Borrelli, P., Diodato, N., and Panagos, P.: Rainfall erosivity in Italy: a
national scale spatio-temporal assessment, Int. J. Digit. Earth, 9,
835–850, https://doi.org/10.1080/17538947.2016.1148203, 2016.
Brown, L. C. and Foster, G. R.: Storm erosivity using idealized intensity
distributions, T. ASABE, 30, 379–0386, https://doi.org/10.13031/2013.31957, 1987.
Chen, H., Wen, D., Du, Y., Xiong, L., and Wang, L.: Errors of five satellite
precipitation products for different rainfall intensities, Atmos. Res., 285,
106622, https://doi.org/10.1016/j.atmosres.2023.106622, 2023.
Chen, W. and Bezak, N.: Comment on “Towards improved USLE-based soil erosion modelling in India: A review of prevalent pitfalls and implementation of exemplar methods” by Majhi et al. (2021), Earth-Sci. Rev., 231, 104095, https://doi.org/10.1016/j.earscirev.2022.104095, 2022.
Chen, Y., Duan, X., Ding, M., Qi, W., Wei, T., Li, J., and Xie, Y.: New gridded dataset of rainfall erosivity (1950–2020) o
n the Tibetan Plateau, Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, 2022.
FAO: Intergovernmental Technical Panel on Soils: Status of the World's Soil
Resources, Rome: Food and Agriculture Organization of the United Nations,
ISBN 978-92-5-109004-6, 2015.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., and Seal, D.: The shuttle
radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183,
2007.
Ferreira, C. S., Seifollahi-Aghmiuni, S., Destouni, G., Ghajarnia, N., and
Kalantari, Z.: Soil degradation in the European Mediterranean region:
Processes, status and consequences, Sci. Total Environ., 805, 150106, https://doi.org/10.1016/j.scitotenv.2021.150106, 2022.
Fournier, F.: Climat et Erosion, Presses Universitaries de France, Paris,
https://doi.org/10.1097/00010694-196108000-00029, 1960.
GSP: Global Soils Partnership: Global Soil Partnership Endorses Guidelines
on Sustainable Soil Management,
http://www.fao.org/global-soil-partnership/resources/highlights/detail/en/c/416516/ (last access: 12 November 2022),
2017.
Hanjra, M. A. and Qureshi, M. E.: Global water crisis and future food
security in an era of climate change, Food Policy, 35, 365–377, https://doi.org/10.1016/j.foodpol.2010.05.006, 2010.
Hong, K. L., Hsu, K.-L., Sorooshian, S., and Gao, X.: Precipitation estimation
from remotely sensed imagery using an artificial neural network cloud
classification system, J. Appl. Meteorol., 43, 1834–1853, https://doi.org/10.1175/JAM2173.1, 2004.
Hovius, N. and Stark, C. P.: Landslide-driven erosion and topographic
evolution of active mountain belts. In Landslides from massive rock slope
failure, Springer, Dordrecht, 573–590, https://doi.org/10.1007/978-1-4020-4037-5_30, 2006.
Hovius, N., Stark, C. P., and Allen, P. A.: Sediment flux from a mountain
belt derived by landslide mapping, Geology, 25, 231–234, https://doi.org/10.1130/0091-7613(1997)025<0231:SFFAMB>2.3.CO;2, 1997.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Nelkin, E. J.: The TRMM
Multi-satellite Precipitation Analysis (TMPA), in: Satellite Rainfall
Applications for Surface Hydrology, edited by: Hossain, F. and Gebremichael, M., Springer, 3–22, https://doi.org/10.1007/978-90-481-2915-7_1,
2010.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C.,
Nelkin, E. J., Sorooshian, S., Tan, T., and Xie, P.: Algorithm theoretical
basis document (ATBD) version 5.2 for the NASA Global Precipitation
Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (I-MERG),
GPM Project Doc., 35
pp.,
https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V5.2.pdf (last access: 3 October 2023), 2019.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method
that produces global precipitation estimates from passive microwave and
infrared data at high spatial and temporal resolution, J.
Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Joyce, R. J., Xie, P., and Janowiakm, J. E.: Kalman filter–based CMORPH, J.
Hydrometeorol., 12, 1547–1563, https://doi.org/10.1175/JHM-D-11-022.1, 2011.
Khouakhi, A., Villarini, G., and Vecchi, G. A.: Contribution of tropical
cyclones to rainfall at the global scale, J. Climate, 30, 359–372, https://doi.org/10.1175/JCLI-D-16-0298.1, 2017.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S.,
Hirose, M., Takayabu, Y. N., Ushio, T., Nakagawa, K., and Iwanami, K.: Global
precipitation map using satellite-borne microwave radiometers by the GSMaP
project: Production and validation, IEEE T. Geosci. Remote, 45,
2259–2275, https://doi.org/10.1109/TGRS.2007.895337, 2007.
Kubota, T., Aonashi, K., Ushio, T., Shige, S., Takayabu, Y.N., Kachi, M.,
Arai, Y., Tashima, T., Masaki, T., Kawamoto, N., and Mega, T.: Global
Satellite Mapping of Precipitation (GSMaP) products in the GPM era,
In Satellite precipitation measurement, Springer, 355–373, https://doi.org/10.1007/978-3-030-24568-9_20, 2020.
Li, X., Li, Z., and Lin, Y.: Suitability of TRMM ducts with different
temporal resolution (3 hly, daily, and monthly) for rainfall erosivity
estimation, Remote Sens.-Basel, 12, 1–21, https://doi.org/10.3390/rs12233924, 2020.
Llauca, H., Lavado-Casimiro, W., León, K., Jimenez, J., Traverso, K.,
and Rau, P.: Assessing near real-time satellite precipitation products for
flood simulations at sub-daily scales in a sparsely gauged watershed in
Peruvian Andes, Remote Sens.-Basel, 13, 826, https://doi.org/10.3390/rs13040826, 2021.
Majhi, A., Shaw, R., Mallick, K., and Patel, P. P.: Towards improved
USLE-based soil erosion modelling in India: A review of prevalent pitfalls
and implementation of exemplar methods, Earth-Sci. Rev., 221, 103786, https://doi.org/10.1016/j.earscirev.2021.103786, 2021.
Mahji, A., Patel, P. P., Shaw, R., and Mallick, K.:
R, you correct? The Curious Case of Arnoldus (1977). Response to “Comment on “Towards improved USLE-based soil erosion modelling in India: A review of prevalent pitfalls and implementation of exemplar methods” by Majhi et al. (2021), Earth-Science Reviews 221, 103,786” by Chen and Bezak (2022), Earth Sci.
Rev., 231, 104096, https://doi.org/10.1016/j.earscirev.2022.104096, 2022.
Marc, O., Behling, R., Andermann, C., Turowski, J. M., Illien, L., Roessner, S., and Hovius, N.: Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides, Earth Surf. Dynam., 7, 107–128, https://doi.org/10.5194/esurf-7-107-2019, 2019.
Marc, O., Oliveira, R. A. J., Gosset, M., Emberson, R., and Malet, J. P.:
Global assessment of the capability of satellite precipitation products to
retrieve landslide-triggering extreme rainfall events, Earth
Interact., 26, 122–138, https://doi.org/10.1175/EI-D-21-0022.1, 2022.
Matthews, F., Panagos, P., and Verstraeten, G.: Simulating event-scale
rainfall erosivity across European climatic regions, Catena, 213, 106157,
https://doi.org/10.1016/j.catena.2022.106157, 2022.
McGregor, K. C., Bingner, R. L., Bowie, A. J., and Foster, G. R.: Erosivity index
values for northern Mississippi, Trans. Amer. Soc. Ag. Engin.,
38, 1039–1047, https://doi.org/10.13031/2013.27921, 1995.
Mekonnen, K., Melesse, A. M., and Woldesenbet, T. A.: Spatial evaluation of
satellite-retrieved extreme rainfall rates in the Upper Awash River Basin,
Ethiopia, Atmos. Res., 249, 105297, https://doi.org/10.1016/j.atmosres.2020.105297,
2021.
Montes, C., Acharya, N., Hassan, S. M. Q., and Krupnik, T. J.: Intense Precipitation Events during the Monsoon Season in Bangladesh as Captured by Satellite-Based Products, J. Hydrometeorol., 22, 1405–1419, https://doi.org/10.1175/JHM-D-20-0287.1, 2021
Nwachukwu, P. N., Satge, F., Yacoubi, S. E., Pinel, S., and Bonnet, M. P.:
From TRMM to GPM: how reliable are satellite-based precipitation data across
Nigeria?, Remote Sens.-Basel, 12, 3964, https://doi.org/10.3390/rs12233964, 2020.
Padulano, R., Rianna, G., and Santini, M.: Datasets and approaches for the
estimation of rainfall erosivity over Italy: A comprehensive comparison
study and a new method, J. Hydrol.-Reg. Stud., 34, 100788, https://doi.org/10.1016/j.ejrh.2021.100788, 2021.
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva,
S., Tadić, M. P., Michaelides, S., Hrabalíková, M., Olsen, P.,
and Aalto, J.: 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., Meusburger, K., Yu, B., Klik, A., Jae Lim, K.,
Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., and Sadeghi, S. H.: Global
rainfall erosivity assessment based on high-temporal resolution rainfall
records, Sci. Rep., 7, 1–12, https://doi.org/10.1038/s41598-017-04282-8, 2017.
Raj, R., Saharia, M., Chakma, S., and Rafieinasab, A.: Mapping rainfall
erosivity over India using multiple precipitation datasets, Catena, 214,
106256, https://doi.org/10.1016/j.catena.2022.106256, 2022.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the
planet: 1. Geographic distribution of global agricultural lands in the year
2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952, 2008 (data available at:
http://www.earthstat.org/cropland-pasture-area-2000/).
Renard, K. G.: Predicting soil erosion by water: a guide to conservation
planning with the Revised Universal Soil Loss Equation (RUSLE), United
States Government Printing, Washington DC, ISBN 0-16-048938-5, 1997.
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.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and
Rudolf, B.: GPCC's new land surface precipitation climatology based on
quality-controlled in situ data and its role in quantifying the global water
cycle, Theor. Appl. Climatol.., 115, 15–40, https://doi.org/10.1007/s00704-013-0860-x,
2014.
Smithen, A. A. and Schulze, R. E.: The spatial distribution in southern
Africa of rainfall erosivity for use in the Universal Soil Loss
Equation, Water SA, 8, 74–78, 1982.
Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I.,
Bennett, E. M., Biggs, R., Carpenter, S. R., de Vries, W., de Wit, C. A.,
Folke, C., Gerten, D., Heinke, J., Mace, G. M., Persson, L. M., Ramanathan,
V., Reyers, B., and Sörlin, S.: Planetary boundaries: Guiding changing
planet, Science, 347, 6223, https://doi.org/10.1126/science.1259855, 2015.
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.-Basel Env., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697,
2020.
Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in
satellite-based precipitation measurements, Geophys. Res. Lett., 37, L24407,
https://doi.org/10.1029/2010GL046008, 2010.
UN: The UN Sustainable Development Goals. United Nations, New
York,
https://sdgs.un.org/goals (last access: 12 November 2022), 2015
USDA: Science Documentation: Revised
Universal Soil Loss Equation Version 2 (RUSLE 2), USDA-Agricultural Research
Service, Washington DC,
https://www.ars.usda.gov/ARSUserFiles/60600505/rusle/rusle2_science_doc.pdf (last access: 3 October 2023), 2013.
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.
