University of Missouri Kansas City
Urban flooding became to be a significant issue for many cities in the world due to urbanization and increased impervious areas (Nigel and Andy 2007). Rain gardens are considered to be an economically-friendly solution for the urban storm water problem. The Marlborough neighborhood, in urban area of Kansas City, MO (USA) was selected to be a large study area with dense rain garden construction(Robert and Leila 2012). The city introduced the rain garden project into this neighborhood to see if the rain gardens can perform well in reducing inflow to the collection system.
Monitoring is needed to quantify design parameters and long-term performance of rain gardens. There are eight rain gardens (part of 135 rain gardens in this six blocks neighborhood) that were monitored by the University of Missouri-Kansas City (UMKC) research team. The monitoring data reveal that the eight rain gardens have different responses from the same rain event. There are many candidate factors which may affect a response, such as garden types, watershed area, surface area, precipitation duration, or precipitation intensity. Therefore, a detailed rain garden hydrology characteristics report can be generated, to be used by future engineers who are planning rain gardens to control urban runoff.
Rain garden hydrology characteristics research have been done for many years by different research groups. However, few studies show the detailed rain garden hydrology characteristics based on field data. Most studies are limited to short monitoring periods or only one or two rain gardens. Our study results can validate rain gardens hydrology features . Thus, it can provide valuable support for future engineering site design and research work.
Step 1: Data Collection:
There were 16 rain events monitored June 2012 until October 2013. However, few heavy rains occurred. The monitoring will continue until May 2015 in order to get additional storm events.
Step 2: Data Analysis
Water level pressure sensors were placed in the garden inlet and deepest point in the garden. A comparison table was generated for each significant storm event.
Based on the tables from different rain events, the statistical Backward Stepwise Regression is an efficient procedure for this study. The regression process in Sigmaplot software was used. Detailed statistical data analysis results figure and table were generated. The factor which most affects the gardens'responses can be identified.
Step 3: Results and discussion
Based on the data from 2011 to 2013, the Backward Stepwise Regression process showed the factors' response on total water volume getting into the gardens.
Table 1 illustrated that the factors of watershed area (square meter), garden type and biofilter percentage do not significantly affect the response of the total water volume getting into gardens. The factors of surface area (square meter) and rainfall (mm) have significant impact on the water volume.
Using the same method, the relationships between CV and all the factors will be analyzed. Data will be updated with new monitoring data from September 2014 to May 2015.
Conclusion and significance:
Long time monitoring data and multiple study sites are the unique part of this study. Few studies show the detailed rain garden hydrology characteristics based on field data. This study's results can validate rain gardens hydrology features. Thus, it can provide more valid support for future engineering site design and research work.
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2.Dunnett , N. and A. Calyden. (2007). Rain Gardens- Managing Water Sustainable in the Garden and Designed Landscape. Section 1, page 13. Timber Press. Portland, OR.
3.Pitt, R., and L. Talebi. (2012). Modeling of Green Infrastructure Components and Large-Scale Test and Control Watersheds at Kansas City, MO.