**Figure 8.**

*Lagoon Environments around the World - A Scientific Perspective*

with 95% confidence (Global Moran's I = −0.025, *p* = 0.111).

the independent variables.

increases in runoff depth.

concentrations in April 2011.

**5.4 Runoff depth during the 2011 super algal bloom**

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

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

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

The IRL ecosystem recently suffered from a recurrence of algae blooms since 2011 which are heavily influenced by anthropogenic stressors within its watershed, such as surrounding developed land with possible higher surface runoff [8]. Based on a visual comparison; the runoff depth was higher prior to the algal bloom events between 2011 and 2016 particularly near the areas of recorded high Chlorophyll *a* concentrations (**Figures 3**–**6**). It is important to note that the monthly runoff reflects precipitation estimates collected at the end of the month. Therefore, the runoff depth map for March should be visually compared to the chlorophyll *a*

Although there is no available MERIS NDCI calculations collected throughout the summer, there was an increase of runoff to 10–18 cm in May and June for the Banana River (**Figures 5** and **6**). The increase may explain the 48.62 μg/L spike in chlorophyll a May 2011 from April 2011. Based on the estimated concentrations by MERIS NDCI and water quality samples from SJRWMD, the algal bloom became higher on the 14 September 2011 with the highest concentrations in October [29].

**134**

*The map above shows the geographically weighted regression locally weighted coefficients throughout the study area.*

The runoff depth for October 2011 showed the high runoff with values above 15 cm in the southern IRL and Northern IRL. Subsequently, concentrations of chlorophyll a gradually decreased throughout October, and further dropped in November and December. Results also indicate that there were also smaller contributions of runoff during those months with decreasing trends. Visual comparisons between the chlorophyll *a* and runoff depth indicate that there may be a correlation positive association between the two variables for 2011. However, further analysis including statistical measures should be performed to assess the relationship.

### **5.5 Implication for coastal water management**

Delineation of PRCs and runoff depths can provide a geographic depiction for assessing lands of interest to implement sustainable developmental designs and structures. In this study, runoff coefficients were calculated for each pixel regardless of surrounding pixel values. Therefore, computational methods used in this study to determine runoff depth were not assessed using methods to incorporate Directly Connected Impervious Areas (DCIA). DCIAs are areas that are considered to be hydraulically connected to the conveyance system according to the Southwest Florida Water Management District Resource Regulation Technical Guide [45]. Other study estimated runoff volumes in various sub-basins of rivers and tributaries within the IRL watershed using DCIA and non-DCIA methods [46]. Runoff from such studies use a measure called the Soil Conservation Survey Runoff Curve Number. The overall runoff was calculated from the sum of the DCIA and non-DCIA runoff, while runoff coefficients were derived by dividing the generated runoff by the total rainfall for the stations which was listed as "C values". While this approach can be used to determine Total Maximum Daily Loads (TMDLs) for nutrients, PRCs from this study can be used to emphasize exact locations within the watershed that are suitable for LC/LU management practices.

Direct surface runoff into waterbodies can be significantly affected by impervious surfaces in close proximity. In other scenarios, runoff from developed lands may travel through vegetation before entering into a waterbody. The harmful effects of surface runoff from and on urban communities call for a need of more stringent regulations, and more efficient coastal urban planning and management. This approach of stormwater management provides a long term adaptation plan to be proactive to the future impacts from climate change. Delineation of potential runoff coefficients and runoff depths can provide a geographic depiction for assessing lands of interest to implement sustainable developmental designs and structures. Mean runoff depth and runoff coefficient values can be used to determine areas of high runoff to apply green infrastructure within a watershed. "Green Infrastructure" is the practice of utilizing natural vegetated areas for runoff treatment by mimicking natural stormwater flow paths, and is composed of many low impact development (LID) designs [47]. 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, 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. 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

**137**

**Acknowledgements**

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

rainfall-runoff relationships. This outcome also can be noticed within the 11-year mean runoff OLS regression from the existence of local relationships between

Mitigation of stormwater runoff often includes developing more sustainable development strategies within urban communities. An aggregation of developmental designs for combating runoff in an urban community showed reductions in runoff, and also projected expansions in bare soil, impervious cover, and soil alteration will lead to higher runoff volumes [48]. As with vegetated and undeveloped surfaces within riverine systems, the hydrological changes in volume and base flows are reduced with this hybrid design. In reference to GIS approaches to correlating surface runoff to LCLU, urban areas and bare land also corresponded to the degra-

The PRCs for the IRL were applied to land surfaces based on soil, land cover, and slope. These coefficients were used as ratios to determine the runoff depth per pixel within the IRL using precipitation data. After calculating the runoff depth for an 11-year period (2006–2016), it was found that the recent years (2014, 2016) were above the average 11-year runoff matched years of strong El Niño. The runoff deviation from the 11-year mean was also calculated per pixel for each year and highlighted higher runoff quantities closer to the shore of the IRL within the watershed. It is well known that impervious surfaces decrease infiltration, thus increasing runoff yields. Even with vegetated landscape, highly developed land can have poor infiltration from compact soil. The linear regression analysis showed that land development has a significant relationship with runoff depth, and there are local trends between the variables. During the 2011 super algal bloom, the months of March and April 2011 showed increases in runoff, which matched the areas with higher chlorophyll *a* mapped with MERIS in the Mosquito Lagoon in the Northern IRL [29]. In October 2011, extremely high concentrations were detected from MERIS and sampled from St. Johns River Water Management District; this research also calculated high runoff depth concentrations, delineated in the IRL watershed for October 2011. Based on these analyses, the output of this research can possibly delineate areas within the coastal communities that experience higher runoff, and help locate more suitable areas for stormwater parks, green infrastructure, and sustainable stormwater structures. Future research can include using the indices such as LDI to further correct the runoff coefficients for a particular watershed. PRCs can be applied to other watersheds of coastal ecosystems for as a visual reference, or

used as a parameter for more advanced hydrologic modeling.

Commerce, National Oceanic and Atmospheric Administration.

This publication was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program award (NA16SEC4810009). Its contents are solely the responsibility of the award recipient and do not necessarily represent the official views of the U.S. Department of

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

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

runoff and the independent variables.

dation of stormwater quality [49].

**6. Conclusion**

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

rainfall-runoff 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.

Mitigation of stormwater runoff often includes developing more sustainable development strategies within urban communities. An aggregation of developmental designs for combating runoff in an urban community showed reductions in runoff, and also projected expansions in bare soil, impervious cover, and soil alteration will lead to higher runoff volumes [48]. As with vegetated and undeveloped surfaces within riverine systems, the hydrological changes in volume and base flows are reduced with this hybrid design. In reference to GIS approaches to correlating surface runoff to LCLU, urban areas and bare land also corresponded to the degradation of stormwater quality [49].
