EVALUATION OF SOIL LOSS EQUATIONS FOR USE IN DISTRIBUTED MODELING OF LARGE BASINS

Name: Gabriella Passamani Moreira de Almeida
Type: MSc dissertation
Publication date: 05/03/2021
Advisor:

Namesort descending Role
Diogo Costa Buarque Advisor *

Examining board:

Namesort descending Role
Daniel Rigo Internal Examiner *
Diogo Costa Buarque Advisor *
Fernando Mainardi Fan External Examiner *

Summary: Erosion is one of the main factors of soil degradation and can cause several changes
in its structure, such as increased production and transportation of sediments and
silting of water bodies. For this reason, hydrosedimentological studies are becoming
increasingly importante. Mathematical models have been widely used to understand
the dynamics of sediments in watersheds. In large-scale basins, conceptual and
distributed models, which integrate a hydrological module in their structure are usually
preferred. The MGB-SED is a hydrosedimentological model coupled to the MGB
model, which uses the MUSLE equation, to calculate sediment production on a daily
time scale. Besides MUSLE, in large scale distributed modeling for continuous
simulation, other soil loss equations have potential for these applications. The objective
of this study was to evaluate the potential of the soil loss equations, MUSLE, USLE-M
and RUSLE2 for use in distributed modeling in the Doce River watershed, using the
MGB-SED model. The seasonal variability of suspended sediments and water quality
parameters, turbidity and total suspended solids were evaluated, in comparison with
observed sediment concentration data, the spatial variability of the annual sediment
load generated in each mini-basin and of suspended sediments in each stretch of river
in addition to the relationship between solid flow and net flow for each equation. The
evaluation carried out showed that the results obtained by the three equations were
able to represent the temporal variability of suspended sediments along the Doce River
watershed and its sediment dynamics, however, the results obtained with MUSLE
showed the adjustments closest to the observed data and the best statistics for most
evaluated stations, followed by the USLE-M equation and finally RUSLE2

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