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USGS2012

The response of Lake Tahoe to climate change

Meteorology is the driving force for lake internal heating, cooling, mixing, and circulation. Thus continued global warming will affect the lake thermal properties, water level, internal nutrient loading, nutrient cycling, food-web characteristics, fish-habitat, aquatic ecosystem, and other important features of lake limnology. Using a 1-D numerical model - the Lake Clarity Model (LCM) - together with the down-scaled climatic data of the two emissions scenarios (B1 and A2) of the Geophysical Fluid Dynamics Laboratory (GFDL) Global Circulation Model, we found that Lake Tahoe will likely cease to mix to the bottom after about 2060 for A2 scenario, with an annual mixing depth of less than 200 m as the most common value. Deep mixing, which currently occurs on average every 3-4 years, will (under the GFDL B1 scenario) occur only four times during 2061 to 2098. When the lake fails to completely mix, the bottom waters are not replenished with dissolved oxygen and eventually dissolved oxygen at these depths will be depleted to zero. When this occurs, soluble reactive phosphorus (SRP) and ammonium-nitrogen (both biostimulatory) are released from the deep sediments and contribute approximately 51 % and 14 % of the total SRP and dissolved inorganic nitrogen load, respectively. The lake model suggests that climate change will drive the lake surface level down below the natural rim after 2085 for the GFDL A2 but not the GFDL B1 scenario. The results indicate that continued climate changes could pose serious threats to the characteristics of the Lake that are most highly valued. Future water quality planning must take these results into account.

California;Nevada↗

USGS2006

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Science of the Total Environment↗