Characterization and monitoring of managed aquifer recharge [electronic resource]

Over a fifth of the world's population lives in an area where there is not enough water to meet demand. However, the problem is not that there is not enough water in the world but that there is not enough of it at either the right time, location or quality. Managed aquifer recharge and recovery (MARR), the intentional act of recharging water into the subsurface for later use, is a water management tool that can help to balance water supply and demand in time, while improving the water's quality. By infiltrating water through the ground via recharge ponds into an aquifer and storing it there until it is needed, MARR has huge potential in the future for mitigating water management challenges. However, for MARR systems to be adopted at a wide scale, better tools are needed to ensure that a system is successful. I define in this dissertation MARR success to mean a project is capable of infiltrating a provided volume of water over a given time period, recovering all of it when desired, and improving its quality to a set level. Actions to meet these objectives must be taken during both project design and operations. The research in this thesis focuses on improving MARR success post-construction. I suggest that one approach for improving MARR success is to define an optimal range of infiltration rates needed for a given recharge pond to maximize the amount of water infiltrated, recovered and cleaned and to monitor in real-time infiltration rates across the pond to ensure they fall within this range. The goal of this research was to advance the knowledge and tools available for carrying out this characterization and monitoring, primarily through the use of electrical resistivity imaging (ERI), which, as is discussed, is well suited for use in the unsaturated zone. I do so by developing MARR specific methods as well as by advancing our understanding of the relationship between hydrologic and electrical properties that can be used in contexts outside of MARR. The contributions of this thesis fall into four categories: 1) development of methods for characterizing the unsaturated zone in order to define optimal infiltration rates using ERI, 2) development of methods for monitoring infiltration, 3) a discovery of a new relationship relating electrical conductivity in the unsaturated zone to hydraulic conductivity, and 4) contribution to understanding of MARR infiltration processes. I develop two methods for using electrical resistivity data to estimate saturated hydraulic conductivity and unsaturated soil properties and demonstrate their use with synthetic data sets. The first method uses a vertical electrical conductivity profile, already inverted for from geophysical data. However, by sequentially performing a hydrologic and geophysical inversion, features can be lost and in the second method, I address this by developing a coupled geophysical-hydrologic inversion method. The method takes as its input the electrical resistivity data obtained in the field and estimates saturated hydraulic conductivity, which can be used to define an upper bound on infiltration rates for optimal MARR operations. The method is unique because it uses heat transport modeling to more accurately estimate the change of fluid conductivity throughout the soil profile due to diurnal and long-term temperature fluctuations, which in other methods is assumed to be known or to not change. It also takes advantage of a new inversion method, the principal component geostatistical approach (PCGA), which reduces the number of forward model runs needed to estimate the Jacobian matrix during inversion. Monitoring infiltration requires real-time information. Therefore, direct relationships between data and desired quantities, such as infiltration rates are key. I introduce in this dissertation the concept of local infiltration efficiency, which can be an alternative metric to infiltration rate for monitoring recharge. I show that with both distributed temperature sensor (DTS) data and electrical resistivity (ER) data that local infiltration efficiency is a more robust metric to monitor because it does not rely on parameters that are difficult to estimate. The metric also clearly shows how a given location is performing over time, allowing for the identification of clogging and other processes that need to be addressed. Next, I demonstrate that even during transient infiltration rates, the vertical pressure gradient within the vadose zone is negligible, which allows infiltration and unsaturated flow rates to be estimated as hydraulic conductivity. This simplification becomes valuable for monitoring infiltration rates because we can relate electrical conductivity, which can be measured in the field, to hydraulic conductivity. I first show how this can be done by combining Archie's equation, which relates electrical conductivity to saturation and a van Genuchten equation, which relates saturation to hydraulic conductivity. Such formulation then provides a direct way of estimating infiltration rates using a single electrical conductivity measurement and a few soil specific parameters and does not require a cumbersome hydrologic inversion. Since, in soils with negligible surface conduction, electrical conductivity and hydraulic conductivity are both controlled by water content and the geometry of the pore space, I explore through pore-scale numerical experiments the relationship between these two quantities. I found that relative hydraulic conductivity (hydraulic conductivity divided by saturated hydraulic conductivity) and relative electrical conductivity (electrical conductivity divided by saturated electrical conductivity) are related by a power law. This finding provides a new petrophysical relationship relating electrical conductivity to a hydrologic parameter of interest. It reduces the parameters necessary to relate hydraulic and electrical conductivity done previously through the van Genuchten and Archie equations and allows for a relatively simple method for estimating infiltration rate directly from electrical conductivity measurement. Lastly, a field experiment was performed at a recharge pond outside Denver, Colorado where DTS and ER data were taken. These data were used to demonstrate the methods and relationships discussed in this thesis. In doing so, some processes were observed. First, I quantified the effects of heterogeneous soil properties on infiltration behavior. The field data showed that infiltration rates to varied over an order of magnitude across the basin and I showed that this heterogeneity caused 78 percent of the influent to infiltrate through 50 percent of the pond. This finding shows the importance of spatial monitoring of infiltration rates because a pond-average infiltration rate could imply that infiltration is slow enough for the water to reach a minimum residence time in the vadose zone and not cause lateral loss of flow but in actual fact a large portion of the water could be infiltrating at a rate much faster than optimal. Additionally, through the estimation of local infiltration efficiency across the basin over time, clogging behavior within the pond was observed. It was seen that the east side decreased in infiltration efficiency faster and more significantly than the west side. This decrease in efficiency on the east side was ascribed to the development of a clogging layer, which was visible on the east side of the pond after the infiltration event but not the west. This finding shows that clogging develops faster across more permeable portions of a pond bottom, which could be due to the fact that more water is moving to these areas, which delivers nutrients that cause bacteria and algae growth and clogging particulates. Additionally, infiltration rates are only limited through soils with a hydraulic conductivity higher than the clogging layer above, so clogging will decrease infiltration rates and efficiency in higher permeability soils before and to a greater extent than in lower permeability soils.

Keywords: Civil and Environmental Engineering.
Advisor: Kitanidis P. K. (Peter K.) primary advisor. Thesis advisor
Knight Rosemary (Rosemary Jane)
Pidlisecky Adam advisor. Thesis advisor