impact of 21st
century land use change on soil erosion, Nat. Commun., 8, 2013, https://doi.org/10.1038/s41467-017-02142-7, 2017.
Borrelli, P., Robinson, D. A., Panagos, P., Lugato, E., Yang, J. E.,
Alewell, C., Wuepper, D., Montanarella, L., and Ballabio, C.: Land use and
climate change impacts on global soil erosion by water (2015–2070), P.
Natl. Acad. Sci. USA, 117, 21994–22001,
https://doi.org/10.1073/pnas.2001403117, 2020.
Brown, L. C. and Foster, G.: Storm erosivity using idealised intensity
distribution, T. ASAE, 30, 379–386, https://doi.org/10.13031/2013.31957, 1987.
Burn, D. H. and Hag Elnur, M. A.: Detection of hydrologic trends and
variability, J. Hydrol., 255, 107–122,
https://doi.org/10.1016/S0022-1694(01)00514-5, 2002.
Carollo, F. G., Ferro, V., and Serio, M. A.: Reliability of rainfall kinetic
power–intensity relationships, Hydrol. Process., 31, 1293–1300,
https://doi.org/10.1002/hyp.11099, 2017.
Chen, H., Chandrasekar, V., Cifelli, R., and Xie, P.: A Machine Learning
System for Precipitation Estimation Using Satellite and Ground Radar Network
Observations, IEEE Trans. Geosci. Remote Sens., 58, 982–994,
https://doi.org/10.1109/TGRS.2019.2942280, 2020.
Chen, Y., Xu, M., Wang, Z., Gao, P., and Lai, C.: Applicability of two
satellite-based precipitation products for assessing rainfall erosivity in
China, Sci. Total Environ., 757, 143975, https://doi.org/10.1016/j.scitotenv.2020.143975, 2021.
Cui, Y., Pan, C., Liu, C., Luo, M., and Guo, Y.: Spatiotemporal variation and
tendency analysis on rainfall erosivity in the Loess Plateau of China,
Hydrol. Res., 51, 1048–1062, https://doi.org/10.2166/nh.2020.030, 2020.
Dabney, S. M., Yoder, D. C., and Vieira, D. A. N.: The application of the
Revised Universal Soil Loss Equation, Version 2, to evaluate the impacts of
alternative climate change scenarios on runoff and sediment yield, J. Soil
Water Conserv., 67, 343–353, https://doi.org/10.2489/jswc.67.5.343, 2012.
Diodato, N., Borrelli, P., Panagos, P., Bellocchi, G., and Bertolin, C.:
Communicating Hydrological Hazard-Prone Areas in Italy With Geospatial
Probability Maps, Front. Environ. Sci., 7, 193, https://doi.org/10.3389/fenvs.2019.00193,
2019.
Dis, M. O., Anagnostou, E., and Mei, Y.: Using high-resolution satellite
precipitation for flood frequency analysis: case study over the Connecticut
River Basin, J. Flood Risk Manag., 11, S514–S526, https://doi.org/10.1111/jfr3.12250,
2018.
Dodson, R. and Marks, D.: Daily air temperature interpolated at high spatial
resolution over a large mountainous region, Clim. Res., 8, 61–73,
https://doi.org/10.3354/cr008001, 1997.
dos Santos Silva, D. S., Blanco, C. J. C., dos Santos Junior, C. S., and
Martins, W. L. D.: Modeling of the spatial and temporal dynamics of
erosivity in the Amazon, Model. Earth Syst. Environ., 6, 513–523,
https://doi.org/10.1007/s40808-019-00697-6, 2020.
ERA5: ERA5, https://doi.org/10.24381/cds.f17050d7, 2021.
Ganasri, B. P. and Ramesh, H.: Assessment of soil erosion by RUSLE model
using remote sensing and GIS – A case study of Nethravathi Basin, Geosci.
Front., 7, 953–961, https://doi.org/10.1016/j.gsf.2015.10.007, 2016.
Gebregiorgis, A. S. and Hossain, F.: How well can we estimate error variance
of satellite precipitation data around the world?, Atmos. Res., 154, 39–59,
https://doi.org/10.1016/j.atmosres.2014.11.005, 2015.
Ghajarnia, N., Daneshkar Arasteh, P., Liaghat, M., and Araghinejad, S.: Error
analysis on PERSIANN precipitation estimations: Case study of Urmia Lake
Basin, Iran, J. Hydrol. Eng., 23, 05018006-1, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001643,
2018.
Gini, C.: On the measurement of concentration and variability of characters,
Metron, 63, 3–38, 1914.
Habib, E., Haile, A. T., Tian, Y., and Joyce, R. J.: Evaluation of the
high-resolution CMORPH satellite rainfall product using dense rain gauge
observations and radar-based estimates, J. Hydrometeorol., 13,
1784–1798, https://doi.org/10.1175/JHM-D-12-017.1, 2012.
Haile, A. T., Yan, F., and Habib, E.: Accuracy of the CMORPH
satellite-rainfall product over Lake Tana Basin in Eastern Africa, Atmos.
Res., 163, 177–187, https://doi.org/10.1016/j.atmosres.2014.11.011, 2015.
Hamed, K. H.: Trend detection in hydrologic data: The Mann-Kendall trend
test under the scaling hypothesis, J. Hydrol., 349, 350–363,
https://doi.org/10.1016/j.jhydrol.2007.11.009, 2008.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, 2018.
Islam, M. A., Yu, B., and Cartwright, N.: Assessment and comparison of five
satellite precipitation products in Australia, J. Hydrol., 590, 125474, https://doi.org/10.1016/j.jhydrol.2020.125474, 2020.
Jiang, Q., Li, W., Wen, J., Qiu, C., Sun, W., Fang, Q., Xu, M., and Tan, J.:
Accuracy evaluation of two high-resolution satellite-based rainfall
products: TRMM 3B42V7 and CMORPH in Shanghai, Water (Switzerland), 10, 40, https://doi.org/10.3390/w10010040, 2018.
Jiang, Q., Li, W., Wen, J., Fan, Z., Chen, Y., Scaioni, M., and Wang, J.:
Evaluation of satellite-based products for extreme rainfall estimations in
the eastern coastal areas of China, J. Integr. Environ. Sci., 16,
191–207, https://doi.org/10.1080/1943815X.2019.1707233, 2019.
Kidd, C., Bauer, P., Turk, J., Huffman, G. J., Joyce, R., Hsu, K.-L., and
Braithwaite, D.: Intercomparison of high-resolution precipitation products
over Northwest Europe, J. Hydrometeorol., 13, 67–83,
https://doi.org/10.1175/JHM-D-11-042.1, 2012.
Kim, J., Han, H., Kim, B., Chen, H., and Lee, J.-H.: Use of a
high-resolution-satellite-based precipitation product in mapping
continental-scale rainfall erosivity: A case study of the United States,
Catena, 193, 104602, https://doi.org/10.1016/j.catena.2020.104602, 2020.
Kim, J. P., Jung, I., Park, K. W., Yoon, S. K., and Lee, D.: Hydrological
utility and uncertainty of multi-satellite precipitation products in the
mountainous region of South Korea, Remote Sens., 8, 608, https://doi.org/10.3390/rs8070608, 2016.
Kinnell, P. I. A.: Event soil loss, runoff and the Universal Soil Loss
Equation family of models: A review, J. Hydrol., 385, 384–397,
https://doi.org/10.1016/j.jhydrol.2010.01.024, 2010.
Kinnell, P. I. A.: CLIGEN as a weather generator for RUSLE2, Catena, 172,
877–880, https://doi.org/10.1016/j.catena.2018.09.016, 2019.
Lehner, B. and Grill, G.: Global river hydrography and network routing:
Baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013.
Li, X., Li, Z., and Lin, Y.: Suitability of trmm products with different
temporal resolution (3-hourly, daily, and monthly) for rainfall erosivity
estimation, Remote Sens., 12, 1–21, https://doi.org/10.3390/rs12233924, 2020.
Liu, Y., Zhao, W., Liu, Y., and Pereira, P.: Global rainfall erosivity
changes between 1980 and 2017 based on an erosivity model using daily
precipitation data, Catena, 194, 104768, https://doi.org/10.1016/j.catena.2020.104768, 2020.
Lorenz, M. O.: Methods of measuring the concentration of wealth, Publ. Am.
Stat. Assoc., 9, 209–219, https://doi.org/10.2307/2276207, 1905.
Masaki, Y., Hanasaki, N., Takahashi, K., and Hijioka, Y.: Global-scale
analysis on future changes in flow regimes using Gini and Lorenz asymmetry
coefficients, Water Resour. Res., 50, 4054–4078,
https://doi.org/10.1002/2013WR014266, 2014.
McLeod, A. I.: Kendall rank correlation and Mann-Kendall trend test, 12, http://www.stats.uwo.ca/faculty/aim (last access: 10 January 2022), 2011.
Mineo, C., Ridolfi, E., Moccia, B., Russo, F., and Napolitano, F.: Assessment
of rainfall kinetic-energy-intensity relationships, Water,
11, 1994, https://doi.org/10.3390/w11101994, 2019.
Nearing, M. A., Unkrich, C. L., Goodrich, D. C., Nichols, M. H., and Keefer,
T. O.: Temporal and elevation trends in rainfall erosivity on a 149 km2
watershed in a semi-arid region of the American Southwest, Int. Soil Water
Conserv. Res., 3, 77–85, https://doi.org/10.1016/j.iswcr.2015.06.008, 2015.
Nearing, M. A., Yin, S.-Q., Borrelli, P., and Polyakov, V. O.: Rainfall
erosivity: An historical review, Catena, 157, 357–362,
https://doi.org/10.1016/j.catena.2017.06.004, 2017.
Nel, W., Reynhardt, D. A., and Sumner, P. D.: Effect of altitude on erosive
characteristics of concurrent rainfall events in the northern kwazulu-natal
drakensberg, Water, 36, 509–512, 2010.
NOAA: CMORPH dataset, https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/, last access 28 March 2022.
Palharini, R. S. A., Vila, D. A., Rodrigues, D. T., Quispe, D. P.,
Palharini, R. C., de Siqueira, R. A., and de Sousa Afonso, J. M.: Assessment
of the extreme precipitation by satellite estimates over South America,
Remote Sens., 12, 2085, https://doi.org/10.3390/rs12132085, 2020.
Panagos, P., Van Liedekerke, M., Jones, A., and Montanarella, L.: European
Soil Data Centre: Response to European policy support and public data
requirements, Land Use Policy, 29, 329–338,
https://doi.org/10.1016/j.landusepol.2011.07.003, 2012 (data available at: https://esdac.jrc.ec.europa.eu/resource-type/datasets, last access: 10 January 2022).
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva,
S., Tadić, M. P., Michaelides, S., Hrabalíková, M., Olsen, P.,
Beguería, S., and Alewell, C.: Rainfall erosivity in Europe, Sci. Total
Environ., 511, 801–814, https://doi.org/10.1016/j.scitotenv.2015.01.008, 2015.
Panagos, P., Borrelli, P., Spinoni, J., Ballabio, C., Meusburger, K.,
Beguería, S., Klik, A., Michaelides, S., Petan, S.,
Hrabalíková, M., Banasik, K., and Alewell, C.: Monthly rainfall
erosivity: Conversion factors for different time resolutions and regional
assessments, Water, 8, 119, https://doi.org/10.3390/w8040119, 2016a.
Panagos, P., Ballabio, C., Borrelli, P., and Meusburger, K.: Spatio-temporal
analysis of rainfall erosivity and erosivity density in Greece, Catena, 137,
161–172, https://doi.org/10.1016/j.catena.2015.09.015, 2016b.
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K.,
Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi,
Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Gorobets, A. V, Levi, Y.,
Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P. T. S., Bonilla,
C. A., Meddi, M., Nel, W., Al Dashti, H., Boni, M., Diodato, N., Van Oost,
K., Nearing, M., and Ballabio, C.: Global rainfall erosivity assessment based
on high-temporal resolution rainfall records, Sci. Rep., 7, 4175,
https://doi.org/10.1038/s41598-017-04282-8, 2017.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Petan, S., Rusjan, S., Vidmar, A., and Mikoš, M.: The rainfall kinetic
energy-intensity relationship for rainfall erosivity estimation in the
mediterranean part of Slovenia, J. Hydrol., 391, 314–321,
https://doi.org/10.1016/j.jhydrol.2010.07.031, 2010.
Petek, M., Mikoš, M., and Bezak, N.: Rainfall erosivity in Slovenia:
Sensitivity estimation and trend detection, Environ. Res., 167, 528–535,
https://doi.org/10.1016/j.envres.2018.08.020, 2018.
Prakash, S.: Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA
precipitation products across India, J. Hydrol., 571, 50–59,
https://doi.org/10.1016/j.jhydrol.2019.01.036, 2019.
Prakash, S., Mitra, A. K., AghaKouchak, A., and Pai, D. S.: Error
characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42)
products over India for different seasons, J. Hydrol., 529, 1302–1312,
https://doi.org/10.1016/j.jhydrol.2015.08.062, 2015.
Rahmawati, N. and Lubczynski, M. W.: Validation of satellite daily rainfall
estimates in complex terrain of Bali Island, Indonesia, Theor. Appl.
Climatol., 134, 513–532, https://doi.org/10.1007/s00704-017-2290-7, 2018.
Reder, A. and Rianna, G.: Exploring ERA5 reanalysis potentialities for
supporting landslide investigations: a test case from Campania Region
(Southern Italy), Landslides, 1909–1924, https://doi.org/10.1007/s10346-020-01610-4, 2021.
Renard, K. G. and Freimund, J. R.: Using monthly precipitation data to
estimate the R-factor in the revised USLE, J. Hydrol., 157, 287–306,
https://doi.org/10.1016/0022-1694(94)90110-4, 1994.
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D.
C.: Predicting Soil Erosion byWater: A Guide to Conservation Planning with
the Revised Universal Soil Loss Equation (RUSLE) (Agricultural Handbook
703), US Department of Agriculture, ISBN 0-16-048938-5, 1997.
Rodrigues da Silva, V. D. P., Belo Filho, A. F., Rodrigues Almeida, R. S.,
de Holanda, R. M., and da Cunha Campos, J. H. B.: Shannon information entropy
for assessing space-time variability of rainfall and streamflow in semiarid
region, Sci. Total Environ., 544, 330–338,
https://doi.org/10.1016/j.scitotenv.2015.11.082, 2016.
SAGA GIS: http://www.saga-gis.org/ (last access: 10 January 2022), 2021.
Sanchez-Moreno, J. F., Mannaerts, C. M., and Jetten, V.: Rainfall erosivity
mapping for Santiago Island, Cape Verde, Geoderma, 217–218, 74–82,
https://doi.org/10.1016/j.geoderma.2013.10.026, 2014.
Seo, B.-C., Krajewski, W. F., Quintero, F., ElSaadani, M., Goska, R., Cunha,
L. K., Dolan, B., Wolff, D. B., Smith, J. A., Rutledge, S. A., Rutledge, S.
A., and Petersen, W. A.: Comprehensive evaluation of the IFloodS Radar
rainfall products for hydrologic applications, J. Hydrometeorol., 19,
1793–1813, https://doi.org/10.1175/JHM-D-18-0080.1, 2018.
Skok, G., Žagar, N., Honzak, L., Žabkar, R., Rakovec, J., and Ceglar,
A.: Precipitation intercomparison of a set of satellite- and
raingauge-derived datasets, ERA Interim reanalysis, and a single WRF
regional climate simulation over Europe and the North Atlantic, Theor. Appl.
Climatol., 123, 217–232, https://doi.org/10.1007/s00704-014-1350-5, 2016.
Stampoulis, D. and Anagnostou, E.: Evaluation of global satellite rainfall
products over Continental Europe, J. Hydrometeorol., 13, 588–603,
https://doi.org/10.1175/JHM-D-11-086.1, 2012.
Stampoulis, D., Anagnostou, E. N., and Nikolopoulos, E. I.: Assessment of
high-resolution satellite-based rainfall estimates over the mediterranean
during heavy precipitation events, J. Hydrometeorol., 14, 1500–1514,
https://doi.org/10.1175/JHM-D-12-0167.1, 2013.
Sun, R., Yuan, H., and Yang, Y.: Using multiple satellite-gauge merged
precipitation products ensemble for hydrologic uncertainty analysis over the
Huaihe River basin, J. Hydrol., 566, 406–420,
https://doi.org/10.1016/j.jhydrol.2018.09.024, 2018.
Sunilkumar, K., Narayana Rao, T., Saikranthi, K., and Purnachandra Rao, M.:
Comprehensive evaluation of multisatellite precipitation estimates over
India using gridded rainfall data, J. Geophys. Res., 120, 8987–9005,
https://doi.org/10.1002/2015JD023437, 2015.
Sutanto, S. J., Vitolo, C., Di Napoli, C., D'Andrea, M., and Van Lanen, H. A.
J.: Heatwaves, droughts, and fires: Exploring compound and cascading dry
hazards at the pan-European scale, Environ. Int., 134, 105276, https://doi.org/10.1016/j.envint.2019.105276, 2020.
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have
satellite precipitation products improved over last two decades? A
comprehensive comparison of GPM IMERG with nine satellite and reanalysis
datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697, 2020.
Teng, H., Ma, Z., Chappell, A., Shi, Z., Liang, Z., and Yu, W.: Improving
rainfall erosivity estimates using merged TRMM and gauge data, Remote Sens.,
9, 1134, https://doi.org/10.3390/rs9111134, 2017.
Tian, Y., Peters-Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G.
J., Adler, R. F., Hsu, K., Turk, F. J., Garcia, M., and Zeng, J.: Component
analysis of errors in satellite-based precipitation estimates, J. Geophys.
Res.-Atmos., 114, D24101, https://doi.org/10.1029/2009JD011949, 2009.
Verstraeten, G., Poesen, J., Demarée, G., and Salles, C.: Long-term (105
years) variability in rain erosivity as derived from 10 min rainfall depth
data for Ukkel (Brussels, Belgium): Implications for assessing soil erosion
rates, J. Geophys. Res.-Atmos., 111, D22109, https://doi.org/10.1029/2006JD007169, 2006.
Wang, M., Yin, S., Yue, T., Yu, B., and Wang, W.: Rainfall erosivity estimation using gridded daily precipitation datasets, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-633, 2020.
Wei, G., Lü, H., Crow, W. T., Zhu, Y., Wang, J., and Su, J.:
Comprehensive evaluation of GPM-IMERG, CMORPH, and TMPA precipitation
products with gauged rainfall over mainland China, Adv. Meteorol., 2018, 3024190, https://doi.org/10.1155/2018/3024190, 2018.
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.:
Reprocessed, bias-corrected CMORPH global high-resolution precipitation
estimates from 1998, J. Hydrometeorol., 18, 1617–1641,
https://doi.org/10.1175/JHM-D-16-0168.1, 2017.
Xie, P., Joyce, R., Yoo, S., Yarosh, S. H., Sun, Y., and Lin, F.: NOAA
Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution
Global Precipitation Estimates, Version 1, https://doi.org/10.25921/w9va-q159, 2021.
Yin, S., Nearing, M. A., Borrelli, P., and Xue, X.: Rainfall erosivity: An
overview of methodologies and applications, Vadose Zone J., 16, 1–16, https://doi.org/10.2136/vzj2017.06.0131, 2017.
Yu, B. and Rosewell, C. J.: Rainfall erosivity estimation using daily
rainfall amounts for South Australia, Aust. J. Soil Res., 34, 721–733,
https://doi.org/10.1071/SR9960721, 1996.
Short summary
Rainfall erosivity is one of the main factors in soil erosion. A satellite-based global map of rainfall erosivity was constructed using data with a 30 min time interval. It was shown that the satellite-based precipitation products are an interesting option for estimating rainfall erosivity, especially in regions with limited ground data. However, ground-based high-frequency precipitation measurements are (still) essential for accurate estimates of rainfall erosivity.
Rainfall erosivity is one of the main factors in soil erosion. A satellite-based global map of...
