As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.

Several Sediment Delivery Ratio (SDR) models have been used to estimate Sediment Yield (SY), mainly in data-scarce and ungauged basins, such as in many regions of Brazil. However, it is difficult to choose the most suitable SDR model, mainly because of the lack of investigations of this approach using observed data. Here, we investigated the performance of five widely used SDR models (SDR_EST) to estimate sediment yield values (SY_EST ) based on observed data in a tropical watershed. We used observed sediment yield values (SY _OBS) during September 2011 to July 2017 in three sub-basins of the Guariroba Basin, Midwestern Brazil. To estimate the average annual soil loss, we used the Revised Universal Soil Loss Equation. The SDR_OBS and SY_OBS ranged from 5.56 to 10.54% and 940.76 to 5,400.32 t yr^-1, respectively. The Williams and Berndt (1972) method presented the best performance, with a percent bias ranging from -2.34 to 3.30% in SRD estimation. Therefore, this model provided suitable SDR and SY estimates, and may be useful to estimate SY in other tropical data-scarce and ungauged basins.

Bedload sediment transport in rivers can cause impacts, such as bed erosion/deposition, sandbank formation and changes in flow capacity. Bedload sampling techniques have limitations related to spatial and temporal resolution. These constraints are more relevant in rivers with dunes and high sediment transport. This paper presents a comparison between bedload transport rates estimated with direct and indirect methods in a river with sand dunes. The case study area is a stretch of the Taquari River, in Brazil. Surveys were carried out on three consecutive days, during a flood season. A SonTek M9-ADCP with HydroSurveyor capabilities activated was used to simultaneously measure bathymetry and water velocities throughout a river reach, and also to perform moving-bed tests at six verticals along a predefined cross-section. A mechanical trap (Helley–Smith) was used to collect bedload samples at the same time and positions where the moving-bed tests were performed. Sediment transport was calculated and compared following different approaches: (1) ADCP-BT (Bottom Tracking); (2) modified ISSDOTv2 method (dune tracking); (3) HelleySmith mechanical trap; (4) and five empirical equations. The results showed good agreement between the methodologies, indicating the potential of using ADCPs for hydro sedimentological studies due to the advantages of integrating bathymetry, flow velocity and bedload data.

Modelagem Hidrológica Combinado à Mudança Climática, Uso e Ocupação do Solo

Both the excess and alteration of bed sediments in river systems can cause socioeconomic and environmental damage; thus, the quantification of bedload transport is an important tool to assess the health of rivers and help in decision-making imposed by the agencies responsible for water resource management. This work aims to evaluate the efficiency of pressure-difference samplers (Helley–Smith) qualitatively and quantitatively when used in environments with sandy characteristics. The experiments were carried out in a stream with full transparency and two pressure-difference samplers with nozzle dimensions of 7.20 × 7.20 cm and 8.89 × 7.50 cm. The Particle Image Velocimetry technique was used to analyze the sampler efficiency simultaneously with an Acoustic Doppler Current Profiler. Qualitative results showed that the way the equipment is allocated at the bottom of the river can generate overestimated or underestimated sediment transport measurements. Additionally, evaluating it quantitatively, we see that the collection efficiency of the equipment varied between 15.45% and 534.78% when compared to the results obtained by the Particle Image Velocimetry technique.