Evaluating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil

Sulimar Caparoci Nogueira,Maurício Moreira,Margarete Lordelo Volpato

doi:10.3390/rs10020313

Abstract

Precipitation estimates derived from the Eta model and from TRMM (Tropical Rainfall Measuring Mission) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) remotely sensed data were compared to the precipitation data of the INMET (National Institute of Meteorology) meteorological stations in the south-southeast region of Minas Gerais state, Brazil, in the period between July 2009 and June 2015. Then, information about evapotranspiration (ETR), water deficit (DEF), and water surplus (EXC) was obtained from the precipitation data, using the sequential water balance (SWB) separately for each type of precipitation data (INMET, TRMM, Eta, and CHIRPS). Subsequently, the components of the SWB were comparatively analyzed. The results indicate that all three products overestimate rainfall. The strongest relationships between the INMET data and the estimated data were observed for the TRMM, in terms of precipitation estimates, as well as DEF, EXC, and ETR components. The Eta precipitation estimates are overestimated relative to those from INMET, resulting in underestimation of the water deficit (DEFETA) and overestimation of evapotranspiration (ETRETA). In general, the CHIRPS data presented a pattern similar to the station data, though statistical analyses were lower than those of the TRMM data.

Highlights

Precipitation is one of the main meteorological variables affecting soil preparation and the harvest, transportation, growth, development, and productivity of agricultural crops, in addition to having a large impact on water and energy resources [1]
The best results between CHIRPS and INMET were obtained for the crop years 2011/2012 (R2 = 0.98) and 2010/2011 (R2 = 0.96), and the lowest coefficients were observed for the crop years 2013/2014 (R2 = 0.88) and 2014/2015 (R2 = 0.90)
We evaluated the components of the sequential water balance: the observed data of INMET and the estimates from the Eta model and remotely sensed Tropical Rainfall Measuring Mission (TRMM) and CHIRPS data were used to determine water surplus, water deficit and evapotranspiration

Summary

Introduction

Precipitation is one of the main meteorological variables affecting soil preparation and the harvest, transportation, growth, development, and productivity of agricultural crops, in addition to having a large impact on water and energy resources [1] In this context, the information on meteorological conditions provided by ground stations is of utmost importance for planning agricultural activities, monitoring crops, and making decisions. Remotely sensed data and numerical predictions have helped to overcome the lack of meteorological stations, serving as an alternative source of time series data on global and/or regional scales and enabling event detection and decision-making. These resources are rarely used in the agricultural sector [5,6]

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Conclusion