No abstract

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

Ten-year monitoring and data assessment of a petroleum-hydrocarbon spill site were introduced to measure the effectiveness of natural attention at the site. For one decade, the monitoring of groundwater was conducted and collected data were analyzed to be used for the remediation of groundwater and soil for the future land development. The project site was previously used as gas station and car wash business. Typical groundwater contaminants such as TPH-G, BTEX, and oxygenates (MTBE, DIPE, ETBE, TAME, and TBA) were persistently found at the studied site. The plume and concentrations of monitored contaminants kept changing for the entire monitoring period. For this specific site, natural attenuation was unlikely feasible due to unfavorable environmental conditions. As a remediation strategy, other remediation options must be considered. The combinational method including a clean-up technology and natural attenuation is also recommended.

Two industrial sites were investigated based on years of available hydrogeologic information and monitoring data for soil and groundwater. Collected data were forensically evaluated using age-dating and fingerprinting methods. The previous business uses of the project sites were as a gas station, laundry/dry-cleaning service, and car wash with petroleum underground storage tanks (USTs). As a result, these sites were exposed to a number of toxic contaminants at relatively high concentrations. Source control was necessary for successful remediation and the ultimate removal of the remaining compounds from these industrial sites. Although contaminated soil around the source was excavated during the remedial action and the high concentrations of contaminants were reduced, typical groundwater contaminants such as petroleum hydrocarbons as gasoline (TPH-G), benzene, toluene, ethylbenzene, xylenes (BTEX), and oxygenates including methyl tert-butyl ether (MTBE), diisopropyl ether (DIPE), ethyl tert-butyl ether (ETBE), tert-amyl methyl ether (TAME), and tert-butyl alcohol (TBA) were persistently found at the studied sites around the source points. The plume and concentration of contaminants had changed their shapes and strength for all monitoring periods. Thus, additional source control seems to be a requirement for the complete removal of source contamination, which must be ascertained with groundwater and soil monitoring on a regular time base. For the study sites, monitored natural attenuation was relatively feasible for the long-term plan; however, it did not offer a perfect remediation solution for an ultimate goal because of residual toxic compounds that might have affected the surrounding residential areas at higher concentrations than their health limits. Therefore, as a remediation strategy, the combination of clean-up technology and natural attenuation with monitoring activities are more highly recommended than either clean-up or natural attenuation used separately.