RESUMO
A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below (RMSE <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.
RESUMO
Vegetated buffers and filter strips are a widely used Best Management Practice (BMP) for enhancing streamside ecosystem quality and water quality improvement through nonpoint source pollutant removal. Most existing studies are either site-specific, rely on limited data points, or evaluate buffer width and slope as the only design variables for predicting sediment reduction, not considering other parameters such as soil texture, vegetation types, and runoff loads that can significantly influence the buffer efficiency. In this paper, we carry out a meta-analysis of published studies and fit regression models to explore the sediment removal capacity of riparian buffers. We compiled 905 data points from over 90 studies (including data from an online BMP database) documenting sediment trapping by vegetated buffers and recorded data regarding buffer characteristics such as buffer width, slope, area, vegetation type, sediment loading, water flow rates, and sediment removal efficiency. We found that an exponential regression model describing the relationship between sediment removal efficiency by the buffer and water inflow/outflow volume ratio explained 44% of the variance. Adding the square root of roughness increased the R 2 to 0.50. The model performance was compared with other sediment reduction regression models reported in the literature. The results point towards the importance of considering flow parameters in vegetative buffer design. The improved empirical relationships derived here can be used at local scales to understand sediment trapping potential by vegetated buffers for water quality mitigation purposes and can be built into extant hydrologic models for improved watershed-scale assessments.
RESUMO
The integrated effects of the many risk factors associated with West Nile virus (WNV) incidence are complex and not well understood. We studied an array of risk factors in and around Atlanta, GA, that have been shown to be linked with WNV in other locations. This array was comprehensive and included climate and meteorological metrics, vegetation characteristics, land use / land cover analyses, and socioeconomic factors. Data on mosquito abundance and WNV mosquito infection rates were obtained for 58 sites and covered 2009-2011, a period following the combined storm water - sewer overflow remediation in that city. Risk factors were compared to mosquito abundance and the WNV vector index (VI) using regression analyses individually and in combination. Lagged climate variables, including soil moisture and temperature, were significantly correlated (positively) with vector index as were forest patch size and percent pine composition of patches (both negatively). Socioeconomic factors that were most highly correlated (positively) with the VI included the proportion of low income households and homes built before 1960 and housing density. The model selected through stepwise regression that related risk factors to the VI included (in the order of decreasing influence) proportion of houses built before 1960, percent of pine in patches, and proportion of low income households.