Exploring Groundwater Recoverability in Texas: Maximum Economically Recoverable Storage texaswaterjournal.org An online, peer-reviewed journal published in cooperation with the Texas Water Resources Institute Volume 11 Number 1 | 2020 https://www.texaswaterjournal.org Volume 11, Number 1 2020 ISSN 2160-5319 texaswaterjournal.org THE TEXAS WATER JOURNAL is an online, peer-reviewed journal devoted to the timely consideration of Texas water resources management, research, and policy issues. The journal provides in-depth analysis of Texas water resources management and policies from a multidisciplinary perspective that integrates science, engineer-ing, law, planning, and other disciplines. It also provides updates on key state legislation and policy changes by Texas administrative agencies. For more information on TWJ as well as TWJ policies and submission guidelines, please visit texaswaterjournal.org. The Texas Water Journal is published in cooperation with the Texas Water Resources Institute, part of Texas A&M AgriLife Research, the Texas A&M AgriLife Extension Service, and the College of Agriculture and Life Sciences at Texas A&M University. Jude A. Benavides, Ph.D. University of Texas, Rio Grande Valley Managing Editor Chantal Cough-Schulze Texas Water Resources Institute Layout Editor Sarah Richardson Texas Water Resources Institute Staff Editor Ava English Texas Water Resources Institute Kerry Halladay Texas Water Resources Institute Kristina J. Trevino, Ph.D. Trinity University Editorial Board Todd H. Votteler, Ph.D. Editor-in-Chief Collaborative Water Resolution LLC Kathy A. Alexander, Ph.D. Gabriel Collins, J.D. Center for Energy Studies Baker Institute for Public Policy Robert E. Mace, Ph.D. Meadows Center for Water and the Environment Texas State University Ken A. Rainwater, Ph.D. Texas Tech University Rosario Sanchez, Ph.D. Texas Water Resources Institute Cover photo: Tres Palacios River at FM 1468 near Clemville, Texas. ©2019 Ed Rhodes, TWRI. As a 501(c)(3) nonprofit organization, the Texas Water Journal needs your support to provide Texas with an open-accessed, peer-reviewed publication that focuses on Texas water. Please consider donating. https://twj-ojs-tdl.tdl.org/twj/index.php/twj/support https://www.texaswaterjournal.org https://www.texaswaterjournal.org Texas Water Journal, Volume 11, Number 1 Texas Water Resources Institute Texas Water Journal Volume 11, Number 1, December 10, 2020 Pages 152-171 Exploring Groundwater Recoverability in Texas: Maximum Economically Recoverable Storage Abstract: The 2017 Texas state water plan projects total supply deficits of 4.8 and 8.9 million acre-feet under drought-of-record conditions by the year 2020 and 2070, respectively, driven by a growing population concurrent with declining available water supplies. Reductions in groundwater supply account for 95% of anticipated declines in total water supply. Meanwhile, restrictive groundwater management plans may be creating a regulation-induced shortage of groundwater in Texas, given the significant groundwater storage volumes that are unutilized under many management plans. However, these estimates do not account for many of the physical and none of the economic constraints to groundwater recoverability. We report an analysis of groundwater extraction feasibility and simulate maximum economically recoverable storage for conditions representative of the central section of the Carrizo-Wilcox Aquifer under economic constraints associated with agricultural uses. Two key limitations are applied to simulate recoverability: (1) the value of water pumped relative to pumping costs and (2) the capacity of the aquifer and well to meet demand. Our results indicate that these constraints may limit certain uses to as little as 1% of current groundwater avail- ability estimates. We suggest that Texas groundwater managers, stakeholders, and policymakers assessing groundwater availability need an alternate approach for estimating recoverability. Keywords: groundwater availability, groundwater recoverability, pumping costs, total estimated recoverable storage, TERS, maximum economically recoverable storage, MERS 1 Graduate Research Assistant - Bureau of Economic Geology, PhD Candidate - Jackson School of Geosciences, The University of Texas at Austin 2 Retired - Energy and Earth Resources Graduate Program, Jackson School of Geosciences, The University of Texas at Austin 3 Senior Research Scientist - Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin * Corresponding author: justin.thompson@utexas.edu Citation: Thompson JC, Kreitler CW, Young MH. 2020. Exploring groundwater recoverability in Texas: maximum economically recoverable storage. Texas Water Journal. 11(1):152-171. Available from: https://doi.org/10.21423/twj.v11i1.7113. © 2020 Justin C. Thompson, Charles W. Kreitler, Michael H. Young. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ or visit the TWJ website. Justin C. Thompson1*, Charles W. Kreitler2, and Michael H. Young3 https://doi.org/10.21423/twj.v11i1.7113 https://creativecommons.org/licenses/by/4.0/ https://twj-ojs-tdl.tdl.org/twj/index.php/twj/about#licensing Texas Water Journal, Volume 11, Number 1 153Maximum Economically Recoverable Storage INTRODUCTION Is Texas running out of groundwater, blessed with abun- dance, or somewhere in the middle? This question, historically shrouded in scientific uncertainty and political controversy, represents a complex nexus of hydrogeology, economics, and policy with many relevant and potentially conflicting consider- ations. Hydrogeologic conditions and management objectives vary significantly across the state, and as a consequence there is no universal yield solution. Nonetheless, one key element common to all human ground- water demand is recoverability, defined as the relative ease or difficulty of extraction. Recoverability is constrained by aquifer characteristics, well design, and economics. While recoverabil- ity data is crucial to groundwater planning and management, particularly with respect to availability assessments, Texas’ best estimates of recoverable groundwater volumes reflect only the volume in storage and take no account of well design or eco- nomic constraints. This study therefore addresses the question: What are the economic and physical limits to recoverability? By establishing these limits, we can better estimate potentially available groundwater for given uses and infrastructure. Goals and objectives We seek here to (a) develop improved methods for quan- tifying groundwater recoverability by integrating aquifer and well dynamics with economics and (b) contextualize our results within existing policy frameworks and discussions. The key purpose of this study is to facilitate the exploration of planned and potential changes in groundwater recoverability by devel- oping methods for analytically calculating the physical and economic constraints and limitations to pumping associated with changes in depth-to-water over time. This study does not seek to establish a yield prescription for groundwater management, but it does estimate a reference limit we term maximum economically recoverable storage (MERS). While not designed to be economically efficient, MERS is intended to establish clear and rational limits to groundwa- ter recoverability for the purpose of evaluating groundwater availability under variable uses and infrastructure. Moreover, because MERS is, in part, a function of depth-to-water, its limits are directly comparable to existing or proposed depth- to-water based groundwater management goals. For any pumping groundwater well, the maximum volume of recoverable water is a subset of total aquifer storage, which may be numerically simulated using simplified hydrogeolog- ic and economic constraints. The maximum yield a well can physically produce is limited by the relationship between the aquifer, well, and pumping rate. We anticipate that aquifer and pumping characteristics introduce capacity constraints where demand is constant. We further expect some percentage of sat- urated thickness to be unavailable for production (a groundwa- ter “dead pool”) at any given pumping rate, and a relationship to exist between the pumping rate and the saturated thickness available for production. In terms of economics, increasing depth-to-water increases pumping costs where other factors are held constant. We expect these changes can be significant to Terms used in paper Acronym/Initialism Descriptive Name DFC Desired Future Conditions GCD Groundwater Conservation District GMA Groundwater Management Area MAG Modeled Available Groundwater MERS Maximum Economically Recoverable Storage TERS Total Estimated Recoverable Storage TWC Texas Water Code TWDB Texas Water Development Board Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:154 under drought-of-record conditions in the amount of 4.8 and 8.9 million acre-feet by the year 2020 and 2070, respectively, resulting from an anticipated 70% increase in the population concurrent with an 11% projected decline in total water sup- plies (TWDB 2016). The plan further estimates that, if left unresolved into 2070, these deficits would result in approx- imately $151 billion of annual economic losses and roughly a third of the projected population having less than half the projected municipal water demand (TWDB 2016). The plan considers drought-of-record conditions. Under unprecedent- ed drought driven by climate change (Nielsen-Gammon et al. 2020), supply deficits and economic losses may be even higher. Even without this consideration, the plan findings establish a central theme: demonstrating the necessity of responsive water development financing while sounding a call to action for pol- icymakers. But how were these conclusions reached? What key assump- tions were made? First, an important distinction should be noted between water resource availability and water resource supply as those terms are defined by the plan. Section 6.1 of the plan clarifies: “Water availability refers to the maximum volume of raw water that could be withdrawn annually from each source (such as a reservoir or aquifer) during a repeat of the drought of record. Availability does not account for whether the supply is connected to or legally authorized for use by a specific water user group. Water availability is analyzed from the per- spective of the source and answers the question: How much water from this source could be delivered to water users as either an existing water supply or, in the future, as part of a water management strategy? […] [Then], planning groups evaluate the subset of the water availability volume that is already connected to water user groups. This subset is defined as exist- ing supply.” (TWDB 2016, p. 61 [emphasis added]) Recognizing this distinction, the plan reveals a projected 20% decline in available groundwater (from 12.3 million to 9.8 mil- lion acre-feet) and a 24% decline in groundwater supply (from 7.2 million to 5.5 million acre-feet) over the planning period (2020 through 2070) “… due primarily to reduced availabili- ty from the Ogallala Aquifer, based on its managed depletion, and the Gulf Coast Aquifer, based on regulatory limits aimed at reducing long-term groundwater pumping to limit land sur- face subsidence” (TWDB 2016, p. 70). Indeed, reductions in groundwater supply considered by the plan account for 95% of the anticipated 11% decline in total water supply (TWDB 2016). If the impacts of popula- tion growth are assumed valid and held constant (i.e., only the decline in total supply is considered), the total water resource agricultural and other uses. Therefore, we address two hypoth- eses in this study: • H1: In shallow and unconfined settings, physical con- straints related to the capacity of the aquifer and well to meet demand, not economic constraints, will limit groundwater recoverability. • H2: In deep and confined settings, economic constraints, not physical constraints, will limit groundwater recover- ability for some uses, restricting them to producing from confined, pressurized storage. Groundwater management in Texas Groundwater in Texas is managed at the local level by approx- imately 100 groundwater conservation districts (GCD(s)). However, in 2005, the 79th Texas Legislature enacted House Bill 1763, which amended the Texas Water Code (TWC) to regionalize groundwater availability decision making under groundwater management areas (GMA(s)). House Bill 1763 further instructs GCDs within a GMA on how they should cooperate with each other and the Texas Water Development Board (TWDB) to determine groundwa- ter volumes available for permitting. Chapter 36 §108 of the TWC states that “[GCDs] shall propose for adoption desired future conditions for the relevant aquifers within the [GMA].” Desired future conditions (DFC(s)) are further defined by Title 31, Part 10, §356.10(6) of the Texas Administrative Code to be “the desired, quantified condition of groundwater resources (such as water levels, spring flows, or volumes) within a [GMA] at one or more specified future times as defined by participat- ing [GCDs] within a [GMA] as part of the joint planning pro- cess.” Our evaluation of currently adopted DFCs shows that, while spring flow and saturated thickness metrics are common, groundwater in Texas is most commonly managed as a func- tion of depth-to-water over time (i.e., x feet of drawdown over y years). Once DFCs are adopted, Chapter 36 §108(b) of the TWC requires the TWDB to calculate values for the volume of modeled available groundwater (MAG) that comply with the adopted DFC given the hydrologic properties of the aquifer in question. Finally, Chapter 16 §053(e)(3) of the TWC requires that GCDs honor MAG volumes in their groundwater man- agement plans. In this way, the DFCs adopted by GCDs create a regulatory target or cap for groundwater extraction in the form of the derived MAG volumes provided by the TWDB (Mace et al. 2008). 2017 State Water Plan: Water for Texas The latest iteration of the Texas state water plan, 2017’s “Water for Texas,” predicts a deficit of total water supplies Texas Water Journal, Volume 11, Number 1 155Maximum Economically Recoverable Storage Figure 1. Change in groundwater availability by county from the state water plan in 2012 to 2017 (TWDB 2016). deficits portended by the plan are driven almost entirely by anticipated declines in groundwater availability. Second, we note that this water plan determines, for the first time, groundwater availability volumes as the sum of the MAG volumes provided by the TWDB in accordance with the DFCs adopted by GCDs (TWDB 2016). This change in account- ing methodology from the previous state water plan (2012) to the current plan (2017) has produced significant changes in regional groundwater availability estimates, in many juris- dictions increasing or decreasing volume by 50% or more (TWDB 2016) (Figure 1). However, MAG volumes derived from DFCs do not strictly adhere to the definition of availability given by the plan. Spe- cifically, MAG volumes from DFCs are the total volume of groundwater that is “legally authorized for use” (TWDB 2016, p. 61). TOTAL ESTIMATED RECOVERABLE STORAGE Prior to adopting a DFC, Chapter 36 §108(d)(3) of the TWC requires GCDs to consider, among nine potentially con- flicting issues, the total estimated recoverable storage (TERS) volumes provided by the TWDB for each area aquifer. TERS is defined by Rule §356.10.23 of the Texas Administrative Code as “the estimated amount of groundwater within an aquifer that accounts for recovery scenarios that range between 25% and 75% of the porosity-adjusted aquifer volume.” Given the statutory definition of TERS and the statutory definition of total storage provided in Chapter 36 §001(24) of the TWC as “the total calculated volume of groundwater that an aquifer is capable of producing,” the TWDB has developed a working definition of TERS as a two-step calculation.   Figure 1 Exploring Groundwater Recoverability in Texas:156 In the first step, the hydrologic properties and geometries of the aquifer (such as transmissivity, water levels, and storage coefficients) are established according to the relevant TWDB groundwater availability model (where available). Those values are then used to derive total storage (Bradley 2016). The calcu- lation differs among confined and unconfined aquifers and is provided by the TWDB (Bradley 2016) as: total unconfined storage = (1) area × (water level - bottom) × Sy total confined storage = (2) (area × (water level - top) × St) + (area × (top - bottom) × Sy) where total unconfined storage is the storage volume of water released due to water draining from an unconfined setting (i.e., dewatering); area is the land surface area of the aquifer; water level is the depth of potentiometric head; bottom is the depth of the bottom of the aquifer; Sy is the specific yield storage coefficient; total confined storage is the storage volume of water released due to the elastic properties of the aquifer, plus the volume of water released due to dewatering; top is the depth of the top of the aquifer; and St is the confined storativity storage coefficient. In the second step, the calculated total storage is multiplied by 25% and 75% to “account for recovery scenarios that range between 25% and 75% of the porosity-adjusted aquifer vol- ume” (Wade and Shi 2014b., p. 4) and thereby arrive at final TERS volumes. We are unaware of any rationale provided in the public record for why 25% and 75% were chosen to represent the limits of potentially recoverable groundwater in TERS. We therefore assume these bounds are arbitrary reference points and that none of the potential physical and economic constraints and limitations associated with the recoverability of groundwater extraction are captured by TERS. The total storage component of TERS is the state’s closest approximation of groundwater availability, or “the maximum volume of raw water that could be withdrawn” (TWDB 2016, p. 61), as it incorporates depth-to-water and spatially variable aquifer characteristics. Thus, we compile total storage volumes (Tables 1 and 2), published by the TWDB as of April 2018 for the nine major aquifers of the state within each GMA (Fig- ure 2). Note that total storage data are not available for the Hueco-Mesilla Bolsons Aquifer and GMA 5 because no GCDs administer this area. The Carrizo-Wilcox Aquifer and the Gulf Coast Aquifer reported the largest total storage volumes at 5.227 and 4.163 billion acre-feet (respectively) and together constitute 81% of the sum total volume of water in storage for all nine major aquifers, calculated at 11.575 billion acre- feet. By contrast, the Seymour, Edwards (Balcones Fault Zone), and Edwards-Trinity (Plateau) Aquifers reported the smallest total storage volumes at 5.128, 24.951, and 45.491 million acre-feet, respectively. The total storage volume for the Ogallala Aquifer is reported to be 380.544 million acre-feet, represent- ing only 3% of the total volume of water in storage for all nine major aquifers. Even at the 25% TERS metric, the TERS volume reported for the Carrizo-Wilcox Aquifer alone (1.306 billion acre-feet) is far more than sufficient to satisfy the 2070 deficits projected by the 2017 state water plan (8.9 million acre-feet by 2070). The difference between these volumes could mean that, while the state is projecting water supply deficits, it is ignoring signif- icant reserves of recoverable groundwater. We are not the first to acknowledge TERS volumes in light of potential future deficits. A 2016 report by Brady et al. (2016), addressed to the Texas Comptroller of Public Accounts, crit- icized the current groundwater management approach as reverse-engineered and politicized, resulting in a “regula- tion-induced [groundwater] shortage” (Brady et al. 2016, p. 2). They recommended that the approach be revised in favor of more objective, economic constraints and presumably greater volumes of groundwater available for production. The report “assumes that prudent aquifer management would allow the TERS in each GCD to be drawn down by 5% over a 50-year period—or .1% of TERS annually” (Brady et al. 2016, p. 9) and proposes that such a metric replace the MAG from DFC volume regulations mandated by the current form of the TWC. TERS estimates report significant volumes of groundwater in storage that could potentially be available to meet the deficits projected by the state water plan. However, this critique dis- regards the apparently arbitrary recoverability constraints of TERS (25% and 75% of total storage). SIMULATING RECOVERABILITY To test H1 and H2 and quantitatively evaluate the physical and economic impacts to groundwater recoverability associat- ed with changes in depth-to-water, we develop a simplified, single-cell pumping simulation using numerical processors to generate MERS. This is done through a linear convex optimi- zation constrained by hydrogeology, pumping dynamics from given well specifications and pumping demand, and the given agricultural value of the water pumped over derived pumping costs. The MERS model is applied to a variety of user inputs and hydrogeologic conditions but was conceptualized for a sin- gle well pumping for agricultural uses. Texas Water Journal, Volume 11, Number 1 Table 1. Total storage and total estimated recoverable storage (25% and 75%) of the nine major aquifers of Texas in GMA 1-8. Source: Boghici et al. 2014, Jones et al. 2013a., Jones et al. 2013b., Kohlrenken et al. 2013a., Kohlrenken et al. 2013b., Kohlrenken 2015, Shi et al. 2014. TWDB major aquifers Aquifer (million acre-feet) Groundwater management area (million acre-feet) 1 (Kohlrenken 2015) 2 (Kohlrenken et al. 2013a.) 3 (Jones et al. 2013a.) 4 (Boghici et al. 2014) 6 (Kohlrenken et al. 2013b.) 7 (Jones et al. 2013b.) 8 (Shi et al. 2014) Carrizo - Wilcox Total storage 25% 75% 5,227.077 1,306.769 3,920.308 Gulf Coast Total storage 25% 75% 4,163.507 1,040.877 3,122.630 Trinity Total storage 25% 75% 1,405.166 0.471 0.523 1,359.625 351.292 0.118 0.131 339.906 1,053.875 0.353 0.392 1,019.719 Ogallala Total storage 25% 75% 380.545 232.700 139.210 0.010 2.285 6.340 95.136 58.175 34.803 0.002 0.571 1.585 285.408 174.525 104.408 0.007 1.714 4.755 Pecos Valley Total storage 25% 75% 323.860 2.000 309.000 1.490 11.370 80.965 0.500 77.250 0.373 2.843 242.895 1.500 231.750 1.118 8.528 Edwards - Trinity (Plateau) Total storage 25% 75% 45.491 0.142 0.390 3.780 38.821 11.373 0.036 0.098 0.945 9.705 34.118 0.107 0.293 2.835 29.116 Edwards (BFZ) Total storage 25% 75% 24.952 0.095 6.238 0.024 18.714 0.071 Seymour Total storage 25% 75% 5.128 0.001 0.057 5.070 0.001 1.282 0.000 0.014 1.268 0.000 3.846 0.001 0.043 3.803 0.000 Gross storage 25% Gross storage 75% Gross storage 11,575.726 232.701 141.409 309.400 5.270 7.826 57.055 1,359.625 2,893.932 58.175 35.352 77.350 1.318 1.957 14.264 339.906 8,681.795 174.526 106.057 232.050 3.953 5.870 42.791 1,019.719 Texas Water Journal, Volume 11, Number 1 Maximum Economically Recoverable Storage157 Table 2. Total storage and total estimated recoverable storage (25% and 75%) of the nine major aquifers of Texas in GMA 9-16. Source: Jigmund and Wade 2013, Jones and Bradley 2013, Jones et al. 2013c., Wade and Anaya 2014, Wade and Bradley 2013, Wade et al. 2014, Wade and Shi 2014a., Wade and Shi 2014b. TWDB major aquifers Aquifer (million acre-feet) Groundwater management area (million acre-feet) 9 (Jones and Bradley 2013) 10 (Jones et al. 2013c.) 11 (Wade and Shi 2014a.) 12 (Wade and Shi 2014b.) 13 (Wade and Bradley 2013) 14 (Wade et al. 2014) 15 (Wade and Anaya 2014) 16 (Jigmund and Wade 2013) Carrizo - Wilcox Total storage 25% 75% 5,227.077 2,061.633 1,019.320 1,951.720 19.804 69.900 104.700 1,306.769 515.408 254.830 487.930 4.951 17.475 26.175 3,920.308 1,546.225 764.490 1,463.790 14.853 52.425 78.525 Gulf Coast Total storage 25% 75% 4,163.507 1.447 0.450 2.460 2,776.000 368.800 1,014.350 1,040.877 0.362 0.113 0.615 694.000 92.200 253.588 3,122.630 1.085 0.338 1.845 2,082.000 276.600 760.763 Trinity Total storage 25% 75% 1,405.166 5.280 23.057 0.500 11.100 4.705 351.292 1.320 5.764 0.125 2.775 1.176 1,053.875 3.960 17.293 0.375 8.325 3.529 Ogallala Total storage 25% 75% 380.545 95.136 285.408 Pecos Valley Total storage 25% 75% 323.860 80.965 242.895 Edwards - Trinity (Plateau) Total storage 25% 75% 45.491 2.358 11.373 0.590 34.118 1.769 Edwards (BFZ) Total storage 25% 75% 24.952 0.261 22.878 1.718 6.238 0.065 5.719 0.430 18.714 0.196 17.158 1.289 Seymour Total storage 25% 75% 5.128 1.282 3.846 Gross storage 25% Gross storage 75% Gross storage 11,575.726 7.899 45.935 2,063.580 1,030.870 1,960.603 2,795.804 438.700 1,119.050 2,893.932 1.975 11.484 515.895 257.718 490.151 698.951 109.675 279.763 8,681.795 5.924 34.451 1,547.685 773.153 1,470.453 2,096.853 329.025 839.288 Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:158 Texas Water Journal, Volume 11, Number 1 159Maximum Economically Recoverable Storage Figure 2. Map of the state’s 16 Groundwater Management Areas (numbered) and nine major aquifers (colored). Solid aquifer colors indicate outcrop areas (the part of an aquifer that lies at the land surface) and hatched aquifer colors indicate sub-crop areas (the part of an aquifer that lies or dips below other formations). Gray areas indicate areas regulated by groundwater conservation and subsidence districts. Gray outlines indicate Texas counties. Map generated by ArcGIS with data available from the TWDB at: https://www.twdb.texas.gov/mapping/gisdata.asp.   Figure 2 Methods To test and develop the MERS model we simulate hydro- geologic characteristics and approximate conditions in the cen- tral section of the Carrizo-Wilcox Aquifer under confined and unconfined conditions. This area was selected in part because the Carrizo-Wilcox Aquifer, with the largest total storage in the state, is in close proximity to development corridors and population centers, and in part because much of its water is stored at significant depths under confined conditions. Sim- ilarly, hypothetical well characteristics (presumably available to stakeholders and managers applying these methods but estimated here) were derived from representative agricultural demand and approximated aquifer characteristics. Carrizo-Wilcox Aquifer characteristics were estimated from the literature to represent a simplified version of the general- ized conditions present in Bastrop, Burleson, Caldwell, Gon- zales, Guadalupe, Lee, Milam, and Wilson counties located within GMA 12 (four counties) and GMA 13 (four counties). Due to limitations in the scope of this study, we assume that the Carrizo-Wilcox Aquifer is both homogenous and isotropic within the study area and this construction is characterized by https://www.twdb.texas.gov/mapping/gisdata.asp Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:160 the above idealized and simplified hydrogeological properties (Table 3). Key assumptions The limitations of this MERS analysis are akin to those applied to TERS; no consideration is given to subsidence, sur- face water interaction, or water quality. These are all clearly important issues for groundwater managers and must be con- sidered when adopting DFCs pursuant to Chapter 36 §108(d) of the TWC. We simulate agricultural uses because this economic sector generally returns the smallest monetized benefit per volumet- ric unit of water consumed. When compared to industrial or municipal/domestic uses, the volumes demanded are compar- atively high and the economic value of the product (crops) is comparatively low (Aylward et al. 2010; Young and Loomis 2014). We therefore assume agricultural users may be consid- ered the most sensitive of all users to prospective changes in recoverability driven by increasing depth-to-water. Addition- ally, we assume that agricultural users represent a substantial proportion of groundwater ownership under Texas law (which links groundwater ownership to the area of owned overly- ing land and historical use—see Edwards Aquifer Authority v. Day-McDaniel) and therefore those users have significant agen- cy in DFC adoption. We also assume that agricultural daily water demand is con- stant, cannot be deferred during the growing season, and can- not be satisfied by alternative sources. We calculate constant daily demand as a function of the irrigated area and the requi- site irrigation depth as follows: (3) where demand is in units of gallons per minute, irdepth is the target daily irrigation depth in units of inches (simulated here as 0.5 unless otherwise noted), irarea is the area to be irrigated in units of acres (simulated here as 100), 325,851 is the con- version constant from acre-feet to gallons, and t is the time of pumping in units of minutes (assumed here to be 1440 min- utes, or one day, in all cases). Reference agricultural harvest values in units of dollars per acre per year are assumed in this simulation to be inclusive of any relevant subsidies and net of all costs external to pumping (such as fertilizer, labor, machinery). Reference harvest values are given by Shaw (2005) as: alfalfa = $440, onions = $778, tomatoes = $1,018, grains = $1,153, and potatoes = $2,792. These values are likely overestimates of the actual net value of all costs unrelated to pumping, but such crop-specific data are difficult to obtain. Thus, we assume that groundwater man- agers and agricultural users will input this key variable to the MERS model with more precise values for local uses. Well efficiency, or the energy loss of the well due to friction, is given as a user input to the model and held constant. As most modern pumps have an efficiency of between 50% and 85% (Stringman 2013),depending upon the age of the system, the type of construction, accumulated well screen fouling, the type of power plant, and other factors, we hold operational well efficiency constant at 75% for all calculations. Finally, we assume that where hypothetical depth-to-water in the confined setting falls below the depth of the top of the aqui- fer, the groundwater system fully transitions to the unconfined setting. In this way, the same demand-capacity constraints that are applied to the unconfined setting also apply to the confined setting but occur at greater depth. Furthermore, the depth of the bottom of the aquifer in the confined setting is assumed to be the depth of the base of potable water, approximately 2,000 feet in our study area (Dutton et al. 2003). Aquifer and well performance Here we use specific capacity to capture the hydrogeologic limitations to production at a given well. Specific capacity has units of length squared per time but is frequently reported in units of volume per time per length of drawdown. For example, a specific capacity of 5 square feet per minute may be report- Table 3. Hydrogeologic properties assumed for the study area simulation. Property Setting Value Source Depth to aquifer bottom Unconfined 350 feet (Dutton et al. 2003) Depth to aquifer bottom Confined 2,000 feet Depth to aquifer top Confined 1,650 feet Initial saturated thickness All 350 feet Specific yield All 0.15 Storativity Confined 10(-3.52) (Mace et al. 2000) Hydraulic conductivity All 7 feet per day (Dutton et al. 2003) Texas Water Journal, Volume 11, Number 1 161Maximum Economically Recoverable Storage ed as 37.4 gallons per minute per foot of drawdown, where the conversion from one form to the other is accomplished by multiplying square feet per minute by the constant 7.48052 gallons per cubic foot. A relationship between specific capacity and pumping dynamics was developed from the Theis (1935) non-equilibrium solution by Theis (1963) and is presented in this form in Mace et al. (2000): specific capacity = (4 × π × T ) ÷ [ln((2.25 × T × t) ÷ (r2 × S))] (4) where specific capacity is in units of length squared per time (such as feet squared per minute), T is the transmissivity of the aquifer in units of length squared per time (also equal to the product of hydraulic conductivity and saturated thickness), t is the time of pumping (one day or 1440 minutes), r is the well radius (simulated here as 1 foot to include the gravel pack), and S is the dimensionless storativity of the aquifer (Sy in the unconfined setting and St in the confined setting). As we are interested in increasing depth-to-water over time (as might occur under DFCs), we iteratively calculate specific capacity by applying transmissivities that decrease as a function of declining saturated thickness (in single foot increments here) to simulate planned and potential changes in depth-to-water. A representative depth of the top of the well screen (the depth of the bottom of the aquifer minus the length of the well screen interval) is calculated for this MERS simulation from demand and the well screen intake capacity. A representative well screen intake capacity is estimated from the maximum well entry velocity (assumed here at 0.1 feet per second) and the well screen open area (i.e. slot size) derived from grain size distribution of the Carrizo-Wilcox Aquifer which is estimated from hydraulic conductivity using the Hazen (1893) approx- imation. Here we simulate the smallest well screen interval capable of supporting demand in order to minimize the well screen dead pool. We then iteratively calculate the maximum pumping rate supported by the hydrogeologic and well characteristics (at all possible depths-to-water) as a function of the specific capacity and the available saturated thickness as: maximum pumping rate = specific capacity × s_max (5) where maximum pumping rate is in units of volume per time (such as gallons per minute), specific capacity is in units of vol- ume per time per unit of drawdown (such as gallons per min- ute per foot) as converted from Equation 4, and s_max is the maximum possible drawdown given available saturated thick- ness, simulated here as the difference, in length, between the iterated depth-to-water and the top of the well screen. Note that where maximum pumping rate values are signifi- cantly greater than demand the results may not be plausible with the given well screen (due to well entry velocity and oth- er factors) and are provided for reference only. The maximum pumping rate declines with declining transmissivity and avail- able s_max associated with hypothetical dewatering (decreasing saturated thickness) occurring in the unconfined or transitioned setting over time. To avoid pumping air, a certain amount of saturated thickness must be reserved from production to sup- port the well screen interval dead pool and the pumping period drawdown (s, which is assumed here equivalent to s_max where the maximum pumping rate equals demand). Thus, where the maximum pumping rate equals demand a binding constraint is applied to the MERS model; beyond this depth-to-water, defined here as h_max, the aquifer and well can no longer sat- isfy demand (Figure 3). While it is possible to pump beyond h_max (i.e., where the top of the well screen is exposed), the MERS model does not allow such over pumping as we assume the introduction of air to the system has significant impacts to efficiency and may damage the well. The difference between the initial depth- to-water and h_max is defined here as the production range (Figure 3). Within the production range, the aquifer and the well have the physical capacity to satisfy demand. Similarly, we dub the saturated thickness required to support pumping period drawdown which is variable with pumping rate and well characteristics the pumping range (Figure 3). Importantly, the production range and pumping range vary significantly with demand. Pumping costs Pumping costs at the well head (or marginal extraction costs) are identified here as the hypothesized binding constraint for agricultural users in deep and confined settings. These are defined as the energy costs required to pump water to the sur- face at the given hydrogeologic, well and demand conditions. Fixed costs are not considered in this study. Water horsepower, or the amount of horsepower required to do the work of lifting the given output of water to the discharge point if the well was 100% efficient (Fipps 2015), is defined as: water horsepower = (h × demand) ÷ 3960 (6) where h is the iterated hypothetical depth-to-water in feet and 3,960 is the conversion constant to horsepower. However, because no well is 100% efficient, the wire-to-water efficiency of the pumping system must adjust water horsepower to calculate the true horsepower applied to run the pump at the observed pumping rate. The pumping rate demand, as adjusted for well efficiency losses, is then directly relatable to dollar costs per unit of pumping time to meet the given demand volume by introducing an applicable power cost rate for the study area to calculate a pumping cost rate at depth-to-water as: Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:162 Figure 3. Representation of the aquifer and well constraints associated with pumping applied to the simulation in order to generate demand-capacity constraints.   Figure 3 pumping cost rate = (7) where pumping cost rate is in units of dollars per minute, 745.7 is the conversion constant from horsepower to watts, and power cost rate is the applicable power cost rate in dollars per watt-minute (assumed $0.07 per kilowatt-hour here). We can then simplify the pumping cost rate at depth-to-wa- ter and demand to dollars per gallon, a form we refer to here as recoverability: recoverability = pumping cost rate ÷ demand (8) Pumping costs in the MERS model is then expressed in dol- lars per pumping period as a function of demand and recover- ability as: pumping costs = (demand × t) × recoverability (9) While we choose to express depth-to-water as all possibilities between the land surface and the aquifer bottom for this study, the range of h may be adjusted by the user to evaluate any rel- evant range of potential depth-to-water changes (such as exist- ing or proposed DFCs). Depth maximization Given that most of the simplified relationships evaluated by this simulation are functionally linear, we modify an analytical solution (originally developed by Domenico 1972) for linear optimization of groundwater yields to implement the limita- tions associated with an aquifer bottom and declines in trans- missivity associated with increasing depth-to-water over time. We define value as the estimated daily dollar value of irrigation as: value = (harvest value × irarea) ÷ irrigation days (10) where harvest value is in units of dollars per area of agricultural production per year (such as dollars per acre per year, a com- mon metric), irarea is the user defined area to be irrigated (100 acres simulated here), and irrigation days is the number of days in the annual growing season to be irrigated (simulated here as Texas Water Journal, Volume 11, Number 1 163Maximum Economically Recoverable Storage 111 days per year = 37 growing season weeks per year multi- plied by 3 irrigation days per week). With pumping costs and value determined we are able to generate a simple profit function in terms of dollars per irriga- tion day: profit = value - pumping costs (11) Because value is constant here and pumping costs increase lin- early with increasing depth-to-water, profit falls linearly to zero where pumping costs are equivalent to value. Beyond this point the irrigator is theoretically losing money if pumping contin- ues and, if no other constraint is limiting, this constraint is binding on the MERS model. This ensures a global solution to the optimization problem and creates an objective limit to economic recoverability. Altogether, the MERS simulation applies three key limita- tions as constraints upon recoverability: (1) saturated thickness screened by the well, (2) the saturated thickness necessary to accommodate drawdown at demand, and (3) the depth-to-wa- ter at which value is equivalent to pumping costs. The smallest depth-to-water value (i.e., the most constraining limitation) is then applied to derive the maximum recoverable depth-to-wa- ter. Results Shallow and unconfined storage (addressing H1) Two factors limit physical yield capacity: (1) dewatering (increasing depth-to-water which reduces saturated thickness), and (2) variability in pumping rates. In effect, the well screen dead pool and the pumping range together serve to simulate an effective aquifer bottom and thereby introduce physical con- straints on yields in the form of production capacity. As the saturated thickness of the aquifer decreases, the maxi- mum pumping rate supported by the well and aquifer decreas- es non-linearly (Figure 4). The DFC with the largest increase Figure 4. Relationship between maximum pumping rate, demand, and depth-to-water in the unconfined setting given input aquifer, well, and use parameters. The (solid blue) curve is the maximum pumping rate. The only horizontal line (dashed blue) is demand at the given irrigation rate. From left to right: The first vertical line (solid green) is the deepest depth-to-water based DFC found in the representative study area of the Carrizo-Wilcox Aquifer (+65 feet), the second vertical line (solid red) represents h_max, the third vertical line (solid black) indicates the top of the well screen. Note that where maximum pumping rate values are significantly greater than demand the results may not be plausible with the depicted well screen interval due to well entry velocity and other factors. Simulation generated by MATLAB.   Figure 4 0 50 100 150 200 250 300 350 Depth-to-Water: Land Surface to Aquifer Bottom [feet] 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 P um pi ng R at e [1 ,0 00 g al lo ns /m in ut e] Demand-Capacity Constraints Maximum Pumping Rate Demand @ 0.5 in/acre Irrigation DFC Max (+65 feet) h-max @ Demand Top of Well Screen Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:164 in depth-to-water within the simulated study area is 65 feet of drawdown over 50 years (in Burleson and Milam counties, GCD #71) and provided for reference. Specific capacity, the first component of the maximum pumping rate, falls with declines in transmissivity (Equation 3), which in turn falls with declining saturated thickness. Similarly, the maximum distance between the initial depth-to-water and the top of the well screen (i.e., s_max), the second component of maximum pumping rate, falls linearly with declining saturated thickness. Thus, at some depth-to-water, the transmissivity and avail- able pumping range are insufficient to support the demanded pumping rate and resultant drawdown under pumping. Here a binding constraint is applied to the model: beyond this depth (h_max) the aquifer and well do not have sufficient capacity to meet irrigation demand. The higher the pumping rate demanded is, the greater the drawdown under pumping and resultant pumping range are. Naturally, where the pumping range increases, the production range decreases as additional saturated thickness is reserved from production to accommodate the increased drawdown. Importantly, our results indicate that impacts to the pump- ing and production ranges are significant within the potential range of irrigation demand for various crops. Here we simu- late irrigation depths (which drive demand) from 0.25 inch- es per acre per day to 1.00 inch per acre per day to evaluate the changes in the pumping range (Figure 5). When irrigation demand is 0.25 inches, h_max is over 250 feet (over 80% of the unscreened saturated thickness is physically recoverable); but when the irrigation demand is 1.00 inch, h_max is less than 150 feet (approximately 50% of the unscreened saturat- ed thickness is physically recoverable). Thus, smaller pumping rates may extract from greater depths than larger pumping rates before reaching the demand-capacity constraints of the well and aquifer. Simulated pumping costs increase linearly with depth-to-wa- ter to a maximum of $33.41 per acre-foot at the aquifer bot- Figure 5. Relationship between maximum pumping rate, varying demand, and depth-to-water in the unconfined setting given input aquifer, well, and use parameters. The (solid blue) curve is the maximum pumping rate. From left to right: The first vertical line (solid green) is the deepest depth-to-water based DFC found in the representative study area of the Carrizo-Wilcox Aquifer (+65 feet), the four red vertical lines indicate h_max at irrigation demand of 1.00 inches per acre per day (solid), 0.75 inches (dashed), 0.50 inches (dash-dot), and 0.25 inches (dotted), and the fifth vertical line (solid black) indicates the top of the well screen (generated for demand at 0.5in/acre irrigation). Simulation generated by MATLAB.   Figure 5 0 50 100 150 200 250 300 350 Depth-to-Water: Land Surface to Aquifer Bottom [feet] 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 P um pi ng R at e [1 ,0 00 g al lo ns /m in ut e] Demand-Capacity Constraints: Variable Demand Maximum Pumping Rate DFC Max (+65 feet) h-max @ 1.00 in/acre Demand h-max @ 0.75 in/acre Demand h-max @ 0.50 in/acre Demand h-max @ 0.25 in/acre Demand Top of Well Screen Texas Water Journal, Volume 11, Number 1 165Maximum Economically Recoverable Storage tom (a depth of 350 feet) while profit falls linearly with increas- ing depth-to-water. The harvest value point at which profit is equivalent to pumping costs at the depth of the bottom of the aquifer (350 feet) is found to be $154.51 per acre per year. At this harvest value, profit is $13.27 per acre-foot of groundwater pumped at the above h_max depth of 211 feet—less than 40% of the initial value. Importantly, a $154.51 harvest value falls well below even the lowest reference harvest value considered here, which is alfalfa at the price of $440 per acre per year. This suggests that many or all harvest values may be sufficient to dewater the full production range before profit falls to zero in shallow and unconfined settings. Thus, where irrigation demand is 0.50 inches per acre per day, the irrigated area is 100 acres, and the harvest value is $154.51 per acre per year, the binding MERS constraint in the unconfined setting is the demand-capacity constraint (h_ max), simulated at a maximum depth of 211 feet or 71% of the unscreened saturated thickness (Figure 6). The demand-capaci- ty constraint (h_max) simulated here in the unconfined setting exceeds this maximum DFC depth by over 140 feet. These results confirm H1: simulated recoverability is con- strained by demand-capacity limitations in shallow and uncon- fined settings for all irrigation demand rates and harvest values. However, the reference harvest values noted here are estimates and may not represent true agricultural values net of all costs beyond those explicitly considered here. Moreover, pumping costs are not insignificant to agricultural users. Determin- ing what reduction in profit irrigators are willing to accept as pumping costs rise is another matter not considered here beyond the economically inefficient limit of profit = 0. Deep and confined storage (addressing H2) The methods for calculating MERS in the confined setting have several important distinctions from the methods used in Figure 6. Maximum economically recoverable storage where harvest value is $154.51 per acre per year and irrigation demand is 0.5 inches per acre per day in the unconfined setting. The (solid blue) diagonal line reflects the linear change in profit as pumping costs increase with depth-to-water. The only horizontal line (dashed blue) is profit at the binding demand-capacity constraint (h_max). From left to right: The first vertical line (solid green) is the deepest depth-to-water based DFC found in the representative study area of the Carrizo-Wilcox Aquifer (+65 feet), the second vertical line (solid red) represents h_max (binding here), and the third vertical line (solid black) indicates the top of the well screen. Simulation generated by MATLAB.   Figure 6 50 100 150 200 250 300 350 Depth-to-Water: Land Surface to Aquifer Bottom [feet] 0 5 10 15 20 25 30 P ro fit [$ /a cr e- fo ot ] Maximum Economically Recoverable Storage - Unconfined Setting Profit @ $154.51 Harvest Value Profit @ h-max DFC Max (+65 feet) h-max @ 0.5 in/acre Demand Top of Well Screen Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:166 the unconfined setting. In this construct of the Carrizo-Wil- cox Aquifer the simulated depth to the bottom of the aquifer is much deeper in the confined setting (2,000 feet) than the unconfined setting (350 feet). The depth to the top of the aqui- fer (1,650 feet) is introduced as a new variable to create a dis- tinction between the pressurized storage of the aquifer and pore space storage. Accordingly, the well screen and pumping range occur at significant depth (within the saturated thickness of the aquifer). Thus, the demand-capacity constraint considered by the MERS model is also at great depth (Figure 7) and, while present, may not be binding in light of economic impacts. Pumping cost impacts to recoverability within the produc- tion range are significant in deep and confined settings. Pump- ing costs at the depth of the bottom of the aquifer (2,000 feet) reflect the increased depth and are found to be $190.90 per acre-foot, or roughly 5.71 times the $33.41 pumping costs at the aquifer bottom in the shallower, unconfined case (350 feet). Similarly, the harvest value point where profit = 0 at the depth of the bottom of the aquifer (2,000 feet) is found to be $882.47 per acre per year; again, this is 5.71 times the compa- rable $154.51 harvest value above as changes in pumping costs are linear (5.71 is equivalent to the change in depth, 2,000 feet / 350 feet). Where harvest value is $882.47 per acre per year, profit is $13.27 per acre-foot of groundwater pumped at the h_max depth of 1,860 feet—less than 7% of the initial value. Agricultural users experience much greater changes in pump- ing costs over the full production range in the confined setting because the range of depths is greater, and those changes are sufficient to make a clear difference in recoverability among crop types (Figure 8). For example, alfalfa harvest values are insufficient to allow positive profit long before depth-to-wa- ter reaches the top of the aquifer (and transitions it from the confined to the unconfined state), but tomato harvest values are sufficient to reach the demand-capacity constraint (Figure 8). Note that demand is constant at an irrigation rate of 0.5 inches per acre per day for all simulated harvest values shown here (Figure 8), but higher value crops may require greater irri- gation demand than lower value crops. Additionally, simulated harvest values are likely overestimates of the actual net value of all costs unrelated to pumping (see key assumptions). Figure 7. Maximum economically recoverable storage where harvest value is $882.47 per acre per year and irrigation demand is 0.5 inches per acre per day in the confined setting. The (solid blue) diagonal line reflects the linear change in profit as pumping costs increase with depth-to-water. The only horizontal line (dashed blue) is profit at the binding demand-capacity constraint (h_max, binding here). From left to right: The first vertical line (dashed black) is the depth of the top of the aquifer, the second vertical line (solid red) represents h_max (binding here), and the third vertical line (solid black) indicates the top of the well screen. Simulation generated by MATLAB.   Figure 7 200 400 600 800 1000 1200 1400 1600 1800 2000 Depth-to-Water: Land Surface to Aquifer Bottom [feet] 0 20 40 60 80 100 120 140 160 180 P ro fit [$ /a cr e- fo ot ] Maximum Economically Recoverable Storage - Confined Setting Profit @ $882.47 Harvest Value Profit @ h-max Top of Aquifer h-max @ 0.5 in/acre Demand Top of Well Screen Texas Water Journal, Volume 11, Number 1 167Maximum Economically Recoverable Storage These results confirm H2, that simulated recoverability in deep and confined settings is constrained by economic limita- tions for some uses (harvest values) at all irrigation (demand) rates, restricting them to producing from pressurized storage. DISCUSSION Whether Texas is running out of groundwater or experi- encing a regulation-induced shortage depends upon how one assesses groundwater availability. At the same time, there is no universal groundwater availability assessment method for the state as availability is a function of many, potentially conflicting management objectives. The methods developed here define MERS as a simplified simulation of the physical and econom- ic limitations to groundwater recoverability; key elements of availability common to all human groundwater demand absent from total storage and TERS. Our results indicate that recoverability is a function of use, aquifer characteristics, and well infrastructure. Here we show the capacity of an aquifer to meet demand is a function of transmissivity where transmissivity declines with increasing depth-to-water. Together with well screen limitations and Figure 8. Profit function over increasing depth-to-water in the confined setting for a range of reference and representative harvest values. The diagonal lines reflect the linear changes in profit as pumping cost increases with depth-to-water for given harvest values. From left to right: The first vertical line (dashed black) is the depth of the top of the aquifer, the second vertical line (solid red) represents h_max (where irrigation demand is 0.5 inches per acre), and the third vertical line (solid black) indicates the top of the well screen. Simulation generated by MATLAB.   Figure 8   200 400 600 800 1000 1200 1400 1600 1800 2000 Depth-to-Water: Land Surface to Aquifer Bottom [feet] 0 20 40 60 80 100 120 140 160 180 P ro fit [$ /a cr e- fo ot ] Economic Constraints: Variable Harvest Value Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:168 drawdown under pumping, a maximum depth-to-water with the capacity to satisfy the demanded pumping rate is estab- lished as a binding constraint. While simple in concept, these constraints are absent from many publications in the literature that assume a bottomless aquifer of infinite areal extent. This demand-capacity constraint is found to be binding in shallow and unconfined settings simulated here and exceeds maximum established DFCs for all agricultural uses. Changes in pump- ing costs are shown to be significant to agricultural users and directly associated with changes in depth-to-water in both the confined and unconfined settings. Indeed, while the capaci- ty of deep and confined aquifers to meet demand is high, the costs associated with reaching the depth-to-water necessary to extract much of that storage may be economically prohibitive for some uses. In all cases, users are economically incentivized to minimize pumping costs (and thereby depth-to-water) irre- spective of confined or unconfined setting. Critically, our results further suggest that storage-based esti- mates that do not incorporate the physical and economic con- straints of pumping (such as TERS, at either percentile bench- mark) may overestimate groundwater availability in deep and confined settings by orders of magnitude due to the change in storage coefficient assumed when an aquifer transitions from confined to unconfined state (Equation 2). This manifests for uses where pumping from depth-to-water at or below the top of the confined aquifer is infeasible. For example, the local total storage volume for a 100-acre farm pumping in deep and confined settings, where the initial depth-to-water is 350 feet above land surface (artesian), would be 5,313.25 acre-feet (Equation 2 and Table 3). Related TERS volumes would be 3,984.93 acre-feet (at 75% of local total storage) and 1,328.31 acre-feet (at 25% of local total storage). However, if we apply the above conditions and assumptions to an alfalfa farm, we see that the MERS model constrains the maximum recoverable depth-to-water to the depth where profit = 0 at approximately 1,000 feet (Figure 8). We can then calculate the local MERS volume by integrating this simulated depth-to-water recoverability limit with the relevant elements of the total storage calculation (Equation 2). The MERS model would thus estimate that only 42.69 acre-feet is recoverable for this use, about 0.8% of the local total storage or 1.1% and 3.2% of comparable TERS estimates. Thus, while the Carrizo-Wilcox Aquifer stores 5.227 billion acre-feet of water, or 45% of the total 11.575 billion acre-feet stored by all major aquifers of the state (Tables 1 and 2), the overwhelming majority of that storage may be unrecoverable, by these standards, for some uses and locations due to the change in depth necessary to transition the aquifer from the confined to unconfined state. Importantly, while we choose to simulate agricultural uses operating in the central section of the Carrizo-Wilcox Aqui- fer, the MERS model may be applied to any aquifer and any use to estimate groundwater recoverability where demand and the economic value generated by pumped groundwater are known and effectively constant. Moreover, the MERS model is deliberately designed to be calculable with commonly held data (such as specific capacity) without the need for advanced computing and mathematics, perhaps increasing accessibility. We suggest that groundwater policymakers, managers, and producers consider including MERS (or a similar metric) along with TERS and the other considerations of Chapter 36 §108(d) (3) of the TWC, especially in jurisdictions operating under a depth-to-water based DFC. Even a simple estimate of how groundwater recoverability changes with depth-to-water for variable uses, such as when certain pumping demands become infeasible for various crop or other use values, may prove use- ful. Failure to account for demand-capacity constraints and the economic impact to pumping costs arising from prospec- tive changes in depth-to-water may result in overestimates of groundwater availability. CONCLUSION We conclude that Texas groundwater managers, stakehold- ers, and policymakers assessing groundwater availability need an alternate approach for estimating recoverability. The cur- rent metrics employed by the state for estimating groundwater storage and recoverability, total storage and TERS, are highly limited in scope and function. Irrespective of the name, TERS values do not scientifically account for many of the physi- cal and none of the economic constraints upon groundwater recoverability, as noted by the TWDB (Bradley 2016). The system of equations described above, which constitute the MERS model, represents one method for estimating the limits of groundwater recoverability that accounts for some of the physical and economic constraints upon yields. These con- straints can be significant and may limit recoverability to as little as 1% of local storage (or 1.1% and 3.2% of comparable TERS estimates) in deep and confined settings. This suggests that the majority of water stored in the Carrizo-Wilcox Aquifer (45% of major aquifer storage in Texas) may not be economi- cally recoverable for some agricultural uses. Conversely, recov- erability of water stored in shallow and unconfined settings may be limited only by the capacity of the well and aquifer to meet demanded pumping rates. Future studies expanding on these methods may refine draw- down estimates by replacing specific capacity estimates with drawdown solutions that account for partial well penetration, Texas Water Journal, Volume 11, Number 1 169Maximum Economically Recoverable Storage though the analyses would become more complex. These or similar methods could also be integrated with the TWDB groundwater availability model and groundwater database data to estimate local recoverability for any use and aquifer. Ultimately, what is recoverable for a microchip manufacturer may not be the same as what is recoverable for a farmer, and what is recoverable for an alfalfa farmer may not be the same as what is recoverable for a tomato farmer. Moreover, the limits to what is economically recoverable for any user are not econom- ically efficient and pumping costs increase for all users in all cases where depth-to-water increases. Nonetheless, quantifying planned and potential changes to groundwater recoverability using scientific methods with known assumptions, conditions, and infrastructure provides important information for Texas policymakers and stakeholders looking to the future. ACKNOWLEDGEMENTS The authors acknowledge funding for a graduate research assistantship to Justin C. Thompson from the University of Texas at Austin Planet Texas 2050 program, and the helpful comments by dissertation committee members Danielle Rem- pe, Jay Banner, and Sheila Olmstead, and by the anonymous reviewers. REFERENCES Aylward B, Seely H, Hartwell R, Dengel J. 2010. United Nations Food and Agriculture Organization. The eco- nomic value of water for agricultural, domestic and indus- trial uses: a global compilation of economic studies and market prices. Prepared by Ecosystem Economics LLC. Available from: https://www.researchgate.net/publica- tion/284848775_The_economic_value_of_water_for_ agricultural_domestic_and_industrial_uses_a_global_ compilation_of_economic_studies_and_market_prices. Boghici R, Jones IC, Bradley RG, Shi J, Goswami RR, Thork- ildsen D, Backhouse S. 2014. GAM Task 13-028: total estimated recoverable storage for aquifers in groundwater management area 4. Austin (Texas): Texas Water Devel- opment Board, Groundwater Resources Division. Avail- able from: http://www.twdb.texas.gov/groundwater/docs/ GAMruns/Task13-028.pdf. Bradley R. 2016. Aquifer assessment 16-01: supplemental report of total estimated recoverable storage for ground- water management area 10. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/ docs/AA/AA16-01_TERS.pdf. Brady R, Beckerman W, Capps A, Kennedy B, McGee P, Northcut K, Parish M, Qadeer A, Shan A, Griffin J. 2016. Reorganizing groundwater regulation in Texas. Bush School of Government and Public Service. Col- lege Station (Texas): Texas A&M University. Available from: https://oaktrust.library.tamu.edu/bitstream/han- dle/1969.1/187041/2016%20Final%20Report%20Reor- ganizing%20Groundwater%20Regulation%20in%20 Texas%20%283%29.pdf?sequence=1&isAllowed=y. Domenico P. 1972. Concepts and models in groundwater hydrology. New York (New York): McGraw-Hill. 395 p. ISBN 07-017535-7. Dutton A, Harden B, Nicot JP, O’Rourke D. 2003. Ground- water Availability Model for the Central Part of the Car- rizo-Wilcox Aquifer in Texas. Prepared for the Texas Water Development Board under contract no. 2001-483-378. Austin (Texas): Bureau of Economic Geology, the Uni- versity of Texas at Austin. 405 p. Available from: http:// www.twdb.texas.gov/groundwater/models/gam/czwx_c/ czwx_c_full_report.pdf. Edwards Aquifer Authority v. Day-McDaniel. Supreme Court of Texas. Case Number 08-0964. Decided February 24, 2012. Available from: https://caselaw.findlaw.com/tx-su- preme-court/1595644.html. Fipps G. 2015. Calculating horsepower requirements and sizing irrigation supply pipelines. Texas Agricultural Extension Service. College Station (Texas): Texas A&M University. 11 p. Available from: http://faculty.missouri. edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calcu- lating%20Horsepower%20Requirements%20and%20 Sizing%20Irrigation%20Supply%20Pipelines.%20 Texas%20Agricultural%20Extension%20Service,%20 Texas%20A&M%20University%20System..pdf. Hazen A. 1893. Some physical properties of sands and grav- els. Massachusetts Board of Health. 24th Annual Report. 539–556. Jigmund M, Wade S. 2013. GAM Task 13-025: total estimated recoverable storage for aquifers in groundwater manage- ment area 16. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ GR12-025.pdf. Jones IC, Boghici R, Kohlrenken W, Shi J. 2013a. GAM Task 13-027: total estimated recoverable storage for aquifers in groundwater management area 3. Austin (Texas): Texas Water Development Board, Groundwater Resources Divi- sion. Available from: http://www.twdb.texas.gov/ground- water/docs/GAMruns/Task13-027.pdf. https://www.researchgate.net/publication/284848775_The_economic_value_of_water_for_agricultural_domestic_and_industrial_uses_a_global_compilation_of_economic_studies_and_market_prices https://www.researchgate.net/publication/284848775_The_economic_value_of_water_for_agricultural_domestic_and_industrial_uses_a_global_compilation_of_economic_studies_and_market_prices https://www.researchgate.net/publication/284848775_The_economic_value_of_water_for_agricultural_domestic_and_industrial_uses_a_global_compilation_of_economic_studies_and_market_prices https://www.researchgate.net/publication/284848775_The_economic_value_of_water_for_agricultural_domestic_and_industrial_uses_a_global_compilation_of_economic_studies_and_market_prices http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-028.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-028.pdf http://www.twdb.texas.gov/groundwater/docs/AA/AA16-01_TERS.pdf http://www.twdb.texas.gov/groundwater/docs/AA/AA16-01_TERS.pdf https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/187041/2016%20Final%20Report%20Reorganizing%20Groundwater%20Regulation%20in%20Texas%20%283%29.pdf?sequence=1&isAllowed=y https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/187041/2016%20Final%20Report%20Reorganizing%20Groundwater%20Regulation%20in%20Texas%20%283%29.pdf?sequence=1&isAllowed=y https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/187041/2016%20Final%20Report%20Reorganizing%20Groundwater%20Regulation%20in%20Texas%20%283%29.pdf?sequence=1&isAllowed=y https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/187041/2016%20Final%20Report%20Reorganizing%20Groundwater%20Regulation%20in%20Texas%20%283%29.pdf?sequence=1&isAllowed=y http://www.twdb.texas.gov/groundwater/models/gam/czwx_c/czwx_c_full_report.pdf http://www.twdb.texas.gov/groundwater/models/gam/czwx_c/czwx_c_full_report.pdf http://www.twdb.texas.gov/groundwater/models/gam/czwx_c/czwx_c_full_report.pdf https://caselaw.findlaw.com/tx-supreme-court/1595644.html https://caselaw.findlaw.com/tx-supreme-court/1595644.html http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://faculty.missouri.edu/~schumacherl/Fipps,%20G.%20(n.d.)%20Calculating%20Horsepower%20Requirements%20and%20Sizing%20Irrigation%20Supply%20Pipelines.%20Texas%20Agricultural%20Extension%20Service,%20Texas%20A&M%20University%20System..pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/GR12-025.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/GR12-025.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-027.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-027.pdf Texas Water Journal, Volume 11, Number 1 Exploring Groundwater Recoverability in Texas:170 Jones IC, Bradley RG. 2013. GAM Task 13-032: total estimat- ed recoverable storage for aquifers in groundwater man- agement area 9. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-032.pdf. Jones IC, Bradley RG, Boghici R, Kohlrenken W, Shi J. 2013b. GAM Task 13-030: total estimated recoverable storage for aquifers in groundwater management area 7. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.tex- as.gov/groundwater/docs/GAMruns/Task13-030.pdf. Jones IC, Shi J, Bradley R. 2013c. GAM Task 13-033: total estimated recoverable storage for aquifers in groundwater management area 10. Austin (Texas): Texas Water Devel- opment Board, Groundwater Resources Division. Avail- able from: http://www.twdb.texas.gov/groundwater/docs/ GAMruns/Task13-033.pdf. Kohlrenken W. 2015. GAM Task 15-006: total estimated recoverable storage for aquifers in groundwater manage- ment area 1. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: https://www.twdb.texas.gov/groundwater/docs/GAM- runs/Task15-006.pdf Kohlrenken W, Boghici R, Jones I. 2013a. GAM Task 13-026: total estimated recoverable storage for aquifers in ground- water management area 2. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/ docs/GAMruns/Task13-026.pdf. Kohlrenken W, Boghici R, Shi J. 2013b. GAM Task 13-029: total estimated recoverable storage for aquifers in ground- water management area 6. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/ docs/GAMruns/Task13-029.pdf. Mace R, Petrossian R, Bradley R, Mullican WF, Christian L. Texas Water Development Board. 2008. A streetcar named desired future conditions: the new groundwater availability for Texas (revised). In: Proceedings of The Changing Face of Water Rights in Texas; 2008 May 8–9; Bastrop (Texas): State Bar of Texas; 21 p. Available from: http://www.twdb. texas.gov/groundwater/docs/Streetcar.pdf. Mace R, Smyth R, Xu L, Liang J. 2000. Transmissivity, hydrau- lic conductivity, and storativity of the Carrizo-Wilcox Aquifer in Texas. Prepared for the Texas Water Develop- ment Board under contract no. 99-483-279, Part 1. Austin (Texas): Bureau of Economic Geology, University of Texas Austin. Available from: http://www.beg.utexas.edu/files/ publications/contract-reports/CR2000-Mace-1.pdf. Nielsen-Gammon JW, Banner JL, Cook BI, Tremaine DM, Wong CI, Mace R, Gao H, Yang ZL, Gonzalez MF, Hoffpauir R, Gooch T, Kloesel K. 2020. Unprecedent- ed drought challenges for Texas water resources in a changing climate: what do researchers and stakehold- ers need to know? Earth’s Future. 8(8):1-20. Available from: https://agupubs.onlinelibrary.wiley.com/doi/ full/10.1029/2020EF001552. Shaw WD. 2005. Water resource economics and policy: an introduction. Cheltenham (United Kingdom): Edward Elgar Publishing. 364 p. ISBN 1-84376-917-4. Shi J, Bradley RG, Wade S, Jones I, Anaya R, Seiter-Weather- ford C. 2014. GAM Task 13-031: total estimated recov- erable storage for aquifers in groundwater management area 8. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http:// www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-031.pdf. Stringman B. 2013. Understanding horsepower and water horsepower efficiency and fuel consumption costs for irri- gation pumps. Las Cruces (New Mexico): New Mexico State University. 2 p. Available from: http://aces.nmsu. edu/pubs/_m/M227.pdf. Texas Administrative Code Texas, Title 31 – Natural Resourc- es and Conservation, Part 10 – Texas Water Develop- ment Board, Chapter 356 – Groundwater Management, Subchapter A – Definitions, Rule § 356.10. Available from: http://texreg.sos.state.tx.us/public/readtac$ext. TacPage?sl=R&app=9&p_dir=&p_rloc=&p_tloc=&p_ ploc=&pg=1&p_tac=&ti=31&pt=10&ch=356&rl=10. Texas Water Code, Title 2 – Water Administration, Subtitle C – Water Development, Chapter 16 – Provisions Generally Applicable to Water Development, Subchapter A – Gen- eral Provisions. Available from: http://www.statutes.legis. state.tx.us/Docs/WA/htm/WA.16.htm. Texas Water Code, Title 2 – Water Administration, Subtitle E – Groundwater Management, Chapter 36 – Groundwa- ter Conservation Districts, Subchapter A – General Provi- sions. Available from: http://www.statutes.legis.state.tx.us/ Docs/WA/htm/WA.36.htm. [TWDB] Texas Water Development Board. 2016. 2017 state water plan – water for Texas. Austin (Texas): Texas Water Development Board. 150 p. Available from: http://www. twdb.texas.gov/waterplanning/swp/2017/index.asp. Theis CV. 1935. The relation between the lowering of the pie- zometric surface and the rate and duration of discharge of a well using groundwater storage. American Geophysics Union. 16:519-524. Available from: https://water.usgs. gov/ogw/pubs/Theis-1935.pdf. http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-032.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-032.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-030.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-030.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-033.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-033.pdf https://www.twdb.texas.gov/groundwater/docs/GAMruns/Task15-006.pdf https://www.twdb.texas.gov/groundwater/docs/GAMruns/Task15-006.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-026.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-026.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-029.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-029.pdf http://www.twdb.texas.gov/groundwater/docs/Streetcar.pdf http://www.twdb.texas.gov/groundwater/docs/Streetcar.pdf http://www.beg.utexas.edu/files/publications/contract-reports/CR2000-Mace-1.pdf http://www.beg.utexas.edu/files/publications/contract-reports/CR2000-Mace-1.pdf https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001552 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020EF001552 http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-031.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-031.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-031.pdf http://aces.nmsu.edu/pubs/_m/M227.pdf http://aces.nmsu.edu/pubs/_m/M227.pdf http://texreg.sos.state.tx.us/public/readtac$ext.TacPage?sl=R&app=9&p_dir=&p_rloc=&p_tloc=&p_ploc=&pg=1&p_tac=&ti=31&pt=10&ch=356&rl=10 http://texreg.sos.state.tx.us/public/readtac$ext.TacPage?sl=R&app=9&p_dir=&p_rloc=&p_tloc=&p_ploc=&pg=1&p_tac=&ti=31&pt=10&ch=356&rl=10 http://texreg.sos.state.tx.us/public/readtac$ext.TacPage?sl=R&app=9&p_dir=&p_rloc=&p_tloc=&p_ploc=&pg=1&p_tac=&ti=31&pt=10&ch=356&rl=10 http://www.statutes.legis.state.tx.us/Docs/WA/htm/WA.16.htm http://www.statutes.legis.state.tx.us/Docs/WA/htm/WA.16.htm http://www.statutes.legis.state.tx.us/Docs/WA/htm/WA.36.htm http://www.statutes.legis.state.tx.us/Docs/WA/htm/WA.36.htm http://www.twdb.texas.gov/waterplanning/swp/2017/index.asp http://www.twdb.texas.gov/waterplanning/swp/2017/index.asp https://water.usgs.gov/ogw/pubs/Theis-1935.pdf https://water.usgs.gov/ogw/pubs/Theis-1935.pdf Texas Water Journal, Volume 11, Number 1 171Maximum Economically Recoverable Storage Theis CV. 1963. Estimating the transmissivity of a water-table aquifer from specific capacity of a well. Washington (Dis- trict of Columbia): United States Department of the Inte- rior. U.S. Geological Survey Water-Supply Paper 1536-I. 332-336. https://pubs.usgs.gov/wsp/1536i/report.pdf. Wade S, Anaya R. 2014. GAM Task 13-038: total estimated recoverable storage for aquifers in groundwater manage- ment area 15. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-038.pdf. Wade S, Bradley R. 2013. GAM Task 13-036: total estimated recoverable storage for aquifers in groundwater manage- ment area 13. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-036revised.pdf. Wade S, Shi J. 2014a. GAM Task 13-034: total estimated recoverable storage for aquifers in groundwater manage- ment area 11. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-034.pdf. Wade S, Shi J. 2014b. GAM Task 13-035: total estimated recoverable storage for aquifers in groundwater manage- ment area 12. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/docs/GAMruns/ Task13-035_v2.pdf. Wade S, Thorkildsen D, Anaya R. 2014. GAM Task 13-037: total estimated recoverable storage for aquifers in ground- water management area 14. Austin (Texas): Texas Water Development Board, Groundwater Resources Division. Available from: http://www.twdb.texas.gov/groundwater/ docs/GAMruns/Task13-037.pdf. Young RA, Loomis JB. 2014. Determining the economic value of water: concepts and methods. 2nd edition. New York (New York): RFF Press. 337 p. ISBN 978-0-415-83846-7. https://pubs.usgs.gov/wsp/1536i/report.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-038.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-038.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-036revised.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-036revised.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-034.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-034.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-035_v2.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-035_v2.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-037.pdf http://www.twdb.texas.gov/groundwater/docs/GAMruns/Task13-037.pdf Chapter 36 §108 of the TWC Title 31, Part 10, §356.10(6) of the Texas Administrative Code Chapter 36 §108(b) of the TWC Chapter 16 §053(e)(3) of the TWC Mace et al. 2008 TWDB 2016 Nielsen-Gammon et al. 2020 TWDB 2016 Chapter 36 §108(d)(3) of the TWC Rule §356.10.23 of the Texas Administrative Code Bradley 2016 Wade and Shi 2014b. TWDB 2016 Brady et al. (2016) Brady et al. 2016 Kohlrenken 2015 Kohlrenken et al. 2013a. Jones et al. 2013a. Boghici et al. 2014 Kohlrenken et al. 2013b Jones et al. 2013b. Shi et al. 2014 Jones and Bradley 2013 Jones et al. 2013c. Wade and Shi 2014a. Wade and Shi 2014b. Wade and Bradley 2013 Wade et al. 2014 Wade and Anaya 2014 Jigmund and Wade 2013 Dutton et al. 2003 Mace et al. 2000 Chapter 36 §108(d) of the TWC Aylward et al. 2010 Young and Loomis 2014 Edwards Aquifer Authority v. Day-McDaniel Shaw (2005) Stringman 2013 Dutton et al. 2003 Theis (1935) Theis (1963) Mace et al. (2000) Hazen (1893) Fipps 2015 Domenico 1972 Chapter 36 §108(d)(3) of the TWC Bradley 2016