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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Global rainfall erosivity assessment based on high-temporal resolution rainfall records</title><author><name sortKey="Panagos, Panos" sort="Panagos, Panos" uniqKey="Panagos P" first="Panos" last="Panagos">Panos Panagos</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author><author><name sortKey="Borrelli, Pasquale" sort="Borrelli, Pasquale" uniqKey="Borrelli P" first="Pasquale" last="Borrelli">Pasquale Borrelli</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0642</institution-id><institution-id institution-id-type="GRID">grid.6612.3</institution-id><institution>Environmental Geosciences,</institution><institution>University of Basel,</institution></institution-wrap>Basel, Switzerland</nlm:aff></affiliation></author><author><name sortKey="Meusburger, Katrin" sort="Meusburger, Katrin" uniqKey="Meusburger K" first="Katrin" last="Meusburger">Katrin Meusburger</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0642</institution-id><institution-id institution-id-type="GRID">grid.6612.3</institution-id><institution>Environmental Geosciences,</institution><institution>University of Basel,</institution></institution-wrap>Basel, Switzerland</nlm:aff></affiliation></author><author><name sortKey="Yu, Bofu" sort="Yu, Bofu" uniqKey="Yu B" first="Bofu" last="Yu">Bofu Yu</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0437 5432</institution-id><institution-id institution-id-type="GRID">grid.1022.1</institution-id><institution>School of Engineering,</institution><institution>Griffith University,</institution></institution-wrap>Nathan, Australia</nlm:aff></affiliation></author><author><name sortKey="Klik, Andreas" sort="Klik, Andreas" uniqKey="Klik A" first="Andreas" last="Klik">Andreas Klik</name><affiliation><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2298 5320</institution-id><institution-id institution-id-type="GRID">grid.5173.0</institution-id><institution>BOKU,</institution><institution>University of Natural Resources and Life Sciences,</institution></institution-wrap>Vienna, Austria</nlm:aff></affiliation></author><author><name sortKey="Jae Lim, Kyoung" sort="Jae Lim, Kyoung" uniqKey="Jae Lim K" first="Kyoung" last="Jae Lim">Kyoung Jae Lim</name><affiliation><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0707 9039</institution-id><institution-id institution-id-type="GRID">grid.412010.6</institution-id><institution></institution><institution>Kangwon National University,</institution></institution-wrap>Chuncheon-si, Gangwon-do, South Korea</nlm:aff></affiliation></author><author><name sortKey="Yang, Jae E" sort="Yang, Jae E" uniqKey="Yang J" first="Jae E." last="Yang">Jae E. Yang</name><affiliation><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0707 9039</institution-id><institution-id institution-id-type="GRID">grid.412010.6</institution-id><institution></institution><institution>Kangwon National University,</institution></institution-wrap>Chuncheon-si, Gangwon-do, South Korea</nlm:aff></affiliation></author><author><name sortKey="Ni, Jinren" sort="Ni, Jinren" uniqKey="Ni J" first="Jinren" last="Ni">Jinren Ni</name><affiliation><nlm:aff id="Aff6"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2256 9319</institution-id><institution-id institution-id-type="GRID">grid.11135.37</institution-id><institution>College of Environmental Sciences and Engineering,</institution><institution>Peking University,</institution></institution-wrap>Beijing, P.R. China</nlm:aff></affiliation></author><author><name sortKey="Miao, Chiyuan" sort="Miao, Chiyuan" uniqKey="Miao C" first="Chiyuan" last="Miao">Chiyuan Miao</name><affiliation><nlm:aff id="Aff7"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1789 9964</institution-id><institution-id institution-id-type="GRID">grid.20513.35</institution-id><institution>College of Global Change and Earth System Science,</institution><institution>Beijing Normal University,</institution></institution-wrap>Beijing, P.R. China</nlm:aff></affiliation></author><author><name sortKey="Chattopadhyay, Nabansu" sort="Chattopadhyay, Nabansu" uniqKey="Chattopadhyay N" first="Nabansu" last="Chattopadhyay">Nabansu Chattopadhyay</name><affiliation><nlm:aff id="Aff8"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0498 1600</institution-id><institution-id institution-id-type="GRID">grid.466772.6</institution-id><institution></institution><institution>India Meteorological Department,</institution></institution-wrap>Pune, India</nlm:aff></affiliation></author><author><name sortKey="Sadeghi, Seyed Hamidreza" sort="Sadeghi, Seyed Hamidreza" uniqKey="Sadeghi S" first="Seyed Hamidreza" last="Sadeghi">Seyed Hamidreza Sadeghi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Hazbavi, Zeinab" sort="Hazbavi, Zeinab" uniqKey="Hazbavi Z" first="Zeinab" last="Hazbavi">Zeinab Hazbavi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Zabihi, Mohsen" sort="Zabihi, Mohsen" uniqKey="Zabihi M" first="Mohsen" last="Zabihi">Mohsen Zabihi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Larionov, Gennady A" sort="Larionov, Gennady A" uniqKey="Larionov G" first="Gennady A." last="Larionov">Gennady A. Larionov</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Krasnov, Sergey F" sort="Krasnov, Sergey F" uniqKey="Krasnov S" first="Sergey F." last="Krasnov">Sergey F. Krasnov</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Gorobets, Andrey V" sort="Gorobets, Andrey V" uniqKey="Gorobets A" first="Andrey V." last="Gorobets">Andrey V. Gorobets</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Levi, Yoav" sort="Levi, Yoav" uniqKey="Levi Y" first="Yoav" last="Levi">Yoav Levi</name><affiliation><nlm:aff id="Aff11">Israel Meteorological Service, Beit Dagan, Israel</nlm:aff></affiliation></author><author><name sortKey="Erpul, Gunay" sort="Erpul, Gunay" uniqKey="Erpul G" first="Gunay" last="Erpul">Gunay Erpul</name><affiliation><nlm:aff id="Aff12"><institution-wrap><institution-id institution-id-type="ISNI">0000000109409118</institution-id><institution-id institution-id-type="GRID">grid.7256.6</institution-id><institution>Faculty of Agriculture - Soil Science Departement,</institution><institution>Ankara University,</institution></institution-wrap>Ankara, Turkey</nlm:aff></affiliation></author><author><name sortKey="Birkel, Christian" sort="Birkel, Christian" uniqKey="Birkel C" first="Christian" last="Birkel">Christian Birkel</name><affiliation><nlm:aff id="Aff13"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0706</institution-id><institution-id institution-id-type="GRID">grid.412889.e</institution-id><institution></institution><institution>University of Costa Rica,</institution></institution-wrap>San Jose, Costa Rica</nlm:aff></affiliation></author><author><name sortKey="Hoyos, Natalia" sort="Hoyos, Natalia" uniqKey="Hoyos N" first="Natalia" last="Hoyos">Natalia Hoyos</name><affiliation><nlm:aff id="Aff14"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0486 8632</institution-id><institution-id institution-id-type="GRID">grid.412188.6</institution-id><institution></institution><institution>Universidad del Norte,</institution></institution-wrap>Barranquilla, Colombia</nlm:aff></affiliation></author><author><name sortKey="Naipal, Victoria" sort="Naipal, Victoria" uniqKey="Naipal V" first="Victoria" last="Naipal">Victoria Naipal</name><affiliation><nlm:aff id="Aff15">Laboratoire des Sciences du Climat et de l′Environnement, IPSL-LSCE, Gif sur Yvette, France</nlm:aff></affiliation></author><author><name sortKey="Oliveira, Paulo Tarso S" sort="Oliveira, Paulo Tarso S" uniqKey="Oliveira P" first="Paulo Tarso S." last="Oliveira">Paulo Tarso S. Oliveira</name><affiliation><nlm:aff id="Aff16"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2163 5978</institution-id><institution-id institution-id-type="GRID">grid.412352.3</institution-id><institution></institution><institution>Federal University of Mato Grosso do Sul,</institution></institution-wrap>Campo Grande, Brazil</nlm:aff></affiliation></author><author><name sortKey="Bonilla, Carlos A" sort="Bonilla, Carlos A" uniqKey="Bonilla C" first="Carlos A." last="Bonilla">Carlos A. Bonilla</name><affiliation><nlm:aff id="Aff17"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2157 0406</institution-id><institution-id institution-id-type="GRID">grid.7870.8</institution-id><institution>Departamento de Ingeniería Hidráulica y Ambiental,</institution><institution>Pontificia Universidad Católica de Chile,</institution></institution-wrap>Región Metropolitana, Chile</nlm:aff></affiliation></author><author><name sortKey="Meddi, Mohamed" sort="Meddi, Mohamed" uniqKey="Meddi M" first="Mohamed" last="Meddi">Mohamed Meddi</name><affiliation><nlm:aff id="Aff18">Ecole Nationale Supérieure d’Hydraulique de Blida, Soumaâ, Algeria</nlm:aff></affiliation></author><author><name sortKey="Nel, Werner" sort="Nel, Werner" uniqKey="Nel W" first="Werner" last="Nel">Werner Nel</name><affiliation><nlm:aff id="Aff19"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2152 8048</institution-id><institution-id institution-id-type="GRID">grid.413110.6</institution-id><institution>Department of Geography and Environmental Science,</institution><institution>University of Fort Hare,</institution></institution-wrap>Alice, South Africa</nlm:aff></affiliation></author><author><name sortKey="Al Dashti, Hassan" sort="Al Dashti, Hassan" uniqKey="Al Dashti H" first="Hassan" last="Al Dashti">Hassan Al Dashti</name><affiliation><nlm:aff id="Aff20">Department of Meteorology, Kuwait, Kuwait</nlm:aff></affiliation></author><author><name sortKey="Boni, Martino" sort="Boni, Martino" uniqKey="Boni M" first="Martino" last="Boni">Martino Boni</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author><author><name sortKey="Diodato, Nazzareno" sort="Diodato, Nazzareno" uniqKey="Diodato N" first="Nazzareno" last="Diodato">Nazzareno Diodato</name><affiliation><nlm:aff id="Aff21">Met European Research Observatory, Benevento, Italy</nlm:aff></affiliation></author><author><name sortKey="Van Oost, Kristof" sort="Van Oost, Kristof" uniqKey="Van Oost K" first="Kristof" last="Van Oost">Kristof Van Oost</name><affiliation><nlm:aff id="Aff22"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2294 713X</institution-id><institution-id institution-id-type="GRID">grid.7942.8</institution-id><institution></institution><institution>Université Catholique de Louvain,</institution></institution-wrap>Louvain-la-Neuve, Belgium</nlm:aff></affiliation></author><author><name sortKey="Nearing, Mark" sort="Nearing, Mark" uniqKey="Nearing M" first="Mark" last="Nearing">Mark Nearing</name><affiliation><nlm:aff id="Aff23"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0478 6311</institution-id><institution-id institution-id-type="GRID">grid.417548.b</institution-id><institution>USDA-ARS,</institution><institution>Southwest Watershed Research Center,</institution></institution-wrap>Tucson, AZ USA</nlm:aff></affiliation></author><author><name sortKey="Ballabio, Cristiano" sort="Ballabio, Cristiano" uniqKey="Ballabio C" first="Cristiano" last="Ballabio">Cristiano Ballabio</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28646132</idno><idno type="pmc">5482877</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482877</idno><idno type="RBID">PMC:5482877</idno><idno type="doi">10.1038/s41598-017-04282-8</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000E22</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000E22</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Global rainfall erosivity assessment based on high-temporal resolution rainfall records</title><author><name sortKey="Panagos, Panos" sort="Panagos, Panos" uniqKey="Panagos P" first="Panos" last="Panagos">Panos Panagos</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author><author><name sortKey="Borrelli, Pasquale" sort="Borrelli, Pasquale" uniqKey="Borrelli P" first="Pasquale" last="Borrelli">Pasquale Borrelli</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0642</institution-id><institution-id institution-id-type="GRID">grid.6612.3</institution-id><institution>Environmental Geosciences,</institution><institution>University of Basel,</institution></institution-wrap>Basel, Switzerland</nlm:aff></affiliation></author><author><name sortKey="Meusburger, Katrin" sort="Meusburger, Katrin" uniqKey="Meusburger K" first="Katrin" last="Meusburger">Katrin Meusburger</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0642</institution-id><institution-id institution-id-type="GRID">grid.6612.3</institution-id><institution>Environmental Geosciences,</institution><institution>University of Basel,</institution></institution-wrap>Basel, Switzerland</nlm:aff></affiliation></author><author><name sortKey="Yu, Bofu" sort="Yu, Bofu" uniqKey="Yu B" first="Bofu" last="Yu">Bofu Yu</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0437 5432</institution-id><institution-id institution-id-type="GRID">grid.1022.1</institution-id><institution>School of Engineering,</institution><institution>Griffith University,</institution></institution-wrap>Nathan, Australia</nlm:aff></affiliation></author><author><name sortKey="Klik, Andreas" sort="Klik, Andreas" uniqKey="Klik A" first="Andreas" last="Klik">Andreas Klik</name><affiliation><nlm:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2298 5320</institution-id><institution-id institution-id-type="GRID">grid.5173.0</institution-id><institution>BOKU,</institution><institution>University of Natural Resources and Life Sciences,</institution></institution-wrap>Vienna, Austria</nlm:aff></affiliation></author><author><name sortKey="Jae Lim, Kyoung" sort="Jae Lim, Kyoung" uniqKey="Jae Lim K" first="Kyoung" last="Jae Lim">Kyoung Jae Lim</name><affiliation><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0707 9039</institution-id><institution-id institution-id-type="GRID">grid.412010.6</institution-id><institution></institution><institution>Kangwon National University,</institution></institution-wrap>Chuncheon-si, Gangwon-do, South Korea</nlm:aff></affiliation></author><author><name sortKey="Yang, Jae E" sort="Yang, Jae E" uniqKey="Yang J" first="Jae E." last="Yang">Jae E. Yang</name><affiliation><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0707 9039</institution-id><institution-id institution-id-type="GRID">grid.412010.6</institution-id><institution></institution><institution>Kangwon National University,</institution></institution-wrap>Chuncheon-si, Gangwon-do, South Korea</nlm:aff></affiliation></author><author><name sortKey="Ni, Jinren" sort="Ni, Jinren" uniqKey="Ni J" first="Jinren" last="Ni">Jinren Ni</name><affiliation><nlm:aff id="Aff6"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2256 9319</institution-id><institution-id institution-id-type="GRID">grid.11135.37</institution-id><institution>College of Environmental Sciences and Engineering,</institution><institution>Peking University,</institution></institution-wrap>Beijing, P.R. China</nlm:aff></affiliation></author><author><name sortKey="Miao, Chiyuan" sort="Miao, Chiyuan" uniqKey="Miao C" first="Chiyuan" last="Miao">Chiyuan Miao</name><affiliation><nlm:aff id="Aff7"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1789 9964</institution-id><institution-id institution-id-type="GRID">grid.20513.35</institution-id><institution>College of Global Change and Earth System Science,</institution><institution>Beijing Normal University,</institution></institution-wrap>Beijing, P.R. China</nlm:aff></affiliation></author><author><name sortKey="Chattopadhyay, Nabansu" sort="Chattopadhyay, Nabansu" uniqKey="Chattopadhyay N" first="Nabansu" last="Chattopadhyay">Nabansu Chattopadhyay</name><affiliation><nlm:aff id="Aff8"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0498 1600</institution-id><institution-id institution-id-type="GRID">grid.466772.6</institution-id><institution></institution><institution>India Meteorological Department,</institution></institution-wrap>Pune, India</nlm:aff></affiliation></author><author><name sortKey="Sadeghi, Seyed Hamidreza" sort="Sadeghi, Seyed Hamidreza" uniqKey="Sadeghi S" first="Seyed Hamidreza" last="Sadeghi">Seyed Hamidreza Sadeghi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Hazbavi, Zeinab" sort="Hazbavi, Zeinab" uniqKey="Hazbavi Z" first="Zeinab" last="Hazbavi">Zeinab Hazbavi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Zabihi, Mohsen" sort="Zabihi, Mohsen" uniqKey="Zabihi M" first="Mohsen" last="Zabihi">Mohsen Zabihi</name><affiliation><nlm:aff id="Aff9"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</nlm:aff></affiliation></author><author><name sortKey="Larionov, Gennady A" sort="Larionov, Gennady A" uniqKey="Larionov G" first="Gennady A." last="Larionov">Gennady A. Larionov</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Krasnov, Sergey F" sort="Krasnov, Sergey F" uniqKey="Krasnov S" first="Sergey F." last="Krasnov">Sergey F. Krasnov</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Gorobets, Andrey V" sort="Gorobets, Andrey V" uniqKey="Gorobets A" first="Andrey V." last="Gorobets">Andrey V. Gorobets</name><affiliation><nlm:aff id="Aff10"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</nlm:aff></affiliation></author><author><name sortKey="Levi, Yoav" sort="Levi, Yoav" uniqKey="Levi Y" first="Yoav" last="Levi">Yoav Levi</name><affiliation><nlm:aff id="Aff11">Israel Meteorological Service, Beit Dagan, Israel</nlm:aff></affiliation></author><author><name sortKey="Erpul, Gunay" sort="Erpul, Gunay" uniqKey="Erpul G" first="Gunay" last="Erpul">Gunay Erpul</name><affiliation><nlm:aff id="Aff12"><institution-wrap><institution-id institution-id-type="ISNI">0000000109409118</institution-id><institution-id institution-id-type="GRID">grid.7256.6</institution-id><institution>Faculty of Agriculture - Soil Science Departement,</institution><institution>Ankara University,</institution></institution-wrap>Ankara, Turkey</nlm:aff></affiliation></author><author><name sortKey="Birkel, Christian" sort="Birkel, Christian" uniqKey="Birkel C" first="Christian" last="Birkel">Christian Birkel</name><affiliation><nlm:aff id="Aff13"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0706</institution-id><institution-id institution-id-type="GRID">grid.412889.e</institution-id><institution></institution><institution>University of Costa Rica,</institution></institution-wrap>San Jose, Costa Rica</nlm:aff></affiliation></author><author><name sortKey="Hoyos, Natalia" sort="Hoyos, Natalia" uniqKey="Hoyos N" first="Natalia" last="Hoyos">Natalia Hoyos</name><affiliation><nlm:aff id="Aff14"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0486 8632</institution-id><institution-id institution-id-type="GRID">grid.412188.6</institution-id><institution></institution><institution>Universidad del Norte,</institution></institution-wrap>Barranquilla, Colombia</nlm:aff></affiliation></author><author><name sortKey="Naipal, Victoria" sort="Naipal, Victoria" uniqKey="Naipal V" first="Victoria" last="Naipal">Victoria Naipal</name><affiliation><nlm:aff id="Aff15">Laboratoire des Sciences du Climat et de l′Environnement, IPSL-LSCE, Gif sur Yvette, France</nlm:aff></affiliation></author><author><name sortKey="Oliveira, Paulo Tarso S" sort="Oliveira, Paulo Tarso S" uniqKey="Oliveira P" first="Paulo Tarso S." last="Oliveira">Paulo Tarso S. Oliveira</name><affiliation><nlm:aff id="Aff16"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2163 5978</institution-id><institution-id institution-id-type="GRID">grid.412352.3</institution-id><institution></institution><institution>Federal University of Mato Grosso do Sul,</institution></institution-wrap>Campo Grande, Brazil</nlm:aff></affiliation></author><author><name sortKey="Bonilla, Carlos A" sort="Bonilla, Carlos A" uniqKey="Bonilla C" first="Carlos A." last="Bonilla">Carlos A. Bonilla</name><affiliation><nlm:aff id="Aff17"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2157 0406</institution-id><institution-id institution-id-type="GRID">grid.7870.8</institution-id><institution>Departamento de Ingeniería Hidráulica y Ambiental,</institution><institution>Pontificia Universidad Católica de Chile,</institution></institution-wrap>Región Metropolitana, Chile</nlm:aff></affiliation></author><author><name sortKey="Meddi, Mohamed" sort="Meddi, Mohamed" uniqKey="Meddi M" first="Mohamed" last="Meddi">Mohamed Meddi</name><affiliation><nlm:aff id="Aff18">Ecole Nationale Supérieure d’Hydraulique de Blida, Soumaâ, Algeria</nlm:aff></affiliation></author><author><name sortKey="Nel, Werner" sort="Nel, Werner" uniqKey="Nel W" first="Werner" last="Nel">Werner Nel</name><affiliation><nlm:aff id="Aff19"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2152 8048</institution-id><institution-id institution-id-type="GRID">grid.413110.6</institution-id><institution>Department of Geography and Environmental Science,</institution><institution>University of Fort Hare,</institution></institution-wrap>Alice, South Africa</nlm:aff></affiliation></author><author><name sortKey="Al Dashti, Hassan" sort="Al Dashti, Hassan" uniqKey="Al Dashti H" first="Hassan" last="Al Dashti">Hassan Al Dashti</name><affiliation><nlm:aff id="Aff20">Department of Meteorology, Kuwait, Kuwait</nlm:aff></affiliation></author><author><name sortKey="Boni, Martino" sort="Boni, Martino" uniqKey="Boni M" first="Martino" last="Boni">Martino Boni</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author><author><name sortKey="Diodato, Nazzareno" sort="Diodato, Nazzareno" uniqKey="Diodato N" first="Nazzareno" last="Diodato">Nazzareno Diodato</name><affiliation><nlm:aff id="Aff21">Met European Research Observatory, Benevento, Italy</nlm:aff></affiliation></author><author><name sortKey="Van Oost, Kristof" sort="Van Oost, Kristof" uniqKey="Van Oost K" first="Kristof" last="Van Oost">Kristof Van Oost</name><affiliation><nlm:aff id="Aff22"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2294 713X</institution-id><institution-id institution-id-type="GRID">grid.7942.8</institution-id><institution></institution><institution>Université Catholique de Louvain,</institution></institution-wrap>Louvain-la-Neuve, Belgium</nlm:aff></affiliation></author><author><name sortKey="Nearing, Mark" sort="Nearing, Mark" uniqKey="Nearing M" first="Mark" last="Nearing">Mark Nearing</name><affiliation><nlm:aff id="Aff23"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0478 6311</institution-id><institution-id institution-id-type="GRID">grid.417548.b</institution-id><institution>USDA-ARS,</institution><institution>Southwest Watershed Research Center,</institution></institution-wrap>Tucson, AZ USA</nlm:aff></affiliation></author><author><name sortKey="Ballabio, Cristiano" sort="Ballabio, Cristiano" uniqKey="Ballabio C" first="Cristiano" last="Ballabio">Cristiano Ballabio</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</nlm:aff></affiliation></author></analytic><series><title level="j">Scientific Reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Freebairn, Jw" uniqKey="Freebairn J">JW Freebairn</name></author><author><name sortKey="Zillman, Jw" uniqKey="Zillman J">JW Zillman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tapiador, Fj" uniqKey="Tapiador F">FJ Tapiador</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Orlowsky, B" uniqKey="Orlowsky B">B Orlowsky</name></author><author><name sortKey="Seneviratne, Si" uniqKey="Seneviratne S">SI Seneviratne</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Olsson, J" uniqKey="Olsson J">J Olsson</name></author><author><name sortKey="Berggren, K" uniqKey="Berggren K">K 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sci Rep</journal-id><journal-id journal-id-type="iso-abbrev">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type="epub">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28646132</article-id><article-id pub-id-type="pmc">5482877</article-id><article-id pub-id-type="publisher-id">4282</article-id><article-id pub-id-type="doi">10.1038/s41598-017-04282-8</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Global rainfall erosivity assessment based on high-temporal resolution rainfall records</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Panagos</surname><given-names>Panos</given-names></name><address><email>panos.panagos@ec.europa.eu</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-4767-5115</contrib-id><name><surname>Borrelli</surname><given-names>Pasquale</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Meusburger</surname><given-names>Katrin</given-names></name><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Yu</surname><given-names>Bofu</given-names></name><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Klik</surname><given-names>Andreas</given-names></name><xref ref-type="aff" rid="Aff4">4</xref></contrib><contrib contrib-type="author"><name><surname>Jae Lim</surname><given-names>Kyoung</given-names></name><xref 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contrib-type="author"><name><surname>Nearing</surname><given-names>Mark</given-names></name><xref ref-type="aff" rid="Aff23">23</xref></contrib><contrib contrib-type="author"><name><surname>Ballabio</surname><given-names>Cristiano</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1758 4137</institution-id><institution-id institution-id-type="GRID">grid.434554.7</institution-id><institution>European Commission,</institution><institution>Joint Research Centre,</institution></institution-wrap>I-21027 Ispra (VA), Italy</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0642</institution-id><institution-id institution-id-type="GRID">grid.6612.3</institution-id><institution>Environmental Geosciences,</institution><institution>University of Basel,</institution></institution-wrap>Basel, Switzerland</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0437 5432</institution-id><institution-id institution-id-type="GRID">grid.1022.1</institution-id><institution>School of Engineering,</institution><institution>Griffith University,</institution></institution-wrap>Nathan, Australia</aff><aff id="Aff4"><label>4</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2298 5320</institution-id><institution-id institution-id-type="GRID">grid.5173.0</institution-id><institution>BOKU,</institution><institution>University of Natural Resources and Life Sciences,</institution></institution-wrap>Vienna, Austria</aff><aff id="Aff5"><label>5</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0707 9039</institution-id><institution-id institution-id-type="GRID">grid.412010.6</institution-id><institution></institution><institution>Kangwon National University,</institution></institution-wrap>Chuncheon-si, Gangwon-do, South Korea</aff><aff id="Aff6"><label>6</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2256 9319</institution-id><institution-id institution-id-type="GRID">grid.11135.37</institution-id><institution>College of Environmental Sciences and Engineering,</institution><institution>Peking University,</institution></institution-wrap>Beijing, P.R. China</aff><aff id="Aff7"><label>7</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1789 9964</institution-id><institution-id institution-id-type="GRID">grid.20513.35</institution-id><institution>College of Global Change and Earth System Science,</institution><institution>Beijing Normal University,</institution></institution-wrap>Beijing, P.R. China</aff><aff id="Aff8"><label>8</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0498 1600</institution-id><institution-id institution-id-type="GRID">grid.466772.6</institution-id><institution></institution><institution>India Meteorological Department,</institution></institution-wrap>Pune, India</aff><aff id="Aff9"><label>9</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 1781 3962</institution-id><institution-id institution-id-type="GRID">grid.412266.5</institution-id><institution>Faculty of Natural Resources,</institution><institution>Tarbiat Modares University,</institution></institution-wrap>Jalal, Iran</aff><aff id="Aff10"><label>10</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2342 9668</institution-id><institution-id institution-id-type="GRID">grid.14476.30</institution-id><institution>Faculty of Geography,</institution><institution>Lomonosov Moscow State University,</institution></institution-wrap>Moscow, Russian Federation</aff><aff id="Aff11"><label>11</label>Israel Meteorological Service, Beit Dagan, Israel</aff><aff id="Aff12"><label>12</label><institution-wrap><institution-id institution-id-type="ISNI">0000000109409118</institution-id><institution-id institution-id-type="GRID">grid.7256.6</institution-id><institution>Faculty of Agriculture - Soil Science Departement,</institution><institution>Ankara University,</institution></institution-wrap>Ankara, Turkey</aff><aff id="Aff13"><label>13</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1937 0706</institution-id><institution-id institution-id-type="GRID">grid.412889.e</institution-id><institution></institution><institution>University of Costa Rica,</institution></institution-wrap>San Jose, Costa Rica</aff><aff id="Aff14"><label>14</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0486 8632</institution-id><institution-id institution-id-type="GRID">grid.412188.6</institution-id><institution></institution><institution>Universidad del Norte,</institution></institution-wrap>Barranquilla, Colombia</aff><aff id="Aff15"><label>15</label>Laboratoire des Sciences du Climat et de l′Environnement, IPSL-LSCE, Gif sur Yvette, France</aff><aff id="Aff16"><label>16</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2163 5978</institution-id><institution-id institution-id-type="GRID">grid.412352.3</institution-id><institution></institution><institution>Federal University of Mato Grosso do Sul,</institution></institution-wrap>Campo Grande, Brazil</aff><aff id="Aff17"><label>17</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2157 0406</institution-id><institution-id institution-id-type="GRID">grid.7870.8</institution-id><institution>Departamento de Ingeniería Hidráulica y Ambiental,</institution><institution>Pontificia Universidad Católica de Chile,</institution></institution-wrap>Región Metropolitana, Chile</aff><aff id="Aff18"><label>18</label>Ecole Nationale Supérieure d’Hydraulique de Blida, Soumaâ, Algeria</aff><aff id="Aff19"><label>19</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2152 8048</institution-id><institution-id institution-id-type="GRID">grid.413110.6</institution-id><institution>Department of Geography and Environmental Science,</institution><institution>University of Fort Hare,</institution></institution-wrap>Alice, South Africa</aff><aff id="Aff20"><label>20</label>Department of Meteorology, Kuwait, Kuwait</aff><aff id="Aff21"><label>21</label>Met European Research Observatory, Benevento, Italy</aff><aff id="Aff22"><label>22</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2294 713X</institution-id><institution-id institution-id-type="GRID">grid.7942.8</institution-id><institution></institution><institution>Université Catholique de Louvain,</institution></institution-wrap>Louvain-la-Neuve, Belgium</aff><aff id="Aff23"><label>23</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 0478 6311</institution-id><institution-id institution-id-type="GRID">grid.417548.b</institution-id><institution>USDA-ARS,</institution><institution>Southwest Watershed Research Center,</institution></institution-wrap>Tucson, AZ USA</aff></contrib-group><pub-date pub-type="epub"><day>23</day><month>6</month><year>2017</year></pub-date><pub-date pub-type="pmc-release"><day>23</day><month>6</month><year>2017</year></pub-date><pub-date pub-type="collection"><year>2017</year></pub-date><volume>7</volume><elocation-id>4175</elocation-id><history><date date-type="received"><day>9</day><month>2</month><year>2017</year></date><date date-type="accepted"><day>2</day><month>6</month><year>2017</year></date></history><permissions><copyright-statement>© The Author(s) 2017</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id="Abs1"><p id="Par1">The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.</p></abstract><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2017</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1" sec-type="introduction"><title>Introduction</title><p id="Par2">Given the growing concerns about climate change, climatic data is particularly important for the scientific community and society in general, as decisions of individuals, business and governments are dependent on available meteorological data<sup><xref ref-type="bibr" rid="CR1">1</xref></sup>. At present, a large number of the large-scale precipitation datasets are publicly available, although formats and completeness of the records vary widely<sup><xref ref-type="bibr" rid="CR2">2</xref></sup>. Heavy rainfall and extreme events are of major importance for climate change, economy and society<sup><xref ref-type="bibr" rid="CR3">3</xref></sup>. However, extreme events are typically rare events of short duration and in many regions of the world only limited observational data of sufficient temporal resolution is available to capture them<sup><xref ref-type="bibr" rid="CR4">4</xref></sup>. High temporal resolution rainfall measurements are important in this context, but also have been recognised to be very useful for urban drainage models<sup><xref ref-type="bibr" rid="CR5">5</xref></sup>, climate change modelling<sup><xref ref-type="bibr" rid="CR6">6</xref></sup>, cropping patterns and crop production<sup><xref ref-type="bibr" rid="CR7">7</xref></sup>.</p><p id="Par3">Further, the patterns of heavy and violent rainfall, as captured by the rainfall erosivity factor, influence hydrological and erosive processes and as such are essential for the definition of soil and water conservation practices in the adaptation of agriculture to climate change<sup><xref ref-type="bibr" rid="CR8">8</xref></sup>. Rainfall erosivity is one of the most important input parameters for describing erosive processes and proposing conservation measures by using soil erosion prediction models. Since soil erosion is difficult to measure at large scales, models are required for estimating soil loss by water erosion at regional, national and continental scales. Large scale and global model predictions are of utmost importance, since soil erosion is, in addition to soil sealing, the major threat to soil sustainability and consequently to water- and food security.</p><p id="Par4">As a consequence, recent developments in the soil modelling and climate change communities aim at addressing major scientific gaps in describing key soil processes such as water erosion<sup><xref ref-type="bibr" rid="CR9">9</xref></sup>, based on updated global datasets. Nonetheless, policy initiatives such as the United Nations Convention to Combat Desertification (UNCCD) for zero net land degradation<sup><xref ref-type="bibr" rid="CR10">10</xref></sup>, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the Land Degradation and Restoration Assessment<sup><xref ref-type="bibr" rid="CR11">11</xref></sup>, and the Food Agricultural Organisation (FAO) World Soil Resources<sup><xref ref-type="bibr" rid="CR12">12</xref></sup> still remarked the lack of an updated scientific dataset on global soil erosion.</p><p id="Par5">A vital component of such a global soil erosion map is a spatial assessment of rainfall erosivity. However, methods that estimate erosivity based on annual rainfall data are problematic and highly biased, since rainfall intensity is typically not considered<sup><xref ref-type="bibr" rid="CR13">13</xref></sup>. In order to include rainfall intensity on the calculation of rainfall erosivity, it is necessary to have high temporal resolution rainfall data for long time periods. In this study, we aim to tackle the challenging task of compiling and processing the first global erosivity dataset from long-term high-resolution rainfall data using sub-hourly and hourly pluviographic records. We have used this global erosivity station dataset combined with a set of exhaustive secondary environment variables to generate a global rainfall erosivity map in order to improve our understanding of the global patterns of high intensity rainfall events.</p></sec><sec id="Sec2" sec-type="results"><title>Results</title><sec id="Sec3"><title>Global Rainfall Erosivity Database - GloREDa</title><p id="Par6">At global scale, this is the first time ever that an erosivity database of such dimension is compiled. The Global Rainfall Erosivity Database, named hereafter as <bold>GloREDa</bold>, contains erosivity values estimated as R-factors (refer to the method section) from 3,625 stations distributed in 63 countries worldwide. This is the result of an extensive data collection of high temporal resolution rainfall data from the maximum possible number of countries in order to have a representative sample across different climatic and geographic gradients. GloREDa has three components, which are described in the methods section: a) the Rainfall Erosivity database at European Scale (REDES)<sup><xref ref-type="bibr" rid="CR14">14</xref></sup> b) 1,865 stations from 23 countries outside Europe and c) 85 stations collected from a literature review.</p><p id="Par7">The number of GloREDa stations varied greatly among continents (Fig. <xref rid="Fig1" ref-type="fig">1</xref>). Europe had the largest contribution to the dataset, with 1,725 stations (48% of total), while South America had the lowest number of stations (141 stations or ~4% of total). Africa has very low density of GloREDa stations (5% of the total). In North America and the Caribbean, we collected erosivity values from 146 stations located in 6 countries (Unites States, Canada, Mexico, Cuba, Jamaica and Costa Rica). Finally, Asia and the Middle East were well represented in GloREDa, with 1,220 stations (34% of the total) distributed in 10 countries including the Siberian part of the Russian Federation (Fig. <xref rid="Fig1" ref-type="fig">1b</xref>). The geographic distribution within each continent also differed substantially. For instance, stations in Europe, Oceania and North America covered most of the territory, while those in Africa and South America were largely clustered. However, the stations are well distributed among different erosivity classes (Fig. <xref rid="Fig2" ref-type="fig">2b</xref>).<fig id="Fig1"><label>Figure 1</label><caption><p>(<bold>a</bold>) Global distribution of rainfall erosivity stations (red dots) compiled in the Global Rainfall Erosivity Database (GloREDa); (<bold>b</bold>) Distribution of rainfall erosivity stations by continent. Maps generated with ESRI ArcGIS ver. 10.4 (<ext-link ext-link-type="uri" xlink:href="http://www.esri.com">http://www.esri.com</ext-link>).</p></caption><graphic xlink:href="41598_2017_4282_Fig1_HTML" id="d29e758"></graphic></fig><fig id="Fig2"><label>Figure 2</label><caption><p>(<bold>a</bold>) Global Rainfall Erosivity map (spatial resolution 30 arc-seconds). Erosivity classes correspond to quantiles. Map generated with ESRI ArcGIS ver. 10.4 (<ext-link ext-link-type="uri" xlink:href="http://www.esri.com">http://www.esri.com</ext-link>); (<bold>b</bold>) number and cumulative percentage of GloREDa stations grouped by erosivity; <bold>(c)</bold> mean erosivity by continent; <bold>(d)</bold> mean erosivity by climate zone.</p></caption><graphic xlink:href="41598_2017_4282_Fig2_HTML" id="d29e785"></graphic></fig></p></sec><sec id="Sec4"><title>Global erosivity map</title><p id="Par8">The Gaussian Process Regression (GPR) model used to interpolate the erosivity (R-factor) point values to a map showed a good performance for the cross-validation dataset [R<sup>2</sup> = 0.722, RMSE (Root Mean Square Error) = 1,629 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>]. The annual global erosivity map (Fig. <xref rid="Fig2" ref-type="fig">2</xref>) is presented at 30 arc-seconds (~1 km) spatial resolution and subdivided in 10 erosivity classes corresponding to the quantiles. The mean of the global R-factor map is 2,190 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> with high variability as expressed by the standard deviation of 2,974 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. The median (50<sup>th</sup> percentile) of the global erosivity map is 1,150 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> while 20% of the erosivity values (20<sup>th</sup> percentile) are lower than 200 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> and the highest 20% (80<sup>th</sup> percentile) are higher than 5,200 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> (Fig. <xref rid="Fig2" ref-type="fig">2</xref>). According to the global erosivity map, the highest values are located in south-eastern Asia (Cambodia, Indonesia, Malaysia, the Philippines and Bangladesh), Central Africa (Congo and Cameroon), South America (Brazil, Colombia and Peru), Central America and the Caribbean. The lowest erosivity is mainly located in Siberia, the Middle East, Northern Africa, Canada and Northern Europe. The Polar Regions have been masked out in the global erosivity map.</p><p id="Par9">We found that the spatial patterns of the highest erosivity values (Fig. <xref rid="Fig2" ref-type="fig">2</xref>) are coincident with the corresponding patterns of extreme rainfall events reported by Zipser <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR14">14</xref></sup>. Zipser <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR14">14</xref></sup> defined intense storms based on the convective vertical velocity of rain. The authors compiled a 7-year period (1998–2004) database of intense storms and they generated global maps of extreme rainstorm events based on lightening flash, brightness, temperature and noise. According to their study the highest frequency of extreme rainfall events (similar to high annual erosivity values) occurs in the central part of Latin America, Gulf of Mexico, central and western Africa, Madagascar, south-eastern Asia (mainly Bangladesh, south China), Indonesia and North Australia.</p></sec><sec id="Sec5"><title>Continental assessments</title><p id="Par10">At the continental level, South America experiences the highest mean R-factor with 5,874 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, followed by Africa (3,053 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), Asia and the Middle East (1,487 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). In Oceania, the mean R-factor was estimated at 1,675 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>(Fig. <xref rid="Fig2" ref-type="fig">2c</xref>).</p><p id="Par11">Africa exhibits the highest erosivity estimates at the country level; Mauritius and Comoros have the highest worldwide mean annual erosivity values with an R-factor close to 20,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. In Western Africa (Liberia, Sierra Leone and Equatorial Guinea), Central Africa (D.R of Congo, Republic of Congo and Cameroon) and Madagascar mean annual R-factor is higher than 7,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. These patterns agree with those from other continental-scale assessments<sup><xref ref-type="bibr" rid="CR15">15</xref>, <xref ref-type="bibr" rid="CR16">16</xref></sup> which indicated highest erosivity values (>10,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) along the Guinea coast of west and central Africa, the Congo basin and Madagascar. Ethiopia and South Africa have mean R-factor values close to 2,500 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, but the spatial patterns are highly variable with the Ethiopian highlands having extremely high erosivity (>7,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) while the lowlands have 3–4 times smaller values. The lowest mean R-factor, with values less than 115 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, was estimated for Western Sahara, Libya and Egypt.</p><p id="Par12">Within Asia, the Middle East has the lowest erosivity values, with a mean annual R-factor less than 220 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> in Jordan, Saudi Arabia, Kuwait, Syria, Iran and Iraq (Fig. <xref rid="Fig2" ref-type="fig">2</xref>). China has a mean value of 1,600 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, but exhibits high variability with zero erosivity in the arid north-west areas (Taklimakan desert), and extreme erosivity (>15,000) in the south-eastern coastal zones. Regional studies conducted by Zhu and Yu<sup><xref ref-type="bibr" rid="CR17">17</xref></sup> and Qin <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR18">18</xref></sup> show very similar spatial patters compared to our rainfall erosivity distribution in China. In Japan, the mean annual erosivity was estimated as 4,815 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, a value close to the 5,130 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> modelled by Shiono <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR19">19</xref></sup>.</p><p id="Par13">As expected the Siberian part of the Russian Federation and the former Union of Soviet Socialist Republics (Kazakhstan, Turkmenistan and Uzbekistan) have very low mean erosivity values (<250 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) due their continental climate. On the contrary, Southeast Asia falls almost completely within the highest erosivity class (>7,400 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), in agreement with national assessments for Peninsular Malaysia<sup><xref ref-type="bibr" rid="CR20">20</xref></sup>. Their erosivity values, generated from pluviographic data range from 7,500 to 20,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>.</p><p id="Par14">In South America, Chile has the lowest R-factor with a mean annual value 1,320 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, followed by Argentina (2,232 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). The rest of the South American countries have high mean erosivity values (>3,700 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), with the highest ones in Brazil, Colombia and Ecuador (>7,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). The erosivity gradient created by the Andes is clearly visible in the erosivity map. There were few national assessments on rainfall erosivity in south America<sup><xref ref-type="bibr" rid="CR21">21</xref>–<xref ref-type="bibr" rid="CR24">24</xref></sup>. Most of the data used for these studies have been used as input for GloREDa and their spatial patterns are in broad agreement to ours.</p><p id="Par15">In North America and the Caribbean, the mean R-factor is 1,409 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> with very low values in Canada and the Northern part of the United States, and extremely high values (>8,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) along the Gulf of Mexico and the Caribbean countries. The erosivity map for the United States<sup><xref ref-type="bibr" rid="CR25">25</xref></sup> also shows high values along the Gulf of Mexico and southern Florida (>8,500 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), while overall low values are observed in the Midwestern region (<690 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>).</p><p id="Par16">In Australia, the mean R-factor is 1,535 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> close to 1,767 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> estimated by Teng <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR26">26</xref></sup> based on 11 years (2002–2012) satellite derived Tropical Rainfall Measuring Mission data. In terms of spatial patterns, Teng <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR26">26</xref></sup> also found maximum erosivity values along the northern and eastern coastal areas (>8,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), which decreased towards the south-central region (<300 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). In New Zealand, the high erosivity values (>4,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) occur on the west coast of the South Island and decrease towards the east similar to the patterns observed by Klik <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR27">27</xref></sup> based on 35 weather stations.</p><p id="Par17">The mean erosivity value for Europe was 488 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>, which is much lower than the one estimated by Panagos <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR28">28</xref></sup> for the European Union (722 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). This is due to the inclusion of European Russia, Ukraine (422 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) and Belarus (365 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>), all of which have low values compared to the other European countries.</p></sec><sec id="Sec6"><title>Analysis by Climate zones</title><p id="Par18">The global rainfall erosivity map was further analysed per climate zones. The updated world Kopper-Geiger climate classification<sup><xref ref-type="bibr" rid="CR29">29</xref></sup> is the most widely used and accepted climate map in the scientific community. As expected, tropical climate group showed the highest mean erosivity with 7,104 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. Within this group the tropical rainforest (Af) and monsoon (Am) climatic types had the highest mean erosivity and the lowest variability (Fig. <xref rid="Fig3" ref-type="fig">3</xref>). Second highest mean erosivity (3,729.3 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) occurs in the temperate climate group.<fig id="Fig3"><label>Figure 3</label><caption><p>R-factor descriptive statistics per Kopper-Geiger climate type. Colour bars are the mean values per climate zone. Error bars represent the standard deviation. Percentages below each main climate category represent its proportion within the study area. Climate zones: Af (tropical rainforest), Am (tropical monsoon), Aw (tropical savannah), BWh (hot desert), BWk (cold desert), BSh (hot steppe), BSk (cold steppe), Csa (dry hot summer), Csb (dry warm summer), Cwa (subtropical dry winter), Cwb (dry winter and dry summer), Cfa (temperate without dry season and hot summer), Cfb (temperate without dry season and warm summer), Cfc (temperate without dry season and cold summer), DSa (cold and dry hot summer), Dsb (cold and dry warm summer), Dsc (cold and dry cold summer), Dwa (cold and dry winter, and hot summer), Dwb (cold and dry winter, and warm summer), Dwc (cold and dry winter, and cold summer), Dwd (cold and dry winter, and very cold winter), Dfa (cold without dry season and hot summer), Dfb (cold without dry season and warm summer), Dfc (cold without dry season and cold summer), Dfd (cold without dry season and very cold winter), E (polar).</p></caption><graphic xlink:href="41598_2017_4282_Fig3_HTML" id="d29e1343"></graphic></fig></p><p id="Par19">The humid temperate, and temperate with dry winter climate type (Cfa, Cwa), mainly present in the southeastern United States, eastern Australia and southeast China, have mean erosivity values higher than 4,600 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. The Mediterranean (Csa, Csb) and the Oceanic (Cfb) climate zones have mean erosivity values lower than 2,000 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> (Fig. <xref rid="Fig3" ref-type="fig">3</xref>).</p><p id="Par20">The arid climate group has a relatively low mean erosivity (842 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>) characterised by the highest spatial variability (e.g. the Cold desert (BWk) type). In this group, the hot desert (BWh) has the largest spatial share (13.9% of global area) with low mean erosivity values (537 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). The cold desert climate (BWk), characteristic of northwest China and large areas of Kazakhstan, Turkmenistan, Uzbekistan, North Chile and Argentina, has a very low mean erosivity of 362 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>. The hot steppe climate (BSh), which is a transition from hot dessert to the tropical group (mainly in Africa and India), had medium mean erosivity of 2,371 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>.</p><p id="Par21">The cold climate group had the lowest mean erosivity, with 493 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup> whereof the subarctic or boreal climate type (Dsc, Dwc, Dfc), covering major areas of Scandinavia, Siberia and Canada, had minimum mean erosivity values (<285 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). By comparison, the climate zones immediately north of hot continental summers (Dfb, Dwb) that cover most of central and eastern Europe, European Russia and the northern United States, have much higher mean erosivity values (526 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>). The polar areas, mainly located in the Alps, Pyrenees and part of the Tibetan plateau, have a mean erosivity of approximately 990 MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>.</p><p id="Par22">The greatest uncertainty of the global erosivity map is likely related to transition areas between different climatic zones. The different climatic conditions, which result in high variability of rainfall amount, duration, magnitude and intensity, is the main reason for different spatial patterns of erosivity between climatic zones. The standard deviation shows the variability inside the climatic zone (Fig. <xref rid="Fig3" ref-type="fig">3</xref>). Moreover, the seasonal variation of climatic conditions play an important role in rainfall erosivity variability.</p></sec></sec><sec id="Sec7" sec-type="discussion"><title>Discussion</title><p id="Par23">The increasing availability of rainfall data with high temporal resolution, the growing computing power, and the development of sophisticated geostatistical models, enabled the development of a global rainfall erosivity dataset at 30 arc-seconds (~1 km) spatial resolution. We acknowledge that this achievement was only feasible through the scientific cooperation between scholars from all over the globe. The global erosivity map was possible thanks to the contribution of data providers (see the long list of meteorological services, organisations, and institutions in the Acknowledgements section), tested methodologies and geostatistical models suitable for such a scale.</p><sec id="Sec8"><title>Comparison with past studies</title><p id="Par24">Compared to previous works on global R-factor estimation<sup><xref ref-type="bibr" rid="CR30">30</xref>–<xref ref-type="bibr" rid="CR32">32</xref></sup>, our study presents a data-driven approach including measured hourly and sub-hourly data on rainfall intensity for erosivity assessment. In contrast previous global erosivity assessments by Nachtergaele <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR30">30</xref></sup> and Yang <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR32">32</xref></sup> that used annual rainfall to estimate erosivity. However, as demonstrated by past literature<sup><xref ref-type="bibr" rid="CR13">13</xref>, <xref ref-type="bibr" rid="CR28">28</xref>, <xref ref-type="bibr" rid="CR33">33</xref></sup> and by the erosivity density statistics, the relation between rainfall amount and erosivity is not straightforward and as shown by Naipal <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR31">31</xref></sup> lead to an overestimation of rainfall erosivity. As a consequence, Naipal <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR31">31</xref></sup>, included a simple precipitation intensity index calibrated to high resolution R-factor data of the USA.</p><p id="Par25">In order to compare the three past erosivity maps<sup><xref ref-type="bibr" rid="CR30">30</xref>–<xref ref-type="bibr" rid="CR32">32</xref></sup>, and the current one, four Ordinary Least Square (OLS) models were fitted using the GloREDa measured values as an independent variable (Fig. <xref rid="Fig4" ref-type="fig">4</xref>). An optimal model should have a zero valued intercept and a regression coefficient as close as possible to one, thus matching the grey line of Fig. <xref rid="Fig4" ref-type="fig">4</xref>. Given these criteria our study outperforms the other models<sup><xref ref-type="bibr" rid="CR30">30</xref>–<xref ref-type="bibr" rid="CR32">32</xref></sup> with a regression line very close to a coefficient of one. All the other models clearly show a significant bias (over-estimating the predictions) in either the slope or the intercept or both. Moreover, all the other models suffer from high variability as evidenced by the dispersion of the points cloud (Fig. <xref rid="Fig4" ref-type="fig">4</xref>).<fig id="Fig4"><label>Figure 4</label><caption><p>Comparison of predicted vs. measured R-factor values (values below 10,000 MJ mm<sup>−1</sup> ha<sup>−1</sup> yr<sup>−1</sup>) for the three previous and the presented global models. Grey line is the result of an optimal model (Intercept = 0 and regression coefficient = 1); Blue line is the regression result of each model; Grey zone is the 99% confidence interval for the coefficient.</p></caption><graphic xlink:href="41598_2017_4282_Fig4_HTML" id="d29e1553"></graphic></fig></p><p id="Par26">Table <xref rid="Tab1" ref-type="table">1</xref> shows the comparison of the fitted models when the full measured range is considered. The intercept of the four models is shown in the first line as “B” column while the second line show the regression coefficients for each of the global models. The past studies<sup><xref ref-type="bibr" rid="CR30">30</xref>–<xref ref-type="bibr" rid="CR32">32</xref></sup> have a high intercept bias with values deviating from 0 by more than 1,000, whereas the model proposed in this study has a much smaller value for the intercept (−204.2). The regression coefficient of the current study has a relatively small deviation from 1 (0.13), while the other models have a deviation between −0.27 and −0.85. Also in terms of R<sup>2</sup> our model clearly outperforms all other models as it was fitted directly to the measured data (Table <xref rid="Tab1" ref-type="table">1</xref>). Solely, the model from Nachtergaele <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR30">30</xref></sup>. performs remarkably well compared to the models of Yang <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR32">32</xref></sup> and Naipal <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR31">31</xref></sup>.<table-wrap id="Tab1"><label>Table 1</label><caption><p>Ordinary least squares coefficients (B) of global models for the assessment of rainfall erosivity (R-factor).</p></caption><table frame="hsides" rules="groups"><thead><tr><th></th><th colspan="3"><italic>Naipal et al</italic>.<sup><xref ref-type="bibr" rid="CR31">31</xref></sup></th><th colspan="3"><italic>Nachtergaele et al</italic>. (<italic>2010</italic>)</th><th colspan="3"><italic>Yang et al</italic>.<sup><xref ref-type="bibr" rid="CR32">32</xref></sup></th><th colspan="3"><italic>Panagos et al</italic>.</th></tr></thead><tbody><tr><td></td><td><italic>B</italic></td><td><italic>std. Error</italic></td><td><italic>p-value</italic></td><td><italic>B</italic></td><td><italic>std. Error</italic></td><td><italic>p-value</italic></td><td><italic>B</italic></td><td><italic>std. Error</italic></td><td><italic>p-value</italic></td><td><italic>B</italic></td><td><italic>std. Error</italic></td><td><italic>p-value</italic></td></tr><tr><td>Intercept</td><td><bold>1458.2</bold></td><td>279.3</td><td><0.001</td><td><bold>−1149.0</bold></td><td>141.0</td><td><0.001</td><td><bold>1223.8</bold></td><td>225.7</td><td><0.001</td><td><bold>−204.2</bold></td><td>81.2</td><td>0.012</td></tr><tr><td>Regression coefficient</td><td><bold>0.15</bold></td><td>0.12</td><td>0.187</td><td><bold>0.73</bold></td><td>0.04</td><td><0.001</td><td><bold>0.20</bold></td><td>0.08</td><td>.009</td><td><bold>1.13</bold></td><td>0.05</td><td><0.001</td></tr><tr><td>Observations</td><td colspan="3">3530</td><td colspan="3">3530</td><td colspan="3">3530</td><td colspan="3">3530</td></tr><tr><td>R<sup>2</sup></td><td colspan="3"><bold>0.155</bold></td><td colspan="3"><bold>0.385</bold></td><td colspan="3"><bold>0.207</bold></td><td colspan="3"><bold>0.811</bold></td></tr></tbody></table></table-wrap></p></sec><sec id="Sec9"><title>Sources of uncertainty</title><p id="Par27">Our inclusive approach to compile the maximum possible number of erosive events assumes that the data collected in one period is comparable with data from other periods. The inter-comparison of different time periods and the non-existence of other alternatives has been extensively discussed<sup><xref ref-type="bibr" rid="CR28">28</xref></sup>, and has also been followed in similar data collection exercises such as the updated world map of the Koppen-Geiger climate classifications<sup><xref ref-type="bibr" rid="CR29">29</xref></sup>. Obviously, this simplifying assumption is not valid at local scale, where observed trends in erosivity have been measured over a considerable period (e.g. 30 years). However, at the global scale, the inclusion of 3,625 stations smooths the potential bias due to the presence of trends and long-term temporal variability. Further, we preferred this inclusive approach over the alternative of selecting a common measurement period, since the latter would significantly reduce the station density entailing a reduction of the spatial prediction accuracy.</p><p id="Par28">Past studies recommended a minimum period of 22-years for calculating long term R-factor, while 10-years measurements may lead to under or over-estimation<sup><xref ref-type="bibr" rid="CR25">25</xref>, <xref ref-type="bibr" rid="CR34">34</xref>, <xref ref-type="bibr" rid="CR35">35</xref></sup>. As 12% of GloREDa includes stations with less than 10-years measurements, we recognize that this may cause a bias due to the temporal variation of R-factor. This bias, even if we are convinced that it is limited, should be considered.</p><p id="Par29">Other uncertainties are related to the methodological approach such as the a) discrepancies in case of using a different algorithm for rainfall energy estimation (>97.7% of the calculated R-factor stations are based on the original RUSLE equation); b) the application of temporal resolution calibration factors found in European Union to the global dataset; c) the under-representativeness of measured R-factor data in highlands (11.6% of the total dataset is located in areas > 1000 m a.s.l. while the respective area amounts up to 20%), tropical and arid areas and d) the Gaussian Process Regression interpolation model. With regard to the first point the algorithm for unit rainfall energy (Equation <xref rid="Equ2" ref-type="">2</xref>) may underestimate the rainfall erosivity for instance in high erosive regions (e.g. Ethiopian highlands) due to large drop sizes<sup><xref ref-type="bibr" rid="CR36">36</xref></sup>. The effect of the above listed uncertainties is considered not significant for the objectives of this product which is the model application at global scale. However, we propose that further improvements can be implemented in regional studies taking into account the best suitable algorithm for rainfall unit energy estimation, using more regional-based calibration factors to account for time resolution discrepancies and having a more representative (in terms of climate and topography) pool of stations with measured R-factor data.</p></sec><sec id="Sec10"><title>Implications of global erosivity map and data availability</title><p id="Par30">The new global erosivity map is proposed for global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Therefore, the aim of the global erosivity dataset is not to challenge other local (or national) erosivity maps, developed from local data with better quality that may not have been available for the present study. Nevertheless, our global R-factor map can potentially cover gaps, where erosivity has not been estimated (due to lack of data), or where it has been calculated solely from rainfall amounts.</p><p id="Par31">Current global estimates on soil erosion by water are very uncertain ranging over one order of magnitude from ca. 20 to over 200 Pg yr<sup>−1</sup>. More accurate global predictions of the rill and interrill soil erosion rates can only be achieved when the RUSLE rainfall erosivity factor is thoroughly computed. In this study, we present a robust global rainfall-runoff erosivity factor to measure the erosive potential of rainfall, which is a key input in soil erosion prediction models. The global erosivity map represents the first assessment that is solely based on the original RUSLE approach using sub-hourly measured rainfall data for 3,625 stations, providing a solid and harmonised basis for a robust spatial interpolation results. Our results show new insights into the global geography of rainfall erosivity and the global erosivity map which will be publicly available can be employed by other research groups to perform national, continental and global soil erosion modelling.</p><p id="Par32">The GloREDa can be considered as an important step to bring together a large group of scientists to advance your understanding of large scale patterns related to land degradation, facing the current policy challenges and demands by the Food and Agriculture Organization (FAO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), and the United Nations Convention to Combat Desertification (UNCCD).</p><p id="Par33">The Global erosivity map (GeoTIFF format) at 30 arc-seconds (~1 km) resolution is available for free download in the European Soil Data Centre (ESDAC) website at <ext-link ext-link-type="uri" xlink:href="http://esdac.jrc.ec.europa.eu">http://esdac.jrc.ec.europa.eu</ext-link>. The calculated erosivity values per station in GloREDa will become available in the future pending on the agreed copyright issues with data providers.</p></sec></sec><sec id="Sec11" sec-type="materials|methods"><title>Methods</title><p id="Par34">The generation of the global erosivity map involved the following steps: a) the collection of high-temporal resolution rainfall data; b) the calculation of the erosivity factor (R-factor) for each rainfall station; c) the normalisation of R-factor values calculated with rainfall data collected at different time steps (1 min to 60 min), and d) the spatial interpolation of the R-factor point values.</p><sec id="Sec12"><title>Collection of data</title><p id="Par35">At global scale, this is the first time a data collection of observed (measured) high temporal resolution rainfall data takes place. The collection of high temporal resolution rainfall data from the maximum possible number of countries was considered necessary in order to have a representative sample across climatic and geographic gradients.</p><p id="Par36">The Global Rainfall Erosivity Database (GloREDa) was compiled based on the following three components:<list list-type="bullet"><list-item><p id="Par37">Rainfall Erosivity database at European Scale (<bold>REDES</bold>) which includes 1,675 rainfall stations in the European Union and Switzerland (28 countries in total). REDES has been the data source for the erosivity map of Europe<sup><xref ref-type="bibr" rid="CR28">28</xref></sup> and was updated in 2015 to estimate monthly erosivity in Europe<sup><xref ref-type="bibr" rid="CR13">13</xref>, <xref ref-type="bibr" rid="CR33">33</xref></sup>.</p></list-item><list-item><p id="Par38">A global data collection of both high resolution rainfall data and calculated erosivity values based on high temporal resolution rainfall data (Table <xref rid="Tab2" ref-type="table">2</xref>). The data collection yielded 1,865 additional rainfall stations (around 52% of the total stations in GloREDa) from 23 countries outside of Europe. For the majority of the stations (located in China, Japan, India, Kuwait, Israel, Turkey, the United States of America, Mexico, Costa Rica, Jamaica, South Africa and Suriname) the calculation of the rainfall erosivity, based on high temporal resolution data, was performed for the first time (the calculation of rainfall erosivity, named as R-factor, is presented in the homonymous sub-section). For other regions, we used published erosivity data that were based on high temporal resolution rainfall datasets. This included countries such as Australia<sup><xref ref-type="bibr" rid="CR37">37</xref></sup>, New Zealand<sup><xref ref-type="bibr" rid="CR27">27</xref></sup>, South Korea<sup><xref ref-type="bibr" rid="CR38">38</xref></sup>, Iran<sup><xref ref-type="bibr" rid="CR39">39</xref></sup>, Malaysia<sup><xref ref-type="bibr" rid="CR40">40</xref></sup>, Colombia<sup><xref ref-type="bibr" rid="CR21">21</xref></sup>, Brazil<sup><xref ref-type="bibr" rid="CR22">22</xref></sup>, Chile<sup><xref ref-type="bibr" rid="CR23">23</xref></sup>, Mauritius<sup><xref ref-type="bibr" rid="CR41">41</xref></sup> and Algeria<sup><xref ref-type="bibr" rid="CR42">42</xref></sup>.<table-wrap id="Tab2"><label>Table 2</label><caption><p>Overview of the high resolution rainfall data used to estimate global rainfall erosivity. In addition, erosivity information of 85 stations from 13 countries found in the literature<sup><xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR43">43</xref>–<xref ref-type="bibr" rid="CR56">56</xref></sup> was included in the global map (not shown in the table).</p></caption><table frame="hsides" rules="groups"><thead><tr><th colspan="2">Country</th><th>No. of Stations</th><th>(Main) Period Covered</th><th>Years per station (average)</th><th>(Main) Temporal resolution of rainfall data</th><th>Source of high temporal resolution rainfall data</th></tr></thead><tbody><tr><td>AT</td><td>Austria</td><td>84</td><td>1995–2010</td><td>21</td><td>27 stations: 10Min; 57 stations: 15Min</td><td>Hydrographic offices of Upper Austria, Lower Austria, Burgenland, Styria, Salzburg, Carinthia, Vorarlberg and Tyrol.</td></tr><tr><td>AU</td><td>Australia</td><td>167</td><td>1961–2000</td><td>40</td><td>6 Min</td><td>Bureau of Meteorology in Australia; Yu <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR37">37</xref></sup></td></tr><tr><td>BE</td><td>Belgium - Flanders</td><td>20</td><td>2004–2013</td><td>10</td><td>30 Min</td><td>Flemish Environmental Agency (VMM),</td></tr><tr><td>BE</td><td>Belgium - Wallonia</td><td>29</td><td>2004–2013</td><td>10</td><td>60 Min</td><td>Service Public de Wallonie</td></tr><tr><td>BG</td><td>Bulgaria</td><td>84</td><td>1951–1976</td><td>26</td><td>30 Min</td><td>Rousseva <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR57">57</xref></sup></td></tr><tr><td>BR</td><td>Brazil</td><td>87</td><td>1986–2008</td><td>19</td><td>49 stations: 5 Min; 38 stations: 10 Min</td><td>Oliveira <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR22">22</xref></sup> and updated in 05/2016</td></tr><tr><td>CH</td><td>Switzerland</td><td>71</td><td>1988–2010</td><td>22</td><td>10 Min</td><td>MeteoSwiss</td></tr><tr><td>CL</td><td>Chile</td><td>18</td><td>1976–1995</td><td>17</td><td>15 Min</td><td>General Directorate of Water Resources (DGA), Government of Chile</td></tr><tr><td>CN</td><td>China</td><td>387</td><td>1998–2012</td><td>14</td><td>60 Min</td><td>National Meteorological Information Center of the China Meteorological Administration</td></tr><tr><td>CO</td><td>Colombia</td><td>6</td><td>1987–1996</td><td>10</td><td>5 Min</td><td>Centro Nacional de Investigaciones de Café - Cenicafé</td></tr><tr><td>CR</td><td>Costa Rica</td><td>5</td><td>2011–2015</td><td>6</td><td>30 Min</td><td>University of Costa Rica, Costa Rica</td></tr><tr><td>CY</td><td>Cyprus</td><td>35</td><td>1974–2013</td><td>39</td><td>30 Min</td><td>Cyprus Department of Meteorology</td></tr><tr><td>CZ</td><td>Czech Republic</td><td>32</td><td>1961–1999</td><td>35</td><td>30 Min</td><td>Research Institute for Soil and Water Conservation (Czech Republic)</td></tr><tr><td>DE</td><td>Germany</td><td>148</td><td>1996–2013</td><td>18</td><td>60 Min</td><td>Deutscher Wetterdienst (DWD)</td></tr><tr><td>DK</td><td>Denmark</td><td>30</td><td>1988–2012</td><td>15</td><td>60 Min</td><td>Danish Meteorological Institute (DMI), Aarhus University</td></tr><tr><td>DZ</td><td>Algeria</td><td>120</td><td>1977–2004</td><td>24</td><td>15 Min</td><td>National Agency of Hydraulic Resources, Algeria</td></tr><tr><td>EE</td><td>Estonia</td><td>21</td><td>2007–2013</td><td>7</td><td>60 Min</td><td>Estonian Environment Agency</td></tr><tr><td>ES</td><td>Spain</td><td>146</td><td>2002–2013</td><td>12</td><td>24 stations: 10 Min; 104 stations: 15 Min; 18 stations 30 MIN</td><td>Regional water agencies</td></tr><tr><td>FI</td><td>Finland</td><td>64</td><td>2007–2013</td><td>7</td><td>60 Min</td><td>Finnish Climate Service Centre (FMI)</td></tr><tr><td>FR</td><td>France</td><td>81</td><td>2004–2013</td><td>10</td><td>60 Min</td><td>Météo-France DP/SERV/FDP</td></tr><tr><td>GR</td><td>Greece</td><td>80</td><td>1974–1997</td><td>30</td><td>30 Min</td><td>Hydroskopio</td></tr><tr><td>HR</td><td>Croatia</td><td>42</td><td>1961–2012</td><td>40</td><td>10 Min</td><td>Croatian Meteo & Hydrological Service</td></tr><tr><td>HU</td><td>Hungary</td><td>30</td><td>1998–2013</td><td>16</td><td>10 Min</td><td>Hungarian Meteorological Service</td></tr><tr><td>IE</td><td>Ireland</td><td>13</td><td>1950–2010</td><td>56</td><td>60 Min</td><td>Met Éireann – The Irish National Meteorological Service</td></tr><tr><td>IL</td><td>Israel</td><td>61</td><td>1998–2015</td><td>17</td><td>30 Min</td><td>Israel Meteorological Service</td></tr><tr><td>IN</td><td>India</td><td>247</td><td>2007–2015</td><td>7</td><td>60 Min</td><td>India Meteorological Department, Ministry of Earth Sciences</td></tr><tr><td>IR</td><td>Iran</td><td>70</td><td>1984–2004</td><td>21</td><td>10 Min</td><td>Iranian Meteorological Organization</td></tr><tr><td>IT</td><td>Italy</td><td>251</td><td>2002–2011</td><td>10</td><td>30 Min</td><td>Regional meteorological services, Regional agencies for environmental protection (ARPA)</td></tr><tr><td>JM</td><td>Jamaica</td><td>9</td><td>2003–2014</td><td>12</td><td>2 Min</td><td>Meteorological service Jamaica</td></tr><tr><td>JP</td><td>Japan</td><td>55</td><td>2006–2015</td><td>10</td><td>60 Min</td><td>Japan Meteorological Agency (JMA)</td></tr><tr><td>KR</td><td>South Korea</td><td>75</td><td>1998–2015</td><td>18</td><td>10 Min</td><td>Korea Meteorological Administration (KMA)</td></tr><tr><td>KW</td><td>Kuwait</td><td>15</td><td>2007–2015</td><td>9</td><td>60 Min</td><td>Department of Meteorology, Directorate General of Civil Aviation, State of Kuwait</td></tr><tr><td>LT</td><td>Lithuania</td><td>3</td><td>1992–2007</td><td>16</td><td>30 Min</td><td>Lithuanian Agriculture and Forestry Research Centre</td></tr><tr><td>LU</td><td>Luxembourg</td><td>16</td><td>2000–2013</td><td>11</td><td>60 Min</td><td>Agrarmeteorologisches Messnetz</td></tr><tr><td>LV</td><td>Latvia</td><td>4</td><td>2007–2013</td><td>7</td><td>60 Min</td><td>Latvian Environment, Geology and Meteorology Centre</td></tr><tr><td>MU</td><td>Mauritius</td><td>5</td><td>2005–2008</td><td>5</td><td>6 Min</td><td>Mauritius Meteorological Services (MMS)</td></tr><tr><td>MX</td><td>Mexico</td><td>15</td><td>2006–2014</td><td>9</td><td>60 Min</td><td>CONAGUA, Comisión Nacional Del Agua, Servicio Meteorológico Nacional, Mexico.</td></tr><tr><td>MY</td><td>Malaysia</td><td>2</td><td>1981–1998</td><td>18</td><td>10 Min</td><td>Yu <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR40">40</xref></sup></td></tr><tr><td>NL</td><td>Netherlands</td><td>32</td><td>1981–2010</td><td>24</td><td>60 Min</td><td>Royal Netherlands Meteorological Institute</td></tr><tr><td>NZ</td><td>New Zealand</td><td>35</td><td>2000–2012</td><td>12</td><td>10 Min</td><td>New Zealand Institute of Water and Atmospheric Research (NIWA)</td></tr><tr><td>PL</td><td>Poland</td><td>13</td><td>1961–1988</td><td>27</td><td>30 Min</td><td>Warsaw University of Life Sciences</td></tr><tr><td>PT</td><td>Portugal</td><td>41</td><td>2001–2012</td><td>11</td><td>60 Min</td><td>Agência Portuguesa do Ambiente</td></tr><tr><td>RO</td><td>Romania</td><td>60</td><td>2006–2013</td><td>8</td><td>10 Min</td><td>Meteorological Administration</td></tr><tr><td>RU</td><td>Russian Federation</td><td>218</td><td>1961–1983</td><td>23</td><td>30 Min</td><td>Lomonosov Moscow State University</td></tr><tr><td>SE</td><td>Sweden</td><td>73</td><td>1996–2013</td><td>18</td><td>60 Min</td><td>Swedish Meteorological and Hydrological Institute (SMHI)</td></tr><tr><td>SI</td><td>Slovenia</td><td>31</td><td>1999–2008</td><td>10</td><td>5 Min</td><td>Slovenian Environment Agency</td></tr><tr><td>SK</td><td>Slovakia</td><td>103</td><td>1971–1990</td><td>20</td><td>60 Min</td><td>Slovak Hydrometeorological Institute, Climatological service</td></tr><tr><td>SR</td><td>Suriname</td><td>11</td><td>1987–2010</td><td>25</td><td>60 Min</td><td>Meteorological organization of Suriname</td></tr><tr><td>TR</td><td>Turkey</td><td>160</td><td>2005–2014</td><td>10</td><td>1 Min</td><td>Ministry of Forest and Water Affairs General Directorate of Combating Desertification and Erosion</td></tr><tr><td>UK</td><td>United Kingdom</td><td>38</td><td>1993–2012</td><td>20</td><td>60 Min</td><td>NERC & UK Environ. Change Network(ECN), British Atmospheric Data Centre (BADC)</td></tr><tr><td>US</td><td>United States of America</td><td>92</td><td>2006–2016</td><td>11</td><td>5 Min</td><td>U.S. Climate Reference Network (USCRN), NOAA; Diamond <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR58">58</xref></sup></td></tr><tr><td>ZA</td><td>South Africa</td><td>5</td><td>2001–2005</td><td>5</td><td>5 Min</td><td>Nel and Summer<sup><xref ref-type="bibr" rid="CR41">41</xref></sup></td></tr><tr><td><bold>Total</bold></td><td></td><td><bold>3,540</bold></td><td colspan="4"></td></tr></tbody></table></table-wrap></p></list-item><list-item><p id="Par39">A literature review was used to fill some important data gaps, mainly in Africa, where high temporal resolution rainfall data are scarce. As a result of this exercise, rainfall erosivity values for 85 stations (2.4% of the whole database) from 13 countries were inserted in GloREDa. These countries included Canada<sup><xref ref-type="bibr" rid="CR43">43</xref></sup>, Argentina<sup><xref ref-type="bibr" rid="CR24">24</xref></sup>, Cuba<sup><xref ref-type="bibr" rid="CR44">44</xref></sup>, Cape Verde<sup><xref ref-type="bibr" rid="CR45">45</xref></sup>, Cameroon<sup><xref ref-type="bibr" rid="CR46">46</xref></sup>, Eritrea<sup><xref ref-type="bibr" rid="CR47">47</xref></sup>, Ethiopia<sup><xref ref-type="bibr" rid="CR48">48</xref></sup>, Kenya<sup><xref ref-type="bibr" rid="CR49">49</xref>, <xref ref-type="bibr" rid="CR50">50</xref></sup>, Niger<sup><xref ref-type="bibr" rid="CR51">51</xref></sup>, Nigeria<sup><xref ref-type="bibr" rid="CR52">52</xref>, <xref ref-type="bibr" rid="CR53">53</xref></sup>, Rwanda<sup><xref ref-type="bibr" rid="CR54">54</xref></sup>, Tenerife<sup><xref ref-type="bibr" rid="CR55">55</xref></sup> and Zambia<sup><xref ref-type="bibr" rid="CR56">56</xref></sup>.</p></list-item></list></p><p id="Par40">Summarizing, we collected high temporal resolution rainfall data for 3,540 stations (97.7% of the total GloREDa). The total number of observational years equals 59,380; this results in an average of 16.8 years of high temporal resolution rainfall data per station. As such, GloREDa is the most comprehensive global database including the largest possible number of stations with high temporal resolution rainfall data.</p></sec><sec id="Sec13"><title>Calculation of rainfall erosivity (R-factor)</title><p id="Par41">Rainfall erosivity accounts for the combined effect of rainfall duration, magnitude and intensity. In addition, it is also necessary to take into account the frequency of erosive events over a longer time period. In this study, the original R-factor from the Revised Universal Soil Loss Equation (RUSLE)<sup><xref ref-type="bibr" rid="CR25">25</xref></sup> was used for the vast majority (>97.7%) of the rainfall stations included in GloREDa. Accordingly, the calculation of rainfall erosivity (EI<sub>30</sub>) of a single event was based on the following equation:<disp-formula id="Equ1"><label>1</label><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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\begin{document}$$E{I}_{30}=(\sum _{r=1}^{k}{e}_{r}{v}_{r})\,{I}_{30}$$\end{document}</tex-math><mml:math id="M2" display="block"><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>30</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>k</mml:mi></mml:munderover><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mspace width=".25em"></mml:mspace><mml:msub><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>30</mml:mn></mml:mrow></mml:msub></mml:math><graphic xlink:href="41598_2017_4282_Article_Equ1.gif" position="anchor"></graphic></alternatives></disp-formula>where e<sub>r</sub> is the unit rainfall energy (MJ ha<sup>−1</sup> mm<sup>−1</sup>) and v<sub>r</sub> the rainfall volume (mm) during the r<sup>th</sup> time period of a rainfall event divided in k-parts. I<sub>30</sub> is the maximum 30-minutes rainfall intensity (mm h<sup>−1</sup>). The unit rainfall energy (e<sub>r</sub>) is calculated for each time interval as follows<sup><xref ref-type="bibr" rid="CR59">59</xref></sup>:<disp-formula id="Equ2"><label>2</label><alternatives><tex-math id="M3">\documentclass[12pt]{minimal}
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\begin{document}$${e}_{r}=0.29[1-0.72{e}^{(-0.05{i}_{r})}]$$\end{document}</tex-math><mml:math id="M4" display="block"><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>0.29</mml:mn><mml:mrow><mml:mo stretchy="true">[</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mn>0.72</mml:mn><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mn>0.05</mml:mn><mml:msub><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy="true">]</mml:mo></mml:mrow></mml:math><graphic xlink:href="41598_2017_4282_Article_Equ2.gif" position="anchor"></graphic></alternatives></disp-formula>where i<sub>r</sub> is the rainfall intensity during the time interval (mm h<sup>−1</sup>).</p><p id="Par42">R is the average annual rainfall erosivity (MJ mm ha<sup>−1</sup> h<sup>−1</sup> yr<sup>−1</sup>):<disp-formula id="Equ3"><label>3</label><alternatives><tex-math id="M5">\documentclass[12pt]{minimal}
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\begin{document}$$R=\,\frac{{\sum }_{j=1}^{n}{\sum }_{k=1}^{{m}_{j}}{(E{I}_{30})}_{k}}{n}$$\end{document}</tex-math><mml:math id="M6" display="block"><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mspace width=".25em"></mml:mspace><mml:mfrac><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msubsup><mml:msub><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>I</mml:mi></mml:mrow><mml:mrow><mml:mn>30</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mi>n</mml:mi></mml:mfrac></mml:math><graphic xlink:href="41598_2017_4282_Article_Equ3.gif" position="anchor"></graphic></alternatives></disp-formula>where <italic>n</italic> is the number of years recorded, <italic>m</italic><sub><italic>j</italic></sub> is the number of erosive events during a given year <italic>j</italic> and <italic>k</italic> is the index of a single event with its corresponding erosivity <italic>EI</italic><sub><italic>30</italic></sub>.</p><p id="Par43">Equation (<xref rid="Equ2" ref-type="">2</xref>) was developed by Brown and Foster<sup><xref ref-type="bibr" rid="CR59">59</xref></sup> as part of the Revised Universal Soil Loss Equation (RUSLE, Renard <italic>et al</italic>.<sup><xref ref-type="bibr" rid="CR25">25</xref></sup>), and replaced the original equation of Wischmeier and Smith<sup><xref ref-type="bibr" rid="CR35">35</xref></sup> used in the Universal Soil Loss Equation (USLE). Equation (<xref rid="Equ2" ref-type="">2</xref>) was further modified as part of RUSLE2<sup><xref ref-type="bibr" rid="CR60">60</xref></sup>, but its use has been mostly limited to the United States. However, outside of the United States the RUSLE equation is still the most commonly used and we have therefore applied it for our erosivity calculations. A comparison of the two equations yields slightly higher erosivity values for the RUSLE2 equation<sup><xref ref-type="bibr" rid="CR60">60</xref></sup>. A recent study showed that empirical rainfall kinetic energy relationships compare well to measurement when complete events were considered (R<sup>2</sup> > 0.90). Nonetheless, future research will explore how our current GloREDa results compare with alternative functions for rainfall energy.</p><p id="Par44">According to the RUSLE handbook<sup><xref ref-type="bibr" rid="CR25">25</xref></sup>, the erosive rainfall events were computed based on the following criteria: (i) the cumulative rainfall of an event is greater than 12.7 mm, (ii) the event has at least one peak that is greater than 6.35 mm during a period of 15 min (or 12.7 mm during a period of 30 min) and, (iii)a rainfall accumulation of less than 1.27 mm during a period of six hours splits a longer storm period into two storms. The erosivity factor equations and the above mentioned criteria have been developed according to more than 10,000 plot-years of experiments. The R-factor for each station in GloREDa was calculated using the Rainfall Intensity Summarisation Tool (RIST) software developed by the United States Department of Agriculture (USDA).</p></sec><sec id="Sec14"><title>Calibration of different time resolutions</title><p id="Par45">For such an extensive data collection, it is expected to have a variety in both i) the range of available data-years and ii) the time resolution of the data. According to the GloREDa statistics, 35.7% of the stations had rainfall data at very high resolution (<=15 min); ~25.7% had an intermediate resolution (30 minutes), while the remaining 38.6% had a resolution of 60 minutes. Due to this heterogeneity, a time step calibration of erosivity values was considered necessary.</p><p id="Par46">We selected a 30-minute time resolution as an acceptable compromise between the coarse time resolution of 60 minutes and the higher ones (<=15 minutes). For the calibration, we have selected a pool of 86 rainfall stations as their data were available at multiple temporal resolutions and their geographical coverage is representing 14 countries covering a large climate gradient. The calibration process included three steps: a) The R-factor was calculated at the highest possible resolution (5, 10 or 15 minutes); b) The rainfall data were aggregated to coarser resolutions (30 and 60 minutes) and the corresponding R-factor was calculated at the coarser resolutions and c) The calibration factors was derived from the regression analysis of the R-factor calculations at the highest and coarser resolutions. Those calibration factors have been developed in the European study<sup><xref ref-type="bibr" rid="CR33">33</xref></sup> and were in agreement with range values provided in the literature<sup><xref ref-type="bibr" rid="CR25">25</xref>, <xref ref-type="bibr" rid="CR60">60</xref>, <xref ref-type="bibr" rid="CR61">61</xref></sup> which have been calculated in China, South Italy and U.S.A.</p></sec><sec id="Sec15"><title>Interpolation of GloREDa</title><p id="Par47">Given the correlation between the R-factor and monthly climatic data<sup><xref ref-type="bibr" rid="CR28">28</xref></sup>, a regression approach was used to infer the spatial global distribution of rainfall erosivity from a series of independent climatic covariates derived from the WorldClim database<sup><xref ref-type="bibr" rid="CR62">62</xref></sup>. The covariates used included average monthly precipitation, average minimum and maximum monthly precipitation, average monthly temperature, precipitation of the wettest month, precipitation of the driest month, and precipitation seasonality, as defined in the WorldClim database (www.worldclim.org). These variables represent long term conditions based on the interpolation of observed data for the period 1960–1990<sup><xref ref-type="bibr" rid="CR62">62</xref></sup>. As a result, our erosivity map represents long-term erosivity patterns. Elevation was not included in the model, as it was already used to estimate some of the WorldClim variables. We subsequently assembled a dataset where each location (i.e. rainfall station) had a mean R-factor (independent variable) as well as values for the climatic variables (independent variables), and used it as input for the interpolation.</p><p id="Par48">We used Gaussian Process Regression (GPR)<sup><xref ref-type="bibr" rid="CR63">63</xref>, <xref ref-type="bibr" rid="CR64">64</xref></sup> due to the large number of support covariates, their potential collinearity, and the presence of non-linear relationships between the target variable (R-factor) and the covariates. GPR is a non-linear regression approach that can model non-linear relations by projecting the inputs into a higher dimensional space using basis functions, and by creating a regression model in said space. Considering the regression form <inline-formula id="IEq1"><alternatives><tex-math id="M7">\documentclass[12pt]{minimal}
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\begin{document}$${\rm{y}}=f({\rm{x}})+{\mathscr{N}}(0,\,{\sigma }_{n}^{2})$$\end{document}</tex-math><mml:math id="M8"><mml:mi mathvariant="normal">y</mml:mi><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi mathvariant="normal">x</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo>+</mml:mo><mml:mi mathvariant="script">N</mml:mi><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mspace width=".25em"></mml:mspace><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="41598_2017_4282_Article_IEq1.gif"></inline-graphic></alternatives></inline-formula> with <inline-formula id="IEq2"><alternatives><tex-math id="M9">\documentclass[12pt]{minimal}
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\begin{document}$$f({\rm{x}})={{\rm{x}}}^{T}{\bf{w}}$$\end{document}</tex-math><mml:math id="M10"><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mi mathvariant="normal">x</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="normal">x</mml:mi></mml:mrow><mml:mi>T</mml:mi></mml:msup><mml:mi mathvariant="bold">w</mml:mi></mml:math><inline-graphic xlink:href="41598_2017_4282_Article_IEq2.gif"></inline-graphic></alternatives></inline-formula> (where <italic>y</italic> are the observed responses, <italic>x</italic> the covariates values vector, <italic>f</italic> a set of functions and <bold>w</bold> a vector of weights), the GPR uses a projection from the original input space into a feature space using kernel expansion so that f(x) can be rewritten as <inline-formula id="IEq3"><alternatives><tex-math id="M11">\documentclass[12pt]{minimal}
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\begin{document}$$f(x)=\varphi {({\bf{x}})}^{T}{\bf{w}}$$\end{document}</tex-math><mml:math id="M12"><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>φ</mml:mi><mml:msup><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi mathvariant="bold">x</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mi>T</mml:mi></mml:msup><mml:mi mathvariant="bold">w</mml:mi></mml:math><inline-graphic xlink:href="41598_2017_4282_Article_IEq3.gif"></inline-graphic></alternatives></inline-formula> where <italic>ϕ</italic> is the kernel function.</p><p id="Par49">In this study the Radial Basis Function (RBF) Gaussian kernel was used, which can be written as <inline-formula id="IEq4"><alternatives><tex-math id="M13">\documentclass[12pt]{minimal}
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\begin{document}$$K({\bf{x}},{\bf{x}}^{\prime} )=\exp (-\frac{\parallel {\bf{x}}-{\bf{x}}^{\prime} {\parallel }^{2}}{2{\sigma }^{2}})$$\end{document}</tex-math><mml:math id="M14"><mml:mi>K</mml:mi><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi mathvariant="bold">x</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="bold">x</mml:mi><mml:mo>′</mml:mo></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>exp</mml:mi><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mo>∥</mml:mo><mml:mi mathvariant="bold">x</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant="bold">x</mml:mi><mml:mo>′</mml:mo><mml:msup><mml:mrow><mml:mo>∥</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="41598_2017_4282_Article_IEq4.gif"></inline-graphic></alternatives></inline-formula> where <italic>σ</italic> is free tunable parameter. The Gaussian kernel is highly adaptable and commonly applied in machine learning<sup><xref ref-type="bibr" rid="CR65">65</xref></sup>. The main advantages of GPR are that it can model very complex non-linear relations between covariates and the target variable, and directly model both average and variance estimation, thus providing information about prediction uncertainty. Moreover, GPR is resistant to the issues derived from collinearity among independent variables that can arise in other statistical models. Finally, the GPR has the advantage of adapting in local conditions (climatic) due to its non-parametric nature. In this study the GPR model performance was tested for both, a fitting, and a cross-validation dataset. The cross-validation was carried out by random sampling with 10% replacement of the original dataset used for validation.</p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note:</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><ack><title>Acknowledgements</title><p>The authors would also like to acknowledge the following services for proving their data: Bureau of Meteorology in Australia, New Zealand Institute of Water and Atmospheric Research (NIWA), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Meteorological Information Center of China, India Meteorological Department, Ministry of Earth Sciences, Iranian Meteorological Organization, Department of Meteorology, Directorate General of Civil Aviation - State of Kuwait, Lomonosov Moscow State University, Israel Meteorological Service, Turkish Ministry of Forestry and Water Affairs, U.S. Climate Reference Network (USCRN, NOAA), Comision Nacional Del Agua, Servicio Meteorologico Nacional, Mexico, Meteorological service Jamaica, University of Costa Rica (UCR), Centro Nacional de Investigaciones de Café – Cenicafé (Colombia), General Directorate of Water Resources (Chile), Meteorological organization of Suriname, Mauritius Meteorological Services (MMS), Algerian National Agency of Hydraulic Resources. Plus all the European Organizations listed in Panagos <italic>et al</italic><sup>28</sup>.</p></ack><notes notes-type="author-contribution"><title>Author Contributions</title><p>Panos Panagos, Cristiano Ballabio, Pasquale Borrelli, Katrin Meusburger elaborated the results, developed the erosivity model and wrote major part of the manuscript. Prof. Bofu Yu, Dr. Andreas Klik, Dr. Kyoung Jae Lim, Dr. Jae E. Yang, Prof. Jinren Ni, Prof. Chiyuan Miao, Dr. Nabansu Chattopadhyay, Prof. Seyed Hamidreza Sadeghi, Dr. Zeinab Hazbavi, Dr. Mohsen Zabihi, Prof. Genady Larionov, Dr. Andrey Gorobets, Dr. Sergey Krasnov, Mr. Yoav Levi, Dr. Gunay Erpul, Prof. Christian Birkel, Dr. Natalia Hoyos, Dr. Victoria Naipal, Dr. Paulo Tarso Oliveira, Prof. Carlos Bonilla, Dr. Mohamed Meddi, Dr. Werner Nel, Mr. Hassan Al Dashti, Mr. Martino Boni, Dr. Nazzareno Diodato, Prof. Kristof Van Oost and Dr. Mark A. Nearing provided the data input, contributed with modelling and analysis and reviewed the manuscript.</p></notes><notes notes-type="COI-statement"><sec id="FPar1"><title>Competing Interests</title><p id="Par50">The authors declare that they have no competing interests.</p></sec></notes><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Freebairn</surname><given-names>JW</given-names></name><name><surname>Zillman</surname><given-names>JW</given-names></name></person-group><article-title>Economic benefits of meteorological services</article-title><source>Meteorol. 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