Damodaram, Chandana, Marcio H. Giacomoni, C. Prakash Khedun, Hillary Holmes, Andrea Ryan, William Saour, and Emily M. Zechman, 2010. Simulation of Combined Best Management Practices and Low Impact Development for Sustainable Stormwater Management. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00462.x Abstract:  Urbanization causes increased stormwater runoff volumes, leading to erosion, flooding, and the degradation of instream ecosystem health. Although Best Management Practices (BMPs) are used widely as a means for controlling flood runoff events, Low Impact Development (LID) options have been proposed as an alternative approach to better mimic the natural flow regime by using decentralized designs to control stormwater runoff at the source, rather than at a centralized location in the watershed. For highly urbanized areas, LID practices such as rainwater harvesting, green roofs, and permeable pavements can be used to retrofit existing infrastructure and reduce runoff volumes and peak flows. This paper describes a modeling approach to incorporate these LID practices in an existing hydrologic model to estimate the effects of LID choices on streamflow. The modeling approach has been applied to a watershed located on the campus of Texas A&M University in College Station, Texas, to predict the stormwater reductions resulting from retrofitting existing infrastructure with LID technologies. Results demonstrate that use of these LID practices yield significant stormwater control for small events and less control for flood events. A combined BMP‐LID approach is tested for runoff control for both flood and frequent rainfall events.

[1] The influence of two large-scale circulation patterns (the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO)), and the effect of the interdecadal modulation of ENSO on precipitation in the state of Texas, U.S., was explored. Texas, by virtue of its size, topography, and geographical location, spans a wide range of climatic regions. The state is divided into 10 climate divisions. The precipitation pattern in each division follows different probability distributions. The climate regimes which trigger this difference are discussed. The seasonal correlation between ENSO and PDO with precipitation anomaly in each climate division was established. Copula-based models were developed to examine the dependence structure between the large-scale climate indices and average monthly seasonal precipitation. The choice of copula is discussed in light of the dependence structure. The selected copulas were then used to simulate precipitation anomalies in three climate divisions: one which has a semiarid climate, one located in the wettest region, and one straddling the subtropical humid and subtropical subhumid regions of the state. The statistical performance of bivariate models for ENSO and precipitation, and trivariate models for ENSO, PDO, and precipitation, in simulating precipitation anomalies were compared. In general, inclusion of PDO was found to improve simulation results. The most notable improvement was in simulating negative precipitation anomalies during La Niña and negative PDO. The copula models were also tested for their abilities to predict precipitation anomalies in these three regions. Again, the trivariate models performed better, especially in predicting droughts due to La Niña and negative PDO.Citation: Khedun, C. P., A. K. Mishra, V. P. Singh, J. R. Giardino (2014), A copula-based precipitation forecasting model: Investigating the interdecadal modulation of ENSO's impacts on monthly precipitation, Water Resour. Res., 50, 580-600,

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