Spatial Distribution of Annual and Monthly Rainfall Erosivity in the Jaguarí River Basin

<div><p>ABSTRACT The Jaguarí River Basin forms the main water supply sources for the São Paulo Metropolitan Region and other cities in the state. Since the kinetic energy of rainfall is the driving force of water erosion, the main cause of land and water degradation, we tested the hypothesis of correlation between the erosive potential of rainfall (erosivity) and geographical coordinates and altitude for the purpose of predicting the spatial and temporal distribution of the rainfall erosivity index (EI30) in the basin. An equation was used to estimate the (EI30) in accordance with the average monthly and total annual rainfall at rainfall stations with data available for the study area. In the regression kriging technique, the deterministic part was modeled using multiple linear regression between the dependent variable (EI30) and environmental predictor variables: latitude, longitude, and altitude. From the result of equations and the maps generated, a direct correlation between erosivity and altitude could be observed. Erosivity has a markedly seasonal behavior in accordance with the rainy season from October to March. This season concentrates 86 % of the estimated EI30 values, with monthly maximum values of up to 2,342 MJ mm ha-1 h-1 month-1 between December and January, and minimum of 34 MJ mm ha-1 h-1 month-1 in August. The highest values were found in the Mantiqueira Range region (annual average of up to 12,000 MJ mm ha-1 h-1), a region that should be prioritized in soil and water conservation efforts. From this validation, good precision and accuracy of the model was observed for the long period of the annual average, which is the main factor used in soil loss prediction models.</p></div

Sediment export simulations, in Mg ha^-1y^-1, as a function of forest area for scenarios with different restoration strategies: Riparian Restoration (in blue), the Steepest Slopes Restoration (red), 2-Way restoration (gray), and the Conservador das Águas Project (green).

<p>The shaded areas represent the range between the calibration members, and the lines represent the mean of the calibration members. Each geometric symbol in the graph is a restoration scenario (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192325#pone.0192325.g009" target="_blank">Fig 9G</a> for more details).</p

Simulated soil loss by calibration member 7a, in Mg ha^-1y^-1.

<p>Simulated soil loss by calibration member 7a, in Mg ha^-1y^-1.</p

The calibrated values of pasture <i>C</i> and Sediment Retention Efficiency, and results of simulated soil loss and sediment export are listed for each calibrated member.

<p>In the empty cells, calibration was not successful, and its members were not considered. Mean absolute error from simulated sediment export was calculated using the observed sediment export (of 1.35 Mg ha^-1y^-1).</p

Relationship between sediments trapping and vegetation buffer width suggested by Liu et al., Park et al., and Yuan et al. [42–44], and for the Sediment Retention Efficiency of InVEST between 45% and 65% (in shaded).

<p>Relationship between sediments trapping and vegetation buffer width suggested by Liu et al., Park et al., and Yuan et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192325#pone.0192325.ref042" target="_blank">42</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192325#pone.0192325.ref044" target="_blank">44</a>], and for the Sediment Retention Efficiency of InVEST between 45% and 65% (in shaded).</p

Summary of the scenarios used and restoration strategies and their characteristics.

<p>Summary of the scenarios used and restoration strategies and their characteristics.</p

Parameters and input data used in the model.

<p>Parameters and input data used in the model.</p

Temporal series of twice-a-day data of discharge (observed), turbidity (calculated with (Eq 1)) and sediment export (calculated with (Eq 3)) in the mouth of Posses Watershed, from October 2010 to September 2015.

<p>On the right, the percentage frequency distribution of the same variables.</p