Satellite observed water quality changes in the Laurentian Great Lakes due to invasive species, anthropogenic forcing, and climate change

Long time series of ocean and land color satellite data can be used to measure Laurentian Great Lakes water quality parameters including chlorophyll, suspended minerals, harmful algal blooms (HABs), photic zone and primary productivity on weekly, monthly and annual observational intervals. The observed changes in these water quality parameters over time are a direct result of the introduction of invasive species such as the Dreissena mussels as well as anthropogenic forcing and climate change. Time series of the above mentioned water quality parameters have been generated based on a range of satellite sensors, starting with Landsat in the 1970s and continuing to the present with MODIS and VIIRS. These time series have documented the effect the mussels have had on increased water clarity by decreasing the chlorophyll concentrations. Primary productivity has declined in the lakes due to the decrease in algae. The increased water clarity due to the mussels has also led to an increase in submerged aquatic vegetation. Comparing water quality metrics in Lake Superior to the lower lakes is insightful because Lake Superior is the largest and most northern of the five Great Lakes and to date has not been affected by the invasive mussels and can thus be considered a control. In contrast, Lake Erie, the most southern and shallow of the Laurentian Great Lakes, is heavily influenced by agricultural practices (i.e., nutrient runoff) and climate change, which directly influence the annual extent of HABs in the Western Basin of that lake

Satellite monitoring of harmful algal blooms in the Western Basin of Lake Erie: A 20-year time-series

Blooms of harmful cyanobacteria (cyanoHABs) have occurred on an annual basis in western Lake Erie for more than a decade. Previously, we developed and validated an algorithm to map the extent of the submerged and surface scum components of cyanoHABs using MODIS ocean-color satellite data. The algorithm maps submerged cyanoHABs by identifying high chlorophyll concentrations (\u3e18 mg/m3) combined with water temperature \u3e20 °C, while cyanoHABs surface scums are mapped using near-infrared reflectance values. Here, we adapted this algorithm for the SeaWiFS sensor to map the annual areal extents of cyanoHABs in the Western Basin of Lake Erie for the 20-year period from 1998 to 2017. The resulting classified maps were validated by comparison with historical in situ measurements, exhibiting good agreement (81% accuracy). Trends in the annual mean and maximum total submerged and surface scum extents demonstrated significant positive increases from 1998 to 2017. There was also an apparent 76% increase in year-to-year variability of mean annual extent between the 1998–2010 and 2011–2017 periods. The 1998–2017 time-series was also compared with several different river discharge nutrient loading metrics to assess the ability to predict annual cyanoHAB extents. The prediction models displayed significant relationships between spring discharge and cyanoHAB area; however, substantial variance remained unexplained due in part to the presence of very large blooms occurring in 2013 and 2015. This new multi-sensor time-series and associated statistics extend the current understanding of the extent, location, duration, and temporal patterns of cyanoHABs in western Lake Erie

Spatial and temporal variability of inherent and apparent optical properties in western Lake Erie: Implications for water quality remote sensing

Lake Erie has experienced dramatic changes in water quality over the past several decades requiring extensive monitoring to assess effectiveness of adaptive management strategies. Remote sensing offers a unique potential to provide synoptic monitoring at daily time scales complementing in-situ sampling activities occurring in Lake Erie. Bio-optical remote sensing algorithms require knowledge about the inherent optical properties (IOPs) of the water for parameterization to produce robust water quality products. This study reports new IOP and apparent optical property (AOP) datasets for western Lake Erie that encapsulate the May–October period for 2015 and 2016 at weekly sampling intervals. Previously reported IOP and AOP observations have been temporally limited and have not assessed statistical differences between IOPs over spatial and temporal gradients. The objective of this study is to assess trends in IOPs over variable spatial and temporal scales. Large spatio-temporal variability in IOPs was observed between 2015 and 2016 likely due to the difference in the extent and duration of mid-summer cyanobacteria blooms. Differences in the seasonal trends of the specific phytoplankton absorption coefficient between 2015 and 2016 suggest differing algal assemblages between the years. Other IOP variables, including chromophoric, dissolved organic matter (CDOM) and beam attenuation spectral slopes, suggest variability is influenced by river discharge and sediment re-suspension. The datasets presented in this study show how these IOPs and AOPs change over a season and between years, and are useful in advancing the applicability and robustness of remote sensing methods to retrieve water quality information in western Lake Erie

Rapid and highly variable warming of lake surface waters around the globe

In this first worldwide synthesis of in situ and satellite-derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade-1) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors - from seasonally ice-covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade-1) to ice-free lakes experiencing increases in air temperature and solar radiation (0.53°C decade-1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes

Lake Superior ice cycle--1979

Master of ScienceRemote SensingUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/114767/1/39015003276915.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/114767/2/39015003276915.pd

Long term chlorophyll observations in the Great Lakes from oceancolor satellite data using multiple retrieval approaches

Ocean color satellites such as CZCS, SeaWiFS, MODIS, MERIS, and VIIRS have been imaging the Great Lakes and providing chlorophyll (chl) concentrations since 1979. Historically, the chl concentrations have been produced using an empirical band ratio technique developed by NASA for the open ocean. An alternative to the ratio technique is the use of semi-analytical Inverse Radiative Transfer Models (IRTM) such as the MTRI CPA-A. The IRTM approach requires knowledge of the optical properties of the Great Lakes and can provide more robust chl estimates in case II coastal waters where the color producing agents (CPAs) in the water include dissolved organic carbon and suspended minerals in addition to chl. Chl estimates from the NASA OC3 band ratio, CPA-A (IRTM), and EPA ship surveys were compared for the period 1998 to 2013. The comparisons which utilized Sea- WiFS and MODIS satellite data show the changing water optical properties of the Great Lakes as a function of nutrient loading and invasive species presence. The comparison also indicated in the present day offshore areas of the Lakes where the dominant CPA is chl, the ratio and CPA-A methods both produce acceptable retrievals. In complex case II water such as Lake Erie and near shore regions throughout the Lakes the CPA-A estimates of chl are significantly more robust

Estimation of absorption and backscatter values from in-situ radiometric water measurements

Water color remote sensing algorithms for case II waters like the Great Lakes require accurate characterization of the inherent optical properties (IOPs) within the water column to successfully estimate concentrations of chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals(sm). IOP coefficients (i.e. absorption and backscatter) necessary to create hydro-optical(HO) models for each of the Great Lakes can be measured directly using expensive in situ instrumentation (such as ac-s and bb9) or derived from in situ radiometric measurements of light entering and exiting the water surface (ie. Ed and Lu) . We have utilized data collected from a Satlantic Profiling Radiometer and the MODIS satellite to generate through a model, absorption and backscatter values for CHL, and SM and absorption for the visible portion of DOC (CDOM) for data collected in 2010 on Lakes Michigan and Huron. These calculated values were then compared to coincident in situ measurements of the absorption and backscatter for the three parameters

A model for determining satellite-derived primary productivity estimates for Lake Michigan

A new MODIS-based satellite algorithm to estimate primary production (PP) has been generated and evaluated for Lake Michigan. The Great Lakes Primary Productivity Model (GLPPM) is based on the work of Fee (1973) and Lang and Fahnenstiel (1996) but utilizes remotely sensed observations as input for model variables. The Color Producing Agent Algorithm (CPA-A) developed by Pozdnyakov et al. (2005) and Shuchman et al. (2006, in press 2013) is utilized to generate robust chlorophyll values and the NASA KD2M approach is used to obtain the diffuse attenuation coefficient (Kd). Only incident PAR and carbon fixation rates are additionally needed to generate the PP estimate. Comparisons of the satellite-derived PP estimates from single monthly images to average monthly field measurements made by NOAA/ GLERL found good agreement between estimates. Satellite derived PP estimates were used to calculate a preliminary Lake Michigan annual production of 8.5 Tg C/year. The GLPPM can be easily adapted to work on all the Great Lakes and therefore can be used to generate time series dating back to late 1997 (launch of SeaWiFS). These time series can contribute to improved assessment of Great Lakes primary productivity changes as a result of biological events, such as Dreissenid mussel invasions, climate change, and anthropogenic forcing

Phytoplankton Group Determination using Hyperspectral Remote Sensing in Western Lake Erie

Determination of phytoplankton groups (green algae, diatoms, cryptophytes, and cyanobacteria) in natural water bodies is important information for many stakeholders including scientists, water intake managers, and recreational users. These determinations can be made in the field using fluorescence instrumentation or in the laboratory using microscopy and particle imaging. There is potential to determine phytoplankton groups from remote sensing hyperspectral data measured on the ground, in the air, and from space. Remote sensing provides the unique ability to provide synoptic coverage of a water body without the need to be in the field. These techniques rely on the ability to detect specific spectral absorption features associated with different pigments that vary with phytoplankton group. An extensive data set was collected from May-October 2015 in western Lake Erie that included weekly coincident measurements of phytoplankton composition and water surface reflectance. This robust data set was used to evaluate and generate remote sensing based phytoplankton classification approaches with variable success. Supervised classification methods were able to determine the dominant phytoplankton type from others (cyanobacteria vs. diatoms) while complex machine learning techniques could differentiate types and concentrations