Abstract:With an area of 6,200 km2, the Gilbués badlands region in the Brazilian drylands is the largest desertification site in the Country. It is located upstream the Boa Esperança Hydroelectric Power Plant and is contiguous to an important Brazilian agricultural area. However, primary quantitative data on erosive processes are scarce or nonexistent. We analyzed on‐site data (2018–2019) concerning small‐scale (hillslope and micro basin) processes: inter‐rill gross erosion, vegetation coverage factor, sediment yield, … Show more
“…The efficiency of the model for total siltation rate is 0.96 (Table 6), its classification ranges from very good (Moriasi et al, 2007) to good (Ritter and Muñoz-Carpena, 2013). It supports the argument that stationary parameters such as relief (in our temporal analysis scale) play a relevant role for sediment delivery mechanisms (Simplício et al, 2020); they, therefore, increase the performance of the model over time.…”
Section: Sediment Yield Modellingsupporting
confidence: 75%
“…Gaiser et al (2003) found that, for the Brazilian northeast region, the most fit among those equations is the one by Maner Maner (1958, hereafter Equation 10). Simplício et al (2020) had the same result for the dry Cerrado region of Gilbués (Fig. 2).…”
Section: Gross Erosion and Siltation Assessmentsupporting
confidence: 67%
“…The stations in Gilbués, Aiuaba and Sumé (Fig. 2) were maintained by research groups (Simplício et al, 2020;de Figueiredo et al, 2016;Srinivasan and Galvão, 2003) and only the station of Sobral is maintained by the Brazilian Water Management Agency (ANA). Those four stations presented consistent measurements over at the least two years without gaps.…”
Section: Study Areamentioning
confidence: 99%
“…For each catchment we obtained the time series of daily rainfall from FUNCEME (2019). Sub-daily measurements are scarce and available for the whole study period only in one station in Gilbués (Simplício et al, 2020). and one in Aiuaba (de Figueiredo et al, 2016), the basins with the shortest and most recent time series.…”
Section: Study Areamentioning
confidence: 99%
“…The USLE does not directly address gully erosion (Wischmeier and Smith, 1978). Nevertheless, gullies may be major sediment sources (Bennett and Wells, 2019), especially in degraded areas such as Sumé 4 and Gilbués (Srinivasan and Galvão, 2003;Simplício et al, 2020).…”
Abstract. Scarcity of precipitation data is yet a problem in erosion modelling, especially when working in remote and data scarce areas. While much effort was made to use remote sensing and reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with area varying from 10−3 to 10+2 km2 and broad timespan of monitoring (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for the rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21 % and a Nash-Sutcliffe Efficiency of 0.96 (rather than 105 % and −4.49, respectively
“…The efficiency of the model for total siltation rate is 0.96 (Table 6), its classification ranges from very good (Moriasi et al, 2007) to good (Ritter and Muñoz-Carpena, 2013). It supports the argument that stationary parameters such as relief (in our temporal analysis scale) play a relevant role for sediment delivery mechanisms (Simplício et al, 2020); they, therefore, increase the performance of the model over time.…”
Section: Sediment Yield Modellingsupporting
confidence: 75%
“…Gaiser et al (2003) found that, for the Brazilian northeast region, the most fit among those equations is the one by Maner Maner (1958, hereafter Equation 10). Simplício et al (2020) had the same result for the dry Cerrado region of Gilbués (Fig. 2).…”
Section: Gross Erosion and Siltation Assessmentsupporting
confidence: 67%
“…The stations in Gilbués, Aiuaba and Sumé (Fig. 2) were maintained by research groups (Simplício et al, 2020;de Figueiredo et al, 2016;Srinivasan and Galvão, 2003) and only the station of Sobral is maintained by the Brazilian Water Management Agency (ANA). Those four stations presented consistent measurements over at the least two years without gaps.…”
Section: Study Areamentioning
confidence: 99%
“…For each catchment we obtained the time series of daily rainfall from FUNCEME (2019). Sub-daily measurements are scarce and available for the whole study period only in one station in Gilbués (Simplício et al, 2020). and one in Aiuaba (de Figueiredo et al, 2016), the basins with the shortest and most recent time series.…”
Section: Study Areamentioning
confidence: 99%
“…The USLE does not directly address gully erosion (Wischmeier and Smith, 1978). Nevertheless, gullies may be major sediment sources (Bennett and Wells, 2019), especially in degraded areas such as Sumé 4 and Gilbués (Srinivasan and Galvão, 2003;Simplício et al, 2020).…”
Abstract. Scarcity of precipitation data is yet a problem in erosion modelling, especially when working in remote and data scarce areas. While much effort was made to use remote sensing and reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration – MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with area varying from 10−3 to 10+2 km2 and broad timespan of monitoring (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for the rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21 % and a Nash-Sutcliffe Efficiency of 0.96 (rather than 105 % and −4.49, respectively
Our understanding of the movement and storage of water in typical Caatinga plants is still limited and often disregarded in water balance calculations. This is why the objective of this work was to evaluate the water storage dynamic in typical trees of the Caatinga biome during the dry, rainy and transition period by gauging the water content levels that cause the onset of leaf emergence. In a preserved Caatinga forest, soil and stem water content of six trees of the representative species catingueira (Caesalpinia pyramidalis Tul.) were monitored with low‐cost capacitive sensors. Leaf moisture, leaf area index, leaf and stem water volume, and sap flow density were measured. The emergence of leaves occurred with a stem moisture of 0.32 m3 m−3, and the leaf area index was maximum with a stem moisture of 0.34 m3 m−3. Catingueira plants are able to absorb water below the soil water potential commonly determined as the permanent wilting point (−1.5 MPa). The volume of water stored in the plants represents 108% of the average volume stored in the Boqueirão reservoir during the study period.
High erosion and runoff rates have been measured on road infrastructure, indicating that unpaved roads might be significant sources of sediment in catchments. In this paper, the production of surface sediments from unpaved rural roads is assessed in Northeastern Brazil, in a semiarid area of the caatinga biome, vulnerable to desertification. Sediment production data from road surface segments were monitored for 2 years (2013–2014) under conditions of natural precipitation. By using hydrosedimentological modeling and a geographical information system (GIS), the sediment budget was calculated at the meso‐scale catchment (aprox. 930 km2), in order to identify the relative contribution of roads to the sediment balance. The universal soil loss equation (USLE) associated with Maner's sediment delivery ratio (SDR) equation, proved to be an adequate approach for predicting sediment yield on the road segment scale. The best results were obtained for the road without traffic, due to the non‐interference in this segment of external factors, such as traffic and maintenance activities, not explicitly considered in the model formulation. The modeling procedure showed that the roads, which occupy only 0.7% of the catchment surface, were responsible for approximately 7% of soil loss in the area. Furthermore, sediment connectivity might be enhanced by roads, which cross the river network and, therefore, deliver more directly the sediment generated at hillslopes. This is particularly important in the studied environment, where sediment connectivity is low due to limited runoff and the existence of a dense network of surface water reservoirs.
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