abstract: The Phoenix Metropolitan region is subject to intense summer monsoon thunderstorms that cause highly localized flooding. Due to the challenges in predicting these meteorological phenomena and modeling rainfall-runoff transformations in urban areas, the ability of the current operational forecasting system to predict the exact occurrence in space and time of floods in the urban region is still very limited. This thesis contributes to addressing this limitation in two ways. First, the existing 4-km, 1-h Stage IV and the new 1-km, 2-min Multi-Radar Multi-Sensor (MRMS) radar products are compared using a network of 365 gages as reference. It is found that MRMS products consistently overestimate rainfall during both monsoonal and tropical storms compared to Stage IV and local rain gauge measurements, although once bias-corrected offer a reasonable estimate for true rainfall at a higher spatial and temporal resolution than rain gauges can offer. Second, a model that quantifies the uncertainty of the radar products is applied and used to assess the propagation of rainfall errors through a hydrologic-hydraulic model of a small urban catchment in Downtown Phoenix using a Monte Carlo simulation. The results of these simulations suggest that for this catchment, the magnitude of variability in the distribution of runoff values is proportional to that of the input rainfall values. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2020

Soil moisture influences the water and energy cycles of terrestrial environments, and thus plays an important climatic role. However, the behavior of soil moisture at large scales, including its impact on atmospheric processes such as precipitation, is not well characterized. Satellite remote sensing allows for indirect observation of large-scale soil moisture, but validation of these data is complicated by the difference in scales between remote sensing footprints and direct ground-based measurements. To address this problem, a method, based on information theory (specifically, mutual information), was developed to determine the useful information content of satellite soil moisture records using precipitation observations. This method was applied to three soil moisture datasets derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements over the contiguous U.S., allowing for spatial identification of the algorithm with the least inferred error. Ancillary measures of biomass and topography revealed a strong dependence between algorithm performance and confounding surface properties. Next, statistical causal identification methods (i.e. Granger causality) were used to examine the link between AMSR-E soil moisture and the occurrence of next day precipitation, accounting for long term variability and autocorrelation in precipitation. The probability of precipitation occurrence was modeled using a probit regression framework, and soil moisture was added to the model in order to test for statistical significance and sign. A contrasting pattern of positive feedback in the western U.S. and negative feedback in the east was found, implying a possible amplification of drought and flood conditions in the west and damping in the east. Finally, observations and simulations were used to demonstrate the pitfalls of determining causality between soil moisture and precipitation. It is shown that ignoring long term variability and precipitation autocorrelation can result in artificial positive correlation between soil moisture and precipitation, unless explicitly accounted for in the analysis. In total, this dissertation evaluates large-scale soil moisture measurements, outlines important factors that can cloud the determination of land surface-atmosphere hydrologic feedback, and examines the causal linkage between soil moisture and precipitation at large scales.

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Due to the large amount of impervious surface cover, urban areas are at high risk for flooding and, in cities with combined sewer systems, subject to sewer overflow during heavy storm events. The Pennsylvania Department of Transportation (PennDot) is currently reconstructing and expanding parts of Interstate 95 (I-95) through the city of Philadelphia. Due to both federal and local laws, PennDOT must account for the stormwater runoff and minimize outflow to the sewer system. To do so, PennDOT has plans to construct a series of stormwater management practices (SMPs) adjacent to I-95 to control the volumes of highway runoff. In partnership with Villanova University, Temple University has been tasked with monitoring these SMPs, known as bioswales, to provide insight and guidance as the project moves forward and to ensure mistakes aren’t reproduced in future construction. This research is contributing to the overall project goals by testing the application of geophysical monitoring to one of the bioswales known as SMP A. Unlike commonly used point measurements, geophysical surveys are non-invasive and provide extensive spatial coverage. Specifically, this research involves the use of electrical resistivity tomography (ERT), in which a series of cable-connected electrodes are placed in the ground and measure electric potential differences when an electric current is applied. Once processed, the results are a contoured subsurface image of the distribution of electrical resistivity (the inverse of electrical conductivity). If multiple surveys are taken over time, the data can be differenced, known as time lapse inversion, to quantify changes in electrical resistivity. ERT is a favorable for these SMPs as survey results are sensitive to changes in soil moisture and fluid conductivity, which are essential parameters when tracking infiltration and road salt influx at these SMPs. Additionally, the ERT data can be converted to soil moisture values using Archie’s law, which is important for determining soil moisture at points where no sensors are currently placed. We built and installed three ERT survey lines connected to an on-site monitoring station in April 2019 and collected quasi-daily measurements until monitoring seized in November 2021. One way to test SMPs is through a simulated runoff test, in which an SMP is flooded with water from an external source and the SMP’s response is recorded. During September 2020, Villanova University performed an SRT at SMP A, while we performed ERT surveys before, during, and after the SRT to track the infiltration and dry-out cycle. Knowing how long the soil at an SMP takes to recover to pre-storm soil moisture levels is essential in understanding an SMP’s performance and functionality. We were successfully able to capture the wet-up associated with the SRT and the corresponding dry-out period with the ERT data, which showed around a 20% decrease in resistivity when soil sensors indicated saturation. This resistivity change began to decrease and finally reached pre-SRT levels (0 – 5% change) after 68 hours, leading to our estimate of a three day recovery time for SMP A. Interestingly, inflow/outflow measurements at SMP A showed that only 24% of the input water exited the SMP via the overflow drain, meaning the rest of the water remained in the SMP. This discrepancy was solved with our ERT data, which showed that the decrease in resistivity, and therefore increase in soil moisture, was seen at depths beyond the 0.60 m layer of amended fill the SMP contained. Overall, the water was infiltrating past this layer and into the urban soil below. Initially it was thought that the native urban soil would impede infiltration, hence SMP A was designed around this assumption. However, our geophysical results indicate that the native urban soil underlying the SMP has an infiltration rate of 10 cm/hr and is contributing to the overall function of the SMP. This was unknown as previous monitoring was focused on the layer of amended fill material, not the underlying native soil. The relationship between electrical resistivity and soil moisture, fluid conductivity, and porosity is known as Archie’s law, who derived an empirical formula that allows electrical resistivity data to be converted to soil moisture values. However, this equation requires quantifying two parameters, m (also known as the cementation factor) and n, the saturation exponent. Researchers commonly use pre-published values for m and n, or establish site-specific values by fitting Archie’s law to a set of soil moisture and conductivity data. However, as soil is heterogeneous, one set of m and n values may not be accurate across an entire site, especially with the presence of hysteresis, where one soil moisture value can correspond to multiple conductivity values depending on whether the soil is experiencing imbibition or drainage. Additionally, m and n can change over time as soil fabric changes, as well as soil conductivity changes due to the influx of road salt during winter months. In December 2019, we finished installing 16 TEROS12 soil sensors at SMP A, which recorded soil volumetric water content (VWC) and bulk electrical conductivity (bulk EC) every five minutes for nearly two years. These sensors were at six different locations within SMP A at depths of either 0.10 m, 0.30 m, or 0.60 m. We selected 13 storm events and fit Archie’s law to the soil VWC and bulk EC data to get values for m and n. While we were able to find m and n for all events, including events that exhibited hysteresis in soil VWC and bulk EC, each sensor had a different pair of m and n values. This discrepancy was surprising, given that the soil at SMP is a homogeneous, sandy-loam fill with no more than 10% clay. However, even sensors at the same depth show statistically significant differences. We also found that m and n were changing over time, notably m was increasing over time, possibly due to porosity changes. This result indicates that multiple sensors are needed to accurately calculate m and n, even at sites with relatively homogeneous soil. Most notably, the reason why we had success in fitting Archie’s law for every sensor was due to our accounting for changes in porewater conductivity. Most researchers assume a constant value for porewater (fluid) conductivity in Archie’s law. However, we found that not accounting for porewater conductivity changes lead to severe misestimation of soil VWC, even getting physically impossible values (VWC > 1.0 m3/m3) in some cases. Therefore, accounting for changes in porewater conductivity is essential when using Archie’s law. Road salt transport in SMPs is a concern, especially in Philadelphia, which is subject to winter storms and freezing conditions. In some PennDOT SMPs, the presence of road salt in the soil during leaf-out has been suspected to be the cause of stunted plant growth and pre-mature plant mortality. Vegetation is an important aspect of the SMPs, as they provide evapotranspiration pathways, aesthetics, and soil erosion control. Thus, vegetation impairment affects SMP functionality, and plants often need to be replaced, increasing maintenance costs. To track and assess the spatial distribution of road salt, we performed ERT surveys along three lines, with two lines in the topographically lower portion of the SMP, or flood zone, and the other line on the elevated bank parallel to the other lines. All three of these lines had vegetation. In total, we collected 900 ERT surveys from October 2020 to September 2021, sufficiently covering the winter months and growing season. During February 2021, the soil sensors indicated significant increases in conductivity, with sensors ranging from 5.0 – 20.0 mS/cm, compared to pre-winter values of 0.1 – 0.6 mS/cm. The winter ERT surveys show the formation of a shallow conductive (< 10 Ω) layer in the top 0.25 m of soil, and an overall decrease in resistivity of up to 70%. This change decreased over the spring and summer months, indicating that dilute runoff was flushing the salt through the soil column. However, flood-zone ERT data still showed a 20% decrease in resistivity in June when compared to pre-winter data, indicating that road lingered in the soil during the spring and summer months. In May, we began taking bimonthly measurements of plant height, width, and leaf chlorophyll content (SPAD) on plants along the ERT lines, then in July took leaf tissue, root tissue, and root-zone soil samples and analyzed them for sodium content. We found that the plants along Lines 2 and 3 (flood-zone) had statistically significant stunted growth when compared to the plants along the elevated bank, as well as elevated sodium levels (> 400 mg/kg) in root tissue. No detectable sodium was found in leaf tissue samples. The stunted growth and elevated root sodium in the flood-zone plants indicate that early spring storms are not enough to flush out the road salt, and therefore artificial flooding may be required before leaf-out to ensure plant survival. We also suggest planting salt-tolerant plant species in areas of SMPs prone to flooding, such as the topographically lower portions. ERT can also be used to guide the placement of these plant species, as ERT can delineate areas of higher conductivity. / Geoscience