Abstract
Precipitation is one of the main factors of soil erosion, and the intensity, duration and frequency of precipitation can aggravate the erosive process. The objective of the study was to determine the spatial and temporal distribution of rainfall erosivity in the Amazon. Data were collected from 334 rainfall gauge stations distributed throughout the region, composing a series of 20 years (1997–2016). The gaps in this series were filled with satellite data using the CPC MORPHing technique. The erosivity equations used were those available in the literature and based on the modified Fournier index. The values of precipitation and erosivity were interpolated using GIS software, and thematic maps were generated for these variables. The annual value of rainfall erosivity ranged from 7060 to 36767 (Mj mm ha−1 h−1 year−1). On the monthly scale, the highest rates of erosivity were recorded in the rainy season, i.e., February and March, at approximately 1548 and 2651 (Mj mm ha−1 h−1 month−1), respectively. In the context of erosion risk, the region was classified as having very strong erosivity. Therefore, it is imperative that land management and conservation policies be implemented to minimize erosion in the region, which, at its borders, undergoes intense land use change, i.e., forests are being transformed into pastures and grain crops.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Almagro A, Oliveira PTS, Nearing A, Hagemam S (2017) Projected climate change impacts in rainfall erosivity over Brazil. Sci Rep 7:8130. https://doi.org/10.1038/s41598-017-08298-y
Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G (2013) Koppen’s climate classification, map for Brazil. Meteorol Z 22:711–728
Carvalho AAV, Oyama MD (2013) Variabilidade da largura e intensidade da Zona de Convergência Intertropical atlântica: aspectos observacionais. Revista Brasileira de Meteorologia 28(3):305–316. https://doi.org/10.1590/S0102-77862013000300007
Cohen JCV, Fitzjarrald DR, D’Oliveira FAF, Saraiva I, Barbosa IRS, Gandu AW, Kuhn PA (2014) Radar-observed spatial and temporal rainfall variability near the Tapajós-Amazon confluence. Revista Brasileira de Meteorologia 29:23–30. https://doi.org/10.1590/0102-778620130058
De Oliveira Vieira S, Satyamurty P, Andreoli RV (2013) On the South Atlantic convergence zone affecting southern Amazonia in austral summer. Atmos Sci Lett 14:1–6. https://doi.org/10.1002/asl2.401
EMBRAPA e INPE (2013) Levantamento de informações de uso e cobertura da terra na Amazônia – 2010. Sumário executivo. 1ª Impressão, Embrapa Amazônia Oriental, Belém, 7p
Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) (2015) Bioma cerrado: Latossolo. http://www.agencia.cnptia.embrapa.br/Agencia16/AG01/arvore/AG01_96_10112005101956.html. Accessed 28 Jan 2019
Farhan Y, Sregat D, Farhan I (2013) Spatial estimation of soil erosion risk using RUSLE approach, RS, and GIS techniques: a case study of Kufranja Watershed, Northern Jordan. J Water Resour Prot 5:1247–1261. https://doi.org/10.4236/jwarp.2013.512134
Ferraro RR (1997) Special sensor microwave imager derived global rainfall estimates for climatological applications. J Geophys Res 102:16715–16735. https://doi.org/10.1029/97JD01210
Ferraro RR, Weng F, Grody NC, Zhao L (2000) Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys Res Lett 27:2669–2672. https://doi.org/10.1029/2000GL011665
Foster GR, McCool DK, Renard KG, Moldenhauer WC (1981) Conversion of the universal soil loss equation to SI metric units. J Soil Water Conserv 36:355–359
Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS—a case study of Nethravathi Basin. Geosci Front 7:953–961. https://doi.org/10.1016/j.gsf.2015.10.007
García-Ruiz JM, Nadal-Romero E, Lana-Renault N, Beguería S (2013) Erosion in Mediterranean landscapes: changes and future challenges. Geomorphology 198:20–36. https://doi.org/10.1016/j.aaspro.2015.03.006
Grimm AM (2011) Interannual climate variability in South America: impacts on seasonal precipitation, extreme events and possible effects of climate change. Stoch Environ Res Risk Assess 25(4):537–554. https://doi.org/10.1007/s00477-010-0420-1
Haile AT, Yan F, Habib E (2015) Accuracy of the CMORPH satellite-rainfall product over lake Tana Basin in Eastern Africa. Atmos Res 163:177–187. https://doi.org/10.1016/j.atmosres.2014.11.011
Ishihara JH, Fernandes LL, Duarte AAAM, Duarte ARCLM, Ponte MX, Loureiro GE (2014) Quantitative and spatioal assessment of precipition in the Brasilian Amazon (Legal Amazon)—(1978 to 2007). Revista Brasileira de Recursos Hídricos 19:29–39
Jones C, Carvalho LMV (2002) Active and break phases in the South America monsoon system. J Clim 15:905–914. https://doi.org/10.1175/1520-0442(2002)015%3c0905:AABPIT%3e2.0.CO;2
Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503
Kummerow CD, Hong Y, Olson WS, Yang S, Adler RF, Mccollum J, Ferraro R, Petty G, Shin DB, Wilheit TT (2001) Evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J Appl Meteorol 40:1801–1820. https://doi.org/10.1175/1520-0450(2001)040%3c1801:TEOTGP%3e2.0.CO;2
Limberger L, Silva MES (2016) Precipitação na bacia amazônica e sua associação à variabilidade da temperatura da superfície dos oceanos Pacífico e Atlântico: uma revisão. Geousp Espaço e Tempo 20(3):657–675. https://doi.org/10.11606/issn.2179-0892
Mello CR, Viola MR, Beskow S, Norton LD (2013) Multivariate models for annual rainfall erosivity in Brasil. Geoderma 202–203:88–102. https://doi.org/10.1016/j.geoderma.2013.03.009
Mondal A, Khare D, Kundu S, Mukherjee S, Mukhopadhyay A, Monndal S (2017) Uncertainty of soil erosion modelling using open source high resolution and aggregated DEMs. Geosci Front 8:425–436. https://doi.org/10.1016/j.gsf.2016.03.004
Morais LFB, Silva V, Naschenveng TMC, Hardoin PC, Almeida JEL, Weber OLS, Boel E, Durigon V (1991) Índice EI30 de Chuva e sua Relação com o Coeficiente de Chuva do Sudoeste de Mato Grosso. Revista Brasileira de Ciência do Solo 15:339–344
Napoli M, Cecchi S, Orlandini S, Mugnai G, Zanchi CA (2016) Simulation of field-measured soil loss in Mediterranean hilly areas (Chianti, Italy) with RUSLE. Catena 145:246–256. https://doi.org/10.1016/j.catena.2016.06.018
National Institute of Meteorology of Brazil (INMET) (2019) Climatological normal of the Brazil. http://www.inmet.gov.br/portal/index.php?r=clima/normaisclimatologicas. Accessed 20 Nov 2019
Oliveira RC Jr, Medina BFA (1990) Erosividade das Chuvas em Manaus (AM). Revista Brasileira Ciência do Solo 14:235–239
Oliveira RC Jr (1988) A Erosividade das Chuvas na Parte Leste do Pará. Dissertação de Mestrado. Faculdade de Ciências Agrárias do Pará, Belém, 52p
Oliveira PTS, Wendland E, Nearing MA (2012) Rainfall erosivity in Brazil: a review. Catena 100:139–147. https://doi.org/10.1016/j.catena.2012.08.006
Panagos P, Ballabio C, Borrelli P, Meusburger K, Klik A, Rousseva S, Tadić MP, Michaelides S, Hrabalikova M, Olsen P, Aalto J, Lakatos M, Rymszewicz A, Dumitrescu A, Begueía S, Alewell C (2015) Rainfall erosivity in Europe. Sci Total Environ 511(2015):801–814. https://doi.org/10.1016/j.scitotenv.2015.01.008
Panagos P, Borrelli P, Meusburger K, Yu B, Klik A, Lim KJ, Yang JE, Ni J, Miao C, Chattopadhyay N, Sadeghi SH, Hazbavi Z, Zagihi M, Larionov GA, Krasnov SF, Gorobets AV, Levi Y, Erpul G, Birkel C, Hoyos N, Naipal V, Oliveira PTS, Bonilla CA, Meddi M, Nel W, Dashti HAL, Boni M, Diodato N, Oost KV, Nearing M, Ballabio C (2017) Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Sci Rep 7:2017. https://doi.org/10.1038/s41598-017-04282-8
Pereira Filho AJ, Carbone RE, Janowiak JE, Arkin P, Joyce R, Hallak R, Ramos CGM (2010) Satellite rainfall estimates over South America—possible applicability to the water management of large watersheds. Water Resour Assoc 46(2):344–360. https://doi.org/10.1111/j.1752-1688.2009.00406.x
Pham GT, Degener J, Kappas M (2018) Integrated universal soil loss equation (USLE) and geographical information system (GIS) for soil erosion estimation in a Sap Basin: central Vietnam. Int Soil Water Conserv Res 6:99–110. https://doi.org/10.1016/j.iswcr.2018.01.001
Reboita MS, Gan MA, Rocha RP, Ambrizzi T (2010) Regimes de Precipitação na América do Sul: uma revisão bibliográfica. Revista Brasileira de Meteorologia 25(2):185–204. https://doi.org/10.1590/S0102-77862010000200004
Sanchez-Moreno JF, Mannaerts CM, Jetten V (2014) Rainfall Erosivity Mapping for Santiago Island, Cape Verde. Geoderma 218:74–82. https://doi.org/10.1016/j.geoderma.2013.10.026
Santos EB, Lucio PS, Silva CMS (2015) Precipitation regionalization of the Brazilian Amazon. Atmos Sci Lett 16:185–195. https://doi.org/10.1002/asl2.535
Silva AM (2001) Indice de Erosividade e sua Relação com a Pluviometria e Coeficiente de Chuva em Juazeiro (BA). Piracicaba, São Paulo, p 2001
Silva AM (2004) Rainfall erosivity map for Brazil. Catena 57:251–259. https://doi.org/10.1016/j.catena.2003.11.006
Sodré GRC, Rodrigues LLM (2013) Comparação entre Estimativa da Precipitação Observada pela Técnica CMORPH e Estações Meteorológicas do INMET em Diferentes Regiões do Brasil. Revista Brasileira de Geografia Física 2(6):301–307
Sun R, Yuam H, Liu X, Jiang X (2016) Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River Basin. J Hydrol 536:302–319. https://doi.org/10.1016/j.jhydrol.2016.02.054
Trindade ALF, Oliveira PTS, Anache JAA, Wendland D (2016) Variabilidade Espacial da Erosividade das Chuvas no Brasil. Pesquisa Agropecuária Brasileira 51(12):1918–1928. https://doi.org/10.1590/s0100-204x2016001200002
Willmott CJ (1985) Statistics for evaluation and comparison of models. J Geophys Res Wash 0(C5):8995–9005
Wischmeier WH, Smith DD (1965) Predicting rainfall erosion losses in the Eastern U.S.—a guide to conservation planning. In: Agricultural handbook No. 282. US
WMO—World Meteorological Organization (1989) Calculation of monthly and annual 30-year standard normals. WMO, Geneva. Technical document, 341
Yang Y, Zhao R, Shi Z, Rossel RAV, Wan D, Liang Z (2018) Integrating multi-source data to improve water erosion mapping in Tibet, China. Catena 169:31–45. https://doi.org/10.1016/j.catena.2018.05.021
Acknowledgements
The authors would like to thank the Coordination for the Improvement of Higher Education Personnel- Brazil (CAPES) - Finance Code 001 and Amazon Foundation for Studies and Research Support (FAPESPA). The second author would like to thank the CNPq for funding a research productivity Grant (Process 303542/2018-7). We would like to thank the office for research (PROPESP) and the Foundation for Research Development (FADESP) of the Federal University of Pará through Grant No. PAPQ 2019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
dos Santos Silva, D.S., Blanco, C.J.C., dos Santos Junior, C.S. et al. Modeling of the spatial and temporal dynamics of erosivity in the Amazon. Model. Earth Syst. Environ. 6, 513–523 (2020). https://doi.org/10.1007/s40808-019-00697-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-019-00697-6