Abstract:-The objective of this work was to evaluate the influence of anthropic activities on the effective precipitation (eP) and soil loss in watersheds under different land uses in a tropical dry forest region. The experimental area was located in the central part of the State of Ceará, Brazil. The land uses evaluated were: fallow Caatinga (FC), thinned Caatinga (TC) and deforested Caatinga followed by a burning procedure and pasture cultivation (DBP). The areas were monitored in the rainy season (January to May, 20… Show more
“…In contrast, high magnitude events which occurred over very wet (six events), and wet (10 events) vegetation density scenarios had the hillslope connectivity processes buffered (Santos, Andrade, Medeiros, Palácio, & Araújo Neto, 2017). This behaviour represents the typical hillslope dryland processes where pre-rainy season rains generate a small portion of the total annual runoff, but are responsible for substantial erosion volume due to the significant exposure of the soils (Lima et al, 2013;Palácio et al, 2016;Santos, Andrade, Medeiros, Palácio, & Araújo Neto, 2017). Simultaneously, it is important to emphasize the highly variable monthly rainfall behaviour, indicating that the vegetation fluctuation analysis should be done by the precipitation behaviour not by fixed seasons or months.…”
Section: Discussionmentioning
confidence: 97%
“…Different C‐factors for each density class in each rainfall/vegetation scenario enable understanding of how the natural Caatinga dynamics affect the potential connectivity. In this way, the C‐factor was defined, based on the literature (Anache et al, 2017; Andrade et al, 2017; C. A. Lima et al, 2013; Palácio et al, 2016) for the different NVDI responses (Table 3): bare land and sparse vegetation (0.5), savanna (0.11), shrubland (0.04), dry forest (0.0167), and rain forest (0.0004). To understand the vegetation seasonality impact on the connectivity the IC models were run for each vegetation scenario, allowing the creation of five connectivity scenarios.…”
Section: Methodsmentioning
confidence: 99%
“…based on research on runoff, infiltration, erosion, sediment transport, models and experimental plots effects of different vegetation, including Caatinga areas and similar vegetation stages/ densities and rainfall seasons(Figueiredo et al, 2016;Santos, et al, 2013;Palácio et al, 2016) for the different NVDI responses (Table3): bare land and sparse vegetation (0.5), savanna (0.11), shrubland (0.04), dry forest (0.0167), and rain forest (0.0004). To understand the vegetation seasonality impact on the connectivity the IC models were run for each vegetation scenario, allowing the creation of five connectivity scenarios.T A B L E 2…”
Seasonally dry forests in tropical regions show over 300% inter-annual biomass variability that directly affects the runoff and erosion dynamics. However, biomass fluctuation is mostly overlooked in hydrosedimentological analysis, including in connectivity analysis. The aim of this paper is to understand how the dryland vegetation seasonality in Brazilian drylands affects the potential runoff and sediment connectivity using the Index of Connectivity (stream and outlet targets). Two main analytical steps were used to identify the influence of dry forest biomass fluctuation on connectivity: Creation of vegetation scenarios based on the relationship between rainfall patterns and NDVI fluctuations (Landsat images); Identification of the effect of the vegetation scenarios on Index of Connectivity. The method was applied to a 90 km 2 watershed in NE Brazil, creating a daily vegetation classification using five vegetation scenarios related to rainfall parameters, with average NDVI values from 0.18 during very dry scenarios (<20 mm of antecedent rainfall) to 0.62 in very wet scenario (>500 mm of antecedent rainfall). The primary connectivity behaviour is controlled by a continuous connectivity decrease, reaching 32%, related to increase of humidity and vegetation biomass. At the same time, due to rainfall irregularity, high magnitude rainfall events can occur even during very dry scenarios, when the watershed shows very high potential connectivity. It indicates that connectivity in runoff-dominated regions is temporally variable due to the highly seasonal vegetation and variable incidence of intense rainstorms.
“…In contrast, high magnitude events which occurred over very wet (six events), and wet (10 events) vegetation density scenarios had the hillslope connectivity processes buffered (Santos, Andrade, Medeiros, Palácio, & Araújo Neto, 2017). This behaviour represents the typical hillslope dryland processes where pre-rainy season rains generate a small portion of the total annual runoff, but are responsible for substantial erosion volume due to the significant exposure of the soils (Lima et al, 2013;Palácio et al, 2016;Santos, Andrade, Medeiros, Palácio, & Araújo Neto, 2017). Simultaneously, it is important to emphasize the highly variable monthly rainfall behaviour, indicating that the vegetation fluctuation analysis should be done by the precipitation behaviour not by fixed seasons or months.…”
Section: Discussionmentioning
confidence: 97%
“…Different C‐factors for each density class in each rainfall/vegetation scenario enable understanding of how the natural Caatinga dynamics affect the potential connectivity. In this way, the C‐factor was defined, based on the literature (Anache et al, 2017; Andrade et al, 2017; C. A. Lima et al, 2013; Palácio et al, 2016) for the different NVDI responses (Table 3): bare land and sparse vegetation (0.5), savanna (0.11), shrubland (0.04), dry forest (0.0167), and rain forest (0.0004). To understand the vegetation seasonality impact on the connectivity the IC models were run for each vegetation scenario, allowing the creation of five connectivity scenarios.…”
Section: Methodsmentioning
confidence: 99%
“…based on research on runoff, infiltration, erosion, sediment transport, models and experimental plots effects of different vegetation, including Caatinga areas and similar vegetation stages/ densities and rainfall seasons(Figueiredo et al, 2016;Santos, et al, 2013;Palácio et al, 2016) for the different NVDI responses (Table3): bare land and sparse vegetation (0.5), savanna (0.11), shrubland (0.04), dry forest (0.0167), and rain forest (0.0004). To understand the vegetation seasonality impact on the connectivity the IC models were run for each vegetation scenario, allowing the creation of five connectivity scenarios.T A B L E 2…”
Seasonally dry forests in tropical regions show over 300% inter-annual biomass variability that directly affects the runoff and erosion dynamics. However, biomass fluctuation is mostly overlooked in hydrosedimentological analysis, including in connectivity analysis. The aim of this paper is to understand how the dryland vegetation seasonality in Brazilian drylands affects the potential runoff and sediment connectivity using the Index of Connectivity (stream and outlet targets). Two main analytical steps were used to identify the influence of dry forest biomass fluctuation on connectivity: Creation of vegetation scenarios based on the relationship between rainfall patterns and NDVI fluctuations (Landsat images); Identification of the effect of the vegetation scenarios on Index of Connectivity. The method was applied to a 90 km 2 watershed in NE Brazil, creating a daily vegetation classification using five vegetation scenarios related to rainfall parameters, with average NDVI values from 0.18 during very dry scenarios (<20 mm of antecedent rainfall) to 0.62 in very wet scenario (>500 mm of antecedent rainfall). The primary connectivity behaviour is controlled by a continuous connectivity decrease, reaching 32%, related to increase of humidity and vegetation biomass. At the same time, due to rainfall irregularity, high magnitude rainfall events can occur even during very dry scenarios, when the watershed shows very high potential connectivity. It indicates that connectivity in runoff-dominated regions is temporally variable due to the highly seasonal vegetation and variable incidence of intense rainstorms.
The USLE and the RUSLE are two common erosion prediction models that are used worldwide, and soil erodibility (K-factor) is one parameter used to calculate them. The objectives of this study were to investigate the variability of soil-erodibility factors under different soil-texture classes and evaluate the efficiency of diffuse reflectance spectroscopy (DRS) in the near-infrared range at predicting the USLE and RUSLE K-factors using a partial least squares regression analysis. The study was conducted in Fluvisols in dry tropical forest (the Caatinga). Sampling was undertaken in the first 20 cm of soil at 80 sites distributed 15 m apart on a 70 m × 320 m spatial grid. Results show that the clay fraction is represented mainly by 2:1 phyllosilicates. Soil organic matter content is low (<0.2%), which is typical of tropical dry forests, and this is reflected in the high values of the calculated USLE and RUSLE K-factors. An empirical semivariogram was used to investigate the spatial dependence of both K-factors. Pedometric modeling showed that DRS can be used to predict both USLE (R2adj = 0.53; RMSE = 8.37 10−3 t h MJ−1 mm−1) and RUSLE (R2adj = 0.58; RMSE = 6.78 10−3 t h MJ−1 mm−1) K-factors.
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