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This study reports on a blending approach using snowpack measurements from a wireless-sensor network, gauge precipitation, and atmospheric-moisture data to estimate mountain precipitation amount and phase. We applied the approach in California's American River basin, using dense measurements from a network consisting of over 130 sensor nodes distributed across the upper, more snow-dominated part of the basin (≥1,500 m elevation). Analysis of 60 precipitation events in water years 2014–2017 showed that the approach provides estimates of precipitation and orographic enhancement that reduce uncertainty from apparent snow undercatch by limited gauges. This approach also infers total precipitation based on snow measurements during rain-on-snow events. The sensor network and blending approach yielded median upper-basin orographic precipitation gradients (OPGs) of 0.57 km−1, smaller than the also-positive lower-basin (<1,500 m) medians OPGs from precipitation gauges and a gauge-based gridded data set of 1.23 and 1.00 km−1, respectively. However, during 73% of the events, both gauges and the gridded product showed negative OPGs in the upper basin, inconsistent with typically positive values from the distributed sensor network. Upper-basin OPGs from gauges and the gridded product were more negative (p-values < 0.03) during heavy events related to atmospheric rivers and Sierra barrier jets than during milder events, revealing the challenges for gauges to reliably measure precipitation from large moisture transport by strong winds. In snow-dominated headwater areas, precipitation from the blending approach is recommended as being more accurate for decision support, providing critical rain-versus-snow amounts and complementing precipitation-gauge data.

Common methods for spatial distribution, such as hydrologic response units, are subjective, time-consuming, and fail to capture the full range of basin attributes. Recent advances in statistical-learning techniques allow for new approaches to this problem. We propose the use of Gaussian Mixture Models (GMMs) for spatial distribution of hydrologic models. GMMs objectively select the set of modeling locations that best represent the distribution of watershed features relevant to the hydrologic cycle. We demonstrate this method in two hydrologically distinct headwater catchments of the Sierra Nevada and show that it meets or exceeds the performance of traditionally distributed models for multiple metrics across the water balance at a fraction of the time cost. Finally, we use univariate GMMs to identify the most-important drivers of hydrologic processes in a basin. The GMM method allows for more robust, objective, and repeatable models, which are critical for advancing hydrologic research and operational decision making.

To provide complementary information on the hydrologically important rain–snow-transition elevation in mountain basins, this study provides two estimation methods using ground measurements from basin-scale wireless sensor networks: one based on wet-bulb temperature Twet and the other based on snow-depth measurements of accumulation and ablation. With data from 17 spatially distributed clusters (178 nodes) from two networks, in the American and Feather River basins of California’s Sierra Nevada, we analyzed transition elevation during 76 storm events in 2014–18. A Twet threshold of 0.58C best matched the transition elevation defined by snow depth. Transition elevations using Twet in upper elevations of the basins generally agreed with atmospheric snow level from radars located at lower elevations, while radar snow level was;100 m higher due to snow-level lowering on windward mountainsides during orographic lifting. Diurnal patterns of the difference between transition elevation and radar snow level were observed in the American basin, related to diurnal ground-temperature variations. However, these patterns were not found in the Feather basin due to complex terrain and higher uncertainties in transition-elevation estimates. The American basin tends to exhibit 100-m-higher transition elevations than does the Feather basin, consistent with the Feather basin being about 18 latitude farther north. Transition elevation averaged 155 m higher in intense atmospheric river events than in other events; meanwhile, snow-level lowering was enhanced with a 90-m-larger difference between radar snow level and transition elevation. On-the-ground continuous observations from distributed sensor networks can complement radar data and provide important ground truth and spatially resolved information on transition elevations in mountain basins.

One of the principal questions in hydrology is how and when water leaves the critical zone storage as either stream flow or evapotranspiration. We investigated subsurface water storage and storage selection of the Southern Sierra Critical Zone Observatory (California, USA) within the age-ranked storage selection framework, constrained by a novel combination of cosmogenic radioactive and stable isotopes: tritium, sodium-22, sulfur-35, and oxygen-18. We found a significant positive correlation between tritium and stream flow rate and between sulfur-35 and stream flow rate, indicating that the age distribution of stream flow varies with stream flow rate. Storage selection functions that vary with stream flow rate are better able to reproduce tritium concentrations in stream flow than functions that are constant in time. For the Southern Sierra Critical Zone, there is a strong preference to discharge the oldest water in storage during dry conditions but only a weak preference for younger water during wet conditions. The preference of evapotranspiration for younger water, constrained by oxygen-18 in stream water, is essential to parameterize subsurface storage but needs to be confirmed by isotopic or other investigations of evapotranspiration. This is the first study to illustrate how a combination of cosmogenic radioactive isotopes reveals the hydrochronology and water storage dynamics of catchments, constrains the subsurface architecture of the critical zone, and provides insight into landscape evolution.

The work summarizes the results of a studies conducted with six different flushing agents, i.e. two anionic surfactants (SDS and DOWFAX 8390), two complexing agents (cyclodextrins), a humid acid, and an alcohol (ethanol), for solubilizing and removing residual-phase immiscible liquid from porous media. Batch experiments were conducted to measure the degree of trichloroethene (TCE) and tetrachloroethene (PCE) solubilization induced by these agents. These studies revealed that the solubility of TCE and PCE was enhanced significantly. Column experiments were conducted to compare water and agent-enhanced flushing of Borden sand containing residual saturations of TCE and PCE. The results of these studies indicate that the total flushing volume necessary to remove the contaminant was reduced substantially in the presence of all applied agents. The relative effectiveness of the agents varied based on the method of evaluation. It was shown that a volatile contaminant, e.g. toluene, TCE, and PCE, can be separated from cyclodextrin solution by mean of air-sparging. The cyclodextrin solution was successfully reused for continuos flushing of a contaminated porous material. The simulation of selected experiments revealed that the mass transfer rate coefficients are generally increasing if evaluated based on the aqueous driving force approach. However, a relation based on the enhanced driving force approach was found to be less significant. Even though areas of additional research have been identified, this research contributes to a better understanding of the processes governing chemically enhance flushing of contaminated porous media.

Aerosol carbon concentrations have been measured under wintertime conditions at two sites in the Los Angeles basin. Samples were analyzed for total carbon by the Gamma Ray Analysis of Light Elements (GRALE) technique and for elemental carbon by reflectance. Total carbon concentrations at downtown Los Angeles averaged 24 µgm^(-3) and elemental carbon concentrations averaged 9 µgm^(-3) during the 7 am to 1 pm period. The light absorption efficiency of ambient elemental carbon particles was determined to be 11.9 + 0.9 m^2g^(-1), and it was estimated that light absorption by elemental carbon could account for up to 17% of total light extinction in downtown Los Angeles. Baseline conditions for elemental carbon concentrations in the Los Angeles area prior to the introduction of large numbers of diesel passenger cars into the vehicle fleet are established by these measurements. A method for reconstruction of a long time series of past historical air monitoring data on elemental carbon concentrations is described.

Aerosol carbon concentrations have been measured under wintertime conditions at two sites in the Los Angeles basin. Samples were analyzed for total carbon by the Gamma Ray Analysis of Light Elements (GRALE) technique and for elemental carbon by reflectance. Total carbon concentrations at downtown Los Angeles averaged 24 µgm^(-3) and elemental carbon concentrations averaged 9 µgm^(-3) during the 7 am to 1 pm period. The light absorption efficiency of ambient elemental carbon particles was determined to be 11.9 + 0.9 m^2g^(-1), and it was estimated that light absorption by elemental carbon could account for up to 17% of total light extinction in downtown Los Angeles. Baseline conditions for elemental carbon concentrations in the Los Angeles area prior to the introduction of large numbers of diesel passenger cars into the vehicle fleet are established by these measurements. A method for reconstruction of a long time series of past historical air monitoring data on elemental carbon concentrations is described.