High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology
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Abstract
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h–1) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial–aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology |
| Series title | Environmental Science & Technology |
| DOI | 10.1021/es504773x |
| Volume | 49 |
| Issue | 1 |
| Year Published | 2015 |
| Language | English |
| Publisher | ACS Publications |
| Contributing office(s) | National Research Program - Central Branch |
| Description | 9 p. |
| First page | 442 |
| Last page | 450 |
| Country | United States |
| State | Wisconsin |
| Other Geospatial | Lake Mendota |
| Google Analytic Metrics | Metrics page |