Assessment of managed aquifer recharge sites using GIS and numerical modeling

Tess A. Russo*1,2, Andrew T. Fisher1, Brian S. Lockwood3, Randall T. Hanson4 (?)

1Department of Earth and Planetary Sciences, University of California, Santa Cruz, CA2Columbia Water Center, Earth Institute, Columbia University, NY3Pajaro Valley Water Management Agency, Watsonville, CA4United States Geological Survey, San Diego, CA

*tr2378@columbia.edu, (347) 913-6835

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Abstract

We completed a geographic information system (GIS) analysis to assess suitability for managed

aquifer recharge (MAR) using the Pajaro Valley Groundwater Basin, central coastal California

(PVGB), as a case study. With results from the GIS study, we used a groundwater model to

assess the hydrologic impact of potential MAR operating scenarios, illustrating how a

comprehensive analysis of MAR suitability can help with regional water supply planning. The

GIS analyses used topographic, land use, surficial geology, soil infiltration capacity, aquifer and

associated confining layer locations, properties, thicknesses, and historical changes in water

levels. A map of MAR site suitability and comparison with an existing project suggests that

about 7% (15 km2) of the basin may be highly suitable for MAR. Model results show simulated

MAR projects in locations identified as “highly suitable” for MAR reduce seawater intrusion

more than projects simulated in “unsuitable” locations, supporting the GIS analysis results.

Results from the model also illustrate the variability in seawater intrusion reduction and head

level changes throughout the basin and over time. Projects distributed throughout the PVGB

were more effective at reducing seawater intrusion than projects along the coast, at the time scale

of several decades. Collectively, these studies introduce a novel data integration method and help

to evaluate management options for improving long-term groundwater conditions throughout the

PVGB.

1. Introduction

Groundwater overdraft can occur when the sum of extraction and other aquifer outputs exceeds

the sum of inputs over the long term. Groundwater overdraft can lead to numerous undesirable

consequences, including increased pumping costs associated with lowering of aquifer water

levels; drying of streams, lakes and wetlands; land subsidence and an associated loss of storage

capacity; and seawater intrusion. As water demand rises, unsustainable groundwater use is

expected to continue to increase around the world, especially in developing nations, making

groundwater management increasingly important (Foster and Chilton 2003; Giordano 2009;

Konikow and Kendy 2005; Rosegrant and Cai 2009).

Strategies for mitigating groundwater overdraft include limiting extraction (water

conservation, pumping moratoriums), and enhanced aquifer inputs. The latter can be 2

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accomplished with injection wells, aquifer storage and recovery (ASR, with injection and

extraction through the same wells), and managed aquifer recharge (MAR, using surface

infiltration systems). Enhanced recharge has become a common and frequently effective method

for water resource management (Dillon et al. 2009; Ma and Spalding 1997; Maliva and Missimer

2012). ASR can help to reduce overdraft, (Shammas 2007), but must be planned carefully, as it

can have high energy requirements and requires creation and maintenance of conveyance and

pumping systems (Bouwer 2002). In contrast, MAR can be part of a more passive operational

system, potentially involving less engineering and lower operating costs. Water is diverted to a

natural depression or constructed retention area, where it infiltrates into the subsurface over time.

MAR projects have also demonstrated improvements in water quality through denitrification

during the infiltration process (Fryar et al. 2000; Ma and Spalding 1997; Missimer et al. 2011;

Rauch-Williams et al. 2010; Schmidt et al. 2011). These improvements can be particularly

important for sites lacking reliable access to pristine surplus surface water supplies, such as

basins in which there is extensive agricultural development or widespread use of septic systems,

resulting in elevated nutrient levels. The primary disadvantages of MAR include relatively large

land area requirements, and the challenge in identifying locations with amenable surface and

subsurface conditions for infiltration to an unconfined aquifer.

Identifying areas suitable for MAR projects and estimating the influence of these projects

on groundwater levels and fluxes are challenging problems with numerous solutions. The first

step is to locate regions where surface water can infiltrate and flow to available space within an

aquifer. These assessments are often made on a regional basis, within which there may be limited

data on complex surface and subsurface geology. In addition, there is a need to determine how

the benefits of managed recharge could vary with project location, size, and operating conditions.

Some of these questions can be resolved through field testing, but computational tools can play

an important role in evaluating project scenarios and screening potential MAR sites, on the basis

of their broader hydrologic impact.

Studies of the spatial distribution of recharge have been developed and applied to assess

groundwater vulnerability to contamination. Aller et al. (1987) developed a method for

evaluating the potential for groundwater degradation, DRASTIC, which uses a relative ranking

system. The method combines multiple datasets related to groundwater infiltration, including net

recharge, aquifer and soil properties, and impact of infiltrating water through the vadose zone on

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water quality. The parameters for each dataset are classified (generally to values 1 to 10), then

multiplied by a dataset weight (1 to 5). The product of value and weight for each area (or

subarea) are then summed for all the datasets, resulting in a relative vulnerability index for each

area in the study region. This method provides the basis for identifying recharge areas using

geographic information system (GIS) based integration. We follow the general structure of this

method, but use a different ensemble of datasets, different classification and integration

techniques, and different parameter weights.

Many hydrologic applications, including identification of locations for potential MAR

projects, are well suited for GIS analysis (Jha et al. 2007). Several studies have used GIS-based

integration of spatial coverages pertinent to groundwater recharge, with various data values

being classified and weighted before combining (Adham et al. 2010; Chenini et al. 2010;

Chitsazan and Akhtari 2009; Jasrotia et al. 2007; Murray and Mcdaniel 2003; Piscopo 2001;

Saraf and Choudhury 1998; Shankar and Mohan 1998; Yeh et al. 2009). Methods used for

classification and weighting generally differ from study to study, due to variations in data

availability, local geology, and perceived level of dataset importance to groundwater recharge.

Chowdhury et al. (2010) polled a group of geologists and hydrogeologists to determine a

weighting system for their GIS-based recharge location assessment, and found that half the group

thought equal weighting was appropriate while the other half agreed on a variable weighting

method. As a practical matter, all classification schemes of this kind are somewhat arbitrary, but

initial approaches and values can be refined over time as new data becomes available and

individual recharge projects are tested and implemented. Some studies have used GIS with a

multi-criteria decision analysis that accounts for local preferences, and attempt to reduce the

arbitrary nature of weight assignment by using an analytical hierarchy process (Chowdhury et al.

2010; Rahman et al. 2012). Though more rigorous, this decision analysis method still requires

(largely heuristic) estimation of the relative importance of each parameter.

Numerical modeling can also help to identify sites amenable for MAR, and be used for

estimating the potential benefits of MAR projects on regional hydrologic conditions during a

range of future climatic, water use, and management scenarios(Munevar and Marino 1999).

Groundwater models may be combined with an optimization algorithm to test water management

strategies, including artificial recharge (Abarca et al. 2006). These models tend to use simple

governing equations and highly generalized aquifer properties. Another option for reducing

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computation time is to employ an ensemble of analytical models based on simplified lumped

parameters (Smith and Pollock 2012). Combination of the GIS-based integration methods with

numerical modeling allows a more detailed and quantitative assessment of MAR opportunities

and impacts, and takes advantage of overlapping data requirements for GIS and numerical

modeling studies (for example, aquifer geometry and depth and soil properties) (Chenini and

Mammou 2010). The use of numerical modeling as a follow-up to a GIS-based study also

provides an opportunity to assess the MAR location suitability analysis developed using GIS,

and on time requirements to see specific improvements to resource conditions.

True assessment of MAR location suitability requires field testing to determine how

project placement influences local and regional hydrologic conditions. Ultimately this requires

field-scale implementation of MAR projects, but budgetary and time constraints generally limit

opportunities for large-scale installations purely for testing purposes. Thus, numerical modeling

has an important role to play in pre-implementation evaluation of project options, based on a

MAR suitability analysis, helping to reduce the number of choices made in selecting appropriate

management strategies. Similarly, evaluation of actual hydrologic responses to implementation

of MAR projects can be used to "validate" individual and ensemble groups of groundwater

models, contributing to a better understanding of system function and improved basin-wide

management of scarce resources.

The present study combines GIS and numerical analyses to address the following

questions, as applied to the Pajaro Valley Groundwater Basin (PVGB), central coastal California

(Figure 1): 1) How should surface and subsurface information datasets be combined to assess

MAR site suitability? 2) How does MAR suitability vary within the basin? 3) How might

hypothetical MAR operating scenarios influence groundwater conditions in the basin over the 34

year model simulation? This project limits analysis to MAR options for the PVGB rather than

exploring a more comprehensive assessment of basin management options and anticipated

changes to water usage. An extensive technical and public process is currently underway in the

PVGB to evaluate a wide range of supply and conservation options, and develop a new basin

management plan, in an effort to improve groundwater conditions in the Pajaro Valley in coming

decades. We limit analyses in this study to assessing the spatial distribution of MAR suitability

and potential hydrologic impacts of several MAR options.

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2. Study area

The PVGB (Figure 1) underlies a 322 km2 area managed by the Pajaro Valley Water

Management Agency (PVWMA). The region relies almost entirely on groundwater to satisfy

agricultural and municipal/domestic needs (83% and 17%, respectively). Precipitation in the area

has averaged ~560 mm/yr in recent decades, ranging from 480 mm/yr in the south to 1270

mm/yr in the northern high elevation area, with considerable year-to-year variability.

Precipitation is highly seasonal, with a majority falling between December and April, resulting in

distinct dry and wet seasons. Historic changes in precipitation intensity and air temperature in

central coastal California (Dettinger 2005; Russo et al. 2013), suggest that natural recharge rates

and crop water requirements are not stable and may contribute to groundwater stress (Gleeson et

al. 2010; Taylor et al. 2013).

The PVGB is a coastal basin, bounded to the west by Monterey Bay and to the east by the

San Andreas Fault. Northern and southern boundaries are political rather than (hydro)geologic,

with key aquifer units extending beyond the area managed by the PVWMA. Much of the PVGB

corresponds to the lower drainage basin of the Pajaro River, which flows into the valley from the

east at an average discharge of 1.3 x 108 m3/yr (USGS Gage #11159000), after draining an

upstream area of 3.1 x 103 km2. Corralitos Creek, a tributary of the Pajaro River having a

drainage area contained entirely within the PVGB, contributes an additional 1.4 x 107 m3/yr of

discharge (USGS Gage #11159200), and the Watsonville Sloughs also drain into the lower

Pajaro River before it discharges into Monterey Bay (Figure 1).

The PVGB has six hydrogeologic units that comprise aquifer and confining layers:

alluvium, alluvial clay, the Upper Aromas Aquifer, the Aromas confining unit, the Lower

Aromas Aquifer, and the Purisima Aquifer (Dupre 1990; Hanson 2003; Muir 1972). These layers

are herein referred to as A1, C1, A2, C2, A3 and A4, respectively, where A signifies an aquifer

and C signifies a confining unit (Table 1). The six layers are underlain by hydrogeologic

basement rocks consisting of granite and Oligocene-aged deposits.

Groundwater extraction from the basin currently averages 6.2 x 107 m3/yr, with the

majority of water pumped from Layers A1 and A2 (Hanson et al. 2013; PVWMA 2013). Over

time, total groundwater outflows (including extraction) have exceeded recharge and inflow rates;

the current estimated overdraft in the PVGB is approximately 1.5 x 107 m3/yr (Hanson et al.

2013). This annual overdraft is approximately equivalent to 24% of annual pumpage and 10% of

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precipitation falling on the PVGB. Long-term monitoring in recent decades indicates that water

levels within more than half of the PVGB are below sea level, particularly during the dry part of

the water year, with the greatest depression of water levels below the City of Watsonville

(PVWMA 2013). Due to chronic over-extraction, a zone of seawater intrusion extends up to 5

km inland and is advancing at ~80 m/yr along much of the coastal edge of the basin (Hanson et

al. 2008; Hanson 2003; Wallace and Lockwood 2009) (Figure 1).

Table 1. Model layer IDs and geologic information

Layer ID Layer Name Thickness1 (m) Aquifer lithology2

A1 Alluvial aquifer 0 to 116 Unconsolidated, moderately sorted

silt, sand, and gravel with discontinuous lenses of clay and

silty clayC1 Alluvial clay 0 to 16 --A2 Upper Aromas aquifer 0 to 153 Sequence of eolian

and fluvial sand, silt, clay and gravel

C2 Aromas clay 0 to 35 --A3 Lower Aromas aquifer 0 to 319 Semiconsolidated,

fine-grained, oxidized sand and

siltA4 Purisima aquifer 0 to 500 Thick bedded

tuffaceous and diatomaceous siltstone with

interbeds of fine-grained sandstone

1Layer thickness obtained from the Pajaro Valley Hydrologic Model (Hanson et al. 2013)

2Aquifer lithology summarized from USGS geologic maps (Brabb et al. 1997; Clark et al. 1997)

In 199X, the PVWMA constructed the Harkins Slough MAR system which is permitted

to divert up to 2.5 x 106 m3/yr (~2,000 ac-ft/yr) from near the confluence of Harkins and

Watsonville sloughs when flows and water quality are sufficiently high. Diverted water passes

through a sand pack filter and is pumped through a 1.5 km pipeline to a 7 acre infiltration pond.

Some of this percolated water is subsequently recovered and blended with other water supplies, 7

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then delivered using a coastal delivery pipeline to local farms and ranches. In addition, the

PVWMA and the City of Watsonville jointly developed and operate a water recycling plant that

contributes water to the coastal delivery pipeline, as do inland groundwater wells, allowing

project water to be blended to achieve quality and supply goals.

The PVWMA is currently working with regional stakeholders to update their basin

management plan in an effort to bring the basin back into hydrologic balance (PVWMA 2013).

Several projects have been implemented, including the MAR system and recycled water plant,

and additional projects are planned, including enhanced water conservation, surface storage, and

MAR efforts. The overall goal is to develop about 1.5 x 107 m3/yr of demand reduction and

additional supply. Local stakeholders are exploring options for development of distributed

(smaller-scale) MAR projects that could benefit from capture of stormwater flows during the

rainy seasons. Collectively these options raise questions about how best to identify MAR project

locations, and operate and maintain these projects for benefit of basin as a whole.

3. Methods

3.1. GIS analysis

We used GIS for data management, manipulation, and analysis of eleven surface and subsurface

data sets to generate a basin-wide map of "MAR suitability". As defined for this study, high

MAR suitability indicates that, if a water supply of sufficient quantity and quality is available,

surface and subsurface conditions could be favorable to developing one or more MAR projects.

Surface and subsurface property data sets were initially analyzed separately, and then were

combined to produce a final map. For surface analyses, primary data included: (1) surficial

geology, (2) soil infiltration capacity, (3) land use, (4) elevation (topographic slope), and (5)

verified (measured) infiltration and recharge rates from observational studies. For subsurface

analyses, primary data included: (6) aquifer thickness, (7) aquifer hydraulic conductivity, (8)

confining layer thickness, (9) aquifer storativity, (10) vadose zone thickness, and (11) historical

changes in water table height.

Surficial geology data were obtained from 1:62,500-scale geologic maps of Santa Cruz

and Monterey Counties (Brabb et al. 1997; Clark et al. 1997). Lithologic descriptions were used

to classify individual geologic units in terms of whether or not they corresponded to PVGB

aquifers, or if fine-grained sediment (clay and silt) would be likely to reduce direct connection to

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underlying aquifers. Higher MAR suitability is associated with outcropping aquifers units. Soil

infiltration capacity data were obtained from the Natural Resources Conservation Service

SSURGO database (NRCS 2010a; NRCS 2010b). Infiltration capacity of basin soils was mapped

in irregular polygons having values ranging from 0.2 to 12 m/d. Land use classifications were

developed by the PVWMA and the USGS in collaboration with regional stakeholders, based on

field visits and parcel-specific reports of crops grown, as part of a broader effort to develop a

regional hydrogeologic framework and groundwater model (Hanson 2003; Hydrometrics 2012;

Hanson et al. 2013). Land use classifications include native vegetation, urban, and agricultural

areas designated by crop type or presence of a nursery. Land surface slope values were

calculated from the 10-m resolution USGS National Elevation Dataset (ned.usgs.gov). Locations

of measured seepage rates along losing sections of the Pajaro River were reported in earlier

studies based on differential gauging and streambed geothermometry (Hatch et al. 2010; Ruehl et

al. 2006).

Subsurface data sets were prepared initially during development of a regional

hydrogeologic model (Hanson et al. 2013; Hanson 2003), and modified as needed for use in our

GIS-based analysis. Aquifer properties, including layer thicknesses, hydraulic conductivity, and

storativity, were assembled using data from >300 well logs distributed throughout the basin, and

compiled on a grid having horizontal resolution of 250 x 250 m and variable cell thickness. The

present unsaturated zone thickness was calculated by subtracting the interpolated water table

elevations, using data collected in 2010, from the ground elevation. Water levels in the basin

were compared using 1998 and 2010 data sets, to quantify decadal changes in groundwater

levels. There were sparse water level data from earlier times, in many cases indicating greater

absolute changes in water levels, but 1998 was the earliest year for which data covered a

majority of the study area. Nevertheless, this data set had the smallest spatial coverage of all data

sets used in this study, and thus defined the spatial extent of the final MAR suitability map.

The common approach for combining GIS data sets such as these requires reclassifying

relevant datasets to a common scale (e.g., relative values of 1 to 5) and then assigning a weight to

each dataset in proportion to its perceived importance for the condition or process being

evaluated. For each grid cell in the analysis, an index is calculated by summing the products of

value and weight for each dataset:

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Index(x , y )=∑i =1

n

v i(x , y )wi (1)

where n is the total number of data sets, vi is the classified value for data set i at location (x,y),

and wi is the weight assigned to data set i. We defined a weighting scheme for use in the present

study based on (a) a review of published recharge mapping studies that used a similar GIS-based

approach, (b) consideration of available data sets for the PVGB, and (c) inferences as to how

groundwater recharge in this region might be influenced by coexisting factors (Figure 2).

Our approach differs in several respects from methods applied in earlier GIS-based

studies of natural recharge and potential for development of MAR projects. Most significantly,

we evaluated surface data (sets 1 to 4) and subsurface data (sets 6 to 11) independently, with the

former indicating the ease of surface water infiltration, and the latter indicating the ease of

subsurface transport and extent of available storage. In addition, rather than simply combining all

available datasets as independent indicators through a process of weighted summation (as with

equation 1), we used some data sets as modifiers for other data sets before combining individual

coverages to derive a final assessment of MAR suitability (described below). Finally, locations

for which there were direct measurements of recharge rates (set 5) were subsequently assigned

MAR suitability values based entirely on field observations, which are considered to be the most

reliable of available data types.

3.1.1. Data classification

We standardized several of the datasets by classifying values or properties on a scale of 1 to 5,

where 1 represents an unfavorable attribute for MAR suitability, and 5 represents a favorable

attribute. Both numerical and non-numerical datasets (e.g., soil infiltration capacity and surficial

geology, respectively) were used in this study, requiring different methods for classification

before data could be combined. We used three approaches for classifying numerical datasets: (1)

classify values based on knowledge of field properties and past MAR operations, (2) classify

values using the Jenks optimization method based on the distribution of property values (Jenks

1967), and (3) operate on raw data. The first method was applied to soil infiltration capacity and

locations with stream seepage rates measured in the field (Table 2). The second method uses the

ArcMap modification of the Fisher-Jenks algorithm, and was applied to specific yield,

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unsaturated zone thickness, and historical changes in water table height. The third method was

applied to surface slope values. Non-numerical datasets, including surficial geology and land

use, were classified based on interpretation of associated properties that could influence MAR.

For surficial geology, we assigned each lithologic unit a value based on whether the mapped

lithology and texture (Brabb et al. 1997; Clark et al. 1997) corresponded to a known aquifer or

would likely be connected to a known aquifer. This reduced 54 and 85 (for Santa Cruz and

Monterey Counties, respectively) lithologic categories to three possible connection classes

(Table 2). For land use, we classified descriptions based on associated roughness coefficient

values (Chow 1959) (Table 2), where roughness coefficients range from 14 to 100, for

nursery/pavement to forested/native vegetation. Roughness coefficients were assigned to

agricultural areas according to crops grown in rows, fields, or pasture.

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Table 2. Classification of data based on physical properties

Soil infiltration capacity

Stream seepage Aquifer storage Surficial geology Land use

Rate [m/d]

Value Rate [m/d]

Value1 Sy Tu[m]

Value Connection to aquifer

Value Description Roughness coefficient2

>3 5 >1 80 64.01-131 5 Good 5 Forest/Nat. veg.

100

1.2 4 0.2 to 1 60 40.01-64 4 Moderate 3 Pasture 400.6 3 22.01-40 3 Poor 1 Field crop 380.2 2 8.01-22 2 Row crop 350 1 0-8 1 Fallow 30

Turf 27Pavement 14

1Stream seepage rates were determined from direct observations and assigned values that

represent highly suitable locations for MAR. For locations where L is measured, the MAR

suitability index = L (equation 7)2Roughness coefficients modified from Chow [1959] are used in equation 2

3.1.2. Data integration

Earlier studies of recharge potential have treated infiltration capacity, slope, and/or land use as

independent variables (e.g., Jasrotia et al. 2007; Yeh et al. 2009). We reasoned that the primary

influence of slope and land use should be to modify soil infiltration capacity (IC), and developed

an equation to facilitate this approach. The equation incorporates a dependence on land slope (s)

and roughness (n), similar to those used with the Manning equation for calculating mean runoff

velocity in open channels, to generate an effective infiltration capacity (IE):

I E=IC+ ln [ n/√s( n/√s )max ] (2)

where IC is infiltration capacity (based only on soil type), n is a surface roughness coefficient

(with values ranging from 14 to 100, based on land use classificaiton), and s is slope in radians

(extracted from the regional digital elevation model). The second term in Eqn. 2 is intended to

be proportional to the quantity of water that will not infiltrate. Because the product of square-

root-slope and surface roughness is normalized by the maximum (optimal) conditions for the

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region, the second term is ≤ 0. Calculated effective infiltration values are thus dependent on the

soil infiltration capacity, but modified by the relative likeliness of water running off the site

rather than infiltrating, based on surface slope and roughness (Figure 3). For example, if the soil

infiltration capacity is low, the influence of low-slope and native vegetation (high roughness

coefficient), which might help to maintain good infiltration conditions through a permeable soil,

becomes negligible. Conversely, a soil having a high infiltration capacity located in a region with

high slope and a land use of turf grass (low roughness coefficient), will have an IE value that is

lower than the IC value associated with the land use alone. IE will equal IC for optimal surface and

slope conditions (the second term goes to 0 as the term in brackets goes to 1), but otherwise the

IC value will be reduced by the second term, so that IE ≤ IC.

Transmissivity (T) is in important subsurface parameter and can be difficult to estimate

across a large spatial area. For operating a MAR system, high transmissivity is necessary for

avoiding excessive mounding (which could lead to waterlogging of the root zone of crops or

surface flooding), and for allowing infiltrated water to flow to nearby recovery wells. The

primary constraints on transmissivity with respect to MAR are aquifer hydraulic conductivity (K)

and thickness (b) and the presence or absence of confining layers between the ground surface

and the underlying aquifer (three separate subsurface data coverages). To account for spatially

variable K and b and the presence of confining layers in the subsurface, we use the following

equation to calculate an effective transmissivity (TE) as it applies to MAR suitability:

T E=K A1 bA 1+KC 1 bC 1+F1 [ K A2 bA 2+K C 2bC 2+F2 ( K A 3b A 3+K A 4 bA4 ) ] (3)

F1=1−bC 1−1

9 for 1≤bC 1≤ 10 (4)

F2=1−bC 2−1

9 for 1 ≤bC 2≤ 10 (5)

where F1 and F2 are confining unit factors that affect the influence of underlying aquifer units. F1

and F2 scale linearly between 1 and 0 for confining unit thicknesses ranging between 1 and 10 m,

respectively. Thus, the transmissivities of multiple aquifer layers can be combined (at least in

part), using an arithmetic mean rule, if confining layers between separate aquifer layers are <10

m in thickness. The vertical integration accounts for noncontinuity of thin confining layers that

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were readily apparent in hundreds of well logs and drilling records from across the basin.

Calculated TE values were subsequently classified on a scale of 1 to 5 per method 2, described in

§3.1.1.

Available storage space (V) was assessed by multiplying aquifer specific yield (Sy) by the

unsaturated (vadose zone) thickness (Tu) of each cell: V=S y T u. Specific storage is likely to be

negligible in comparison to Sy, especially with respect to MAR surface infiltration, which was

the focus of this study. MAR suitability was additionally enhanced in areas where there has been

a large drop in water levels during the period of 1998 to 2010.

Following calculations and classifications, each dataset was assigned a weight based on

the perceived importance of individual properties and conditions to positioning of potential MAR

projects. The normalized weights used in this study are comparable to those obtained from a

review of similar peer-reviewed studies (Figure 2), although there is considerable variability

between studies depending on the number and type of available datasets and local hydrogeology

for each study. We note that in all of the earlier studies shown for comparison, individual

parameters were added as independent variables on a cell by cell basis. As described earlier, we

used land use and topographic slope data to modify the MAR suitability implied by soil property

data sets, rather than applying land use and slope data independently. Values shown for these

parameters in Figure 2 are the means of weights applied in the present study when calculating

effective infiltration (Equation 2). In general, the relative weight of each data set is lower in the

present study than other studies, but this is mainly because we used more data sets than most

other studies, distributing the assessment of MAR suitability across more coverages.

A final map of MAR suitability was created by summing the weighted, classified values

(all varying from 1 to 5, from least to most suitable for MAR) for every 10-by-10 m grid cell in

the basin for which all data sets existed:

MAR suitability index=5 I E+4G+5V +4 T E+2 D (6)If L exists, MAR suitability index = L (7)

where: G is surficial geology, D is an historic change in water table height, and L is the index for

a losing stream reach within which recharge rates have been measured and indicate high MAR

suitability. The weights applied to the various indices are somewhat arbitrary, but generally align

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with the perceived importance of each data set from other studies (Figure 2). We reasoned that

effective infiltration properties and the volume of storage space should be weighted most

strongly at 5, with formation transmissivity and outcropping of primary aquifers weight at 4. The

historic change in water level was given the least weight (2) because of the uncertainty

associated with interpolating from only a few measurements. The full process was constructed

using ArcGIS ModelBuilder, which can be modified as additional datasets become available,

field data are collected to test GIS predictions, or weighting methods are changed based on

availability of new information.

3.2. Numerical modeling of MAR scenarios

To model the relative hydrologic impact of hypothetical MAR projects, and the importance of

project placement and operational parameters, we modified a hydrogeologic model developed to

assess a range of conditions and management options for the Pajaro Valley. The details of

developing a hydrogeologic framework for the Pajaro Valley, and of creating and applying a

complex model for assessing historical groundwater extraction and conditions, are presented

elsewhere (Hanson, 2003; Hydrometrics, 2012, PVWMA 2013, and Hanson et al., 2013), and

summarized briefly herein. Surface and subsurface hydrologic processes were simulated using

MODFLOW-2005 (Harbaugh 2005). The model domain extends from the back of the basin

(bounded by the San Andreas Fault) to >10 km offshore (Figure 4A), with grid resolution of 250

x 250 m. The six model layers vary in thickness across the basin, corresponding to aquifer and

confining layer thicknesses (Figure 4B). The model includes nearly 1000 active production

(agricultural, municipal, domestic) groundwater wells, and uses the Farm Process (Hanson et al.

2010; Schmid and Hanson 2009) which modifies agricultural groundwater pumping rates during

the simulation based on changes in land-use, climate, and groundwater availability. The

simulations used in the present study represent 34 years (nominally conditions from 1976 to

2009) divided into 408 (monthly) stress periods, each having two time steps.

We worked with a Basecase simulation developed to represent a 34 year time period

beginning nominally in 2009 (Hydrometrics, 2012). Climate conditions for the Basecase

simulation were constructed to be a "mirror image" of climate during the preceding 34 years, and

land use in the simulation was fixed to be that in 2009. This approach allowed us to assess the

influence of MAR operating scenarios in the context of a historically realistic climatological

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scenario. After this simulation was completed, we ran 31 additional simulations, each with a

different combination of hypothetical MAR projects adding water in different locations and at

different rates around the basin (Table 3). Differences in simulated water levels and the extent of

seawater intrusion, as compared to results from the Basecase model, are considered to be

simulated MAR "benefit."

Table 3. Description of MAR scenario model simulations

Run # LocationInf. Rate (m3/d)

Inf. Rate (ac-ft/yr) Quantity

Active(mo/yr)

Total MAR Water (m3/yr)

Total MAR Water

(ac-ft/yr)1 Coastal 505 50 5 4 3.1x105 2502 Coastal 505 50 10 4 6.2x105 5003 Coastal 169 50 10 12 6.2x105 5004 Coastal 8085 800 5 4 4.9x106 40005 Coastal 8085 800 10 4 9.9x106 80006 Coastal 505 150 10 12 1.9x106 15007 Coastal 674 200 10 12 2.5x106 20008 Coastal 1347 400 10 12 4.9x106 40009 Coastal 2695 800 10 12 9.9x106 800010 Coastal 4043 1200 10 12 1.5x107 1200011 Back-basin 505 50 5 4 3.1x105 25012 Back-basin 505 50 10 4 6.2x105 50013 Back-basin 8085 800 5 4 4.9x106 400014 Back-basin 8085 800 10 4 9.9x106 800015 Back-basin 2695 800 10 12 9.9x106 800016 Good 505 50 5 4 3.1x105 25017 Good 505 50 10 4 6.2x105 50018 Good 505 150 10 12 1.8x106 150019 Good 674 200 10 12 2.5x106 200020 Good 8085 800 5 4 4.9x106 400021 Good 2695 800 5 12 4.9x106 400022 Good 1347 400 10 12 4.9x106 400023 Good 8085 800 10 4 9.9x106 800024 Good 2695 800 10 12 9.9x106 800025 Good 4043 1200 10 12 1.5x107 1200026 Poor 505 50 5 4 3.1x105 25027 Poor 505 50 10 4 6.2x105 50028 Poor 8088 800 5 4 4.9x106 400029 Poor 2695 800 5 12 4.9x106 400030 Poor 8085 800 10 4 9.9x106 8000

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31 Poor 2695 800 10 12 9.9x106 8000

MAR projects were simulated by adding water to the surface aquifer layer using a head-

independent boundary condition. It was assumed that each MAR project existed within a single

model cell (6.3 hectares,15.6 acres). Adding water directly to the subsurface did not allow

evaluation of how surface properties (slope, land use, and soil infiltration capacity) influenced

recharge dynamics, but subsurface storativity, transmissivity and the presence of confining units

did govern flow after infiltration.

MAR project scenarios had four variables: (1) project locations, (2) number of projects,

(3) quantity of applied water per project (and in total), and (4) duration of activity during each

year. We evaluated the influence of locating MAR projects in four general regions: coastal area

(“Coastal”), back (eastern side) of the basin (“Back-basin”), areas identified as being particularly

suitable for MAR (“Good”) and areas identified as being considerably less suitable for MAR

(“Poor”). We expected that the MAR project distance from the coast would have a significant

influence on seawater intrusion, so locations for good and poor simulations were selected in pairs

such that the sites in each pair were equidistant from the coast. MAR sites in each location group

recharge to different layers, depending on which aquifer is exposed at the surface in each

location. For example, sites used for MAR in the back of the basin (simulation group Back-

basin) recharge directly to aquifer layer A4 (Purisima Formation), whereas sites used for MAR

projects based on the most suitable conditions (simulation group Good) are located over a mix of

aquifer layers A1, A2 and A4.

Each modeling scenario had either 5 or 10 MAR projects. The rate of MAR-associated

recharge applied at individual project sites ranged from 6.2 x 104 m3/yr (50 ac-ft/yr) to 1.5 x 106

m3/yr (1200 ac-ft/yr), comparable to the amount of water that might be applied based on

stormwater capture of runoff (near the lower end) or based on diversion from major aquatic

systems (near the higher end). Water was applied evenly during periods of either 4 or 12

months/yr. The 4-month MAR operation was intended to represent projects that run only during

the wet season, when runoff or diversion from other surface water supplies is most likely to be

available. MAR projects that use water supplied by a water recycling plant, or water conveyed

from higher in the river basin using the Pajaro River, might theoretically operate throughout the

water year. This set of model scenarios was not intended to be exhaustive or representative of

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actual basin management plan scenarios under consideration by local stakeholders (PVWMA,

2012). Rather, the goal was to assess from a purely hydrologic perspective how MAR project

number, placement, and operations could influence groundwater conditions over several decades.

To analyze MAR scenario results, we compared model output of head levels and flows

from the ocean into the aquifer below the Pajaro Valley. Changes in head levels were quantified

in two ways: 1) for a given time over the entire basin, and 2) at a given location over the duration

of the model simulation. The first method was applied to compare MAR scenario groundwater

head levels from layer A2 (the volumetrically most significant aquifer layer in the region) during

the final time-step to the respective head levels in the Basecase simulation. Using the second

method, we selected a single grid cell and extracted the head values in each aquifer layer during

six stress periods. Flux of water from the offshore zone to coastal zones was classified as

seawater intrusion. With seawater intrusion as an active concern in the study region, it was

natural to use coastal flux as a metric for comparing MAR scenarios to the Basecase model.

Model coastal flux values were calculated for each stress period, and then summed to provide

flux per year over the entire duration of the model run. Flux values are given for the six model

layers combined either as seawater intrusion (flow inland from the ocean) or flow to the offshore

zone (loss of water to the ocean).

4. Results

4.1. Distribution of Classified Properties and MAR Suitability

Results from classification of six of the surface and subsurface properties are shown in Figure 5.

The majority of the surficial geology in the PVGB indicates moderate to good connectivity to

shallow local aquifers, except on the floodplain of the Pajaro River system, where there are

significant shallow silt and clay layers (Figure 5A). Effective infiltration (IE) shows similar

breadth of suitable areas, except in urban areas or nurseries, and on the floodplain of the Pajaro

River (Figure 5B). As discussed earlier, IE was calculated from soil infiltration capacity (IC),

land use (roughness) and elevation (slope) (Figure 3). The roughness coefficient, which is based

on land use, varies throughout the basin, with urban and turf areas concentrated in and around

Watsonville, CA, near the center of the basin. Urban and turf areas account for 21% of the total

area, whereas agricultural fields and pastures account for 41%. The remaining 38% is native

vegetation and unfarmed land, predominately located in the higher sloped northern and north-

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western edges of the basin. Classified values of effective transmissivity (Te) are highly variable

across the basin, illustrating considerable heterogeneity in aquifer properties at a fine scale

(Figure 5C). The lower values generally correspond to locations with thicker alluvial clay

layers. Te values represent three datasets: aquifer layer thickness, aquifer hydraulic conductivity,

and confining layer thickness. Available storage (V) is low for much of the lower valley and

coastal region, with higher values in the northwest and southeast higher elevation areas (Figure

5D). Groundwater levels are generally lower near the coast and along the most northern and

western parts of the basin, relative to water levels in 1998, but there is a band of higher

groundwater levels that runs north-south through the center and to the southwestern side of the

basin (Figure 5E). Classification of measured infiltration rates are shown for two reaches on the

Pajaro River and Corralitos Creek (Figure 5F).

These and other coverages were combined to generate a map of MAR suitability across

the PVGB (Figure 6). This map has a nominal resolution of 10 x 10 m, although resolution of

the individual datasets varies considerably (Figure 5). The full spatial extent of the MAR

suitability map is limited by the intersection of the extents of all data sets used in the analyses

(228 km2). The normalized weights used to integrate the classified datasets are generally low

compared to weights for similar data sets used in other peer-reviewed studies (Figure 2). The

low weights in the present study result mainly from use of more datasets than were used in the

other studies, which reduces the relative influence of any individual dataset.

Calculated MAR suitability index values from across the PVGB range from 6 to 97 (low

to high suitability) and appear to follow a roughly normal distribution, with a mean of 22 and a

standard deviation of 52 (Figure 7). The upper quartile of this range, comprising land areas

being the most suitable for MAR, accounts for 7% of the analyzed land area in the PVGB (15

km2). These areas are located throughout the basin, but are particularly concentrated along the

coast north and south of the Pajaro River, inland south of the Pajaro River, and along the eastern

side (back) of the basin (Figure 6). The site of the Harkins Slough MAR project (Figure 6),

which is permitted to recharge up to 2.5 x 106 m3/yr diverted water to a perched aquifer, has a

MAR suitability index of 78.

4.2. Modeling the Influence of Distributed MAR Projects Options on Resource Conditions

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Thirty one MAR scenarios (Table 3) illustrate how these projects could help to raise aquifer

water levels and reduce (or reverse) seawater intrusion, relative to the 34 year Basecase model.

Unsurprisingly, groundwater levels increased the most in locations closest to simulated MAR

projects (e.g. Figure 9). The Good location scenario (Figure 9A) shows the greatest increase in

the northwest part of the PVGB, and produces >1 m head level increase in over 80% of the

onshore area. The Coastal location scenario (Figure 9B) raises the head levels mostly along the

coast, on the western side of the PVGB, and produces a >1 m head level increase across ~60% of

the onshore area. There are significantly greater head level increases offshore with MAR projects

located in Coastal positions, compared to Good positions.

Simulated benefits to water levels within the aquifer layers vary based on MAR location

because water is applied to the exposed surface layer, which differs in properties and geometry

(thickness, extent of connection to deeper aquifer layers) throughout the basin. In general, and at

the mid-basin location (Figure 8), head levels increase by similar amounts in the shallowest

aquifer layers, A1 and A2 (Figure 10A and 10B), with coastal and MAR-good model scenarios

showing the most long-term improvement. Increases in head within the deepest aquifer layer,

A4, are more similar for the four sets of MAR project locations (Figure 10C), and net benefit is

generally lower than seen in shallower aquifers.

For all tested scenarios, simulated MAR projects reduced seawater intrusion compared to

the Basecase, with the benefit increasing overall with time (Figure 11). There is a period of

abrupt reduction in the extent of seawater intrusion, between simulation years 21 and 27, which

coincides with a period of increased precipitation and reduced groundwater pumping. The

location of simulated MAR projects has a notable effect on the magnitude of reductions in

seawater intrusion. The greatest benefit is achieved by simulating MAR projects in the MAR-

good locations, followed by Back-basin, Poor, and Coastal, in order of decreasing benefit

(Figure 11A). Though the rate of change of benefit varies with time, the benefit in the Good

scenarios increases approximately twice as quickly as that for the Coastal scenario over the 34

year simulation. MAR project location also affects the quantity of flow from the aquifer to the

ocean, and the rate of change over time (Figure 11B). The increase in flow to the ocean is

greatest early in the model runs and subsequently decreases over time, as head levels rise and/or

more water is extracted from the basin by pumping. The Coastal scenario results in the greatest

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increase in flow to the ocean compared to the Basecase, followed in order by Good, Poor and

Back-basin scenarios.

The reduction of seawater intrusion varies with the amount of water applied in simulated

MAR projects, and as a function of project location and the passage of time. As expected,

increasing water applied to the system through simulated MAR projects results in larger

reductions of seawater intrusion along the coast (Figure 12). But increasing the amount of MAR

water applied at Coastal locations (Figure 12A) produces a smaller reduction in seawater

intrusion than does increasing the amount of MAR water applied at Good locations (Figure

12B). The difference in benefit between the two MAR project locations groups is minimal for the

lowest applied water rate (1.5x106 m3/yr), and increases to ~50% greater benefit at the Good

MAR locations for the highest applied water rate (1.5x107 m3/yr).

We divide reduction in seawater intrusion by the total applied water to measure MAR

efficiency (Figures 12C and 12D). For a given MAR location group, the efficiency is

approximately equal for the full range of applied water quantities at time = 1 yr. In other words,

the initial benefit is linearly related to the amount of MAR water applied to the surface. For

example, with MAR projects in Good locations, the seawater intrusion reduction efficiency is

approximately 1.5% of the total water applied for any given amount of water (Figure 12D). Over

time, the efficiency increases at a rate dependent on the amount of applied water, where

scenarios with less applied water show the greatest increase in efficiency.

Changing the number of MAR projects from 5 to 10 appears to have an influence similar

to that of doubling the total applied water, although locations of the additional 5 MAR projects

are likely to influence specific results. If the additional projects have a different average

proximity to the coast and/or MAR suitability index, then their influence on seawater intrusion

will be different than simply doubling the total applied water.

The intra-annual duration of MAR operations has minimal effect on the reduction of

seawater intrusion over the full (34 year) simulations. The scenarios active for 4 months and 12

months per year have nearly identical influence on seawater intrusion for the first 20 years of the

model simulation, then the projects active year-round tend to have an impact ~5 to 8% greater

than do the projects operating only 4 months per year (assuming the same amount of total MAR

water is applied).

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5. Discussion

5.1. Classification and merging of GIS data sets

There are no "standard" practices for classifying indices used to assess suitability for managed

recharge. Most peer-reviewed, GIS-based studies completed to assess recharge properties and

processes have emphasized natural or incidental recharge, rather than MAR (Figure 2). Each of

these studies used a different weighting system for combining disparate datasets, and few earlier

studies attempted to test the results of GIS-based analyses for accuracy or applicability. We

attempted to address this latter issue, in part, by linking the GIS analysis to deterministic

modeling, discussed below, though this approach cannot confirm the "correctness" of regional

interpretations.

One approach for development of a suitable weighting system for applying GIS data is to

generate a suitability map that follows a desired distribution (e.g., normal, log-normal). If the

fundamental goal is to distinguish between the relative suitability of candidate field sites within a

basin, this approach could be useful, allowing clear delineation of land areas having

characteristics of a desired percentile of analyses (top 10%, best 100 hectares, etc). On the other

hand, application of different data sets and methods for combining them could lead to challenges

in comparing results from multiple basins, particularly if there are fundamental differences in

subsurface geology, weather patterns, land use, and other factors. In either case, using the GIS

analyses can guide or inform (rather than dictate) MAR placement as a component of critical

water resource decisions. The wide distribution of areas amenable for MAR projects may

encourage more landowners to participate in distributed recharge enhancement efforts, not just

those who are experiencing the consequences of aquifer overdraft.

Our data integration approach differed from those taken in earlier studies, in that we

combined several data sets to generate interim interpretations of effective properties. Effective

infiltration capacity encompasses the relationship between traditional soil infiltration capacity,

ground slope, and surface roughness. We reasoned that a greater slope and smoother land surface

would serve mainly to reduce the relative rate of infiltration, given intrinsic soil properties. Slope

and roughness should have less influence for soils that have a low infiltration capacity, but these

factors could result in a larger reduction in infiltration through highly permeable soils. Similarly,

we calculated effective transmissivity values for a series of aquifer layers, by summing all (or

part of) the values of individual layers from the surface downwards until a significant confining

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unit was encountered. A more traditional approach for calculating transmissivity could either

under-represent effective values of areas where there are multiple (partly confined) aquifer units,

but only the shallowest is assessed, or over-estimate transmissivity if the presence of shallow

confining layers were ignored. Our calculations suggest the highest transmissivity values, with

respect to potential MAR projects, are located around the margins of the PVGB, but there are

also areas of moderately high transmissivity near the center of the basin, north of the Pajaro

River (Figure 5C). Different aquifer layers are responsible for these high transmissivity values:

the shallow alluvial and Aromas aquifers (A1 to A3) near the western and central parts of the

basin, and deeper Purisima (A4) aquifer to the west and north (Figures 4 and 5).

5.2 Integration of GIS analyses and numerical modeling

This study was designed from the start to link GIS-based assessment of MAR suitability to

results from a regional numerical model. Several of the surface and subsurface datasets used for

the GIS analysis were created originally as part of regional model development. This was

fortunate both in terms of the effort that would have been required to gather and classify each

dataset independently, and to co-register spatial data and modeling domains, but also for making

sure that data and assumptions applied for the GIS analyses were not violated when moving to

the modeling stage of the study. Resource managers and stakeholders in many groundwater

basins have access to similar datasets, although their resolution, accuracy, and completeness is

highly variable. The availability of a detailed and up-to-date regional groundwater model that

can be run on the basis of a GIS-based analysis of MAR is more unusual.

Modification of the PVGB model to include MAR projects facilitated evaluation of the

relative influence of major MAR characteristics, including project location, number of projects,

amount of water applied, and duration of operation through the year. The original model

included the Harkins Slough MAR project, and was calibrated to PVGB conditions under past

and existing climatological and water use conditions. Results presented in the current study,

evaluating hypothetical MAR projects in comparison to a baseline model, are thus most useful in

assessing relative impacts.

5.3. Implications for MAR in the Pajaro Valley

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Our GIS-based analysis of MAR suitability shows considerable variability throughout the

PVGB, on the basis of eleven physical characteristics (Figure 6). The most prominent feature in

the final MAR suitability map is the Pajaro River floodplain, which has relatively low MAR

suitability primarily due to soil infiltration and surficial geology classifications. This is not

surprising given that floodplain lithology tends to comprise silt and clay lithologies which limit

surface infiltration. The GIS analysis would result in assigning similar properties to the bed of

the Pajaro River, but recent field studies documented streambed losses on the order of 1 m/d

along part of the river near the back of the basin (Ruehl et al. 2006; Hatch et al. 2010). This

discrepancy illustrates another limitation of the GIS-based analysis, within which the assessment

of soil properties is based on surveys near, rather than in, the active channel. In this case, there is

heterogeneity in lithology and in hydrologic behavior that is not captured by regional soil

property maps.

We can assess GIS results with respect to an active MAR project in the PVGB operated

by the PVWMA which recharges approximately 106 m3/yr (Racz et al. 2011; Schmidt et al.

2011). We define this as a successful MAR project, and therefore characterize the location as

highly suitable. The projected MAR suitability index based on the GIS analysis for this site is 78

(Figure 6), an index value met or exceeded by 4% of the basin (8.7 km2). These highly suitable

areas are distributed throughout the basin. There would need to be ~15 similarly performing

MAR projects to offset annual overdraft in the PVGB (PVWMA, 2012), but this would equate

to only ~4.8% of the land area that was classified as equal to or more suitable for MAR than the

Harkins Slough recharge project, or 0.19% of the total area analyzed. Of course, this assessment

does not account for surface water availability to supply MAR projects, and that may be the most

important limiting factor in applying this approach. The new Basin Management Plan

emphasizes conservation and water recycling, in additional to MAR, and development of a

portfolio of approaches is most likely to be successful in this setting. Nevertheless, knowing

about the potential for development of MAR projects, based on an assessment of surface and

subsurface conditions, can assist in efforts to bring the basin back into hydrologic balance.

Model results showed that MAR project location, amount of applied water, and years of

operation affect groundwater conditions in different ways. Projects located close to the coast

provide the greatest immediate benefit through reduction of seawater intrusion, but after ~3

years, seawater intrusion reduction is greatest for scenarios that place MAR projects throughout

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the PVGB or in the back (East side) of the basin. Also, as the amount of water recharged

increases over time, the project efficiency (defined by reduction in seawater intrusion per unit of

water recharged) decreases due to flows to the ocean. These offshore flows may help to improve

groundwater quality, a parameter not assessed in this study.

Results show that the benefit from MAR projects varies depending on which evaluation

metric is used (groundwater rise or seawater intrusion reduction), and for the former, where the

metric is applied in the basin. MAR projects located at coastal sites result in the largest

groundwater head increase along the coast (Figure 9), but also the lowest long-term seawater

intrusion reduction (Figure 11A). MAR projects located where there is the highest suitability

based on the GIS analysis are most effective at reducing seawater intrusion, and on average are

farther from the coast than Coastal MAR projects. This illustrates the importance of assessing

both surface and subsurface properties and conditions when comparing locations for MAR

projects.

Comparing results from the Good and Poor scenarios provides confidence in the

applicability of the MAR suitability map. There was negligible difference in aquifer head levels

for the two simulated location groups of MAR projects. However, on average, there was a 25%

greater reduction of seawater intrusion for MAR projects located in areas identified as highly

suitable in the GIS analysis (Figure 11). The inefficiency of Poor MAR projects located in

unsuitable locations might be partially because they tended to recharge mainly layer A1. With

seawater intrusion occurring in all aquifer layers in the PVGB, it is beneficial to distribute MAR

projects among the regions where each aquifer is unconfined, in addition to selection by

suitability index. For example, if all MAR projects recharge to layer A1, where the presence of

underlying confining units restrict downward flow, layers A2 and A4 might only have limited

reduction of seawater intrusion. Note that Poor sites were not intentionally selected in areas

where layer A1 is unconfined; rather, sites with poor suitability often correspond to locations

where the alluvial layer contains low permeability flood plain deposits.

Modeled groundwater levels indicate that confining units do not restrict all flow between

aquifer layers, though flow between geologic layers is limited in some parts of the basin. While

groundwater head levels increase the most in the layer being recharged, modeled head increases

also occur in under- and over-lying aquifers. For example, the Back-basin MAR projects

recharge to A2, A3 and A4, but the head levels also increase by ~0.3 m in A1 near the City of

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Watsonville over the 34 year model simulation (Figure 10A). With the majority of groundwater

extraction occurring in layer A2 (Hanson et al. 2013), having discontinuous confining units to

accommodate fluid flow between aquifers is critical, because it allows MAR projects in all areas

of the basin, taking advantage of available water supplies.

5.4. Study limitations and next steps

Several critical factors are not accounted for in the GIS and modeling analyses, including

water availability, solute sources and transport, unsaturated zone transport, site access, land use

and climate change, sea level rise, and proximity to areas that are already intruded by seawater

intrusion. These factors should be considered as evaluations are done to identify locations for

field and pilot testing. The GIS analyses were not intended to be the primary basis for making

placement and operational decisions for MAR project sites.

This study does not formally evaluate water availability for MAR projects. The analysis

shows many areas in the PVGB that are highly suitable for MAR, especially near the coast,

which coincides with the blending and distribution system developed for use of water from the

recycling plant. Locations in the Back-basin could use runoff from adjacent hills as a recharge

water source.

Numerical model results suggest that placement of MAR projects according to the GIS

suitability index provides the greatest reduction of seawater intrusion along the coast. The

quantity of water applied using MAR is proportional to the long-term benefit. However, in this

water-stressed area, it will be necessary to optimize the quantity of water applied with respect to

desired reduction in seawater intrusion. Larger applied quantities of water will provide a greater

benefit, though at a lower efficiency than smaller applied quantities of water. Water availability

will likely govern the quantity of applied water on an annual basis.

The current model does not include solute sources or advection, and therefore cannot

estimate the influence of recharge on salinity of the seawater intruded areas or overall water

quality elsewhere in the basin. Future studies could add solute transport capabilities to the MAR

suitability assessment. For example, placing MAR projects within the seawater intruded area

might be a feasible option for reducing the rate of future intrusion, but might not have a strong

enough influence on water quality benefit to allow extraction from areas that are already

intruded. Conversely, recharging onto a local perched aquifer above the seawater intruded area

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can provide an alternate source for users, allowing coastal farming to continue and reducing

demand on overdrafted aquifers below (Racz et al. 2011; Schmidt et al. 2011). Recharging to a

perched aquifer was not considered an advantage, as in this particular case, in the GIS analysis.

The model does not include unsaturated flow, which is important for understanding

aquifer recharge. Within the model, water applied to the surface moves immediately down to the

uppermost saturated cell. Results from studies of the Harkins Slough MAR project suggest that

infiltrating water may recharge the shallow aquifer in just a few days (Schmidt et al. 2011). At

least in this location, the assumption of instantaneous transport through the vadoze zone may be

reasonable when using month-long stress periods. However, this approximation could lead to

errors in assessing MAR projects located where infiltration rates are slower.

The groundwater model uses the Farm Process (Schmid and Hanson 2009), which varies

the amount of water pumped based on land use, climate and water availability. As MAR water

recharges the aquifer, groundwater availability increases, and this allows an increase in pumping.

The rate at which pumping increases is modest relative to the rate of recharge. However, if

higher head levels due to MAR cause pumping in the model to increase beyond realistic

projections, then estimations of seawater intrusion reduction would be conservative. One could

disable the Farm Process and fix factors such as natural recharge and pumping, but this could

result in model scenarios that are less realistic; land use and climate are expected to vary year by

year, and these changes influence the locations and rates of groundwater extraction. For example,

there is an eight-year increase in seawater intrusion reduction starting in model year 21 for all

MAR scenarios (Figures 11 and 12), largely in response to a modeled wet climate period and

associated reductions in water use. This model response also shows how climate and changes to

pumping can compound the benefit provided by simulated MAR projects. But for these reasons,

we consider model results mainly in the context of relative benefits from MAR scenarios, rather

than quantitative predictions. Indeed, the scenarios tested in this study deviate considerably from

those explored in the most recent Basin Management Plan (PVWMA, 2013). The latter focused

on realistic project alternatives based on physical, chemical, economic, and social factors, many

of which were not explored in this paper. Narrowly focused scenarios presented in this paper

should not be viewed as alternatives to those proposed in the Basin Management Plan.

The next step in determining where to implement MAR projects is field testing soil

infiltration properties at locations that have been identified as suitable for MAR by the GIS and

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numerical modeling analyses. Assessing sites for MAR is a complicated problem, as evidenced

by the number of studies and variety of methods. We produced a MAR suitability index map,

and show relative impacts on seawater intrusion and groundwater levels, though this must be an

iterative process. Field testing and MAR implementation are required both to reduce

groundwater overdraft and to help calibrate the suitability mapping method. With more field

data, and comparisons to similar results from other study areas, the GIS-based integration

method will become more robust for use in this region and in other groundwater basins.

6. Conclusions

This paper proposed a physically based, GIS integration method for identifying locations that

may be suitable for MAR projects, and quantified the relative benefit of such projects using a

numerical model. We developed a method that allows data to be combined using traditional

approaches (overlying coverages and adding indices) and by allowing some properties to operate

on other properties before coverages are combined. We propose that this method can provide a

more accurate understanding of relationships between geology, hydrology, and managed

groundwater recharge. Results suggest that ~15 km2 of the PVGB may be highly suitable for

MAR projects, as delineated by having a suitability index in the upper quartile of the quantitative

range. Using a numerical model to simulate MAR projects, we show that project sites on high

MAR suitability areas could reduce seawater intrusion to a greater extent than if MAR projects

were located on low suitability areas. Modeling suggests that reducing seawater intrusion is

achieved most efficiently with MAR projects distributed throughout the PVGB in highly suitable

locations, rather than focusing only along the coast.

Groundwater development is expected to continue increasing around the world, providing

significant economic benefits and maintaining food security for the growing population.

Unfortunately, the financial returns from increased agriculture and industrial development are

rarely used towards water management, resulting in declining water tables, reduction in

groundwater storage, and water quality issues. MAR projects may contribute towards sustainable

groundwater use as a low-cost, low-maintenance, and potentially distributed method. This paper

illustrates a nuanced approach for identifying suitable locations for MAR projects, and for

determining the relative impacts of various recharge project scenarios using numerical modeling.

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Careful field studies and assessment of implemented projects will be required to test and refine

these conclusions.

Acknowledgements

We thank Mike Cloud, Michael Cahn, and Marc Los Huertos for their thoughtful advice on land

use and Pajaro Valley geology and hydrogeology. This work was supported by the National

Science Foundation Graduate Research Fellowship Program (ID# 2009083666), the National

Institute for Water Resources (Grant 08HQGR0054), and The Recharge Initiative

(rechargeinitiative.org).

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Figure 1 Location of the Pajaro Valley, CA, with extent of seawater intrusion measured in

2001(Hanson 2003), elevation and major streams. Area shown is the local water management’s

(Pajaro Valley Water Management Agency) boundary of operation. The Harkins Slough MAR

project is indicated with a square, and the mid-basin measurement point used in the modeling

section is indicated with a white circle. Add slough shapefile.

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Figure 2. Comparison of dataset weights used in other studies to map groundwater recharge with

a GIS. The normalized weights used in this study are shown in orange. Values shown for land

use and slope are calculated means of values used, because these data sets were used as

modifiers for other data sets, as discussed in the text. Replace with Andy’s new B&W version.

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Figure 3 Example calculated effective infiltration (IE) values for a given infiltration capacity (IC)

value of 5, roughness coefficients 14 to 100, and three slope values. The IE curve will move

down for larger slopes and smaller IC values.

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Figure 4 Pajaro Valley Hydrologic Model (PVHM), (A) map view of model domain showing

grid cells, (B) cross section showing model layers along transect A-A’. Modified from (Hanson

et al. 2013).

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Figure 5 Classified surface and subsurface properties used to determine relative MAR

suitability. (A) surficial geology, (B) effective infiltration, (C) effective transmissivity, (D)

storage availability, (E) change in groundwater elevation (2010-1998), (F) measured streambed

infiltration.

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Figure 6 Map of relative MAR suitability determined by GIS-based integration. The location of

the existing Harkin Slough MAR project is indicated with a circle.

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Figure 7 Histogram of the MAR suitability index values for the PVGB. The suitability index

value of the Harkin Slough project site is 78, which represents field tested managed recharge of

approximately 106 m3/yr. Four percent (8.7 km2, 2160 ac) of the PVGB has similar or higher

suitability index values.

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Figure 8 MAR scenario location groups shown on the MAR suitability index map. Figure 4

shows the spatial extent of the model domain. Ten site locations are shown for each of the four

groups: Coastal, Back-basin, Good, and Poor. Head levels were compared to the Basecase

simulation at a location in Watsonville (Figure 10), indicated with a filled black circle. Add mid-

basin monitoring location.

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Figure 9 Increase in head levels in Layer A2 at model yr-34 relative to the Basecase due to MAR

projects simulated in Good Run-22 (A) and Coastal Run-8 (B) locations. Both scenarios have 10

MAR projects applying 4.6 x 105 m3/yr each indicated by black open circles.

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Figure 10 Increase in head levels near the center of the PVGB (location shown in Figure 1)

relative to the Basecase due to MAR projects simulated in four regions of the basin, (A) in Layer

A1, (B) in Layer A2, and (C) in the Layer A4.

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Figure 11 Benefits relative to the Basecase due to MAR projects simulated in four regions of the

basin, respectively, for (A) Reduction of seawater intrusion shown versus time, and (B) Increase

in flow to offshore zone shown versus time. Each scenario has 5 MAR projects, applying 9.8x105

m3/yr each, and operating 12-mo/yr.

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Figure 12 seawater intrusion reduction relative to the Basecase due to simulated MAR projects

with varying rates of total applied water. Each scenario uses 10 MAR projects operating

12-mo/yr, located at (A) Coastal sites, and (B) Good sites. The efficiency, with respect to

seawater intrusion, is shown for MAR projects located at (C) Coastal sites, and (D) Good sites.

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