Climate is one of the important drivers of ecosystems’ health, compositions, and other earth systems [1]. That is, the impact of climatic conditions over the earth’s surface and its variations has the propensity to affect certain areas such as agriculture, public health, water supplies, energy production and use, land use, and development, as well as recreational activities. The climatic fluctuations usually determine the average weather and climate conditions over a period, characterized by variable factors including rainfall, temperature, atmospheric pressure, and humidity. These variations are also attributed to changes in greenhouse gases and aerosols, which are expected to result in regional and global changes in temperature, precipitation, and other climate variables, leading to global changes in soil moisture, global mean sea level, and occurrence of more severe extreme high-temperature events, floods, and droughts in some places [2] - [10].

Considering that the subject of climate change is vast, there is at least one topic within this subject that deserves urgent and systematic attention, and that is the changing pattern of precipitation around the world [11]. Precipitation is considered one of the most important variables for climate and hydrometeorology by which a change in its pattern may lead to floods, droughts, loss of biodiversity, and agricultural productivity [12]. Remarkably, global changes especially the warming of the earth’s surface, are gradually influencing regional climates, especially rainfall patterns. For instance, there have been considerable spatial and temporal variations that have occurred over the past 100 years, and notably, these tendencies of warming and increased precipitation have not been globally uniform [13]. Another example is cited by [12], whereby climate change studies have also demonstrated that the land-surface precipitation shows an increase of 0.5% - 1% per decade in most of the Northern Hemisphere mid and high latitudes, and the annual average of regional precipitation increased 7% - 12% for the areas in 30˚N - 85˚N and by about 2% for the areas 0˚S - 55˚S over the 20^th century.

More so, concerns have been raised about the impacts of climate change which has heightened the need for accurate information about spatial and temporal variations in precipitation over the Earth’s surface [14]. That is, as the earth warms, it causes changes in temperature, rainfall, and other patterns of weather and climate, hence, posing threats to human health [15], plant, and animal lives. To further examine this phenomenon, [1] cited [16] about research carried out an analysis of monthly temperature and precipitation time series for hundreds of weather stations across Canada whereby variations in temperature and precipitation at different temporal and spatial scales were discovered [16]. Therefore concluded that, the differences observed in temperature and precipitation could have been influenced by the variations in atmospheric circulations.

Additionally, the variations of the climate in terms of temperature and precipitation have been examined in different regions of the world over recent decades [17] [18] [19] [20] [21]. This is because clearly, increasing global surface temperatures are very likely to lead to changes in precipitation and atmospheric moisture because of changes in atmospheric movement, a more active hydrological cycle, and increases in the water-holding capacity throughout the atmosphere [11]. Other examinations on understanding this pattern revealed that, variations in precipitation trends and distributions are observed in the Northern and Southern hemispheres due to the physical distribution of more landmass in the North than in the South, thereby inducing a greater thermal effect in the North than in the South [11].

Based on this, it is prudent to discuss the dynamics of precipitation over the earth’s surface as well as its trends in order to make predictions over a period of time. Agreeably, the ability to identify precipitation trends and predict future precipitation values is very important for both industrial and individual purposes [22]. Hence, this study examines rainfall data in Southern Louisiana to determine the precipitation trends and patterns in certain selected cities. This will be done by identifying the factors that are influencing precipitation trends and the shift in precipitation patterns that may impact future predictions. The analyses will mainly be conducted on Southeastern Louisiana’s coastal counties to analyze rainfall patterns as well as temperature. By the end of this study, it is expected that the findings of this research will establish more insights in relation to understanding the hydrologic behavior over the last three decades in Southeastern Louisiana.

For this study, the coastal counties of Southeastern Louisiana coastal counties were selected for temporal analysis of rainfall. Rainfall data for 1990-2020 or the available range of years for the selected parishes (Figure 1) was downloaded from the World Bank Group Climate Change Knowledge Portal for Development Practitioners and Policy Makers [23] and the Applied Climate Information System (ACIS) of the National Weather Service Prediction Center [24]. The monthly rainfall data from Donaldsonville, LA weather station for the years, 1990-2020, was used. It is presented in Tables 1-4, respectively. Annual rainfall values were computed for years that had some monthly rainfall data missing (2008, 2015, and 2016, respectively).

A table for the annual rainfall data in inches versus corresponding years was prepared by extracting years and corresponding annual rainfall data from Tables 1-4, respectively. The data is presented in Table 5 and Table 6, respectively.

Data was then prepared for modeling using the approach by [1]. The average rainfall data for each 4-year blocks was summed up and then divided by 4 to yield the average annual precipitation data, based on 4 years (aggregation of data using batches of 4 years). To illustrate the computation, Tables 7-10, etc. were extracted from the 1990-2020 rainfall data.

The 1990-2020 Donaldsonville’s annual mean rainfall data based on 4-year blocks for was then compiled using the computed means from Table 7, Table 8, etc. and presented in Table 11.

The procedure for preparing data for Donaldsonville’s average annual rainfall

data based on 4 consecutive years was also applied for determination of corresponding rainfall for, Galliano, Lafourche, Gonzales, Ascension, Morgan, New Orleans, Audubon, Plaquemine, Ponchatoula, Tangipahoa, respectively.

To model the associations between variables in this study, regression analysis through curve and line fitting was used. Models for mean rainfall based on data sets of mainly 4 consecutive years were modeled based on the following respective formulations.

Rainfall, R = f ( t )

where f ( t ) represents a function of time, in years. Hence, rainfall, R, is a function of time.

For the general polynomial model,

R = A n t n + A n − 1 t n − 1 + ⋅ ⋅ ⋅ + A 0 t 0 (1)

For cubic model,

R = A 3 t 3 + A 2 t 2 + A 1 t + A 0 (2)

For quadratic model,

R = A 2 t 2 + A 1 t + A 0 (3)

For linear model,

R = A 1 t + A 0 . (4)

R is the average rainfall based on 4 consecutive years in mm/year, n is the polynomial index and t is time in years. A_n, A n − 1 , A n − 2 , ..., A₂, A₁ and A₀, are constants.

Statistical software (SPSS, Microsoft Excel data analysis toolkit, etc.) was used to fit data and build models. The strength of model developed was based on the magnitude of the coefficients of correlations between variables (r square), which ranged from 0 to 1.0 (0% - 100%).

A model was adopted if its coefficient of correlation was close to 100%. Linear and curvilinear curve fitting was applied depending on the associations between data pairs, which attempts to develop single models for data sets spanning from 1901 to 2020 yielded models of low coefficients of correlations for most data sets. To develop models that were more representative of the data sets, illustrating the rises and falls in environmental data, data aggregates from time spans shorter than the entire data collection range of time (1901-2020) were used in modeling. Models for rainfall trends were carried out for data sets from 7 selected weather stations. The modeling concept is illustrated in Figure 2.

Correlations between all possible pairs of data were carried out using Microsoft Excel. The results are presented in Table 5. Separate regression models had to be modeled for each station. The only strong correlations found between data pairs were between, Gonzales and Donaldsonville, and Morgan City and Donaldsonville,

Gonzales and Morgan City, and Galliano and New Orleans Audubon, as shown in the following correlation matrix (Table 12). Hence, different, using separately derived, regression coefficients for each individual station are not a methodologically misguided approach. It is evident from the correlation matrix that some data pairs are negatively correlated, and the magnitudes of correlations vary significantly.

A cubic model was used to fit Donaldsonville’s average annual rainfall data, based on 4-year blocks for the period 1990-2002. With R² of approximately 0.73, the model represented about 73% of the data’s variation (Figure 3). During this period, the mean annual rainfall/4 years rose from 60 in 1990 to 70 inches in 2002 and then decreased according to the model to just below 60in. The model is represented as follows,

y = 0.097 x 3 − 580 x 2 + 1 E + 06 x − 8 E + 0824.58 , R 2 = 0.72

Donaldsonville’s average annual rainfall data for 2002-2011 was fitted to a cubic polynomial model. The model is given as, y = −0.260x³ − 1567.9x²+ 3E+ 06x − 2E + 09, with R² = 1. Hence, this model explains about 100% of the variation of the data. From 2002 to 2004, the average precipitation rose from 60 in to approximately 68 inches, just before 2004. It then dropped gradually to about 45 inches in 2009 and rose to 52 inches in 2011 (Figure 4).

Donaldsonville’s average annual rainfall data for 2011-2020 was fitted to a second order polynomial model. The model is given as, y= −0.4009x² + 1617.6x − 2E + 06, with R² = 0.927 (Figure 5).

The model explains about 97.3% of the variation in the data. The data increases according to the model from about 62 inches to about 68 inches and then decreases to about 65 inches in 2020.

Prediction for Donaldsonville’s average annual rainfall for 2030 was carried out by extrapolation of the model that final general trend (2008-2020). The predicted annual average rainfall was about 85.6 - 86.7 inches (Figure 6).

The model explains 79% of the field data.

The mean of annual rainfall for Galliano (1990-2019) per 4-year basis presented in Table 13.

The rainfall data for Galliano, Lafourche for the time range 1990-2019 was fitted to a quadratic model illustrated in Figure 7.

The model suggests that precipitation for Galliano rose from 50 inches to approximately 65 inches between 1995 and 2000, and then dropped gradually few inches below 50 inches between 2010 and 2015, and then rose gradually to a magnitude to approximately 52 inches.

The model represents about 65% of the climate data.

Stronger models were developed by fitting data from, 1990 to 2005, and 2005 to 2019 to two models, respectively. The models are illustrated in Figure 8 and Figure 9, respectively.

The model suggests that the average rainfall increased cubically from 50 inches in 1990 to almost 70 inches in 1994 and then decreased to settle abbot 60 inches in 2005.

Modeling of 2005-2019 annual means’ data produced the following graph Figure 8. The coefficient of correlation for the model is about 84%. The model explains 84% of the data’s variation.

The coefficient of correlation for the quadratic model is about 84%, and hence, the model represents 84% of the data’s variation. The model suggests that the average annual rainfall/4-year basis decreased from 63 inches to a minimum of between 45 and 50 inches and then increased to settle just below 55 inches.

Galliano’s data is strongly positively correlated to that of New Orleans Audubon. Galliano’s and New Orleans, Audubon’s elevations above the sea level are,3 ft (0.9 m), and 3 feet (0.91 m), and 4 feet (1.2 m), and both are close to large water bodies. Hence, the climatic patterns are strongly correlated. Since the precipitation of New Orleans, Audubon area generally increased from 2014 to 2020, the precipitation of Galliano is also assumed to have generally increased. A linear model fitting of Galliano’s data for 2014-2019 using Microsoft Excel yielded a model of positive slope. This model was extrapolated to estimate the precipitation in 2030 (Figure 10).

The predicted average precipitation for Galliano, Lafourche for 2030 is 75.56 - 76.58 inches.

This study revealed high variabilities in rainfall throughout the range of years considered. The major factors influencing variability in rainfall trends are global warming and climate change. The results and analyses found that Southeastern Louisiana has been experiencing rising and dropping rainfall patterns. However, the general trend was an increase for all stations. Apart from Plaquemine, all the other stations were expected to experience significant rainfall increases from 2011. Stations in areas of relatively higher elevations above the sea level tended to generally experience higher average rainfall.

Analysis of data collected during the last 6 years found a trend of increase in the average annual rainfall for the areas whose rainfall data was studied. The predictions for 2030 have also revealed a general precipitation increase in Southern Louisiana. The increase in precipitation is positively correlated with aerial and soil moisture. Since agriculture contributes significantly to the economy of Louisiana, research may have to be carried out to either maintain or boost the resiliency of the region’s crops against gradual changes in soil and aerial moisture. Research on anticipated crop diseases and pastes, and invasive species associated with an increase in rainfall should be carried out so that stakeholders can be well prepared for possible their impacts on agriculture, forests, and wildlife.

The authors would like to acknowledge the USDA National Institute of Food and Agriculture (NIFA) McIntire Stennis Forestry Research Program funded project with award number NI22MSCFRXXXG077. Also, we would like to extend our sincere gratitude to the Dean of Graduate Studies at Southern University in Baton Rouge, Louisiana, Professor Ashagre Yigletu for providing graduate assistantships to some of the graduate students on this paper in order to promote research and help the students acquire the necessary skill development while earning a graduate degree.

The authors declare no conflicts of interest regarding the publication of this paper.

Twumasi, Y.A., Namwamba, J.B., Ning, Z.H., Merem, E.C., Loh, P.M., Asare-Ansah, A.B., Annan, J.B., Okwemba, R., Yeboah, H.B., Apraku, C.Y., Mjema, J., Armah, R.N.D., Anokye, M., Kangwana, L.A., Oppong, J., Atayi, J., Ogbu, C.C., Oladigbolu, O.I., Frimpong, D.B. and McClendon-Peralta, J. (2022) Analysis of Precipitation Trends and Prediction in Selected Cities in the Southeast Louisiana. Atmospheric and Climate Sciences, 12, 698-727. https://doi.org/10.4236/acs.2022.124039