ASSESSING THE IMPACT OF COVID-19 ON LONG ISLAND'S GROUNDWATER SYSTEM: A FIVE-YEAR (2018-2023) ANALYSIS OF MONITORING STATIONS IN NASSAU AND SUFFOLK COUNTIES

Hart, W. J.1, Gogos, K.1, Gallagher, C.1, Kaplan, A.1, Marsellos, A.E.1, Tsakiri, K.G.2

1Dept. Geology, Environment and Sustainability, Hofstra University, NY, USA, 2Dept. Information Systems, Analytics, and Supply Chain Management, Rider University, NJ, USA.

Abstract:

The groundwater system of Long Island with respect to Nassau and Suffolk counties have been studied to understand its dynamics of managing groundwater supply. Two years ago, a study (Buchbinder et. al, 2021) was conducted to better understand how COVID-19 may have affected groundwater levels and subsequent water usage in Hempstead, Long Island; specifically with regards to the stay-at-home order. It was found that groundwater level dropped and interpreted with a hypothesis that perhaps water consumption increased with the stay-at-home order. As we move into a post-COVID world with more people than ever working from home or within a hybrid manner, it begs the question of how this is affecting Long Island’s groundwater levels with comparison to how it was performing at the beginning of the COVID-19 pandemic. We used the R programming language to access and analyze data from 12 monitoring stations, of the original dataset of approximately 56 active groundwater USGS monitoring stations, from the most populated regions of Nassau and Suffolk counties. Furthermore, we found a correlation to population size and density amongst wells which in theory elucidate the influence of COVID-19 on Long Island’s groundwater supply before, during, and after its effects. Here, we further develop the research in the previously mentioned study by looking at the last five years of groundwater data (2018-2023). This study could be used to expand the existing knowledge in groundwater management during pandemics and especially inform future decisions regarding Long Island’s water supply.

Background Information:

❖ Long Island is home to a dynamic groundwater system that is both the main and most important source of water for Nassau and Suffolk counties. This is independent of other hydrological inputs such as precipitation (see Miller & Frederick, 1969 for more information).

❖ On March 20th, 2020, Governor Andrew Cuomo signed the “New York State on Pause” Executive Order, herein referred to as the stay-at-home order or “lockdown”. This order mandated the closure of nonessential businesses (in-office) and barred gatherings of individuals for any size or any reason, among other things.

❖ A previous study by Hofstra University students in 2021 (Buchbinder et. al, 2021) observed Long Island’s groundwater levels before and during the height of the pandemic. This work was in part focused on building on the knowledge gained from this initial study and the methods used in this research were heavily influenced by the 2021 study.

❖ This study hopes to shed a light on how COVID-19 has impacted Long Island’s groundwater usage as well as call attention to how Long Island’s water was managed during a global pandemic. This is to inform future water management practices (Bera et. al, 2022).

Materials & Methods:

❖ Vast majority of work for this study was conducted in RStudio using R programming language, as it is quite versatile concerning large data processing and visualization of the data. Groundwater level data were retrieved from USGS using this method.

❖ R packages used were:

➢ sf, cartography, mapsf, celestial, and leaflet for spatial processing of the data

➢ kza for the application of the Kolmogorov-Zurbenko moving average

➢ dplyr for data manipulation

➢ lubridate for date format and data structure conversions

➢ BBmisc for standardization of the data.

❖ Trends were determined by applying a linear regression on each interval of the groundwater level data.

❖ A large portion of the code used at the beginning of this study was adapted from a previous Hofstra study (Buchbinder et al, 2021) and was modified to include different data inputs for the time series analysis.

➢ Other wells were selected to be focused on. In the prior-mentioned study, wells around Hempstead were highlighted due to their proximity to Hofstra and the high population of the surrounding area. In this study, priority was given to areas of higher population and density in both Nassau and Suffolk counties, ergo different visualizations were used.

Results

:

❖ Nassau and Suffolk Wells mostly showed a declining trend in groundwater levels.

❖ Westernmost Nassau has a higher water recharge trend post-COVID-19, while Suffolk County also had higher recharge in this same time.

❖ Three maps in total successfully generated that show groundwater levels on Long Island over three time periods.

Figure 2: Groundwater level shown by red dotted line; based on the National Geodetic Vertical Datum of 1929, (NGVD 29 in units of feet) versus the studied time intervals related to the pandemic. KZ-filtered groundwater level with kz(730,3) parameters is shown by blue line. The six line graphs of all 12 wells include Hempstead and Huntington/South Huntington towns that contain more than one well.

Discussion/Conclusion:

❖ When COVID-19 triggered the lockdown order, the draw on Long Island’s groundwater supply increased significantly. The majority of wells analyzed in this study were found to have higher levels of discharge than recharge after the stay-at-home order despite all twelve wells recharging before COVID-19. When visualized, it becomes clear that groundwater is still being used faster than it is being replenished, leading to an overall downward trend.

❖ GIS maps with groundwater level trends computed using moving average data from the USGS monitoring wells, located at the highly populated areas of Nassau and Suffolk counties, may imply water usage fluctuations caused by the residents' behavior change as a result of the pandemic.

❖ The pre-COVID GIS map allows us to observe the spatial pattern of higher groundwater level trends closer to central Long Island, while during COVID and post-COVID times those trends migrated closer to New York City.

❖ We think that as more people were working from home during the pandemic, they likely delayed the retreat of some wells’ water levels. Many people have continued to work from home even after the lockdown order was lifted, providing us with another possible reason why Long Island’s groundwater usage is still higher than pre-pandemic levels.

Figure 3: “Scatter-plot” graph/diagram representing the trends of groundwater level (in ft per day) in respect to the three time intervals related to the pandemic from the 12 wells located at the highest populated areas of Nassau and Suffolk counties. Each individual black circle represents a groundwater-supply well. A threshold that separates the discharging from recharging is reflected by the zero slope of the groundwater level.

References:

1) Bera, A., Das, S., Pani, A., Bera, B., & Shit, P. K. (2022). “Assessment of household water consumption during COVID-19 pandemic: A cross-sectional web-based study in India.” Sustainable Water Resources Management 8(3). https://doi.org/10.1007/s40899-022-00672-7.

2) Buchbinder, B., Phan, E., Renner, F., Liebowitz, A., Achek, M., Marsellos, A.E., Tsakiri, K.G. (2021). An approach to study the COVID-19 stay-at-home effect on water consumption in Long Island, NY using time series analysis of groundwater levels. 28th Conference on the Geology of Long Island and Metropolitan New York.

3) Miller, J. F., and Frederick, R. H. (1969). “The Precipitation Regime of Long Island, New York.” U.S. Geological Survey, Professional Paper 627-A. https://doi.org/10.3133/pp627a.

Credit Authorship Contribution Statement:

Hart, W: editing, formatting, R coding, writing - Abstract, Introduction, Methodology, Results, Discussion; Gogos, K: major R coding, data formatting, data analysis, graphing, mapping; Gallagher, C: data curation, data analysis, editing, writing Abstract, Introduction, Methodology, Discussion, Conclusion; Kaplan, A: reference citing, mapping, data curation; Marsellos, A.E.:supervision, R coding, guidance, editing. Tsakiri, K.G. - R coding/providing KZ-Filter code for data processing.