Modeling the Risks of Climate Change and Global Warming to Humans Settled in Low Elevation Coastal Zones in Louisiana, USA

This paper seeks to identify high risk areas that are prone to flooding, caused by sea level rise because of high impacts of global climate change resulting from global warming and human settlements in low-lying coastal elevation areas in Louisiana, and model and understand the ramifications of predicted sea-level rise. To accomplish these objectives, the study made use of accessible public datasets to assess the potential risk faced by residents of coastal lowlands of Southern Louisiana in the United States. Elevation data was obtained from the Louisiana Statewide Light Detection and Ranging (LiDAR) with resolution of 16.4 feet (5 m) distributed by Atlas. The data was downloaded from Atlas website and imported into Environmental Systems Research Institute’s (ESRI’s) ArcMap software to create a single mosaic elevation image map of the study area. After mosaicking the elevation data in ArcMap, Spatial Analyst extension software was used to classify areas with low and high elevation. time in years were modeled for the low land coastal parishes of Louisiana. The expected years for the populations in the study area to be at risk due to rising sea level were estimated by models. The geographic information systems (GIS) results indicate that areas of low elevation were mostly located along the coastal Parishes in the study area. Further results of the study re-vealed that, if the sea level continued to rise at the present rate, a population of approximately 1.8 million people in Louisiana’s coastal lands would be at risk of suffering from flooding associated with the sea level having risen to about 740 inches by 2040. The population in high risk flood zone was modeled by the following equation: y = 6.6667x − 12,864, with R squared equal to 0.9964. The rate of sea level rise was found to increase as years progressed. The slopes of models for data for time periods, 1880-2015 (entire data) and 1970-2015 were found to be, 4.2653 and 6.6667, respectively. The increase re-flects impacts of climate change and land management on rate of sea level rise, respectively. A model for the variation of years with respect to cumulative sea level was developed for use in predicting the year when the cumulative sea level would equal the elevation above sea level of study area parishes. The model is given by the following equation: y = 0.1219x + 1944.1 with R square equal to 0.9995.

time in years were modeled for the low land coastal parishes of Louisiana. The expected years for the populations in the study area to be at risk due to rising sea level were estimated by models. The geographic information systems (GIS) results indicate that areas of low elevation were mostly located along the coastal Parishes in the study area. Further results of the study revealed that, if the sea level continued to rise at the present rate, a population of approximately 1.8 million people in Louisiana's coastal lands would be at risk of suffering from flooding associated with the sea level having risen to about 740 inches by 2040. The population in high risk flood zone was modeled by the following equation: y = 6.6667x − 12,864, with R squared equal to 0.9964. The rate of sea level rise was found to increase as years progressed. The slopes of models for data for time periods, 1880-2015 (entire data) and 1970-2015 were found to be, 4.2653 and 6.6667, respectively. The increase reflects impacts of climate change and land management on rate of sea level rise, respectively. A model for the variation of years with respect to cumulative sea level was developed for use in predicting the year when the cumulative sea level would equal the elevation above sea level of study area parishes.

Introduction
In the last few decades there had been calls by various governments, environmentalist, journalist and non-governmental organizations for the world to reduce greenhouse gases because of their contribution to global warming and the accompanying risks to the population [1] [2]. The 1992 United Nations Conference on Environment and Development (UNCED) in Brazil also known as the Rio Conference and Earth Summit highlighted the severity of the environment and the need to promote sustainable development in order to reduce greenhouse gases [3]. The goal of the UNECD was to seek common action to protect the planet from environmental degradation that threatens to change the global climate. It also aimed to increase national and international efforts to promote sustainable and environmentally sound development in all countries [3] [4]. The conference culminated in the signing of the 1992 Earth Summit. Following the signing of the 1992 Earth Summit, Kyoto protocol emerged from the UN Framework Convention on Climate Change (UNFCCC) with the legally binding agreement under which the industrialized countries were asked to reduce their collective emissions of greenhouse gases by 5.2% [5] [6]. The recent Paris Agreement, signed in 2015 by 175 parties, also aimed to limit global warming to below 2˚C compared to pre-industrial levels. It focuses on reducing greenhouse

Emissions, Climate Change, and Sea Level Rise Projections
Human activities play an important role in emissions release in the atmosphere, causing global temperatures to rise, warm the oceans and deplete the biodiversity [9] [10] [11]. According to U.S. Environmental Protection Agency (EPA) data in 2019, global CO 2 emissions from fossil fuels have been increasing at an alarming rate since 1900 [12]. For example, Olivier et al. 2015 (Borroto, 1997), other stressors such as ammonia-based fertilizers and solvents, refrigerants and forming agents contribute to nitric oxide and chlorofluorocarbon. These and other sources are thought to be enhancing greenhouse effect leading to global warming [15]. In the United States, U.S. EPA reported that CO 2 emissions from the burning of fossil fuel for energy, transportation and electricity production constituted 77 percent of all U.S. man-made greenhouse-gas emissions in 2018 [16].
The positive correlation between global climate change and sea level rise has been well-documented and gained attention by many scientists [17]- [30]. Rising global temperatures from climate change are leading causes of sea level rise.
There are numerous projections around the globe on sea level rise. The National Research Council [31], projected average Sea Level Rise along California coast south and north of Cape Mendocino in year 2030 will be 6 inches and 2 inches, respectively. Similar projection in the year 2050 shows sea level rise in south of Cape Mendocino will increase to 12 inches and 6 inches in the south and north respectively. The National Oceanic and Atmospheric Administration (NOAA) scientist, Linsey [26], projected that by the end of the century, global mean sea level (GMSL)would rise at least one foot (0.3 meters) above 2000 levels by 2100 [26]. In Europe, Vousdoukas et al. [32] [33]. Beyond 2100, according to the report sea level will continue to rise for centuries due to continuing deep ocean heat uptake and mass loss of the Greenland Ice Sheet (GIS) and Antarctic Ice Sheet (AIS). IPCC's earlier report in 2007 projected an estimate of 60 cm (2 ft) through 2099, but their 2014 report estimated about 90 cm (3 ft) [34]. It is important to note that both projections by Vousdoukas et al. [32] and IPCC, [33]

Models Used in Assessing Risks of Population Living in Low-Lying Coastal Areas
In event of sea level rise resulting from climate change, many coastal populations

The Study Area
Flooding is one of the major problems facing Louisiana. For the past 10 years level. Population change vis-à-vis the elevation above the sea was studied. The elevations above the sea level for all the low land parishes were lower than 40 feet. The map of the study area with all its parishes is presented in Figure 1.

Data Acquisition
Elevation data was obtained from the Louisiana Statewide Lidar distributed by Atlas with resolution of 16.4 feet (5 m) and downloaded from their website [54]. Maps and GIS imagery were used to locate and determine the corresponding elevations above sea level for coastal low land parishes of Louisiana. Kosovich's data [55] derived from USGS Digital Elevation Data was used for this study. Population data from U.S. Census was also used for this study is shown in Table  2. The absolute sea level rise data covering the period 1880 to 2015 was also obtained from U.S. Environmental Protection Agency (EPA) website [56]. Summary of data sources for this paper is presented in Table 3.

Data Analysis
To process the Lidar DEM, each tile in the study area Parishes below South of Latitude 31˚N were downloaded and imported into ESRI ArcMap software to create a single mosaic elevation image of the study area ( Figure 1). After mosaicking the elevation data in ArcMap, Spatial Analyst extension software was used to classify areas with low and high elevation. Population trends and trends with respect to sea level rise were modeled for the study area composed of the low land coastal parishes. The years when parishes would be at risk of flooding were estimated by models. Before analysis of the results, mathematical formulation was presented. The total human population for low land parishes (Cameron, Vermilion, Iberia, St. Mary, Terrebonne, Lafourche, Jefferson, Plaquemine, St. Benard, Orleans and Orleans) was modeled with respect to years to predict the parishes' population by the year 2040 by regression, using Microsoft Excel statistical analysis tool kit. The data used to compute the total human population for these parishes was derived from the human population data for Louisiana coastal parishes for the period 1970-2020 ( Table 2). The data is presented in Table 4.
Third order polynomial curve fitting was used to model the 1970-2020 data, followed by extrapolation to 2040, with the help of the Microsoft Excel statistical tool kit. The model is illustrated in Figure 3. It represents about 83% of the population data variation (R squared = 0.83).   The population in these parishes rose from about 800,000 in 1970 to a number between 1.4 and 1.2 million in 1990. It then dropped to almost 1.2 million by 2010. After 2010 it began to rise. According to the model, the coastal low land parishes will be harboring a human population of about 1.8 million by 2040.
The absolute sea level rise data is available for the period 1880 to 2015 [56]. The annual cumulative sea level rise above the datum level of 1980 was computed from the absolute sea level change data and presented as illustrated in Table 5.
The models for rise in sea level and time, t in years were modeled based on the following logic and formulations. In the following formulation, extra water refers to quantity of water more than quantity required to maintain global sea level constant.
When glaciers and snow in mountains etc. melt because of global warming, water formed contributes to increase in the global water cycle. Water in liquid phase flows to regions of lower elevation. The molten ice contributes to increase in global sea level as illustrated in the following word equation.
Extra water flowing into global sea ∝ Mass of molten snow  The quantity of molten ice is a function of temperature, T and duration time, t. Since the change in sea level is proportional to the quantity of molten ice, which is proportional to duration time, t, it follows that the rise in sea level is a function of time, t [57].
In modelling the variation between the cumulative level, H and time, regression was used.

Rise in sea level,
( ) For cubic model, where, H is the cumulative sea level in units of length, n is the polynomial index, 3 and t is time in years.
For quadratic model, where, H is the cumulative sea level in units of length, n is the polynomial index, 2 and t is time in years.
For linear model,

Results and Discussion
The model illustrated in Figure 5 was extrapolated to 2040 by forwarding the year variable by 20 using Microsoft Excel. The prediction of sea level rise is illustrated in the following graph ( Figure 6). Results of classified LiDAR DEM are shown in Figure 6 and Figure 8. From Figure 2, Figure 7, and Figure 8, most of the study area in the southern section of the state lies below sea level. With the area experiencing some growth in population from 1,164,625 million (Table 2), between 2010 and 2020 respectively, the vulnerability of coastal ecosystems to sea level rise could increase. This could have extreme impacts on both the natural and built up environments particularly around big cities holding vital infrastructure crucial in economic development and productive capacity of petroleum and natural resource assets of South of Louisiana. While the situation is further compounded by the region's propensity to natural disasters and the fragile coastal ecosystem close to enormous network of large-scale energy infrastructure made up of oil and gas fields, refineries, and pipeline. The presence of petrochemical complexes, thriving natural resource base, transportation corridors, burgeoning urban centers and neighborhoods often at the receiving end of recurrent climate hazards over time accentuates the inherent risks, due to environmental, physical, and socio-economic and policy factors located within the larger regional ecosystem. The model illustrated in Figure 6 suggests that when the population will be 1.8 million, the cumulative (absolute) sea level will be 740 inches by 2040.
To appreciate the risk of sea level rise to residents of the Louisiana low land parishes, Microsoft Excel statistical tool kit was used to model population versus sea level rise by curve fitting. In this modeling exercise, the total population data (1970-2040) and sea level data (1880-2015) were used. Absolute (cumulative) sea level used in modeling. The model is illustrated in Figure 9.
According to the model the total population in these parishes rose from about 800,000 when the sea level was less than 300 inches to almost 1.4 million despite the sea level rising from less than 300 inches to about 450 inches. It then began to drop as the sea level rose. Between 500 inches and 600 inches sea levels, the total population in coastal parishes began to rise again while the sea level continued to rise. Hence, as the sea level rises, the population in these lands. The number of people at risk is on the rise. The correlation coefficient between the absolute sea level rise and population growth in the low land parishes is 89% (Figure 9).       Hence, beyond 2017, the model cannot be used to predict sea level rise. in mid-2016 the model yields the maximum year. As the sea continues to rise beyond 625.167, the model yields decreasing years. This model was therefore replaced by a linear model (Figure 11), which was developed by plotting years with respect to the corresponding sea level.
The model (Figure 11) was used to predict years that the sea level would rise to the elevations above sea level of the coastal low land parishes of Southeastern Louisiana. To determine the year that sea level would equal the elevation above sea level for a parish the magnitude of the elevation above sea level is substituted for x (the independent variable) in the model of year versus the elevation above sea level (Figure 11), presented as follows.   (Table 6) illustrates the year when the sea level is expected to have risen and equaled the given elevations above sea level for each of the parishes within the study area.
A plot of sea level rise versus years data suggests that the rate of sea level rise increased as years progress. Hence, the actual sea levels could be greater than the figures presented in table (Table 5). Models representing increased rates of sea level rise with respect to time/years were developed from the data collected between 2006 and 2015 through linear regression using Microsoft Excel tool kit. It is illustrated in Figure 12.
The following equation represents the model. To stress the importance and urgency of mitigation for the risk, Baton Rouge, with an elevation above sea level of 56ft (1196.25 inches above 1880 datum) has also been included in Table 7. This model was used to predict the year that the sea level will rise to equal the elevations above sea level for the parishes being studied.  Table 7. Illustration of elevation of parishes above sea level and the year expected for the sea level to rise the magnitude, based on the model illustrated in Figure 10.
Absolute elevation above sea level in inches to parishes elevations above sea level in inches Predicted year for the sea to rise  The years and corresponding elevations above the sea level are illustrated in Table 7.
The results presented in Table 7 reveal a high sea level rise rate, suggesting that the sea level could equal elevations above the sea level for some parishes sooner than expected. However, the rate of sea level rise is a function of many external factors such as storms, hurricanes, among others, whose pattern is dynamic. Hence, it could decrease to the 1970-2015 rate or even, lower.

Conclusions
An examination of the population of individual parishes indicates that there was drop in population in some of the coastal low land parishes while there was increase in others (Table 2). This could have been a result of people moving from parishes which they believed were high risk to safer parishes, following floods or hurricanes. An analysis of sea level rise suggests that these safe areas risk experiencing floods and other impacts associated with the rise. The rate of sea level rise is gradually increasing as shown from the analysis of the data. While the expected population in Louisiana's coastal lands will be about 1.8 million, the model presented in Figure 5 suggests The rise in sea level may lead to increased risk of total flooding of coastal parishes since these areas will gradually be equal to or lower than the sea level, resulting inability of water to be drained in the event increased surface water flows from storms etc. Water flows from high to low elevations. Any area whose elevation is equal or lower than the sea level is a potential reservoir of water during floods. Once the sea rises above these elevations, sea water can also flood them. As the human population rises in the coastal parish's investments, in the forms of enterprises, schools, and homes etc., in these areas also increase. Flooding of the area could result in loss of human life, investments, and ecosystems, etc. The government should invest heavily in pressure walls (levees) to prevent water from flooding the coastal lowlands to prevent or minimize loss of life, investments, heritage, ecosystems, and injury etc.