Paper N National Soil Erosion Research Lab

Transcription

1 Paper N National Soil Erosion Research Lab

2 Carlos R. Mello Federal University of Lavras (UF, Lavras, MG, Brazil) Marcelo R. Viola (UF, Brazil) Samuel Beskow Federal University of Pelotas (UFPEL, Brazil) Lloyd Darrell Norton USD/RS National Soil Erosion Research Laboratory Purdue University (West Lafayette, IN, US) Presenter

3 Water erosion has been treated as one of the most important worldwide environmental problem, mainly in tropical and subtropical agricultural lands; The most important active agent of soil erosion is the rain and its potential is known as rainfall erosivity; Rainfall erosivity maps have been used as input for spatial distribution of soil erosion on the basis of USLE/RUSLE since many regions or countries have presented scarce climatologic data sets for implementation of more complex models (Beskow et al., 009; Shamshad et al., 008).

4 Great part of these maps are generated by geostatistical procedures or another spatial interpolator like Inverse Weighted Distance (Salako, 010; Mello et al., 007; Silva, 004); Nevertheless, these maps present only spatial distribution of values without taking into account other physiographical properties of landscape such as continental and topographical properties which characterize the general climate behavior of a given region;

5 Mean annual and monthly precipitation have been estimated on the basis of multivariate models as a function of geographical coordinates and altitude (Marquinez et al., 003; Mello and Silva, 009); s both variables can be predicted based on the geographical coordinates, there are evidences that the same procedure may be applied to estimate mean monthly rainfall erosivity; This kind of model has a different practical use: the users can predict values for a specific location without necessity of knowledge about specific complex software which consists in one of the most relevant disadvantage of various interpolators (kkala et al., 010).

6 To develop multivariate models for estimating mean annual rainfall erosivity to Brazilian geographical regions having Latitude, Longitude and altitude as input variables, by means Backward multiple linear regression procedure.

7 Data Base: source Hidroweb/N

8 Fournier s Equation: Main Station on Figure 1; 44 Main Stations over Brazilian Territory; 773 pluviometric stations over Brazilian Territory; 155 pluviometric stations exclusively for validation; Characterization of influence area of each Main Station, identifying the pluviometric stations under influence of respectively Main Station: Thiessen Polygons; Digital Elevation Model for Brazil derived from NS/SRTM for application of models;

9 Influence rea of Main Stations over Brazil and DEM

10 Multivariate linear models: One model for each Brazilian region: Southeast, South, Northeast and North-Midwest (one model); EI30 = f (,, ); Backward procedure was applied to select the variables most significant through SS Statistical Software, running the following model: EI 30 b b = b 1 b 3 b b 11 b 17 3 b b 1 4 b b 18 5 b 13 b 3 6 b 14 b 7 b b b 9 3

11 Validation procedure: Mean absolute error (ME): ME 1 n PV n i i (%) = i= 1 OV OV i 100 Bias (T, %): T 1 n OV PV OVi n i i (%) = i= Chart of residuals and scattered points around 1:1 line; Shapiro-Wilk test: employed to evaluate whether the residuals present a normal distribution and the estimates are unbiased.

12 Matrix algorithms available in rcgis (ESRI, 004) combined with the models was applied taking into account, and values from the layers created, generating raster maps for each region considering 1-km² cells. Then, maps of annual rainfall erosivity for Brazil were created using the multivariate models. These maps were compared to the previous rainfall erosivity map in an attempt of emphasizing the contributions of this study compared to the former study.

13 To evaluate the spatial distribution of mean annual rainfall erosivity, two auxiliary maps were created through rcgis (ESRI, 004), to help interpretation of the rainfall erosivity distribution in Brazil as well as validation of the models: maximum daily rainfall which represents the spatial distribution of rainfall intensity; mean annual precipitation, making it easier analysis of the total annual rainfall distribution over different Brazilian regions.

14 Spatial distribution of maximum daily rainfall and mean annual precipitation over Brazil: Mean nnual Precipitation (mm) Mean Maximum Daily Precipitation (mm)

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16 Statistical quality of multivariate models: Region T (%) ME (%) Maximum E (%) djstr R S-W statistical* (W; p) Southern ;0.095 Southeast ;0.070 North-Mid ;0.134 West Northeast ;0.10

17 Distribution of rainfall erosivity predicted around 1:1 line and behavior of residuals Southeast South

18 Northeast North-Midwest

19 Models adjusted EI = Southeast South EI =

20 Models adjusted Northeast North-Midwest EI = E EI =

21 Models application: Layers created for model application: ltitude, Longitude e Latitude; Raster Maps for each region Raster Maps Models

22 Brazilian Rainfall Erosivity Maps on the basis of models and GIS Intervals of,000 MJ mm (ha h year) -1 Classification proposed by Foster et al.(1981)

23 Precision statistics demonstrated that the models had good accuracy on estimate of annual rainfall erosivity (djustedr > 0.69, mean absolute error less than 15%, residuals unbiased and without pattern and points well scattered around 1:1 lines); We demonstrated that the mean annual rainfall erosivity can be acceptably explained by geographical coordinates and altitude since they define the basic climate characteristics of Brazilian pluvial regimes.

24 We would like thank to Federal University of Lavras (Water Resources Graduate Program), CNPq, Fapemig (PPM IV-060/10) and USD/RS National Soil Erosion Research Laboratory by supporting of this research.