Global Rainfall Erosivity

Introduction

The exposure of the Earth’s surface to rainfall is one of the key factors that determine soil erosion. Rainfall can displace soil particles either through the physical impact of the water droplet or as a result of water running across the surface, which under certain conditions can develop into small shallow channels known as rills, or eventually gullies that can be several meters deep. The capacity of rain to cause soil erosion is known as erosivity. This term reflects the kinetic energy of the rainfall which is based on its intensity (measured as mm/hour), the amount that fell (mm) and total duration.

While the Intergovernmental Technical Panel on Soils (ITPS) Status of World Soil Resources reported that soil erosion by water is the most serious cause of soil degradation globally, patterns of rainfall erosivity across the planet remain poorly quantified, and estimates are prone to 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 minutes) and high fidelity rainfall recordings.

GloREDa: Global Rainfall Erosivity Database

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 GloREDa, 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 relevant publication:

  • The Rainfall Erosivity database at European Scale (REDES) 
  • 1,865 stations from 23 countries outside Europe (Australia, New Zealand, South Korea, Japan, China, India, Malaysia, Iran, Kuwait, Israel, Turkey, Russian Federation, United States of America, Mexico, Costa Rica, Jamaica, Colombia, Suriname, Chile, Brazil, Algeria, South Africa, Mauritius).
  • 85 stations collected from a literature review (12 countries)

The number of GloREDa stations varied greatly among continents. 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, China, India, Japan.

R-factor in the World

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-1 h-1 yr-1 and broadly reflects climatic patterns. The highest values, which are three times greater than the mean, are found in South America (especially around the Amazon Basin) and the Caribbean countries, Central Africa and parts of Western Africa and South East Asia. The lowest values are mainly found in mid- and high-latitude regions such as Canada the Russian Federation, northern Europe, northern Africa, the Middle East and southern Australia. It should be noted that high rainfall erosivity does not necessarily mean high erosion, as factors such as soil characteristics, vegetative cover and land use are also important factors.

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.. Compared to previous works on global R-factor estimation, our study presents a data-driven approach including measured hourly and sub-hourly data on rainfall intensity for erosivity assessment.

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.

Reference

Panagos P., Borrelli P., Meusburger K., Yu B., Klik A., Lim K.J., Yang J.E, Ni J., Miao C., Chattopadhyay N., Sadeghi S.H., Hazbavi Z., Zabihi M., Larionov G.A., Krasnov S.F., Garobets A., Levi Y., Erpul G., Birkel C., Hoyos N., Naipal V., Oliveira P.T.S., Bonilla C.A., Meddi M., Nel W., Dashti H., Boni M., Diodato N., Van Oost K., Nearing M.A., Ballabio C., 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records.  Scientific Reports 7: 4175 . DOI: 10.1038/s41598-017-04282-8.

Data - Maps

The data are also accessible in: Global R-factor

Fig 1:  (a) Global Rainfall Erosivity map (spatial resolution of 30 arc-seconds). Erosivity classes correspond to quantiles;
(b) number and cumulative percentage of GloREDa stations grouped by erosivity; (c) mean erosivity by continent; (d) mean erosivity by climate zone.

 

Fig. 2: (a) Global distribution of rainfall erosivity stations (red dots) compiled in the Global Rainfall Erosivity Database (GloREDa);                                                              (b) Distribution of rainfall erosivity stations by continent.

 

Acknowledgments

The authors would also like to acknowledge the following services for providing access to their data:

  • Australia: Bureau of Meteorology
  • New Zealand: Institute of Water and Atmospheric Research (NIWA),
  • Japan: Japan Meteorological Agency (JMA),
  • South Korea: Korea Meteorological Administration (KMA),
  • China: National Meteorological Information Center of China,
  • India: India Meteorological Department, Ministry of Earth Sciences,
  • Iran: Iranian Meteorological Organization,
  • Kuwait: Department of Meteorology, Directorate General of Civil Aviation,
  • Russian Federation: Lomonosov Moscow State University, I
  • Israel: Israel Meteorological Service,
  • Turkey: Turkish Ministry of Forestry and Water Affairs,  
  • United States of America: U.S. Climate Reference Network (USCRN,  NOAA),
  • Mexcico: Comision Nacional Del Agua, Servicio Meteorologico Nacional, Mexico,  
  • Jamaica: Meteorological service Jamaica,
  • Costa Rica: University of Costa Rica (UCR),
  • Colombia: Centro Nacional de Investigaciones de Café – Cenicafé
  • Chile:  General Directorate of Water Resources (Chile),
  • Suriname: Meteorological organization of Suriname,
  • Algeria:  Algerian National Agency of Hydraulic Resources.
  • Mauritius: Mauritius Meteorological Services (MMS)
  • Austria: Hydrographic offices of Upper Austria, Lower Austria, Burgenland, Styria, Salzburg 
  • Belgium - Flanders: Flemish Environmental Agency (VMM), Operational Water Management. 
  • Belgium - Wallonia: Service public de Wallonie, Direction générale Mobilité et Voies hydrauliques, Direction de la Gestion hydrologique intégrée, Namur. 
  • Bulgaria: Rousseva et al. (2010) 
  • Cyprus: Cyprus Department of Meteorology. 
  • Germany: Deutscher Wetterdienst (DWD), WebWerdis Service 
  • Denmark: Aarhus University, Department of Agroecology 
  • Estonia: Client service department, Estonian Environment Agency, Tallinn 
  • Spain: Confederaciones Hidrográficas del Ebro, Tajo, Duero, Guadalquivir, Segura, Júcar, Miño-Sil, Cantábrico and Sur, Servei Meteorològic de Catalunya, and Meteo Navarra. 
  • France: Météo-France DP/SERV/FDP, Division Fourniture de Données Publiques 
  • Greece: Hydroskopio 
  • Croatia: Meteorological and Hydrological Service 
  • Hungary: Hungarian Meteorological Service 
  • Ireland: Data from Met Éireann, financial support from Irish EPA STRIVE Programme - SILTFLUX (2010-W-LS-4) and UCD Earth Institute 
  • Italy: the Servizio Idrografico Abruzzo, Protezione Civile Regione Basilicata, Ufficio idrografico Bolzano, Servizio Idrografico Friuli-Venezia Giulia, Centro funzionale regione Lazio, Meteotrentino, Agenzia Regionale per lo Sviluppo e l'Innovazione dell'Agricoltura nel Molise, Servizio Meteo-Idro-Pluviometrico Marche, Associazione Regionale dei Consorzi di Difesa della Puglia, Osservatorio delle Acque Sicilia, Servizio Idrologico Regionale Toscana, Servizio Risorse idriche e rischio idraulico Umbria, Diodato Nazzareno from Regione Campagna, Centro funzionale regionale Valle d'Aosta and the Hydro-Meteo-Climate Service of the Environmental Agency ARPA Calabria, ARPA Emilia Romagna, ARPA Liguria, ARPA Lombardia, ARPA Piemonte, ARPA Veneto. 
  • Latvia: Latvian Environment, Geology and Meteorology Centre, Riga 
  • Lithuania: Mazvila et al. (2010) 
  • Luxembourg: Agrarmeteorologisches Messnetz Luxembourg 
  • Netherlands: KNMI, Royal Netherlands Meteorological Institute 
  • Portugal: Agência Portuguesa do Ambiente, Departamento de Monitorização de Recursos Hídricos 
  • Poland: Banasik et al. (2001) 
  • Romania: National Meteorological Administration 
  • Slovakia: Malisek et al. (1992) , Jan Styk and Jozef Kobza from Soil Science and Conservation Research Institute Bratislava 
  • Slovenia: Slovenian Environment Agency, Petan et al. (2010) 
  • Sweden: Swedish Meteorological and Hydrological Institute (SMHI) 
  • Switzerland : MeteoSchweiz, Meusburger et al. (2012) 
  • United Kingdom: NERC & UK Environmental Change Network (ECN), and British Atmospheric Data Centre (BADC)

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Title: Global Rainfall Erosivity
Resource Type: Datasets, Soil Threats Data
Theme/Sub-Theme:
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Italy
Author: Panos Panagos, Cristiano Ballabio
Year: 2017
Publisher: European Commission, Joint research Centre
Language: en
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