Regional measurements and spatial/temporal analysis of CDOM in 10,000+ optically variable Minnesota lakes using Landsat 8 imagery

Olmanson, Leif G.; Page, Benjamin P.; Finlay, Jacques C.; Brezonik, Patrick L.; Bauer, Marvin E.; Griffin, C. G.; Hozalski, Raymond M.

doi:10.1016/j.scitotenv.2020.138141

Cited by 37 publications

(26 citation statements)

References 45 publications

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“…Long‐term data for CDOM and other DOM measures across the Upper Great Lakes region are relatively sparse (Stanley et al 2019), but several studies support climatic variation as a major driver of Mississippi River color variability and change. Remote sensing of lake CDOM across Minnesota (Olmanson et al 2020) found strong climate controls over interannual lake CDOM variability in northern Minnesota, and unusually wet conditions (perhaps representative of future climate in the region) were associated with marked CDOM increases compared with typical annual precipitation levels. Similarly, a study of two Wisconsin bogs showed strong dependence of DOC concentration on climate variables, especially drought, which depressed DOC (Bertolet et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The NLF comprises the upper half of the Headwaters watershed, and is composed of 49% forested area (mixed conifers and hardwoods), 27% wetlands, 9% open water, and only 11% agricultural and urban land (2011 National Land Cover data; Homer et al 2015). Surface waters in the NLF ecoregion have higher CDOM levels than those in the NCHF (Griffin et al 2018; Olmanson et al 2020), and the NLF likely is the source of most of the allochthonous DOM in the river at Minneapolis. The NCHF, a transitional ecoregion between the relatively pristine NLF and heavily agricultural ecoregions of southern Minnesota, was 48% agricultural land, 9% urban land, 12% open water, and only 10% wetlands in 2011 (Homer et al 2015).…”

Section: Site Descriptionmentioning

confidence: 99%

“…The high percentage of forest plus wetlands (76%) and low percentage of agricultural plus urban land (11%) in the NLF in 2011 suggests, however, that land cover changes in the NLF were small over the 64‐yr record analyzed here. Land‐cover changes have been larger in the NCHF part of Headwaters watershed because of urban growth and conversion of prairie to agricultural land, but these changes are less likely to have affected color levels in the Mississippi at Minneapolis because of the relatively low CDOM and DOC levels in prairie streams and rivers (Tsui and Finlay 2011; Hansen et al 2018) and agricultural runoff (Dalzell et al 2011) and dominance of the NLF as a CDOM source (Olmanson et al 2020).…”

Section: Site Descriptionmentioning

confidence: 99%

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Long‐termwater color and flow trends in the Mississippi River Headwaters, 1944–2010

Germolus

Brezonik

Hozalski

et al. 2021

Limnology & Oceanography

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Intensification of brown color in surface waters has been observed over several decades in many areas. We examined a 64‐yr daily record (1947–2010) of visual water color, a measure of chromophoric dissolved organic matter (CDOM), in the Mississippi River at Minneapolis, Minnesota. Although no monotonic trends in daily or mean annual color were evident, our analyses revealed trends in seasonal metrics, for example, mean winter color, on decadal scales related to changes in flow (hence climatic conditions). A pattern of high color (CDOM) in late spring and summer, corresponding with higher flow, was found across the period. Daily flow accounted for ~ 50% of the variance in color, and a lag of four days was found between peak responses of flow and color, supporting a CDOM source from wetlands in northern parts of the basin. The slope of the color‐flow relationship increased over the 64 yr, driven by increased CDOM flushing in late summer‐early fall. Based on trends in seasonally aggregated color and discharge, minimum and mean color and flow increased during winter over the 64 yr, potentially due to higher temperatures. Summer months did not show increases, but color became less variable. As a result, the color difference between summer and winter became smaller over the study period. During high flow events (ice‐out or high precipitation), some hysteretic color patterns were observed consistent with observations on other large rivers. Our results indicate that long‐term color (CDOM) trends in the Mississippi Headwaters reach are related to seasonally dominant changes in climatic conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

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Long‐termwater color and flow trends in the Mississippi River Headwaters, 1944–2010

Germolus

Brezonik

Hozalski

et al. 2021

Limnology & Oceanography

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“…We constructed the LimnoSat‐US database by extracting the surface reflectance values of high confidence water pixels (Jones, 2019) from Landsat 5, 7, and 8 imagery across 56,792 lakes contained within the HydoLAKES database (Messager et al., 2016). While these surface reflectance images were originally developed for terrestrial applications, a growing body of research shows that they can be used to accurately estimate inland water quality parameters and perform on par with water‐specific atmospheric correction algorithms (Griffin et al., 2018; Kuhn et al., 2019; Olmanson et al., 2020). To avoid signal noise from surrounding land pixels and bottom reflectance in shallow waters, we take the median reflectance values from within 120 meters of the deepest point for each waterbody (Shen et al., 2015), where the deepest point refers to the portion of the lake that is furthest away from the lake shoreline.…”

Section: Methodsmentioning

confidence: 99%

Shifting Patterns of Summer Lake Color Phenology in Over 26,000 US Lakes

Topp

Pavelsky

Dugan

et al. 2021

Water Resources Research

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Lakes are critical freshwater resources that are highly sensitive to stressors such as climate change (Woolway et al., 2020) and altered land use (Martinuzzi et al., 2014). Globally, these stressors are shortening the duration of ice cover (Sharma et al., 2019), increasing rates of lake carbon burial (Heathcote & Downing, 2012), increasing evaporative water loss (Wang et al., 2018), warming surface waters (O'Reilly et al., 2015), and changing mixing regimes (Maberly et al., 2020), all of which influence lake productivity and ecological state. These changes manifest themselves in the seasonality of lake processes. Just like a deciduous forest that comes to life in the spring, inland water bodies are characterized by a predictable seasonal succession of biological processes (Sommer et al., 2012). In the spring, many lakes experience a diatom bloom, followed by a "clear-water" phase where zooplankton rapidly devour the newly plentiful phytoplankton (Matsuzaki et al., 2020). Summer algal biomass is constrained by nutrient availability, with nutrient-rich eutrophic lakes experiencing near-constant summer phytoplankton blooms, and nutrient-poor oligotrophic lakes experiencing relatively clear waters (Sommer et al., 1986). The difference between these states is visible to the Abstract Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape-level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale.Plain Language Summary Lakes have seasonal cycles that result in yearly peaks in algal growth. The size and timing of these peak periods depends on the amount of nutrients available and the timing of key events such as freezing and thawing. Bluer lakes with little algae typically have one peak in the spring, while greener, high algae lakes can have m...

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“…Data for model training and validation was derived from a variant of the AquaSat database (15) which combines historical water quality measurements from the Water Quality Portal (3) and LAGOS-NE (2) with coincident (+/-1 day) satellite images from the USGS tier 1 surface reflectance collections for Landsat 5, 7, and 8. While the atmospheric corrections used to generate these surface reflectance products were originally developed for terrestrial applications, a growing body of research shows that they can be used to accurately estimate inland water quality parameters and perform on par with water-specific atmospheric correction algorithms (16)(17)(18). Site IDs from AquaSat were spatially joined to lake polygons from NHDPlusV2 (14) (NHD) and then linked to catchment level metrics from the LakeCat database (19).…”

Section: Data Processing and Acquisitionmentioning

confidence: 99%