Many people contributed to this doctoral thesis and to the student during this process. I'm sure that this section will be too short and my words inadequate to describe the size of my acknowledgements and my sincere gratitude for the pleasure of having all these people in my life. Trully sorry if I have forgotten anyone. Firstly I would like to thank my advisor Eduardo Mario Mendiondo, for accepting to do this straigh PhD and full of 'sandwiches' travels. For giving me so many opportunities for personal and professional growth, and above all, for the trust placed in me which was key during this period of so many doubts. I want to thank my co-supervisor Raghavan Srinivasan, for accepting me at Texas A & M University. For all the teachings and discussions that made this study possible, the willingness to help allways and for the example of dedication, vision and humility.
Erosion process occurs naturally, shaping the Earth's surface. Soil loss can cause harmful effects to the environment when intensive anthropic activities occur. Mathematical models have been used as effective and less costly alternatives for identifying sites highly prone to soil loss, especially at the watershed scale. In Brazil, the Revised Universal Soil Loss Equation (RUSLE) is one of the most commonly used soil loss prediction models. The RUSLE requires information on soil erodibility, rainfall erosivity, topography, land use and cover (C), and conservation practices (P) to estimate average annual soil losses. Images derived from remote sensing techniques are generally used to quantify the spatialization of C factor; however, the variation in land use throughout the year is not usually considered. This study aimed to estimate soil losses in an important subwatershed of Candiota river watershed (CRWsub) by using RUSLE, considering land use and rainfall erosivity in different periods of the year. The periods considered were P1 (January, February and March), P2 (April, May and June), P3 (July, August and September) and P4 (October, November and December). Based on the results, the lowest soil losses occurred in P1. Probably, the high vegetation cover in the soil increases its protection against rainfall erosivity. In P3, the heavy rainfall events are predominantly frontal, occurring in the same months as those when the preparation of the soil for later planting takes place; that is, there is no vegetation cover in this period, thus making the soil more prone to erosion. The use of different images to classify and identify land uses is the best way to understand soil losses throughout the year in the study area. It was possible to observe that agricultural areas are generally associated with greater soil losses in the subwatershed. In addition, the land uses were considered to vary quarterly, thereby making it possible to identify the periods most prone to erosion processes throughout the year. Finally, the erosion percentages in the subwatershed can be linked to the tolerance index for different land-uses, soil classes, and slope categories.
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