Stormwater biofilters are being implemented widely in urban environments to provide green space, alleviate flooding, and improve stormwater quality. However, biofilters with conventional media (sand, soil, and/or mulch or compost) do not reliably remove contaminants from stormwater. Research suggests addition of biochar to the biofilter media can improve the pollutant removal capacity of biofilters. In the current work, we present a systematic review of laboratory and mesocosm studies of biochar-augmented biofilters and an assessment of watershed-scale implementation of biofilters on local water quality. A full text review of 84 papers was conducted; of these, data were extracted from the 14 that met our inclusion criteria. log₁₀ removal of microbial pollutants and trace organic contaminants (TOrCs) by biochar-augmented media is generally greater than those of the controls containing just sand, soil, and/or compost. log₁₀ removal of nitrogen, phosphorous, total organic carbon, and total suspended solids in biochar-augmented biofilters is not clearly higher than those of control experiments. A supplemental analysis of four studies reporting longer-term breakthrough data revealed that TOrC removal effectiveness varies substantially among high temperature wood-based biochars, and that operational lifetimes of full-scale systems constrained by TOrC sorption capacity could range from five months to over seven years depending on the selected biochar. At the watershed-scale, biochar-augmented biofilters can provide enhanced treatment of runoff, resulting in the need for fewer treatment units or a smaller volume of watershed runoff treated to meet water quality criteria compared to their conventional counterparts. While their installation can reduce the load of pollutants to receiving waters, achieving concentration-based water quality targets may prove difficult even when pollutant removal capacity is high. This work highlights the importance of a systems approach to studying how biofilter installation affects water quality within a watershed. We identify several topical areas where further research is needed, especially as installation of biofilters and other stormwater control measures gain popularity in highly urbanized watersheds.

Groundwater monitoring is fundamental to understanding system dynamics, trends in storage, and the long‐term sustainability of an aquifer. Water‐level data are the key source of information used to understand the response. However, groundwater‐level data are often irregularly sampled, leading to temporal gaps in the record, and are not adequately distributed spatially across an aquifer. This presents challenges when spatially interpolating potentiometric surfaces and creating groundwater maps due to data availability. We present a spatiotemporal kriging methodology to improve spatial and temporal confidence in groundwater‐level predictions at unsampled locations. The space–time data set consists of a trend and residual component modeled with a linear regression and utilize a sum‐metric model to represent spatiotemporal covariances. The Arapahoe aquifer is used as a case study to demonstrate the benefits of spatiotemporal kriging over spatial kriging across a sparsely gauged and irregularly sampled aquifer. The Arapahoe aquifer is a major source of water for many residents along the Rocky Mountain Front Range in Colorado. The results show superior performance of spatiotemporal kriging to predict groundwater levels over the traditional spatial kriging. Spatiotemporal kriging represents realistic temporal and spatial changes in water levels and avoids some of the problems inherent to spatial kriging. This study demonstrates the power of spatiotemporal kriging to help inform system dynamics in irregularly sampled aquifers.

The current study evaluates residential water use patterns and driving factors across Los Angeles, California. Ten years of monthly residential water data were obtained from the Los Angeles Department of Water and Power. Socio-economic, vegetation characteristics, climate, and water pricing data were utilized to develop a statistical model to determine controlling factors of single-family residential water use. Key drivers were found to be household income, landscape greenness, water pricing, household volume allocation, precipitation and temperature. Results show that low water users are less sensitive to climate variability than high water users, likely because these customers have reduced outdoor water use. In the lower income group, average household size is a predictor for household water consumption, which increases with more residents. Lower water users are also more sensitive to changes in their first level household water allocation (Tier 1). However, low, medium and high water users all respond more to changes in the Tier 1 rate than the Tier 2 rate, and generally reduce consumption if this block rate is increased.