Abstract

The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry.
Water temperature and dissolved oxygen profiles were taken on sampling days.

Abstract

The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. Color is measured in water samples that are filtered in the field through 0.45 um nucleopore membrane filters. A spectrophotometer is used to quantify color in the lab as absorbance (unitless) at 1 nm intervals between the wavelengths of 200 and 800 nm. Absorbance data are considered suspect for values greater than 2.

Abstract

High frequency continuous data for temperature, dissolved oxygen, pH, chlorophyll a, and phycocyanin in Paul, Peter, and Tuesday lakes from mid-May to early September for the years 2013, 2014 and 2015. Inorganic nitrogen and phosphorus were added to Peter and Tuesday lakes each year while Paul Lake was an unfertilized reference.

Abstract

This dataset contains the daily groundwater level observations and other monitoring well attributes in Wisconsin. It covers 964 groundwater level monitoring wells and has 400,812 observations. The time span of this dataset is between February 2nd, 1929 and December 31st, 2015. The data sources include United States Geological Survey (USGS), Wisconsin Department of Natural Resources (WDNR), University of Wisconsin Extension, counties in Central Sands area, and North Temperate Lakes - Long-Term Ecological Research (NTL-LTER).
The data compilation consists of three major steps. First, the data were retrieved from different data sources. Then the data from different sources were pooled together. No well was monitored by more than one entity so none of the wells’ records were merged. Third, two rounds of quality assurance and quality control (QAQC) were conducted.
Wells in confined aquifers were not included in this dataset. The values of the USGS and Central Sands data are the depth to the water whereas LTER values are mean sea level elevations of the groundwater levels. These data could not be directly compared with each other.
This data compilation was funded by the Wisconsin Groundwater Joint Solicitation.

Abstract

This dataset contains the daily lake level observations and other lake attributes in Wisconsin. It covers 1036 lakes including 461 seepage lakes and 575 drainage lakes. It has 342,319 observations. The time span of this dataset is between January 1st, 1900 and December 31st, 2015. The data sources include USGS, Wisconsin Department of Natural Resources, North Temperate Lakes-Long Term Ecological Research (NTL-LTER), North Lakeland Discovery Center, Waushara County, and City of Shell Lake. Wisconsin Department of Natural Resources has two data sources: historical lake levels recorded in paper files and a recently-initiated citizen monitoring program. The latter are stored in Wisconsin DNR’s Surface Water Integrated Monitoring System (SWIMS).
The data compilation consists of four major steps. First, data were retrieved from different data sources. Then data from different sources but for the same lakes were tied together using the datum information if possible. The WISCID is used to denote unique data sets by lake and data source. If two data sources could be tied to the same datum, they share a WISCID. Third, three rounds of quality assurance and quality control (QAQC) were conducted. Finally, more attributes such as lake area, lake depth, and lake type were added to the lakes. This data compilation was funded by the Wisconsin Groundwater Joint Solicitation.

Abstract

This data set integrates and summarizes daily surface and groundwater inputs to 5 primary study lakes, using model estimates from a data-driven USGS hydrologic model (Hunt et al. 2013; Hunt and Walker 2017), and ice phenology data (number of days since ice-on). The lakes are Allequash, Big Muskellunge, Crystal, Sparkling, and Trout. Powers et al. (2017) used these data to estimate upper and lower bounds for exogenous chemical inputs to the lakes during winter. For a given lake and winter year, cumulative surface water and groundwater inputs were calculated across the ice cover period. For each lake, this data set reports the mean, maximum, and minimum winter water inputs observed across years, in units of water volume, % of average lake volume, and volume per winter day.Sampling Frequency: 1 per lake, with multiple summary values reported (i.e., mean, min, max). Number of sites: 5Hunt, R.J. et al., 2013. Simulation of Climate - Change effects on streamflow, Lake water budgets, and stream temperature using GSFLOW and SNTEMP, Trout Lake Watershed, Wisconsin. USGS Scientific Investigations Report., pp.2013-5159. Available at: https://www.researchgate.net/publication/258363719_Simulation_of_Climate-Change_Effects_on_Streamflow_Lake_Water_Budgets_and_Stream_Temperature_Using_GSFLOW_and_SNTEMP_Trout_Lake_Watershed_WisconsinHunt, R.J., and Walker, J.F., 2017, GSFLOW groundwater-surface water model 2016 update for the Trout Lake Watershed, Wisconsin: U.S. Geological Survey data release, https://dx.doi.org/10.5066/F7M32SZ2Powers SM, Labou SG, Baulch HM, Hunt RJ, Lottig NR, Hampton SE, Stanley EH. In press (expected 2017). Ice duration drives winter nitrate accumulation in north temperate lakes. Limnology and Oceanography Letters.

Abstract

Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.

Abstract

In 2013, Clean Lakes Alliance (CLA) launched a Citizen Water Quality Monitoring pilot. Objectives included evaluating and tracking nearshore water quality conditions on all five Yahara lakes: Lakes Mendota, Monona, Waubesa, Kegonsa and Wingra. In 2016, in order to fully understand the interaction between the offshore and nearshore
environment, CLA volunteers will begin sampling the deepest point (deep hole) of all Yahara lakes. The offshore monitoring program will focus on two components: water clarity sampling and dissolved oxygen and temperature measurement. Data from the offshore monitoring program will be compared to data from the nearshore program.

Abstract

Lake and river ice seasonality (dates of ice freeze and breakup) responds sensitively to climatic change and variability. We analyzed climate-related changes using direct human observations of ice freeze dates (1443–2014) for Lake Suwa, Japan, and of ice breakup dates (1693–2013) for Torne River, Finland. We found a rich array of changes in ice seasonality of two inland waters from geographically distant regions: namely a shift towards later ice formation for Suwa and earlier spring melt for Torne, increasing frequencies of years with warm extremes, changing inter-annual variability, waning of dominant inter-decadal quasi-periodic dynamics, and stronger correlations of ice seasonality with atmospheric CO2 concentration and air temperature after the start of the Industrial Revolution. Although local factors, including human population growth, land use change, and water management influence Suwa and Torne, the general patterns of ice seasonality are similar for both systems, suggesting that global processes including climate change and variability are driving the long-term changes in ice seasonality.

Abstract

This dataset includes predicted and observed values of maximum depth for lakes in the upper Midwest and northeast United States. All observed values came from LAGOS ver 1.040.0 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 40 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, observed maximum depth values (n = 8164) were used to train and validate a predictive mixed effects model for lake depth using terrestrial and lake morphology as predictors (Oliver et al., submitted). Predicted values (n = 50 607) generated by the model had a root mean squared error of 7.1 m. This research was supported by the NSF Macrosystem Biology awards 1065786, 1065818, and 1065649.