Long-Term Citizen-Collected Data Reveal Geographical Patterns and Temporal Trends in Lake Water Clarity Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity. Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938–2012). Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Trend direction and strength were related to latitude and median sample date. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Our objectives were to determine (1) whether temporal trends in lake- water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. We compiled a lake- water clarity database using publically available, citizen volunteer observations made between 19 across eight states in the Upper Midwest, USA.
Cheruvelil, Kendra Spence Webster, Katherine E. Long-term citizen-collected data reveal geographical patterns and temporal trends in lake water clarity Information derived from this model can be used by water-resource managers to assess water quality and evaluate effects of changes in the watershed on water quality. A pixel-level lake map of predicted water clarity or computed trophic state can be produced from the model output. The regression model can be used to reliably predict water clarity anywhere within the lake. The natural log of secchi disk transparency is the dependent variable in the regression and the independent variables are Thematic Mapper band 1 (blue) reflectance and a ratio of the band 1 and band 3 (red) reflectance. A pilot study uses multidate satellite image scenes in conjunction with physical measurements of secchi disk transparency collected by the Lakes of Missouri Volunteer Program to construct a regression model used to estimate water clarity. Water quality of Table Rock Lake in southwestern Missouri is assessed using Landsat Thematic Mapper satellite data. Table Rock Lake Water-Clarity Assessment Using Landsat Thematic Mapper Satellite Data The model combines a physical mixing model with an irradiance model and nutrient cycling model. The Tampa Bay Water Clarity Model was developed as a predictive tool for estimating the impact of changing nutrient loads on water clarity as measured by secchi depth. Tampa Bay Water Clarity Model (TBWCM): As a Predictive Tool
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