Comparing Dual Drainage Model (DDM) with SWMM: a case study in John Street watershed, Champaign IL

This study investigated the capacity and uncertainty of Dual Drainage Model (DDM) in urban storm water management by modeling dual drainage system in John Street watershed, Champaign IL under major and minor storms, by comparing the model performance of DDM to Storm Water Management Model (SWMM) and by examining the sensitivity of Green Infrastructure (GI) application in DDM. Considering storm water dual drainage during severe storms could reduce property damage and economic loss from flooding. Available dual drainage models occupy heavy computational burden, compel demanding setup efforts, or have no interactions between surface and underground flow. Instead, DDM is a one-dimensional (1D) hydrologic-hydraulic model, including innovative surface modules and a traditional SWMM sewer engine. Its execution file is merely 3.14-MB, and the program is easy to set up with auxiliary data from Geographic Information System (GIS). However, there was only one case study and no assessment on model performance. Therefore, in this study a 458-acre dual drainage system in John Street watershed was assessed by DDM, comprising 26 blocks, 76 streets, 66 inlets, 68 manholes and 67 conduits. The storm water runoff from overland, on street and in sewer were compared to those in SWMM under 2-year, 10-year, 50-year and 100-year 60-minute rainfall. Hydrograph and statistical errors were used to visualize and quantify the model performance. A sensitivity analysis for GI was conducted under five scenarios with different catchment and sewer conditions. Results showed DDM worked better under major high-intensity storms, by providing the closest total runoff volume as SWMM (-1.21% error) and a conservative estimation of surface peak flow. Unit change in GI properties (percent impervious, suction head, hydro conductivity, porosity, etc.) resulted in up to 0.3 unit change of overland runoff during minor storms, supporting that DDM is sensitive to GI. More case studies with real observatory data are recommended for DDM future assessment. Former observations suggest: i) using DDM for urban dual drainage modeling during major storms and ii) adding GI module in DDM future development. This study is of importance to hydrologist, engineers and researchers because DDM provides detailed flow properties and interactions. It is also critical to city builders, government and residents in terms of reducing economic loss by identify flooding area and causes

WaterHE-NeRF: Water-ray Tracing Neural Radiance Fields for Underwater Scene Reconstruction

Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. Addressing the limitations of existing underwater NeRF methods in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision, this study proposes WaterHE-NeRF. We develop a new water-ray tracing field by Retinex theory that precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF, through its illuminance attenuation mechanism, generates both degraded and clear multi-view images and optimizes image restoration by combining reconstruction loss with Wasserstein distance. Additionally, the use of histogram equalization (HE) as pseudo-GT enhances the network's accuracy in preserving original details and color distribution. Extensive experiments on real underwater datasets and synthetic datasets validate the effectiveness of WaterHE-NeRF. Our code will be made publicly available