An official website of the United States government
Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).
more »
« less
Global lake area time series 1999-2021
This dataset contains: (1) a time series of lake area extracted from two Landsat-derived datasets (GSWO and GLAD) for ~6 million lakes globally, (2) the annual median lake area of these lakes, calculated separately for each dataset and (3) the monthly median lake area of these lakes, calculated separately for each dataset. For lakes within the northern permafrost zone (i.e., above 50 degrees North and underlain by permafrost), the time series dataset extends 1984-2023. For all other lakes, the time series dataset covers the period 1999-2021. For all lakes, annual median and monthly medians are calculated for the period 1999-2021.
more »
« less
- Award ID(s):
- 2528875
- PAR ID:
- 10656565
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- lakes lake area surface water time series
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. Many studies characterize ice-marginal lake behavior over multi-decadal timescales using either episodic ~annual images or multi-year mosaics. However, ice-marginal lakes are dynamic features that experience short-term (i.e., day to year) variations in area and volume superimposed on longer-term trends. Through aliasing, this short-term variability could result in erroneous long-term estimates of lake change. We develop and implement an automated workflow in Google Earth Engine to quantify monthly behavior of ice-marginal lakes between 2013 and 2019 across south-central Alaska using Landsat 8 imagery. We employ a supervised Mahalanobis minimum-distance land cover classifier incorporating three datasets found to maximize classifier performance: shortwave infrared imagery, the normalized difference vegetation index (NDVI), and spatially filtered panchromatic reflectance. We observe physically-meaningful ice-marginal lake area variance on sub-annual timescales, with the median area fluctuation of an ice-marginal lake found to be 10.8% of its average area. The median signal (slow lake growth) to noise (physically-meaningful short-term area variability) ratio is 1.5:1, indicating that short-term variability is responsible for ~33% of observed area change in the median ice-marginal lake. The magnitude of short-term area variability is similar for ice-marginal and nonglacial lakes, suggesting that the cause of observed variations is not of glacial origin. These data provide a new context for interpreting behaviors observed in multi-decadal studies and encourage attention to sub-annual behavior of ice-marginal lakes even in long-term studies.more » « less
-
This dataset includes monthly dissolved silicon (DSi) concentration data from 198 rivers across the Northern Hemisphere. Concentration and discharge data were sourced from public and/or published datasets and the Weighted Regressions on Time, Discharge, and Season model (Hirsch et al. 2010) was used to estimate monthly concentrations and flow-normalized concentrations for all sites over their period of record. Sites span eight climate zones, ranged from 18 degrees N to 70 degrees N, and vary in drainage area from < 1 km2 to nearly 3 million km2. These monthly concentration data were then used to cluster sites into average (i.e., average of all years) and annual (i.e., each year individually) seasonal regimes using a time-series clustering approach. The annual regimes were used to quantify how often a site moved among regimes over its period of record (i.e., stability). Site characteristics including climate zone, discharge, and concentration-discharge behavior were explored as potential drivers of cluster membership and stability.more » « less
-
This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario).more » « less
