Improvement of simulating BMPs and LID practices in L-THIA-LID model

Yaoze Liu, Purdue University


Best management practices (BMPs) and low impact development (LID) practices are popular approaches used to reduce the negative impacts of urbanization on hydrology and water quality. To assist planners and decision-makers in urban development projects, user-friendly tools are needed to assess the effectiveness of BMPs and LID practices on water quantity and quality. To address this need, the Long-Term Hydrologic Impact Assessment-LID (L-THIA-LID) model was enhanced with additional commonly used BMPs and LID practices represented in the model, improved approaches to estimate hydrology and water quality, and representation of practices in series. The tool was used to evaluate the performance of BMPs and LID practices individually and in series in four types of idealized land use units and watersheds (low density residential area, high density residential area, industrial area, and commercial area). Simulation results were comparable with the observed impacts of these practices in other published studies. Then, the model was enhanced further by creating L-THIA-LID 2.1 for modelling BMPs/LID practices at watershed scales and adding cost estimates of practices. The sensitivity and uncertainty of the enhanced model were analyzed using SobolOefs global sensitivity analysis method and the bootstrap method, respectively. CN (Curve Number) and Ratio_r (Practice outflow runoff volume/inflow runoff volume) were the most sensitive variables before and after BMPs/LID practices were implemented, respectively. The limited observed data in the same study area and results from other urban watersheds in scientific literature were either well within or close to the uncertainty ranges found in this study, indicating the model has good precision. Sixteen implementation scenarios of BMPs and LID practices were evaluated with the model at the watershed scale. The implementation of grass strips in 25% of the watershed where this practice could be applied was the most cost-efficient scenario. The scenario with very high levels of BMP and LID practice adoption provided the greatest reduction in runoff volume and pollutant loads among all scenarios. However, this scenario was not as cost-efficient as most other scenarios. The L-THIA-LID 2.1 model is a valid tool that can be applied to various locations to help identify cost effective BMP/LID practice plans at watershed scales. Finally, a decision support tool, which linked L-THIA-LID 2.1 with the A Multi-ALgorithm Genetically Adaptive Multiobjective (AMALGAM) method using the multilevel spatial optimization (MLSOPT) framework, was developed to optimally select and place BMPs/LID practices. The decision support tool was applied to an urban watershed near Indianapolis, Indiana. Optimization results at the hydrologic response unit scale indicated that for sites with different features, the optimal BMP/LID practice solutions to attain the same environmental goals differed. For sites with the same characteristics, the optimal implementation of practices could vary significantly for different environmental goals. For higher expenditures, the implementation levels and types of favored practices tended to increase relative to those for lower expenditures. Watershed scale results showed that for initial expenditures of practices, the environmental benefits increased rapidly as expenditures increased. However, beyond certain expenditure levels, additional spending did not result in noticeable additional environmental impacts. Compared to random placement of practices, the optimization strategy provided 3.9 to 7.7 times the level of runoff/pollutant load reductions for the same expenditures. To obtain the same environmental benefits, costs of random practices placement were 4.2 to 14.5 times the optimized practice placement cost. Results indicate that the decision support tool is capable of supporting decision makers in optimally selecting and placing BMPs and LID practices at watershed scales.




Bralts, Purdue University.

Subject Area

Agricultural engineering|Environmental engineering

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