Land Use and Runoff Uncertainty
Abstract:Imperviousness is an important indicator for urban watersheds in measuring the impact of land development on drainage systems and aquatic life (Schueler 1994; Arnold and Gibbons 1996; Booth and Jackson 1997). There are a lot of assumptions about how much impervious area is associated with each type of land use, but not too much data available and published to support those assumptions. The purpose of this research was to provide more details on impervious surfaces for different land uses and to measure the variability associated with land surface covers for different land uses in a large urban area in the state of Alabama. We wanted to measure this variability because of the large variability present in runoff characteristics. Also, it is important to understand the role of land use/land cover when dealing with stormwater quality data for urban stormwater management if we want to make the investment in stormwater monitoring useful.
Little Shades Creek watershed and five other highly urbanized drainage areas situated in Jefferson County, AL (around city of Birmingham) were surveyed in detail to determine their actual development characteristics and variability. Data from about 165 neighborhoods, representing ten major land uses, was intensively investigated and showed that the watersheds are highly impervious, with three of them having more than 50% of the watershed area composed of connected impervious cover. Those six watersheds have a mixture of land uses that make the land covers to have a lot of variability within each watershed. Because of their activity, some of the land uses will be more important than others when talking about runoff quantity and quality.
Investigating the total impervious area (TIA), it was found that there is considerable variability among watersheds. The TIAs values are based on the individual homogeneous neighborhoods measured within each watershed. The Little Shades Creek, the largest and the most diverse study area, is a mixture of land uses with TIA that ranges from 0.6-98%. Looking at the average impervious cover for the ten major land uses examined, it was concluded that the most imperviousness and connectivity is found in commercial and industrial land uses with more than 50% of the land use covered by impervious surfaces. There is a lot of imperviousness in freeway land use too, but zero connectivity since they are drained by grass swales. Also, it was found that there is a great deal of connectivity in all other land uses with more than 50% of the total impervious cover connected. The variability between the land uses is greater than the variability within the land uses, so it is critical that the land surface covers within each land use also be examined.
The percent of directly connected impervious cover (DCIA) was determined by direct field observations and was also estimated by empirical equations. Most common forms of equations for determining DCIA developed as part of several different studies (Alley and Veenhuis 1983; Laenen 1983; Sutherland 2000, and others) were use to predict DCIA for our data, but did not give good estimates especially when analyzed at land use level. There was a similarity between “highly connected basins” Sutherland equation and the fitted equation for our overall data. However, the residual analysis for the regression model failed, suggesting that the power equation is not the proper equation to be used for those six drainage areas. Consequently, equations for each existing land use were developed, concluding that a single equation cannot accurately estimate DCIA for all regions and land uses.
Pearson correlation matrix was used to measure the degree of association between field measurements of the land surface covers for the urbanized drainage areas to the modeled runoff volume and show how their variability can be explained. The matrix showed a strong positive relationship between runoff volume and impervious surfaces, suggesting that runoff volume can be accurately predicted by using streets, parking lot areas, and connected roofs which are all parts of DCIA, also showing where the runoff is coming from. There was also high correlation between streets, parking, and connected roofs with TIA and DCIA, suggesting that they were an important component of total imperviousness in a watershed. It is expected that the variability of the land covers (especially the DCIA) would have a similar effect on the variabilities of the runoff volumes.
Principal component analysis was used to transform a number of possible correlated variables into a smaller number of uncorrelated variables. These principal components accounted for most of the variance in the observed variables and helped identify patterns in the data set. It was found that the first component explained 40% of the total variability found in the runoff volume and was made mostly of connected impervious areas. The second component, which explained about 19% of the total variability, was mostly made of disconnected imperviousness and landscaped. There were also some overlaps between components especially when looking at third and fourth principal component. The scatter plot of the first two principal components showed several grouping corresponding to similar neighborhoods, concluding that land use is still an important descriptor of stormwater characteristics used to describe the land surface components and to describe the activities occurring in that area.
This research concluded that Jefferson County's watersheds have a wide range of impervious cover (TIA = 0.6-100%) with almost all impervious surfaces directly connected. The variability within land uses was smaller compared with the variability between land uses for total amount of impervious cover. Also, there was a lot of variability in runoff volume which was closely related to variability in development characteristics. Based on the modeled runoff coefficients, we concluded that the expected biological conditions of the receiving waters were “poor” for all study watersheds. The expected biological conditions of the receiving waters were calculated using WinSLAMM model and were confirmed by in-steam investigations conducted by the local biologists.
Document Type: Research Article
Publication date: January 1, 2009
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