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United States Department of Agriculture

Agricultural Research Service

Research Project: BIOGEOCHEMICAL PROCESSES AND SOIL MANAGEMENT IMPACTS ON SOIL EROSION, SOIL/AIR/WATER QUALITY, AND GREENHOUSE GAS EMISSIONS

Location: National Soil Erosion Research Lab

Title: Sea surface temperature (SST) and rainfall erosivity in the Upper Grande River Basin, Southeast Brazil

Authors
item Mello, C -
item Norton, Lloyd
item Curi, Nilton -
item Yanagi, S -
item Silva, A -

Submitted to: Science and Agrotechnology of Lavras
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 28, 2012
Publication Date: March 1, 2012
Repository URL: http://handle.nal.usda.gov/10113/59358
Citation: Mello, C.R., Norton, L.D., Curi, N., Yanagi, S.N., Silva, A.M. 2012. Sea surface temperature (SST) and rainfall erosivity in the Upper Grande River Basin, Southeast Brazil. Science and Agrotechnology of Lavras. 36(1):53-59.

Interpretive Summary: Soil erosion by water is an important global problem and the driving force is climate. In order to make good predictions of erosion, an understanding of how oceans affect climate are important. Changes in the ocean temperatures can affect regional rainfall and especially the ability of rain to cause erosion. We studied the effect of changes in ocean temperatures on rainfall in two different regions of an important river system in southern Brazil. The purpose was to determine if indices of these changes helped in determining rainfall variables used in erosion predictions and see if they could improve the predictions of erosion. We found using a simple statistical relation, that indeed these variables were affected by the ocean temperatures and could be used to improve predictions. These indices of the occurrence of El Nino or La Nina conditions occurring in the Pacific Ocean aids us in understanding how often storms causing erosion may occur and what the severity of expected storms may be. The practical implication of this work is that conservation measures may be better designed to cope with extreme events caused by ocean variations by obtaining better erosion predictions.

Technical Abstract: Relationships between regional climate and oceanic and atmospheric anomalies are important tools in order to promote the development of models for predicting rainfall erosivity, especially in regions with substantial intra-annual variability in the rainfall regime. In this context, this work aimed to analyze the rainfall erosivity in headwaters of Grande River Basin, southern Minas Gerais State, Brazil. This study considered the two most representative environments, the Mantiqueira Range (MR) and Plateau of Southern Minas Gerais (PSM). These areas are affected by the El Nino Southern Oscillation (ENSO) indicators Sea Surface Temperature (SST) for Niño 3.4 Region and Multivariate ENSO Index (MEI).Rainfall erosivity was calculated for individual rainfall events from January 2006 to December 2010. The analyses were conducted using the monthly data of ENSO indicators and the following rainfall variables: rainfall erosivity (EI30), rainfall depth (P), erosive rainfall depth (E), number of rainfall events (NRE), number of erosive rainfall events (NEE), frequency of occurrence of an early rainfall pattern (ep), occurrence of late rainfall pattern (lp) and occurrence of intermediate rainfall patter (ip). Pearson’s coefficient of correlation was used to evaluate the relationships between the rainfall variables and SST and MEI. The coefficients of correlation were significant for SST in the PSM sub-region. Correlations between the rainfall variables and negative oscillations of SST were also significant, especially in the MR sub-region; however, the Person’s coefficients were lesser than those obtained for the SST positive oscillations. The correlations between the rainfall variables and MEI were also significant but lesser than the SST correlations. These results demonstrate that SST positive oscillations play a more important role in rainfall erosivity, meaning they were more influenced by El-Niño episodes. Also, these results have shown that the ENSO variables have potential to be useful for rainfall erosivity forecasting in this region.

Last Modified: 9/21/2014