Effects of drought on the physicochemical,nutrient, and carbon metrics of flows in the Savannah River,Georgia, USA

Hydrological drought has wide-ranging impacts on water quality, nutrient and carbon metrics, and given the uncertainty of climate change and the predicted increased frequency and intensity of drought in the future, investigations into changes induced by drought become increasingly important. This study compared physicochemical parameters (temperature, conductivity, pH and DO), nutrients (TN, NO_X [NO₂ + NO₃], NH₃ and TP) and carbon (TOC and DOC) between hydrological drought conditions (2006–2008) and hydrological normal conditions (2016–2019) at five sites along the lower Savannah River (Georgia, USA). Although we had predicted that water temperatures would increase from drought, we instead found temperature was significantly lower during drought conditions. Levels of pH and DO were significantly higher during drought. Further, TN, TOC and DOC concentrations were significantly lower during drought, but NO_X concentrations were significantly higher during drought. Conductivity varied at the lower river sites, being significantly higher during drought at sites located below the city of Augusta, GA. These complex changes could be attributed to volume reductions coupled with an increase in the percentage of total flow originating from groundwater as well as limnetic reservoir inputs, persistent point source pollution, reduced natural catchment inputs and/or reduced floodplain interactions. The changes that occurred during drought may be disruptive to aquatic life, not only from reduced water quantity but also due to a scarcity of some biologically essential materials and lower food resources, combined with artificially high levels of some other potentially stressful materials.     

Accelerating Hair Rendering by Learning High-Order Scattered Radiance

Efficiently and accurately rendering hair accounting for multiple scattering is a challenging open problem. Path tracing in hair takes long to converge while other techniques are either too approximate while still being computationally expensive or make assumptions about the scene. We present a technique to infer the higher order scattering in hair in constant time within the path tracing framework, while achieving better computational efficiency. Our method makes no assumptions about the scene and provides control over the renderer's bias & speedup. We achieve this by training a small multilayer perceptron (MLP) to learn the higher-order radiance online, while rendering progresses. We describe how to robustly train this network and thoroughly analyze our resulting renderer's characteristics. We evaluate our method on various hairstyles and lighting conditions. We also compare our method against a recent learning based & a traditional real-time hair rendering method and demonstrate better quantitative & qualitative results. Our method achieves a significant improvement in speed with respect to path tracing, achieving a run-time reduction of 40%-70% while only introducing a small amount of bias.