**5.1 Spatially continuous PRC of the IRL and Halifax River watersheds**

The goal of this research is to calculate spatially continuous potential runoff coefficient (PRC) and runoff depth. In order to demonstrate how spatial and temporal variation in PRC can be estimated within a given estuarine drainage area, this study calculated PRC as a proportion of rainfall becoming surface runoff and calculated the runoff depth that is the amount of rainfall converted to runoff.

The average PRCs increase from forest, grass, agriculture, bare soil, and to impervious. Ideally, forested areas would have the highest interception of precipitation because of high percent cover of vegetation. Forested areas also have an increase in absorption from the abundance of extensive rhizome systems in the substrate. Areas of grass may have high percent cover of vegetation, but the interception of storm water may not as efficient due to the small biomass of plants. The classification image shows that forest (27.4%) and grass (24.7%) are the most dominant land covers within the IRL watershed. Forests mostly cover the northern section of the IRL watershed and the Halifax River watershed westward of coastal cities. The higher PRC values are located along the Halifax River and IRL in more developed urban communities such as Daytona Beach, Melbourne, and Palm Bay, Florida. Throughout the state of Florida, St. Augustine grass (*Stenotaphrum secundatum* [Walt.] Kuntze) is a popular turf grass used for urban lawns. This rhizome structure of this grass is dense, but relatively short in length which increases yields in runoff and shoreline erosion. Regardless of specific lawn grass, runoff coefficients are higher than forest cover. The recommended runoff coefficient value table for Georgia Stormwater Management also shows different values for grass covered lands based on the soil texture and slope [38]. However, there is only one value for forested areas despite the slope and texture. Although different from forests PRCs used in this study, a change in land cover can impact runoff yields particularly in areas of dense vegetation.

Impervious surfaces make up 15.3% of the study area much of which is located along the coastlines. Based on the National Atlas of the United States Spatial Data collected from the Florida Geographic Data Library (FGDL), there is a total of 40 cities within the IRL watershed. For this study, the ten coastal communities with the highest cover of impervious surface were included: Palm Bay, Port St. Lucie, Melbourne, West Melbourne, Daytona Beach, Port Orange, Ormond Beach, New Smyrna Beach, Titusville, and Fort Pierce. The cities of the most impervious surfaces are Palm Bay with ~50,554 acres of impervious surfaces, and Port St. Lucie with ~73,959 acres of impervious cover.

#### **5.2 Temporal variation in runoff depth**

The runoff depth varies with changes in LC/LU and intensity of precipitation. The estimated average runoff depth for the IRL ranges from 2.5–141.5 cm for the 11-year interval. The runoff depth throughout the study area fluctuates among the years (**Figure 7**), due to the changes in precipitation. With PRC values, the areas with potential nonpoint source pollution can be used as target locations for management or mitigation. Runoff depth values above the 11-year mean varied across the area and amongst the years. Runoff deviation from the mean indicated

**133**

**Figure 7.**

*A GIS-Based Approach for Determining Potential Runoff Coefficient and Runoff Depth…*

that heavy runoff depth values above the 11-year mean are years of 2008, 2014, 2015, and 2016. Causes for runoff differences can be contributed to fluctuations in climatic and annual weather patterns for rainfalls. Climatic and temporal trends have been related to changes in the IRL water quality such as El Niño years linked to declines in salinity levels in 1997, and the extended period of La Niña drought events that persisted in autumn of 2006 and summer 2007 [3]. The precipitation data for the IRL were low quantities in 2006 and 2007, and a gradual increase to 2016. The runoff depth appeared to be above the 11-year mean (35.89 cm) throughout the watershed for the years of 2014 and 2016. These are the years of strong El Niño events during which recurrent algal blooms occurred in IRL. La Niña events in Florida have shown nitrate levels to higher in ground water than in streams, which results that nutrients in aquifers accumulates from fertilizer, septic tank effluent, and animal wastes [39, 40]. The average runoff depth for 2006 was

*line represents the average 11-year runoff depth (μ = 35.89 cm) (created in Microsoft Excel 2010).*

*The mean of the total runoff depth for each year with standard error bars for standard deviation. The dotted* 

Nutrient loads vary with different land use. For example, golf courses are suspected to be a major contribution to nutrient loading in waterways aside from agricultural lands. Recorded nitrate and phosphorus concentrations significantly increased at the outflow locations from the inflow concentrations for the Morris Williams Municipal Golf Course in Austin, TX [41]. Above average runoff depths in such locations can be monitored as an indicator for early warnings of algal blooms.

**5.3 Linear regression between runoff depth, precipitation, and land development**

Developed land within the IRL watershed contains impervious surfaces consisting of roads, parking lots, and also vegetated lots that are highly altered by human development. Precipitation undoubtedly contributes to runoff quantities, but LC/ LU, and development can influence runoff yields. The regression analyses were used to test the relationship between runoff and development, as well as between runoff and precipitation. The OLS regression (**Tables 1** and **2**), the test to analyze if precipitation is an important factor for determining areas and timing with high runoff contribution, could not be adequately assessed due to spatial autocorrelation (Moran's I = 0.07, *p* < 0.0001). However, the results appear to be marginally significant at the 95% confidence interval (*p* = 0.05; robust *p* = 0.06). The LDI

the lowest of the years, with 2016 being the highest.

*DOI: http://dx.doi.org/10.5772/intechopen.87163*

*A GIS-Based Approach for Determining Potential Runoff Coefficient and Runoff Depth… DOI: http://dx.doi.org/10.5772/intechopen.87163*

#### **Figure 7.**

*Lagoon Environments around the World - A Scientific Perspective*

**5. Discussion**

areas of dense vegetation.

with ~73,959 acres of impervious cover.

**5.2 Temporal variation in runoff depth**

that the standard deviation of the observed values for runoff depth were relatively

The goal of this research is to calculate spatially continuous potential runoff coefficient (PRC) and runoff depth. In order to demonstrate how spatial and temporal variation in PRC can be estimated within a given estuarine drainage area, this study calculated PRC as a proportion of rainfall becoming surface runoff and calculated the runoff depth that is the amount of rainfall converted to runoff. The average PRCs increase from forest, grass, agriculture, bare soil, and to impervious. Ideally, forested areas would have the highest interception of precipitation because of high percent cover of vegetation. Forested areas also have an increase in absorption from the abundance of extensive rhizome systems in the substrate. Areas of grass may have high percent cover of vegetation, but the interception of storm water may not as efficient due to the small biomass of plants. The classification image shows that forest (27.4%) and grass (24.7%) are the most dominant land covers within the IRL watershed. Forests mostly cover the northern section of the IRL watershed and the Halifax River watershed westward of coastal cities. The higher PRC values are located along the Halifax River and IRL in more developed urban communities such as Daytona Beach, Melbourne, and Palm Bay, Florida. Throughout the state of Florida, St. Augustine grass (*Stenotaphrum secundatum* [Walt.] Kuntze) is a popular turf grass used for urban lawns. This rhizome structure of this grass is dense, but relatively short in length which increases yields in runoff and shoreline erosion. Regardless of specific lawn grass, runoff coefficients are higher than forest cover. The recommended runoff coefficient value table for Georgia Stormwater Management also shows different values for grass covered lands based on the soil texture and slope [38]. However, there is only one value for forested areas despite the slope and texture. Although different from forests PRCs used in this study, a change in land cover can impact runoff yields particularly in

Impervious surfaces make up 15.3% of the study area much of which is located along the coastlines. Based on the National Atlas of the United States Spatial Data collected from the Florida Geographic Data Library (FGDL), there is a total of 40 cities within the IRL watershed. For this study, the ten coastal communities with the highest cover of impervious surface were included: Palm Bay, Port St. Lucie, Melbourne, West Melbourne, Daytona Beach, Port Orange, Ormond Beach, New Smyrna Beach, Titusville, and Fort Pierce. The cities of the most impervious surfaces are Palm Bay with ~50,554 acres of impervious surfaces, and Port St. Lucie

The runoff depth varies with changes in LC/LU and intensity of precipitation. The estimated average runoff depth for the IRL ranges from 2.5–141.5 cm for the 11-year interval. The runoff depth throughout the study area fluctuates among the years (**Figure 7**), due to the changes in precipitation. With PRC values, the areas with potential nonpoint source pollution can be used as target locations for management or mitigation. Runoff depth values above the 11-year mean varied across the area and amongst the years. Runoff deviation from the mean indicated

close to the predicted values calculated for the regression model (σ = 0.90).

**5.1 Spatially continuous PRC of the IRL and Halifax River watersheds**

**132**

*The mean of the total runoff depth for each year with standard error bars for standard deviation. The dotted line represents the average 11-year runoff depth (μ = 35.89 cm) (created in Microsoft Excel 2010).*

that heavy runoff depth values above the 11-year mean are years of 2008, 2014, 2015, and 2016. Causes for runoff differences can be contributed to fluctuations in climatic and annual weather patterns for rainfalls. Climatic and temporal trends have been related to changes in the IRL water quality such as El Niño years linked to declines in salinity levels in 1997, and the extended period of La Niña drought events that persisted in autumn of 2006 and summer 2007 [3]. The precipitation data for the IRL were low quantities in 2006 and 2007, and a gradual increase to 2016. The runoff depth appeared to be above the 11-year mean (35.89 cm) throughout the watershed for the years of 2014 and 2016. These are the years of strong El Niño events during which recurrent algal blooms occurred in IRL. La Niña events in Florida have shown nitrate levels to higher in ground water than in streams, which results that nutrients in aquifers accumulates from fertilizer, septic tank effluent, and animal wastes [39, 40]. The average runoff depth for 2006 was the lowest of the years, with 2016 being the highest.

Nutrient loads vary with different land use. For example, golf courses are suspected to be a major contribution to nutrient loading in waterways aside from agricultural lands. Recorded nitrate and phosphorus concentrations significantly increased at the outflow locations from the inflow concentrations for the Morris Williams Municipal Golf Course in Austin, TX [41]. Above average runoff depths in such locations can be monitored as an indicator for early warnings of algal blooms.

#### **5.3 Linear regression between runoff depth, precipitation, and land development**

Developed land within the IRL watershed contains impervious surfaces consisting of roads, parking lots, and also vegetated lots that are highly altered by human development. Precipitation undoubtedly contributes to runoff quantities, but LC/ LU, and development can influence runoff yields. The regression analyses were used to test the relationship between runoff and development, as well as between runoff and precipitation. The OLS regression (**Tables 1** and **2**), the test to analyze if precipitation is an important factor for determining areas and timing with high runoff contribution, could not be adequately assessed due to spatial autocorrelation (Moran's I = 0.07, *p* < 0.0001). However, the results appear to be marginally significant at the 95% confidence interval (*p* = 0.05; robust *p* = 0.06). The LDI

showed to be a significant (*p* < 0.0001) variable at explaining a significant amount of variation in runoff depth. Presence of significant spatial autocorrelation using the Global Moran's I is based on the assumption of stationary data. In this case there will be clustering of standard residuals from heteroscedasticity, thus indicating a local model such as GWR is more appropriate. The Global Moran's Index indicated no spatial autocorrelation with a negative index and rejecting the null hypothesis at with 95% confidence (Global Moran's I = −0.025, *p* = 0.111).

Although the runoff depth was determined by precipitation, LC/LU can have a higher impact on the quantities of runoff. Empty grass lots within urban communities can have compacted soil from earlier construction activity which may decrease infiltration rates up to 70% in the central Florida region [42]. The runoff coefficients for the agricultural land surfaces include the effects of compacted soil from heavy machinery. Based on the coefficient raster generated from the GWR analysis, LDI values for forested and impervious areas may account for most of the linear relationship between development and runoff (**Figure 8**). The local trends between rainfall and runoff on smaller time intervals may have a strong linear relationship. However, the mean rainfall values may have reduced the weights in local rainfallrunoff relationships. This outcome also can be noticed within the 11-year mean runoff OLS regression from the existence of local relationships between runoff and the independent variables.

Precipitation estimates along the 251-kilometer IRL estuary and ~ 35-kilometer Halifax River varies within locations, with changing LC/LU as a factor. Areas with lower rainfall can have a higher runoff yield than areas with higher precipitation over forested areas that were assigned lower runoff coefficients. As a result, areas consisting of more human disturbance have a linear relationship with more runoff. Urban communities are often primary targets for some studies to analyze rainfallrunoff by enhancing methods to estimate the DCIA in developed catchments [43]. Based on the global trends of urbanization within coastal areas, stronger rainfallrunoff relationships have positive correlations with the increase of impervious cover percentages for urban zones within separate countries [44]. The purpose of choosing LDI was to indicate the contributions of surface runoff from vegetated lands affected by urban development. To further explain this relationship, future research should assess stormwater runoff using the impervious percentage images created by the USGS. The percentage of imperviousness can also be compared to increases in runoff depth.
