This study is focused on nitrogen loading from a wide variety of crop and
land-use types in the Central Valley, California, USA, an intensively farmed
region with high agricultural crop diversity. Nitrogen loading rates for
several crop types have been measured based on field-scale experiments, and
recent research has calculated nitrogen loading rates for crops throughout
the Central Valley based on a mass balance approach. However, research is
lacking to infer nitrogen loading rates for the broad diversity of crop and
land-use types directly from groundwater nitrate measurements. Relating
groundwater nitrate measurements to specific crops must account for the
uncertainty about and multiplicity in contributing crops (and other land uses)
to individual well measurements, and for the variability of nitrogen loading
within farms and from farm to farm for the same crop type. In this study, we
developed a Bayesian regression model that allowed us to estimate
land-use-specific groundwater nitrogen loading rate probability distributions
for 15 crop and land-use groups based on a database of recent nitrate
measurements from 2149 private wells in the Central Valley. The
water and natural, rice, and alfalfa and pasture groups had the lowest
median estimated nitrogen loading rates, each with a median estimate below
5 kg N ha-1 yr-1. Confined animal feeding operations (dairies)
and citrus and subtropical crops had the greatest median estimated nitrogen
loading rates at approximately 269 and 65 kg N ha-1 yr-1,
respectively. In general, our probability-based estimates compare favorably
with previous direct measurements and with mass-balance-based estimates of
nitrogen loading. Nitrogen mass-balance-based estimates are larger than our
groundwater nitrate derived estimates for manured and nonmanured forage,
nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to
be due to groundwater age mixing, dilution from infiltrating river water, or
denitrification between the time when nitrogen leaves the root zone (point of
reference for mass-balance-derived loading) and the time and location of
groundwater measurement.
Introduction
Nitrate contamination of groundwater is a common problem in agricultural
regions across the globe that has also gained increased regulatory attention
in recent years. The European Union Nitrate directive, which strives to
protect groundwater quality across Europe, reported that as of 2010 all
27 member states had developed action programs to cut nitrogen pollution. These
action programs include monitoring networks, nitrogen application limits, and
new technologies for nutrient processing . In California, USA, the
Irrigated Lands Regulatory Program (ILRP) was created in 2003 to regulate
agricultural water discharge to surface water. In 2012, the ILRP was updated
to issue permits for discharge to groundwater. All commercial agriculture is
now regulated under the program .
Several studies have documented the presence of nitrate contamination in
shallow groundwater of the California Central Valley (CV) aquifer system
, where this study is
focused. Many people in the CV rely on shallow private wells for domestic
use. Studies estimate the number of private wells in the CV to be on the
order of 100 000 to 150 000 . The federal
drinking water standard to protect against methemoglobinemia (blue-baby
syndrome) is 10 mg L-1 NO3-N. Background nitrate concentrations
are typically less than 2 mg L-1 NO3-N
. Drinking water with nitrate concentrations above
the background level, but below the drinking water standard, has also been
linked to an increased risk of ovarian cancer , thyroid
cancer , bladder cancer , and non-Hodgkin's
lymphoma .
Nitrate contamination of groundwater may originate from several sources
including synthetic fertilizer, manure, septic systems, and leaky sewer lines
. In the CV, the major contributors to nitrate
contamination are fertilizers and manure applied to crops
. Knowledge of the highest-risk crops can aid
future regulatory efforts and help in defining priority areas on a county or
smaller scale. Previous field-based, often plot-scale research, conducted in
California, has measured the amount of nitrogen leached in
kg ha-1 yr-1 from several different crop
types including grains, vegetables and berries, citrus, nuts, and field crops
.
However, these studies were largely conducted in the 1970s and 1980s, with
little research conducted since then, and are subject to high variability due
to various measurement methods . For example, the historical
measurements of nitrogen loading for vegetable and berry crops range from
about 20 kg N ha-1 yr-1 to over 900 kg ha-1 yr-1. Two studies
estimate nitrogen loading rates from several crop or land-use groups in the CV
based on a field-scale nitrogen mass balance approach .
Loading rates for CV dairy corrals, lagoons, and forage
crops receiving manure applications have been estimated based on groundwater
monitoring wells located on dairies . What
is lacking in the literature is a regional-scale assessment of nitrogen
loading to groundwater based on measured groundwater nitrate data.
The main objective of this paper was to investigate what information may be
obtained from existing groundwater quality data that could reveal nitrate
loading rates from the large diversity of crop types within the CV. More than
250 crops are grown within the CV. By developing an innovative use of
existing Bayesian methodology, we estimated the probability distributions of
crop and other land-use-specific nitrogen loading rates (kg N ha-1 yr-1) to groundwater from well
nitrate data and historic crop and land-use information around each well.
Nitrate loading concentrations vary within specific crop or land-use types due
to differences in farming practices and interactions between soil and
hydrogeologic parameters and those practices (farm-to-farm variation).
Loading rates also vary across an individual farm due to small-scale
hydrologic heterogeneity (within-farm variation). We therefore expect a range
of loading rates to apply to any given crop or land-use type. Furthermore,
significant uncertainty exists about the source area and the resulting
land uses that contributed to a well's nitrate concentration.
Bayesian statistical models have specific benefits when it comes to dealing
with uncertainty and complex interactions in groundwater systems: they can
incorporate prior knowledge, take into account uncertainty, and allow for
variability in predictions. Bayesian methods have been used to estimate
nitrate export coefficients from diffuse sources in Swiss watersheds
and for nitrate source apportionment in surface and
groundwater . We
are not aware of any study that has employed Bayesian methods to estimate
nitrate (or other nonpoint source pollutant) loading rates to groundwater. We
therefore develop a generalized linear model using Bayesian methods and
estimate a probability distribution of nitrate loading for crop or land-use
types. In contrast to prior studies, estimates here were not based on
agronomic data, but on a comprehensive groundwater quality dataset and a
geospatial analysis of crops or other land uses surrounding each well. We
compiled two large datasets: a database of nitrate measurements from private
wells distributed throughout the CV for use in the model and a crop and
land-use analysis in the most likely source area to have affected each
individual well's nitrate concentration.
Project area
The CV is a large asymmetric, alluvial basin, with the trough axis trending
northwest to southeast. The CV is 644 km long (extending from Red Bluff to
Bakersfield, CA), an average of 80 km wide, and has an area of approximately
5 million ha. The boundaries of the CV are the Cascade Range to the north,
the Coast Ranges to the west, the Sierra Nevada to the east, and
the Tehachapi Mountains to the south. The CV consists of two separate
valleys, divided at the Sacramento–San Joaquin Delta: the Sacramento Valley
to the north (northern one-third) and the San Joaquin Valley to the south
(southern two-thirds). The CV is filled with 9.5 to 16 km of marine and
continental deposits. Surface geomorphology consists of overflow lands and
lake bottoms, river floodplains and channels, low alluvial plains and fans,
and dissected uplands. Post-Eocene continental deposits consist of fine to
coarse sediments and compose the major aquifer in the CV .
Spring 2011 depth to groundwater ranged from 3 m b.g.s. (below ground surface)
in the northern section of the CV to 204 m b.g.s. in the southern portion of the
CV .
The CV is a highly productive agricultural region with approximately 2.8 million
of California's nearly 3.6 million ha of irrigated farmland
. Major crops grown in the CV are corn, grain and hay,
rice, tomatoes, oranges, almonds, peaches and nectarines, cotton, and wine
and table grapes. A combination of surface water and groundwater is used for
irrigation. Due to the low relief of the CV (slopes typically less than
0.2 %), irrigation runoff to streams is generally considered negligible
compared to recharge to groundwater. Over 80 % of California's 1.8 million
adult cows live on dairies in the CV. Total human population for the
19 counties associated with the CV was approximately 7 million in 2014. Major CV
cities with over 200 000 residences are Sacramento, Fresno, Bakersfield,
Stockton, and Modesto . Residences located in rural
unincorporated areas, many of them clustered in semi-urban belts around
smaller towns and major cities, rely on shallow private wells for drinking
and household purposes. Private wells are not regulated in California and it
is difficult to know how many may be contaminated by nitrate: a significant
portion of the CV has been estimated to contain shallow groundwater with
elevated nitrate concentration .
MethodsConceptual model
To use well nitrate measurements for estimating nitrate (as nitrogen) losses
to groundwater from specific land uses, we employ a stochastic Bayesian
inference model that we derive from physically based concepts about
groundwater dynamics near a pumping well, the location of its source area,
and the overlying crop or land-use type. Each crop and land-use type is
characterized by water mass flux (recharge rate) and an associated nitrate
mass flux to groundwater.
Domestic wells typically supply a single household. The average per household
water consumption in California is 1.2 × 103 m3 yr-1 (1 acft yr-1,
2 L min-1, 0.5 gpm). At a recharge rate of 0.3 m yr-1 (1 ft yr-1), for example, the source
area is approximately 0.4 ha (1 acre). In productive aquifer systems such as
that of the CV, this source area typically has a long but very narrow shape
. At any time, the water produced by the well is a mixture of
water retrieved from a continuous range of depth along the screen of the well
representing contributions from across the source area:
Qi(t)=∫x∫yqx,y,t-τi(x,y,t)dxdy,
where Qi(t) is the pumping rate at well i at time t; q() is the
recharge rate at the water table at location x, y at time (t-τ)
(vertical length per time); and τi(x, y, t) is the age of the water (as
saturated zone travel time) contributing from point x, y at time t. A
well's nitrate concentration is therefore also a mixture of nitrate
contributions across the source area:
Ci(t)=∫x∫ymx,y,t-τi(x,y,t)qx,y,t-τi(x,y,t)dxdy,
where Ci(t) is the measured nitrate concentration in well i at time t,
and m() is the nitrate mass flux associated with the recharge at the
water table q(). Here, we assume linearity in the transport processes
between the points of recharge and pumping as we do not model sorption or
microbial processes such as denitrification or mineralization. The nitrate
mass flux is defined by the following:
mx,y,t-τi(x,y,t)=cx,y,t-τi(x,y,t)3⋅qx,y,t-τi(x,y,t),
where c is the nitrate concentration in recharge. Note that the above
equation implicitly accounts for variability of water and nitrate flux across
the surface of the well screen.
Under ideal conditions, if m(x, y, t-τ) is a constant but unknown quantity
for a crop or other mappable land-use type, if the source area is known, and
if the recharge rate q(x, y, t-τ) is known, then measurements
of Qi(t) and Ci(t) at N wells is sufficient to compute m(x, y, t-τ)
and, hence, c(x, y, t-τ). Writing Eq. () for each of
N wells, where N is the number of crops and land-use types, yields N equations
with N unknowns that could be solved exactly.
In practice, neither of these quantities is well known. Despite the
availability of regional groundwater flow maps and
models , uncertainty about the location of the source area
arises from hydrological heterogeneity and largely unknown spatiotemporal
variability in large-scale groundwater pumping near sampled wells. These
factors can greatly alter groundwater flow and thus the source area of a
well, often seasonally. Previous studies have therefore used a circular
“buffer” zone around each well as an approximation of the well source
area . Circular well
buffers have been shown to be reasonable approximations of the potential well
source area when the actual contributing source area is unknown .
The exact contributions q(x, y, t-τ) and m(x, y, t-τ) are also not
known. While the crop or land-use type at location x, y is here thought to
have a major influence on the magnitude of m(x, y, t-τ), the specific
loading at any location associated with a specific crop or land-use type
exhibits within-crop spatiotemporal variability, which arise from variation
in farming practices (farm-to-farm variation) and from hydrogeologic,
pedologic, and agricultural practice variability (within farm variation).
When linking well nitrate to nitrate (as nitrogen) mass flux from specific
crops and other land-use types, there are therefore four sources of
uncertainty: first, the actual source area is but a small sliver of the
buffer region. Hence, the contributing sources are uncertain, but constrained
by the crop and other land-use type composition within the buffer. Second, the
total contribution to mass loading from each crop within the source area may
vary due to various factors related to the spatiotemporal variability of
agricultural and environmental conditions within the crop area contributing
to Qi(t). Third, the recharge rate q(x, y, t-τ) is unknown, and
fourth, there are uncertainties about nitrogen transformations in the vadose
and saturated zones.
The following sections explain the sources of well nitrate data, the
computation of the buffer radius, and the mapping of crop and other land-use
types that we employed here to demonstrate the usefulness of this approach
(Sect. ). We then explain the Bayesian inference methodology that
we employed to account for the aforementioned uncertainties (Sect. ).
Well and land-use data
A database of nitrate measurements from private wells located in California
was compiled from several data sources. The California Ambient
Spatio-Temporal Information on Nitrate in Groundwater (CASTING) includes
nitrate measurements from private supply, public supply, irrigation, and
monitoring wells . We selected all well measurements from the
CASTING database from supply wells (not monitoring wells) designated as
private. We selected samples from private wells because they are typically
more shallow than public supply or irrigation wells and are not purposefully
located near sources of contamination as monitoring wells are. Private well
samples within the CASTING database originated from several sources, including
the Central Valley Regional Water Quality Control Board (CVRWQCB) Fresno
Office dairy domestic wells monitoring data (sampled for nitrate as a part of
the Dairy General Order regulations for dairy facilities in the CV), the
California Department of Pesticide Regulation (CDPR), Fresno County, the
United States Geological Survey (USGS), Tulare County Environmental Health (TCEH),
and the State Water Resources Control Board (SWRCB) Groundwater
Ambient Monitoring Assessment (GAMA) Domestic Wells Project in Tulare County.
We expanded the original database, which was geographically limited to the
southern CV, to include the data from the same data sources for the entire
CV. Also, additional private well samples from the following data sources
were added to the CASTING database:
a set of private wells previously sampled for nitrate as a part of the
“Proposition 50 Long-Term Risk of Groundwater and Drinking Water
Degradation from Dairies and Other Nonpoint Sources in the San Joaquin
Valley”, funded by the State Water Resources Control Board
(200 wells total, sampled between 2010 and 2011);
additional SWRCB GAMA private wells for Tehama, El Dorado, and Yuba county
project areas (GAMA Domestic Well Project, http://www.waterboards.ca.gov/gama/domestic_well.shtml, March 2016) downloaded from the
GeoTracker GAMA online database (http://www.waterboards.ca.gov/gama/geotracker_gama.shtml, March 2016); and
CVRWQCB Rancho Cordova Office dairy domestic wells monitoring data provided
by the CVRWQCB office.
In this study, we focused on long-term average nitrogen loading from each
crop and land-use group. Records in the database collected between the years 2000
and 2015 were selected. Locations with data collected in multiple years
were assigned the median nitrate value of all the recorded measurements in
order to prevent multiple samples of the same well and associated land use.
Prior to median aggregation, nondetect nitrate values were replaced with the
detection limit and zero values were replaced with the most common detection
limit of 2.21 mg L-1 NO3-NO3. All nitrate
measurements were then converted to NO3-N. Measurement
uncertainty was not explicitly considered as a source of uncertainty in the
model. Measurement uncertainty will vary among laboratories, analysis, and
field methods. However, these uncertainties are typically very small (less
than 0.50 % difference between duplicates), especially compared to the
concentrations at which nitrate becomes a concern (greater than
5–10 mg L-1 NO3-N).
Well geolocation methods varied depending on the source of the data. When
geographic coordinates (latitude and longitude) of the private wells in the
dairy monitoring program were not available, the wells were located using the
dairy's street address and placed at the centroid of a dairy's land parcels.
The methods for locating the wells varied for each of the other data sources
including geographic coordinates, geocoded addresses, offsets by a random
small distance, United States Public Land Survey System (PLSS) section, and
Assessor's Parcel Number (APN) (Table ). Due to the well location
methods, many wells had overlapping locations. Where multiple wells were
geolocated to a single location, a single well was chosen at random to
represent that location. Wells outside of the alluvial aquifer system
boundary were excluded from the analysis. The final nitrate database had a
total of 2149 wells.
Original data source, number of wells, and well location method for
private wells included in final database (2149 wells total).
Dataset groupDataset subgroupNumber ofLocation method/accuracywellsCASTINGS,Private wells on dairies361Located at reported coordinates ofCVRWQCBthe dairy, the reported streetFresnoaddress of the dairy, or the centroidOfficeof dairy parcel(s) (single, multipleadjacent parcels, or centroid ofmultiple nonadjacent parcels).CASTINGSGAMA Domestic Tulare County134Well locations randomly offset by0.8 km from true location.CASTINGSDepartment of Pesticide Regulations (DPR)62Located at the centroid of theUnited States Public Land SurveySystem (PLSS) section (approximately2.6 km2) in which the well resides.CASTINGSFresno County295Located at street address reportedon well logs or centroid of thereported Assessor's Parcel Number(APN) .CASTINGSThe US Geological Survey's (USGS) National17Unknown .Water Information System (NWIS)CASTINGSTulare County Environmental Health437Located at centroid of the reported APN.None200Geographic coordinates digitizedwith imagery from Google Earth.GAMA DomesticTehama, El Dorado, and Yuba Counties253Well locations randomly offset by0.8 km from true location.CVRWQCB RanchoPrivate wells on dairies390Geocoded using street address.Cordova Office
Intrinsic aquifer properties were evaluated as an indicator for additional
risk for or protection from nitrogen contamination. Here we choose a simple
binary indicator: CDPR
Groundwater Protection Areas (GWPAs) are 1.6 km by 1.6 km square sections
that are vulnerable to the leaching of pesticides and are defined by the
following criteria: has previously had pesticides detected in that section,
contains coarse soils and a depth to groundwater less than 21 m, or contains
runoff-prone soils and depth to groundwater less than 21 m
. These zones are either vulnerable to contamination due
to nonpoint source leaching of irrigation water (“leaching GWPAs”) or
direct flow paths through hardpan soils (ditches, dry wells, poorly sealed
wells, “runoff GWPAs”) . The properties that lead
to vulnerabilities from pesticide contamination – shallow depth to
groundwater, short residence time in the vadose zone, low reactivity of
aquifer sediments – also increase the possibility of nitrate contamination.
Wells located within a GWPA zone were hypothesized to be subject to an
increased risk of nitrate contamination. The nonparametric Kruskal–Wallis
statistical test was performed on the nitrate values for wells in each of the
two groups (GWPA versus non-GWPA wells). The Kruskal–Wallis test is a ranked
one-way analysis of variance which tests whether two groups of values should
be considered independent or from the same distribution.
The groundwater data used in this study represent the average most recent (2000–2015)
impact in domestic wells from the average nitrogen loading in
each crop or land-use group at some time prior to sampling. Each domestic well
water sample represents a mix of water age . showed that land-use and nitrogen leaching
patterns from the 1970s are most closely associated with recent groundwater
nitrate measurements. Land use surrounding wells was analyzed using the
California Augmented Multisource Landcover (CAML) 50 m resolution raster image
file . CAML was developed from various data sources
delineating various natural vegetation types, farmland, and urban areas for
5 periods of 5 years, each centered on 1945, 1960, 1975, 1990, and 2005.
The data sources included the California Department of Conservation Farmland
Mapping and Monitoring Program (FMMP), the United States Geological Survey (USGS)
National Land Cover Dataset (NLCD) (1992), the California Department
of Forestry and Fire Protection Fire and Resource Assessment Program
Multisource Landcover Layers (MSLC), and the California Department of Water
Resources (DWR) Land Use Survey. Importantly, CAML identifies 58 different
crop types mapped by the California Department of Water Resources once or
twice per decade in each county. Digital maps of these crop types are not
available for historic conditions prior to 1990 except through back
simulation . Therefore, we used the 1990 land-use map for
the Bayesian analysis. The nearly 60 agricultural and many nonagricultural
land-use categories were aggregated into the following land type groups:
Water & Natural, Citrus & Subtropical, Tree Fruit, Nuts, Cotton, Field Crops,
Forage Crops, Rice, Alfalfa & Pasture, Confined Animal Feeding Operation (CAFO),
Vegetables & Berries, Peri-Urban, Grapes (including wine and table),
and Urban. The Forage Crop group was further separated into fields likely
receiving liquid manure irrigation and fields not likely to receive liquid
manure based on proximity to CAFO land use (within 1.6 km of dairy corrals,
lagoons, or facility barns). This analysis does not take into consideration
dry manure that may be exported off dairies and applied to crops. Our final
study design had a total of 15 land-use and crop groups (hereby referred to as
scenario 1). Alternatively, we also analyzed a scenario where CAFO land use is
grouped with (not distinguished from) Manured Forage Crops (14 land-use and
crop groups, hereby referred to as scenario 2). The approach presented here
can easily be modified to other land-use or management practice categorizations.
Assuming that a private well has very low flow, is screened along its entire
depth below the water table, and only intercepts passing water, source area
length can be calculated by multiplying well depth by the ratio of specific
discharge to groundwater recharge. Land-use amounts (in m2)
were quantified within a circular well “buffer” of radius 2.4 km
surrounding each well and then converted to a percent of buffer area. The
2.4 km buffer radius was determined by assuming a fixed vertical groundwater
recharge rate of 0.30 m yr-1, effective horizontal
hydraulic conductivity of 30.5 m day-1, and hydraulic gradient of 0.001 and
then by the use of Darcy's law to find specific discharge (calculation
details available in ). The assumed conditions for
groundwater recharge, hydraulic conductivity, and gradient are considered
representative of the CV .
Groundwater recharge rates can also be variable and were not assumed to be
fixed for the purposes of estimating the nitrogen loading rates. Probable
vertical groundwater recharge rates (m yr-1) were estimated
based on results of the Central Valley Hydrologic Model (CVHM)
. The CVHM is a numerical model that simulates monthly
surface and groundwater flow components throughout the CV for a period of
over 40 years. For the purposes of the CVHM, the CV was spatially divided
into 20 000 model grid cells (2.6 km2 each) and 10 depth
layers (to a depth below ground surface of approximately 550 m). The CVHM
further divides the CV into nine textural regions based on estimated aquifer
texture. Temporal discretization of the CVHM consisted of monthly stress
periods beginning in October 1961 and ending October 2003. For the purposes
of our study, flow (m3 day-1) below the
bottom of the upper model layer (15 m deep across the majority of the CV), for
each monthly stress period occurring in the 1990 decade, was averaged by
textural region: we calculated total yearly average flow below the upper CVHM
model layer for each CVHM texture region and each year in the 1990 decade as
an estimate of vertical groundwater recharge per year. We used CVHM 1990s
model outputs in order to remain consistent with the selected land-use time
period. This gave 90 estimates of probable groundwater recharge rates (for
each of 9 textural zones and each of 10 years).
Statistical methods
Bayesian analysis was chosen here as it allows for the estimation of the
entire probability distribution of land-use-specific nitrate leaching
concentrations rather than a deterministic value only. Probability
distributions for loading rates for the land-use or crop groups described in
Sect. were estimated with an exponential distribution generalized
linear model using Bayesian methods. Following the structure of the
deterministic model (2) and lumping the uncertainties about source area,
loading rates, and recharge rates into two stochastic variables representing
loading rates and recharge rates only, the basic model equation, modified
from is given by the following:
Ci=1λi=∑j=1nβjAij⋅(0.1/r)⋅Ii+(1-Ii)⋅k
where Ci is the expected nitrate value for well i (mg L-1 NO3-N), λi is the parameter of the exponential
distribution, n is the number of land-use categories considered, βj is
the unknown nitrogen loading rate from land use j (in kg N ha-1 yr-1), Aij is the percent of
well buffer i that is land use j, 0.1 is a conversion factor to convert
units of mass to units of concentration based on vertical groundwater
recharge rate (with units m mg L-1 (kg N)-1 ha)
, r is the vertical groundwater recharge rate (in
m yr-1), Ii is an indicator variable representing
whether or not well i is located within a GWPA (0 for outside and 1 for
within), and k is a groundwater protection parameter representing a mean
decrease in nitrate values applied only to wells outside GWPA zones. We
assume nitrate is the dominant form of nitrogen within and persisting in the
saturated zone . The percent of each land use surrounding each
well was calculated as described above. Note that Eq. ()
represents a general model that can be applied to an arbitrary number of crop
and other land-use type classes, source area configuration, recharge rate
distribution, and indicator variables, depending on the application.
Bayesian analysis requires the assumption of an initial probability
distribution for each parameter to be estimated and this represents the
current estimate or knowledge of the parameter. The initial probability
distribution assigned to each parameter to be estimated is known as a prior
probability distribution or “prior”. Priors are updated in the modeling
process and the final estimate is known as a “posterior” probability distribution.
Student t-distribution priors, representing an initial approximation of the
potential nitrogen loading rate, were assumed for βj. For an
unbiased assessment, each of the land-use or crop groups were given the same
t distribution prior which represents the potential and unknown loading rate.
We used the form of the t distribution parameterized by the location, scale,
and degrees of freedom . The location parameter of the
common t-distribution prior was set to equal the median measured nitrate
value of approximately 5 mg L-1 (see Sect. ). Reasonable, noninformative
degrees of freedom and scale are 1 and 25, respectively. The choice of
t-distribution parameters reflects a heavy-tailed and high-variance distribution
which gives the model flexibility to move the final predicted loading rates
(posterior distributions) away from the prior distribution based on evidence
observed in the measured nitrate values and surrounding land use. The
t-distribution priors were truncated at zero in order to ensure that the
estimated concentrations cannot be negative.
A noninformative Student-t prior probability distribution was used as an
initial approximation of the GWPA factor k. Appropriate location, scale,
and degrees of freedom were 0.5, 1, and 1, respectively (see Sect. ).
A log-normal distribution was used for the prior probability
distribution of potential recharge rates with location and scale of -2 and 0.6,
respectively (see Sect. ). The prior probability distribution
for the GWPA factor was truncated at zero to eliminate any potential for
negative values. The recharge rate, r was assumed to be positive.
Markov Chain Monte Carlo (MCMC) methods were used to infer the marginal
posterior distributions of nitrogen loading rates for each land use. MCMC was
performed using the Gibbs sampler JAGS . JAGS was run from
within the statistical computing program, R , using the package
rjags . Two chains were run and the first 100 000 realizations
of each chain were discarded as the burn-in period. After
burn-in, each chain was sampled 200 000 times with a thinning interval of 400
to reduce autocorrelation. Trace, running mean, and autocorrelation plots
were visually inspected to confirm convergence, proper mixing of chains, and
adequate burn-in period and thinning interval. Such plots were made with the
R package mcmcplots . The realizations from each chain were
then combined for a total of 1000 realizations per parameter. Credibility intervals (CIs) of 95 and 68 %
were then calculated for each parameter. Final model
run time was approximately 12 h on an Intel Core I-5 4670 processor with
16 GB of 1600 MHz DDR3 RAM.
We calculated a standardized Pearson goodness-of-fit statistic
for each scenario. The goodness-of-fit statistic used
the average of the raw residuals between the measured nitrate value for each
well and the estimated value (realization) divided by the standard deviation
of all of the realizations for each well (1000). The sum of the squared
average raw standardized residuals was then divided by the total number of
wells minus the number of parameters in the model (17 for scenario 1 and
16 for scenario 2) to give the goodness of fit.
Results
The wells in the database compiled for this study had a minimum
NO3-N concentration of nondetect, median of 4.95, and
maximum of 131 mg L-1 (Fig. ). Median nitrate value for wells
located within GWPAs was more than twice the median nitrate value for wells
located outside GWPAs (8.22 versus 3.92 mg L NO3-N). The
nonparametric Kruskal–Wallis test for independence between the two groups of
well's nitrate values was significant at the 95 % confidence level. The fact
that GWPA zone wells had higher nitrate than non-GWPA zone wells justifies
the use of a GWPA-related groundwater protection term in the model
(Eq. ). Median nitrate value was calculated for runoff versus
leaching GWPAs and the values were within 1 mg L-1, and therefore runoff and
leaching GWPA zones were grouped for the context of this study (Sect. ).
The model-estimated posterior distribution for the non-GWPA
protection factor, k, was fairly narrow, with a 95 % credibility
interval of 0.699 to 0.850 and a median of 0.773 (Fig. ).
Median depth to top and bottom of well screen for all wells with depth
information available (915 wells) was 40.5 m (133 ft) and 64 m (210 ft),
respectively. Minimum and maximum depth to top of well screen was 2 m (6 ft)
and 184.5 m (602 ft), respectively, and minimum and maximum depth to bottom of
well screen was 6 m (20 ft) and 274.5 m (900 ft).
Study well locations color coded by nitrate
(NO3-N mg L-1) value overlain with CDPR GWPA zones (runoff and
leaching undifferentiated).
Estimated groundwater recharge from the CVHM model used as the basis for the
recharge rate (r, Eq. ) parameter was between 0.016
and 0.530, with a median of 0.146 (m yr-1) (Fig. ).
Our Bayesian model-estimated (updated, posterior) recharge rate was
slightly greater (posterior 95 % credibility interval of 0.159 to 0.481 and
median of 0.281 m yr-1 for scenario 1), but still within
the range of the CVHM model estimates (Fig. ).
Nitrate concentrations for each well were compared to the percent of each
land-use or crop group within well buffers for scenario 1 with a locally
weighted scatterplot smoothing (lowess) line for each plot (Fig. ).
A lowess line is a smoothed regression line that represents
many locally weighted polynomial fits to the data by weighted least squares
. As Citrus & Subtropical crops, Manured Forage crops,
and CAFO land-use proportions increased, nitrate in well samples appeared to
increase (these groups were expected to have greater predicted nitrogen loading rates).
The standardized Pearson goodness-of-fit statistic, a summary measure of
squared deviations between observations and their estimated values, has a
value of near 1 for good-fitting models . The
standardized Pearson goodness-of-fit statistics were 1.16 and 1.17 for scenario 1 and 2,
respectively, and therefore the model was understood to
fit the data fairly well.
Posterior probability density for the non-GWPA attenuation factor,
k, for scenario 1 (tan) and 2 (teal).
CVHM-estimated annual vertical groundwater recharge (grey bars),
log-normal prior probability density for recharge input to model (grey line)
and model-estimated posterior probability density for the annual recharge
rate for scenario 1 (tan) and 2 (teal).
Scatterplot of proportion of land use within each well buffer versus
well nitrate concentration for each of the 15 land use or crop groups in
scenario 1. The red line is a locally weighted scatterplot smoothing line
. Note that each plot shows nitrate concentrations
between 0 and 25 mg L-1 NO3-N (approximately 5 times the median
value); however, all data were used to calculate the plotted smoothing lines.
The x axis is scaled differently among subplots for better
resolution.
Posterior probability densities of estimated nitrogen loading for
scenario 1 (tan) and 2 (teal). Credibility intervals of 95 % are represented
by the light grey shading (dark grey shading occurs where scenario 1 and 2
estimates overlap).
Estimated nitrogen loading rates across all crop and land-use groups for both
scenarios ranged from between negligibly small and nearly 600 kg N ha-1 yr-1 (Fig.
and Table ). The scenario 1 CAFO group and the scenario 2 Manured
Forage & CAFO group had the greatest estimated nitrogen loading rates, while
Alfalfa & Pasture, Rice, and Water & Natural for both scenario 1 and 2 were
the lowest (Fig. and Table ). The
scenario 1 CAFO group also had the greatest range of estimates, reflecting
the highest degree of uncertainty or spatial variability. Results for both
scenario 1 and 2 were fairly consistent, with the exception of the scenario 1
CAFO and scenario 2 Manured Forage & CAFO groups. When Manured Forage, which
occupies relatively large areas, was grouped with nitrogen-intensive but
small-area CAFO land use in scenario 2, the estimate for Manured Forage & CAFO
was over 3 times lower than the estimated nitrogen loading for CAFO
alone. Similarly, the estimate for Manured Forage & CAFO in scenario 2 was
about 2 times higher than for Manured Forage alone in scenario 1. The
effect of merging the two groups in scenario 2 yielded only small changes in
the estimates for the other crops and land uses; several estimated loading
rates increased slightly, such as for Vegetable & Berry crops, while others
decreased slightly (Field Crops), or remained approximately the same.
CAML land-use groups for scenario 1 are plotted side by side with
corresponding Bayesian model estimates of median nitrogen loading rate in
order to spatially represent relative risk to groundwater from nitrate
contamination (Fig. ). Low estimated nitrogen loading is
concentrated in the northern CV, where Water & Natural land use and Rice
crops are dominant, along the middle eastern and southern eastern and western
edge where Water & Natural land use is dominant, or scattered along the
central axis of the CV following the pattern of Alfalfa & Pasture crops. The
greatest nitrogen loading is scattered randomly throughout the central CV
from north to south (a direct representation of CAFO locations), or echoes
the pattern of Citrus & Subtropical crops along the eastern edge of the
southern CV in Tulare and Kern counties.
Median and 95 % credibility interval bounds for estimated nitrogen
loading rates by group for scenario 1 and 2.
CAML land use for the 15 land-use groups in scenario 1 (a)
and the same land-use groups keyed to the median estimated nitrogen loading
rate in kg N ha-1 yr-1 for the corresponding
group (b).
Discussion
The Bayesian estimation model provides a probabilistic data-driven evaluation
of nitrogen loading rates to groundwater from various land uses. Unlike
previous estimates, the model here represents an “inverse” model
estimate of nitrogen loading to groundwater using nitrate concentrations from
a large number of groundwater production wells. Given the relative proportion
of land-use groups within the source area and an estimate of recharge rates,
groundwater concentrations are transformed to effective nitrogen loading rate
distributions for each land-use group.
To determine whether the Bayesian model yields realistic estimates, we
compare model results to two alternative, mutually independent datasets of
nitrogen loading to groundwater: field measurements of nitrogen loading,
obtained using a variety of field-based measurement techniques, and potential
groundwater nitrogen loading obtained by closure to nitrogen mass-balance-based estimates of historic nitrogen fluxes in the CV. For further evaluation
of the Bayesian loading estimates, we also consider hydrologic conditions
other than mass loading that may affect nitrate concentrations measured in
wells and used for the Bayesian loading estimation.
Credibility intervals of posterior probability densities of
estimated nitrogen loading rates for selected crop groups plotted with
historical field measurements of nitrogen loading from California
.
Thinner lines are 95 % credibility intervals, thicker lines are 68 %
credibility intervals, the solid dot is the median estimated value, and the
open black circles are historical field
measurements.
Studies determining nitrogen loading to groundwater from agricultural crops
have historically used soil samples, anion exchange resin bags, suction
lysimeters, or tile drain samples .
These field measurements are limited to a few crops and are not available for
all crop groups analyzed in this study. Measurements were available for
five crop groups: Citrus & Subtropical, Vegetables & Berries, Cotton,
Alfalfa & Pasture, and Rice. Our Bayesian model results for these crop groups were
generally consistent with the field measurements of nitrogen loading
(Fig. ). For each crop group, multiple field measurements
overlap with the 95 % credibility interval (CI) of the Bayesian nitrogen
loading estimates. For Rice, all three available field measurements overlap
with the Bayesian loading model estimates. Overall, the field measurements
encompass a wider range of values and extend to larger values than the 95 % CI
of our model estimates, especially for Vegetables & Berries.
The high variability of nitrogen loading rates measured (field measurements)
within crop groups, especially for Citrus & Subtropical, Vegetables & Berries,
and Cotton (Fig. ) is the result of several
factors including within-field crop rotation, variable irrigation and farming
nutrient management practices within fields and among farms, and variable
measurement methods. The field nitrogen loading measurements therefore need
to be interpreted with some caution . The Bayesian loading
estimates appear to confirm many of the field measurements, given the overlap
of measured with estimated distribution of nitrogen loading. The range of
loading rates predicted by the Bayesian model may therefore be interpreted as
representing both the potential variability of loading rates within a
land-use group and uncertainty about the loading rate.
Credibility intervals of posterior probability densities of
estimated nitrogen loading rates for selected crop groups plotted with the
1990 loading rates (black) estimated using a mass balance approach with the
Groundwater Nitrogen Loading Model (GNLM)
(black). Thinner lines are 95 % credibility intervals, thicker lines are
68 % credibility intervals, and the dot is the median estimated
value.
Detailed spatially and temporally distributed nitrogen flux analysis for the
Central Valley has been performed and documented for the Central Valley and
Salinas Valley Groundwater Nitrogen Loading Model (GNLM)
. Briefly, the conceptual basis for the
GNLM is a mass balance analysis of nitrogen fluxes into and out of
agricultural crops, at the field scale, including nitrogen in atmospheric
deposition, irrigation water, synthetic fertilizer, manure, wastewater
effluent, harvest, runoff, and atmospheric emissions from soils. Potential
groundwater nitrogen loading from agricultural cropland was computed as
closure to the mass balance. GNLM accounts for typical nitrogen fertilizer
and harvest rates of 58 individual crops, spatially distributed across the
CV. It also considers locally and regionally varying nitrogen deposition,
irrigation water nitrate, and facility-specific manure and wastewater
effluent applications to agricultural crops from CAFOs, wastewater treatment
plants, and food processors. As a result, GNLM estimates of groundwater
nitrogen loading within a crop group can be highly variable due to
variability between crops within a group and due to local variability in
nonfertilizer nitrogen fluxes. For consistency with the approach used here,
we compare 1990 GNLM results to the Bayesian model results.
The Bayesian model results generally agree with GNLM results for Citrus & Subtropical,
Vegetables & Berries, Field Crops, Grapes, and the Water & Natural
group with significant overlap in both methods' CIs for each group
(Fig. ). However, median values for these crop groups
obtained from the mass-balance-based estimates (GNLM) were somewhat higher
than groundwater data-based estimates (with the exception of Water & Natural
which was slightly lower). In the Bayesian analysis, Citrus & Subtropical
and Vegetable & Berries yielded the second and third largest crop group
median rates (scenario 1). The lower end of the range of GNLM estimates for
these two crop groups is lower than predicted by the Bayesian model. The
Bayesian model distribution extends to similar high concentrations as GNLM at
the upper end of the predicted range for Citrus & Subtropical, but is about
50 % lower than the upper end of the GNLM prediction for Vegetables & Berries (Fig. ).
The high loading rates estimated by our model for Citrus & Subtropical and
for Vegetables & Berries appear to confirm the large difference between
fertilizer and harvest rates for crops in this crop group. In contrast,
the similarity between our results and GNLM results for groundwater nitrogen
loading from Field Crops and Grapes confirms the lower fertilization rates
and resulting lower nitrogen surplus typically occurring in these latter
crops (when not manured). For Citrus & Subtropical, the estimated high rates
may also be a result of the significant potential for direct contamination
pathways induced by farming practices thought to be common in the region
where these crops are grown, along the eastern edge of the valley floor in
Tulare and Fresno counties (Fig. ). This region includes a
significant portion of “runoff” designated GWPA zones on soils that
contain a shallow hardpan layer and where dry wells used
for surface drainage are common . In addition, the
water table in these same regions is relatively shallow (7–10 m b.g.s.)
. Infiltration of agricultural surface runoff through
dry wells and/or a shallow depth to water may lead to more rapid nitrogen
loading at the high rates predicted by our model for this crop group than for
other crops or land uses.
Bayesian model loading rates within the 95 % CI of the Alfalfa & Pasture and
Water & Natural groups had the lowest overall values (Fig.
and Table ). The Bayesian results for
these crop groups are driven by the lack of apparent correlation between an
increase in nitrate concentration in wells and increasing proportions of
their respective area within well buffers (Fig. ). From a
nitrogen mass balance perspective, low estimated nitrogen loading rates are
expected for both land-use categories because fertilizers and manure are not
typically applied to these areas: alfalfa is a legume, which has the ability
to fix atmospheric nitrogen rather than relying on synthetic fertilizer;
Water & Natural land uses are only subject to atmospheric nitrogen deposition
and some symbiotic nitrogen fixation). In comparison, GNLM assigned a single
value for groundwater nitrogen loading from alfalfa (30 kg N ha-1 yr-1), based on reported field
measurements . The assigned value in GNLM is much higher and
outside the 95 % CI estimated with the Bayesian model.
For urban land use, GNLM assigned 20 kg N ha-1 yr-1 based on a review of urban nitrogen leaching
, which is within our model-estimated 95 % CI for Urban nitrogen
loading. In the Bayesian model results, Peri-Urban areas have a greater
predicted 95 % CI when compared to Urban (Fig. ).
Peri-Urban areas are defined as rural homesteads. Each well buffer contained
Peri-Urban areas. Peri-Urban areas were expected to have a greater nitrogen
loading rate than Urban due to the use of septic systems. Septic systems are
common outside urban areas not reached by centralized sewer services. Loading
from these areas can be highly variable depending on septic system density.
Our model-estimated 95 % CIs for Peri-Urban areas overlaps with the range for
nitrogen loading from septic systems obtained by considering the density of
households using septic, 10 to over 50 kg N ha-1 yr-1. The Bayesian results for loading rates
from Peri-Urban areas are consistent with research indicating that domestic
wells in areas with higher septic system density are at significant risk to
intercept septic system leachate .
The CAFO land-use group, like the Citrus & Subtropical crop group, exhibited
positive apparent correlation between nitrate concentration in wells and its
area fraction in well buffers (Fig. ). In the Bayesian analysis
(scenario 1), CAFO had the greatest estimated median loading rate among all
crop and land-use groups (269 kg N ha-1 yr-1, Table ). The value is about 50 % higher
than the value used for dairy corrals in the GNLM study (183 kg N ha-1 yr-1), and about one-quarter of the
GNLM value for dairy lagoon loading to groundwater (1171 kg N ha-1 yr-1), which represents the average
loading rate obtained from extensive field monitoring .
However, both dairy corrals and lagoons are included in the CAFO category
for the Bayesian analysis. For the CV, estimated the
total dairy corral area to be 12 200 ha and the total dairy lagoon area to be
nearly 2400 ha. Hence, the area-weighted average loading rate from both
areas is about 340 kg N ha-1 yr-1, well
within the 68 % CI of our Bayesian estimate. At 565 kg N ha-1 yr-1, the upper bound of the
Bayesian 95 % CI estimated for CAFO is much higher than average area-weighted
corral and lagoon loading reflecting the large variability in groundwater
nitrogen loading from this land use apparent in groundwater nitrate values.
Similarly, large variability has been observed in other research,
particularly from dairy lagoons . Estimates specific to two dairies located on well-drained
soil with shallow groundwater were provided by : 872 and 807 kg N ha-1 yr-1 for the dairy corral and dairy lagoons at the site,
respectively. These estimates are greater than the 95 % CI of the Bayesian
model estimate, but confirm that the upper end of our estimated CI is not unreasonable.
Crop groups for which the groundwater-nitrate-based Bayesian model estimates
are much lower than mass-balance-based GNLM estimates include Manured and
Nonmanured Forage, Nuts, Cotton, Tree Fruit, and Rice. GNLM results for
these crops are driven mostly by the difference between applied synthetic
fertilizer or manure and harvested nitrogen (Fig. ):
for Rice, the Bayesian estimate is less than 5 kg N ha-1 yr-1,
while GNLM predicts the median residual root zone
losses to be 21 kg N ha-1 yr-1. Bayesian
estimates of median loading rates for Cotton, Nuts, and Tree-Fruit range from
12 to 27 kg N ha-1 yr-1, while the GNLM
median estimates for these crop groups range between 90 and 118 kg N ha-1 yr-1. There is no overlap in CIs,
between the two method estimates for Cotton, Rice, Tree Fruit, or Nuts.
However, measured field data for Cotton overlap with both methods' (GNLM and Bayesian) CIs.
The discrepancy between the Bayesian analysis and other data for Rice, Tree
Fruit, Nuts, Cotton, and possibly Manured and Nonmanured Forage indicate
that other processes, not explicitly accounted for in the Bayesian analysis,
potentially attenuate the impacts of nitrogen mass loading, relative to field
mass-balance-based estimates. In the Bayesian method, these processes,
discussed below, lead to lower effective loading rate estimates when
considering current groundwater quality data.
The distinct distributions obtained with scenario 1 simulations for CAFO and
Manured Forage indicates a statistically strong signal differentiating
loading from these two land-use groups, despite the fact that the two are
typically located immediately adjacent to one another. CIs for CAFO and
Manured Forage did not contain mutually overlapping values. The results
correspond to differences found in previous research that estimated loading
from manured forage crops to be nearly half of that from lagoons or corrals.
VanderSchans et al. (2009, for the same location as the corrals and lagoons
above) estimated loading rates of 486 kg N ha-1 yr-1 for
Manured Forage crops. In a separate study, shallow
groundwater monitoring well nitrate indicated leaching from manured forage
fields of 280 kg N ha-1 yr-1. While lower than CAFO estimates in these studies, both
estimates are much larger than our Bayesian model estimates for Manured
Forage crops in scenario 1, which estimates the upper end of the 95 % CI to
be less than 100 kg N ha-1 yr-1. The GNLM
median estimate for manured crops was 385 kg N ha-1 yr-1.
Our Manured Forage estimates may partly be lower due
to an overestimation of land area assumed here to be used for manure
application (any forage field within 1.6 km of a dairy). Actual manure
distribution in 1990 varied and may have taken up much less forage crop area.
Nonmanured forage may therefore be partially mixed into the category Manured Forage.
Due to Manured Forage crops and CAFO typically being located adjacently, we
also considered a scenario 2, where CAFO was lumped with Manured Forage into
a single land-use category. The proportion of scenario 1 CAFO land use within
well buffers was small (almost all wells had less than 10 % CAFO land use
within the buffer, Fig. ), while Manured Forage crops occupied
a larger proportion of the area (up to 50 % of well buffers were Manured
Forage crops, Fig. ). For CAFO land use above about 0.05, higher
nitrate concentrations were indicated, while higher concentrations were most
dominant for Manured Forage land use above 0.35 (Fig. ).
Scenario 2 results represent an effective, area-weighted nitrogen loading
across all dairy-related land uses: corrals, lagoons, and manured crop areas.
In the CV, Manured Forage areas are estimated to take up 174 000 ha, more
than a magnitude larger than the CAFO area (corrals and lagoons: 12 200 ha)
. The much larger area of Manured Forage when compared to
CAFO explains why the range of the estimated lumped nitrogen loading rate for
Manured Forage and CAFO in scenario 2 is much closer to the range of scenario 1
Manured Forage crops than to scenario 1 CAFO results. The process of
merging CAFO land use with the surrounding Manured Forage land use reduces the
estimated loading rates that would otherwise be specific to CAFO land use
(mostly lagoons and corrals), but may provide a more representative estimate,
given the uncertainty about past manure application areas, for CAFO and
associated (partially) manured areas as a whole. We note that the similarity
of results between scenario 1 and scenario 2 obtained for other crop and
land-use groups indicates that the Bayesian method is robust to the particular
choice of crop and land-use groupings.
The discrepancy between some mass balance estimates and the Bayesian model
estimates may be due to several hydrologic processes that affect well nitrate
concentrations independent of nitrogen loading rates in the recharge area of
a well. These include dilution with older groundwater, mixing with recharge
water from streams, and denitrification or ammonium volatilization in the
vadose zone or in groundwater . Dilution of nitrogen in
recharge water is most likely to occur through mixing along the well screen
with older, low-nitrogen-containing, water. Mixing with old water (that
recharged prior to the advent of nitrogen fertilizers in the 1930s and 1940s)
within well screens could potentially have affected the model-estimated
loading rates for all crop and land-use groups. Due to the length of well
screens, all domestic well samples contain water of mixed age. A study
located near Fresno, CA (within our study area), found that groundwater
samples from individual wells contained groundwater with an age range
typically greater than 50 years .
attributed the high variance in groundwater residence time within a
single well to the heterogeneity within the alluvial aquifer system which
produced spatially varying flow velocities. also
reported significant positive skewness (tailing) in the distribution of
groundwater ages within individual wells, meaning wells contained some
groundwater which was much older than the median age. The authors reported
the tailing behavior was due to low hydraulic conductivity units within the
aquifer in which slow advection and diffusion dominate the transport process.
These results are similar to an earlier study in the Salinas Valley,
California (an alluvial aquifer system that, at comparable depth, is similar
to the CV and dominated by agriculture) where the authors found significant
dispersion of groundwater ages within simulated groundwater samples
. Simulated water samples from had
groundwater ages ranging from 10 years to greater than 500 years. The authors
pointed out that the water pumped from wells in the Salinas Valley was only
partially from water that was young enough to be contaminated by nitrate and
that this proportion would only increase in the future.
Geostatistical analysis of groundwater age tracers from wells sampled in the
CV has estimated the depth to the top of well screens pumping premodern (age
of 60 years of more) groundwater to be between 30 and 120 m .
According to those results and considering the median depth to bottom of well
screen for wells in our study (64 m), a portion of our study wells' screened
intervals likely penetrate the interface between young and old water.
Therefore, mixing within wells with water recharged prior to the intensive
use of fertilizers, with water with long residence times (tailing effect), or
with water recharged between the 1940s and 1970s when nitrogen losses for
many crops were smaller than in 1990 or later , could have
led to the lower estimates of nitrogen loading for some crops in this study.
Mixing with groundwater recharged prior to 1990 may play a significant role
in the Bayesian estimates obtained for Manure Forage, Cotton, and Nuts:
significant increases in manure application to forage crops occurred only
after the 1960s, with large increases in the 1980s and 1990s
. These changes may not yet have affected much of the water
drawn from the measured wells. For Cotton and Nuts, field mass-balance-based
estimates for nitrogen loading indicate much lower median rates in 1975
and 1960 (about 40 kg N ha-1 yr-1). Also, the
harvested area for nut crops increased sharply between 1960 (64 000 ha)
and 1990 (250 000 ha) . At individual wells, or even across our
set of wells, it is difficult to further assess the dilution effect with
older water without more detailed analysis of groundwater age throughout the
CV and more information on study well screened intervals.
Infiltrating river water with very low nitrate concentration is a significant
source of recharge in some areas. This may also dilute otherwise high
nitrogen concentrations in land surface recharge. Wells near rivers may
receive a significant fraction of river recharge. A study focused on the TLB
(within the CV) geospatially related areas near rivers with lower nitrate
concentration in wells. The study highlighted areas where major rivers flow
into the TLB from the Sierra Nevada Mountains and found that these areas were
also characterized by wells with lower relative nitrate concentrations
. A statistical analysis of CV groundwater nitrate recently
confirmed that proximity to major streams is a significant controlling factor
for a well's nitrate concentration .
point out that agricultural areas near rivers are also
more likely to receive surface water irrigations. Nitrogen loading from
fields receiving low nitrate surface water irrigations is likely to be lower
than from fields irrigated with nitrate-contaminated groundwater .
Irrigation water source may have impacted our model estimates and resulted in
the lower loading rates compared to mass balance estimates for some crops.
Denitrification and ammonium volatilization could also play an important role
in some differences between our model estimates and mass balance estimates,
though we do not suspect widespread regional denitrification. A study focused
in the San Joaquin Valley correlated anoxic groundwater conditions to lower
nitrate concentrations, but the authors did not attribute this to
denitrification . Instead, attributed
the lower nitrate concentrations in wells with water classified as anoxic to
older groundwater with longer residence times (recharged prior to the
intensive use of fertilizers). did not find significant
decreases in nitrate concentration in wells due to denitrification. Results
of a multimodel averaging approach to estimate oxygen and nitrogen reduction
rates in the San Joaquin Valley did estimate denitrification rates to be
significant . However, also estimated
oxygen reduction rates to be low with a median of 0.12 mg L-1 yr-1. Much of the shallow groundwater
in the CV is well oxygenated: the dissolved oxygen content of
study wells with a measurement (Table ) was above
5 mg L-1 on average . In addition, found that
the estimated rates of oxygen and nitrogen reduction would not protect wells
from nitrate contamination, given current nitrogen application rates. We
therefore do not expect that denitrification or ammonium volatilization had a
significant, overall effect on our model results, but rather may have had an
isolated effect in areas with Rice and possibly with Manured Forage. For
example, a study on four rice fields in the Sacramento Valley (northern CV)
found little to no nitrate leaching below the rice root zone (pore water
nitrate levels were typically below approximately 2.5 mg L-1 NO3-N).
This was attributed to denitrification during the
rice growing season when fields are flooded, ammonia volatilization, plant
uptake, and crop management practices that contribute to the development of a
hardpan layer directly below the rice root zone. The study also found very
low nitrate concentrations in groundwater wells near rice fields (median
value less than 1 mg L-1 NO3-N) consistent
with our estimates of Rice nitrogen loading. The GNLM mass balance estimates
are outside the range of our model-predicted CIs for Rice as they reflect
nitrogen losses prior to denitrification or ammonium volatilization
potentially taking place in saturated clay soils of rice fields.
Denitrification may also explain why the attenuation factor for areas outside
GWPA protection areas is significantly lower than 1 (Fig. ). Regions
outside GWPAs are characterized by larger depth to groundwater (greater than
21 m). The 15 to 30 % lower apparent nitrogen concentrations in the less
vulnerable regions may be due to longer travel times in the deep vadose zone
or some additional denitrification and ammonium volatilization in the heavier
soils or the underlying deep vadose zone, or in groundwater.
Our model estimates a greater median groundwater recharge rate (r) compared
to the prior information from the CVHM model. This is likely because the
study wells are concentrated in agricultural areas with greater recharge
rates due to irrigation. The CVHM estimated recharge rates
are calculated for the entire Central Valley, including natural areas with
few wells and little agriculture. Many of our study wells were spatially
clustered in the Tulare–Kern and Kings subbasins, which had median CVHM
estimated recharge rates of 0.21 and 0.35 (m yr-1),
respectively, for the 1990 decade. These median rates are near our model-estimated median recharge rate of 0.281 (m yr-1) (Fig. ).
Conclusions
The novel approach developed here provides a robust statistical methodology
to relate nitrate measurements in wells to the various types of surrounding
land uses as a means to obtain a statistical distribution of nitrate loading
rates. After accounting for some hydrologic processes not explicitly
represented in the approach (denitrification, ammonium volatilization, mixing
with older water or water recharge from streams) the Bayesian model estimates
are consistent with previous independent estimates and measurements of
potential groundwater nitrogen loading. The validation against independently
obtained data demonstrate the general usefulness and accuracy of the Bayesian
nonpoint source pollutant loading model introduced here. The information can
provide a better assessment of land-use impacts to water quality based on
extensive nitrate and other nonpoint source groundwater contaminant data
measured in private wells. The tool can be used to define high nitrogen
loading (high risk) zones (Fig. ). As is apparent from Figs.
and , much of the CV already suffers from or is at
risk for serious groundwater contamination by nitrate. Our results indicate
that the highest nitrogen loading rates are associated with Confined Animal
Feeding Operations (dairies) and their associated feed crops (with the
exception of alfalfa), as well as from Citrus & Subtropical crops and
Vegetable & Berry crops. Yet interactions among the depth to older water,
well construction, direct contamination pathways, groundwater depth, presence
of river water recharge, and land use have likely affected the amount of
nitrate pumped by wells. Estimates of nitrate loading generally correspond to
previous field measurements or mass balance estimates. For Nuts, Cotton, Tree
Fruit, Rice, and Manured and Nonmanured Forage, estimated nitrogen loading
rates were lower than mass balance estimates. Nuts, Cotton, Tree Fruit, and
Manured and Nonmanured Forage crop estimates may have been affected by
dilution of crop leachate water past the root zone by infiltrating
low-nitrate river water or by mixing with older low-nitrate water within the
well screen. Land managers may default to the mass balance estimates for
those crops. Rice estimates were likely lower than mass balance estimates due
to denitrification and ammonium volatilization directly below saturated rice
fields, which mass balance estimates did not consider. Estimates of nitrate
leaching concentration for particular crop and land-use types, obtained with
this tool, may not be generalized and transferred to regions with
substantially different climate, agronomic, geologic, geomorphic, or soils
conditions. However, the statistical modeling approach provided here is
broadly applicable to other semi-arid, irrigated regions underlain by
alluvial aquifers and to nonpoint source pollutants other than nitrate, e.g.,
salinity and pesticides. Our results could potentially be improved with more
information on groundwater age and portion of older water pumped by the wells
in our study. Also, a potential limitation of the method is the limited
availability of historic crop and land-use maps of sufficient resolution,
corresponding to typical groundwater age found in wells. In a similar vein,
regional-scale models capable of simulating physical groundwater flow
processes described by and are
needed to more accurately access the transport of nitrogen in the subsurface.
Code availability
Model code may be available upon request.
Data availability
This dataset is available upon request. Please note well
location information, well data group, and well names are not publicly
available due to confidentiality agreements with well owners.
Author contributions
TH designed the research question and experimental, conceptual, and overall
statistical structure. KMR, with assistance from QB, prepared the initial
well data. AMB processed land-use layers for the dataset. KMR with assistance
from Mark N. Grote and Arash Massoudieh wrote and ran model code and assessed
model results. GK and TH prepared and processed Groundwater Nitrogen Loading
Model (GNLM) results for use in this study. KMR prepared the paper text
and figures. Maps were based on a template from Jo Ann M. Gronberg.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We are extremely grateful to Mark N. Grote and Arash Massoudieh for their
help with model development and assessment, our work would not have been
possible without you. Thank you to Jo Ann M. Gronberg for preparing figure
map templates of the Central Valley, California, and to Claudia C. Faunt and
Jon Traum for providing and processing the CVHM data for our initial
estimates of groundwater recharge rates. We would also like to thank the
anonymous reviewers for their suggestions and comments. Partial funding for
this work was provided through the State Water Resources Control Board (SWRCB)
agreement no. 09-122-250, and through a grant from the California
Department of Agriculture Fertilizer Research and Education Program, project
numbers 11-0301 and 15-0454. The authors gratefully acknowledge the financial
support of the Center for Watershed Sciences made possible by a gift from the
S. D. Bechtel, Jr. Foundation.
Edited by: Philippe Ackerer
Reviewed by: two anonymous referees
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