Leveraging satellite imagery and high-frequency sensors to understand variability in lake water clarity across spatial and temporal gradients
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Authors
Glines, Max, Robert
Issue Date
2024-08
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Biology
Alternative Title
Abstract
Freshwater lakes provide many ecosystem services and are highly influential in global carbon, nutrient, and water cycling. Due to their location at the low-point of a watershed, changes in climate and land cover greatly impact lakes, and lakes are often considered sentinels of environmental change. Because of their importance to both humans and the surrounding environment, many monitoring programs and scientific studies focus on long-term changes in lake water quality. These programs frequently measure water clarity, a measurement of light transmittance through water, both because of its value for human uses such as drinking water and recreational use and because of its importance in regulating ecological variables such as light availability, thermal stratification and water temperature, primary productivity and dissolved oxygen, and fish and zooplankton habitat. Accordingly, water clarity measured as Secchi depth is one of the most frequently measured variables in lakes. Despite this, however, water clarity data is still limited across space and time. Secchi depth measurements are typically taken only monthly in the summer, and a majority of publicly available Secchi depth data is focused on a relatively small portion of large, well-monitored, north-temperate lakes. It is known that water clarity can vary daily in response to discrete disturbances, seasonally in response to phenological variability in stratification, zooplankton and fish species abundance, and meteorological drivers, and over years to decades in response to changes in climate and land cover. However, the current availability of water clarity data limits the comparison of variability across space and time. There has been no investigation of daily to seasonal variation at regional to global scales, and long-term water clarity data is lacking across most regions of the globe aside from North America and Europe. This dissertation examines variability in water clarity, as well as the causes and effects of this variability in lake ecosystems, across a broad range of spatial and temporal scales. The first project relates variability in global water clarity at a scale of days to decades using high-frequency underwater light sensors and public Secchi depth data. The second project explores seasonal variation in water clarity across the contiguous United States using satellite imagery. The third project uses satellite imagery to assess changes in water quality across three decades throughout southern Africa. The fourth project uses large-scale models of water clarity, lake morphometry, and stratification depth to explore variability in the depth of primary productivity across the United States. The sum of these projects examines water clarity and its influence on lake ecosystems at spatial and temporal scales that have not been previously studied.
My first project (Chapter 2) aims to relate short-term variability (days to months) in water clarity to long-term variability (years to decades). To do this, I used data from underwater light sensors across a suite of 35 global lakes to calculate short-term changes in light attenuation. Long-term data was assessed using publicly available Secchi depth measurements spanning an average of 12 years. Using Taylor’s Law, which states that the log of the variance of a positive quantity is linearly related to the log of the mean, I showed that long-term measurements of water clarity can reliably predict short-term variance, and vice versa. Despite the complex drivers of variability in water clarity, this chapter shows that it is predictable across a wide range of time scales, climate and land cover types, and lake morphologies. This project is the first to show that Taylor’s Law applies to high-frequency ecological measurements, providing valuable insight into landscape and macrosystems ecology and linking scales of measurement.
The second project (Chapter 3) explores seasonal patterns of water clarity in relation to climate and land cover. Using satellite remote sensing and machine learning, I created a model to estimate Secchi depth across over 90,000 lakes in the United States. I used time series clustering analysis to show that lakes in the US broadly fall into two distinct seasonal patterns of water clarity. The first pattern corresponds to a spring clear water phase, which is common in eutrophic and algal-dominated lakes and is most common in lakes with agricultural or developed watersheds. The second pattern demonstrates a summertime peak in clarity, and represents lakes that are optically dominated by dissolved organic matter or oligotrophic lakes that experience spring and fall algal blooms. This pattern is more common in lakes with higher forest and wetland cover in the watershed. I also show that the timing of peak water clarity is shifting earlier across all major ecoregions regardless of the seasonal pattern shown. This is likely due to a universal driver such as the increasing duration of thermal stratification and decreasing duration of ice cover that is occurring across many lakes. This project is the first to analyze seasonal patterns in water clarity at large spatial scales, and demonstrates the importance of land cover and climate in driving seasonality in lakes.
My third project (Chapter 4) aims to assess long-term trends in water quality across southern Africa in relation to changing climate and land cover. Southern Africa is a water-scarce region that is heavily impacted by drought and land cover change such as urbanization and deforestation, but has highly limited monitoring of water quality and quantity. This project uses remote sensing to delineate 6,900 lakes and reservoirs and estimate water clarity, chlorophyll, turbidity, and total suspended solids for the first time in most of these lakes and reservoirs. I show that water clarity across the region is heavily impaired, with a median Secchi depth of only 1.3 m. Despite changes in climate and land cover, water clarity has largely remained the same across the region over the past 30 years. However, some reservoirs have experienced large shifts in water clarity and quality due to invasive species, fluctuations in water volume, and watershed remediation efforts.
The final project (Chapter 5) partitions the potential for primary productivity based on light availability between the epilimnion, hypolimnion, and benthos in lakes across the United States. To accomplish this, I developed a machine learning model to estimate thermocline depth using lake morphometry and meteorological driver data. This model correctly determines if a lake is stratified or isothermal with 87% accuracy, and estimates thermocline depth with a mean absolute error of less than 1.2 m. Pairing this model with the water clarity model from Chapter 3 and a lake depth model, I show that primary productivity is possible throughout large portions of the hypolimnetic and benthic zones of lakes. Consistent stratification supports the potential for a deep chlorophyll maximum in one-third of US lakes, and a majority of lakes support benthic productivity. The relative volume epilimnion and hypolimnetic water and the relative surface area of the benthos that supports potential productivity varies seasonally and by ecoregion. Fluctuations in water clarity are the most important drivers of potential productivity at depth for shallow lakes, and in deep lakes the depth of the thermocline is the most important driver. This project is the first to examine the potential for productivity in non-surface waters at a large scale and shows that estimates of regional or global lake productivity need to properly account for variability in water clarity and thermocline depth.
Description
August2024
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY