**2.1 Land cover/land use**

*Lagoon Environments around the World - A Scientific Perspective*

in hydrological modeling for determining the amount of runoff.

of any rainfall [15].

the soil over a given period of time.

Hypothetical land cover change scenarios in a simulated hydrological study within the Lavrinha watershed in Minas Gerais State, Brazil showed that deforestation in the Atlantic Forest biome would lead to increases in soil moisture (5%), runoff (22%), and decreases in runoff interception (71%) from the loss of roots and extensive rhizomes [11]. Impervious surfaces in urban watersheds can influence the biogeochemical processes, organism's abundance, stress, and vulnerability from heated surface runoff during hot summers [12]. Incorporation of the contributing factors such as vegetative cover that may enhance or influence the effects is effective

The characteristics of the land surrounding waterbodies affect the amount of surface runoff. During the process of rainfall becoming runoff, various characteristics of the land's surface, such as land use, soil type, and topography, will heavily impact the quantity of runoff [13]. Vegetative cover of the surrounding land can potentially act as a buffer for aquatic systems receiving runoff [14]. During rainfall events, impervious surfaces such as roads, parking lots, and other pavements increase runoff due to extremely limited infiltration into the ground. Areas with 75–100% imperviousness can yield runoff that represents up to 55%

The potential runoff coefficient (PRC) represents the portion of rain that becomes surface runoff during a rain event, and it is determined by the land use, soil texture, and slope [16]. The potential runoff coefficient was derived from methods of developing a unit hydrograph (UH) for specific depths of rainfall. The hydrograph provided the assumption that discharge at any time is proportional to the volume runoff, and the temporal factors for a given duration are constant [17]. Runoff coefficients have been widely utilized in the hydrological modeling along with other computational factors for research in flood frequency, flood prediction, and storm management [18–20]. Hydrologic simulation model software's have been developed using spatial data and GIS [5]. Another method that includes runoff coefficients to hydrological modeling is the runoff curve number (CN) method created by the United States Department of Agriculture Natural Resources Conservation Service. Unlike the potential runoff coefficient, the CN can be calculated for each watershed and encompass the potential maximum retention of

PRC can be determined for land surfaces with different characteristics along with the quantity of runoff known as the "runoff depth." Given the quantity of runoff being influenced by the determining conditions, spatial variation in PRC can be estimated for a specific time duration within a given estuarine drainage area. In order to demonstrate this, runoff coefficients and runoff depths were calculated using geographic information systems (GIS) for the drainage basin of the Indian River Lagoon (IRL), Florida, which recently had recurring severe algal blooms. Nonpoint source pollution from surface runoff may have had been a cause for the recurring algal blooms in the lagoon [7, 8]. Use of the spatially contiguous PRC across an area of interest provides additional resources and information for stormwater research within a coastal watershed. Runoff coefficients of a watershed along with other information can be utilized for analytical processes to gain further

Since 2011, IRL experienced severe algal bloom events; and non-point source pollution through surface runoff is suspected to be one of the causes for the algal blooms. The goal of this study is to calculate the spatially contiguous PRC and runoff depth for the drainage basin of IRL and the connected estuary, the Halifax River, Florida for an eleven-year period (2006–2016) in order to determine which areas and factors contribute to the runoff. The 2011 monthly runoff depth of the draining areas was compared with the 2011 monthly algal bloom maps of a previous

insight of stormwater dynamics on local and regional scales.

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Land cover and land use (LC/LU) is one of the factors for calculating PRC. The LC/LU was derived by classifying the European Space Agency (ESA) Sentinel 2 Level 1C 10 m satellite imagery from November of 2016. Four images were downloaded from the ESA Sentinel Scientific Data Hub website to encompass the elongated watershed of the IRL (https://scihub.copernicus.eu/dhus/#/home). The images were preprocessed with the ESA Sentinel Application Platform (SNAP) remote sensing software along with the Sentinel 2 toolbox. Before classifying LC/ LU of the study area, an atmospheric correction was applied to the images using the Sen2cor 2.3.2 plugin within ESA SNAP to eliminate the effects of water vapor, aerosols, and cirrus clouds when utilizing spectral reflectance data. The preprocessing output of the Sentinel 2 data produces Sentinel Level-2A data which includes values that represent the radiation at the bottom of the atmosphere (BOA). Once the BOA output was produced, the four images were mosaicked to produce a continuous raster image of the IRL. By applying a supervised maximum likelihood classification in ENVI 5.4, the images were classified into five categories; forest, grass, bare soil, crop, and impervious.
