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Jacob L. Conrad

Publications and source records attributed to Jacob L. Conrad.

3 recordsLinked to original sources

Groundwater response to managed aquifer recharge at the Southeast Houghton Artificial Recharge Project in Tucson, Arizona

Managed aquifer recharge is a widespread practice for storing water in the subsurface as groundwater. At a managed aquifer recharge facility in southern Arizona, groundwater-level and repeat microgravity data were collected to monitor aquifer response. These data were used to inform parameter identification for an unsaturated-zone flow model used to simulate the recharge process. The facility, the Southeast Houghton Artificial Recharge Project (SHARP), consists of 3 surface basins (about 27,600 square meters [6.8 acres] total surface area) where recycled water is distributed in recharge cycles lasting several months, with dry periods in between. During the study period, December 2020–December 2022, Tucson Water (the City of Tucson’s water utility) reported 6.56×10 6 cubic meters of water (5,320 acre-feet) recharged. Monitoring included groundwater-level observations at 3 monitoring wells and repeat microgravity measurements at as many as 22 locations (some stations were destroyed between surveys). Six gravity surveys were carried out using absolute- and relative-gravity meters. Large gravity increases, more than 250 microgals, were observed during the first repeat survey, 3.5 months after the start of recharge, but only in the immediate vicinity of the recharge basins. Data show that water moved downward to the water table, and storage changes in the unsaturated zone away from the facility were likely minimal. Gravity decreased at stations more than 1 kilometer from the facility, consistent with regional groundwater-level changes. Groundwater-level increases in wells adjacent to the recharge basins began 2 months after the second repeat gravity survey, and 5.5 months after recharge began. Unsaturated-zone flow modeling was carried out using software that simulates water movement and parameter estimation. Model calibration was carried out by minimizing an objective function calculated from the differences between simulated and observed groundwater levels, and between simulated and observed repeat microgravity data. Including repeat microgravity data in the objective function reduced the uncertainty in estimated parameter values for saturated hydraulic conductivity and saturated water content. Modeling indicated that the unsaturated zone between the recharge basins and the water table does not become saturated even after 685 days of simulated infiltration. This gradual wetting may account for increasing infiltration rates over time, as hydraulic conductivity increases with increasing water content. Unsaturated-zone water content decreased rapidly between recharge cycles. Model-simulated groundwater mounding extended about 1 kilometer from the center of SHARP after the 685-day period following the onset of recharge.

Arizona↗

Monitoring aquifer-storage change from artificial recharge with repeat microgravity along Santa Cruz River, Tucson, Arizona, 2019–22

The City of Tucson water utility, Tucson Water, began releasing treated effluent into the Santa Cruz River channel near downtown Tucson in 2019. This recharge project—the Heritage Project—is intended to create a reach of consistent flow in the channel and recharge water to the aquifer. Tracking the dispersal of recharged water is important for management decisions because groundwater movement depends on spatially variable characteristics of the subsurface and cannot be fully predicted in advance. Groundwater-level measurements in wells are useful, but the relation between water storage and groundwater-level change depends on the unknown storage coefficient of the aquifer. To estimate storage changes caused by recharge of reclaimed effluent released into the channel for the Heritage Project, the U.S. Geological Survey (USGS) collected repeat microgravity data along the Santa Cruz River in Tucson, Arizona, from 2019 to 2022. This method augments groundwater-level monitoring by providing a direct quantitative measurement of changes in the quantity of water stored in the subsurface. Preliminary results of the monitoring through 2022 showed consistent storage increases only near and upstream from the Heritage Project outfall site. Initially high storage increases at some locations west of the channel and in line with Sentinel Peak reached roughly steady state in later times. North of Sentinel Peak, a storage increase from 2020 to 2021 was followed by a storage decrease from 2021 to 2022. Storage changes in the area north of Sentinel Peak appear to be related to the number of days flows in the channel were observed farther downstream from the outfall site (at USGS streamgage 09482500). This observation is likely due to the potential formation of a clogging layer that would allow surface water to disperse farther horizontally (downstream) before infiltrating. This phenomenon has been observed downstream of other recharged effluent projects and has been reduced by large flows in the channel, such as those occurring during large runoff events. There were no large or consistent storage increases near the Water Quality Assurance Revolving Fund (WQARF) sites included in this study area.

Arizona↗

HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

Water↗