An official website of the United States government
{"Abstract":["The Lake Mendota Microbial Observatory collects routine water clarity measurements\n alongside their microbial samples. This dataset includes measurements of water clarity\n collected at the central Deep Hole, collocated with a weather buoy (43°05'58.2"N\n 89°24'16.2"W). All measurements were collected with handheld Secchi discs. When multiple\n personnel performed the Secchi disc measurements, the average and standard deviation are\n reported. To take the Secchi depth, sunglasses are removed and the disc is lowered on the\n shaded side of the boat. The Secchi depth is the average between where the Secchi disc\n disappears while lowering it and where it reappears while raising it. Routine microbial\n observatory sampling continues into the present."]}
more »
« less
Lake Mendota Microbial Observatory Secchi Disk Measurements 2012-present
The Lake Mendota Microbial Observatory collects routine water clarity measurements alongside their microbial samples. This dataset includes measurements of water clarity collected at the central Deep Hole, collocated with a weather buoy (43°05'58.2"N 89°24'16.2"W). All measurements were collected with handheld Secchi discs. When multiple personnel performed the Secchi disc measurements, the average and standard deviation are reported. To take the Secchi depth, sunglasses are removed and the disc is lowered on the shaded side of the boat. The Secchi depth is the average between where the Secchi disc disappears while lowering it and where it reappears while raising it. Routine microbial observatory sampling continues into the present.
more »
« less
- Award ID(s):
- 2011002
- PAR ID:
- 10518533
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The Lake Mendota Microbial Observatory collects routine water physical and chemical measurements alongside their microbial samples. This dataset includes measurements of water temperature, dissolved oxygen, pH, and conductivity collected at the central Deep Hole, collocated with a weather buoy (43°05'58.2"N 89°24'16.2"W). All measurements were collected with handheld probes. Data from 2006-2014 was compiled from multiple sources and includes only water temperature and dissolved oxygen. Data from 2014-2019 is from the same probe, a YSI Pro Plus instrument, and also includes pH and specific conductance. Routine microbial observatory sampling continues into the present.more » « less
-
{"Abstract":["The Lake Mendota Microbial Observatory collects routine water physical and chemical\n measurements alongside their microbial samples. This dataset includes measurements of water\n temperature, dissolved oxygen, pH, and conductivity collected at the central Deep Hole,\n collocated with a weather buoy (43°05'58.2"N 89°24'16.2"W). All measurements were collected\n with handheld probes. Data from 2006-2014 was compiled from multiple sources and includes only\n water temperature and dissolved oxygen. Data from 2014-2019 is from the same probe, a YSI Pro\n Plus instrument, and also includes pH and specific conductance. Routine microbial observatory\n sampling continues into the present."]}more » « less
-
This dataset contains water quality measurements made on Green Lakes 1, 2, 3, 4, 5, and Lake Albion. Green Lake 4 was initially sampled in 2000 and is ongoing. Ongoing sampling of Green Lakes 1 was started in 2014 and ongoing sampling of Lake Albion was started in 2016. Water samples were collected for analysis of chlorophyll a and nutrient analysis (which is available in glvwatsolu.dm.data) and field measurements for pH, temperature, specific conductivity, dissolved oxygen (DO), % saturation, secchi depth, PAR. Secchi depth is recorded at the 0m row however it is a measurement of depth and so the units are meters. Most samples were collected between 0800 and 1200 MST. The first sampling date each summer occurs shortly after the ice had melted. Data are collected from an inflatable raft at the point of deepest depth or from the lake inlet and outlet when surface flow is present. The majority of chlorophyll-a the measurements were taken at the surface (0m), the metalimnion (3m), and the hypolimnion nine (usually 8-11m). However, additional measurements were taken for side projects of the long-term dataset during several of the years and are included in this dataset. Water samples from the metalimnion or hypolimnion were collected using a Van Dorne sampler, and surface samples were collected as grab samples from the water column surface, the inlet and outlet. Field measurements were conducted using a YSI either DO or multiple probe meter (2014-2017, YSI MPS 556)(2018-ongoing, YSI ProPlus) and a Li-Cor meter with a quantum sensor. Chlorophyll-a was extracted from filtered samples and absorbance was measured before and after acidification to quantify chlorophyll a concentration.more » « less
-
Abstract Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future.more » « less
