RESULTS
Nitrate concentrations under different land uses. Nitrate data
from 228 sites across the contiguous United States were analyzed (See
Detailed Methods). This includes 147 sites from the National
Water-Quality Assessment (NAWQA) project (Spahr et al., 2010) and 71
sites from the Water Quality Watch (WQW)
(https://waterwatch.usgs.gov/wqwatch/) within the United States
Geological Survey (USGS), and 10 sites from intensively monitored sites
via research networks in the U.S.. Higher stream nitrate concentrations
generally cluster in the Midwestern “Corn Belt” that is dominated by
agricultural and mixed lands, whereas undeveloped and urban sites
consistently have lower nitrate concentrations (Figure 2). The soil
water and groundwater nitrate concentrations (Figure S1, 81 sites)
estimated from stream concentrations generally follow the spatial
pattern of stream nitrate concentration, indicating the penetration of
nitrate into deeper groundwater.
Statistical boxplot (Figure 3) confirmed that stream nitrate
concentrations (median and mean in mg/L) decrease from Agriculture (3.2,
4.0), to Mixed (1.4, 2.5), to Urban (0.58, 0.94), and to Undeveloped
(0.16, 0.26). The deep water concentrations (mg/L) have a sequence
similar to stream water concentrations, decreasing from Agriculture
(2.9, 3.0) to Urban (1.2, 1.7) to Undeveloped land (0.13, 0.25). These
values are close to the national median groundwater concentration (mg/L)
of agricultural land (3.1), urban land (1.4), and major aquifer wells
(0.56) (Burow et al., 2010). Compared to pristine sites, agriculture and
urbanization have elevated nitrate level by 3 to 20 times. Agriculture
and Mixed lands also show much higher shallow water concentrations
(Csw) compared to deep water concentrations
(Cdw), whereas Undeveloped lands exhibit the opposite
trend. The Urban sites are the only land type with Cdw> Csw. Stream and soil water concentrations
correlate strongly with the percentage of agricultural land with the
correlation Cstream = 0.28e0.031 ×
%Ag (R2 = 0.54) and Csw =
0.35e0.034 × %Ag (R2 = 0.54),
respectively. Deep nitrate concentrations do not correlate well with
agricultural area fraction (R2 = 0.25), indicating
that other factors might be important in determining nitrate in deep
water (Figure S3).
Estimated versus measured concentrations in shallow and deep
waters. Co-located measurements
of stream, soil water, and groundwater concentrations are rare, except
in a few intensively measured sites. End-member estimation such as
hydrograph separation and chemical mixing analysis are often used to
quantify baseflow and quick flow contribution (Raffensperger et al.,
2017) and solute concentrations in end-members (Miller et al., 2017). To
validate the estimation of Csw and Cdwusing stream concentration at the 95th and
5th percentiles, we compare estimation from this work
(Figure 3a and Figure S1) against literature data and the National
Ground-Water Monitoring Network (NGWMN) database (see Detailed Methods).
Results show that the linear
relationship between Cstream and estimated
Csw from this work (slope = 0.9, R2 =
0.94) is close to the linear relationship estimated for 94 sites of
diverse land uses covering 4 orders of magnitude (Sudduth et al., 2013)
(Figure 4a), suggesting a strong correlation between Cswand Cstream. The agricultural sites dominate the top
right corner with high Csw and Cstream.
Comparison of estimated and measured groundwater concentrations as a
proxy for Cdw exhibits a consistent spatial pattern from
closely located sites in the NGWMN (Figure S5). Most estimated and
measured Cdw falls on the 1:1 line in Figure 4b,
indicating a close match (Figure 4b). The relatively large error bars of
measured Cdw may be due to varying sampling depths in
different wells.
Nitrate export patterns under different land uses. Figure 5
(and Figure S2) showed that although different export patterns (i.e.,
multiannual scale C-Q relationship) occur in all land use conditions,
more agricultural sites have high b values and more urban sites
have low b values. In other words, flushing patterns dominate in
agriculture and mixed lands. This challenges the existing perception
that agricultural lands typically have chemostatic or biogeochemical
stationary patterns (Basu et al., 2010; Basu et al., 2011; Thompson et
al., 2011). Diamond and Cohen (2018) also found that a greater
agricultural land cover was not associated with chemostasis patterns.
For urban watersheds, chemostasis and dilution patterns are most
commonly observed but flushing also occurs. In pristine sites, both
chemostasis and flushing patterns are common.
Concentration contrasts in shallow and deep waters drive C-Q
patterns. To explain nitrate export patterns under different land use
conditions, we resort to a recently-developed, process-based watershed
reactive transport model BioRT-Flux-PIHM (Zhi et al., 2019) to explore
the drivers of C-Q patterns. The model was set up first to reproduce
hydrological and nitrate data in Conewago Creek, a watershed with
~ 47% agricultural land in the Chesapeake Bay. The
model has three major water components contributing to the stream:
surface runoff, soil water, and deep water. The shallow water combines
surface runoff and shallow soil interflow water, and the deep water is
the groundwater that interacts with the stream. Annually the shallow and
deep water comprise 80.7% and 19.3% of the total discharge,
respectively, although at daily scale the deep water can vary from 1%
under dry conditions to 99% during large storm events. The hydrology
aspect of the model captured the general trend that shallow water
dominates stream discharge at high flow and deep water predominates at
low flow. The model reproduced the daily discharge dynamics that was
highly responsive to precipitation, as well as daily concentration
range, temporal trends, and C-Q patterns (Figure S6). The slope bfrom the C-Q data is 0.13, and the annual concentrations of shallow
water (Csw) and deep water (Cdw) were
3.8 and 2.1 mg/L, respectively, yielding a Cratio of
1.8.
To cast the model to broader conditions, 500 Monte-Carlo simulations
were run using the hydrology and reaction kinetic conditions from the
base case and variable soil N concentrations that yield the
representative range of shallow and deep waters in the four land use
types in Figure 1. The model reproduced the range of soil water and
groundwater concentrations and Cratio in Figure 2 (see
Methods and Figure S7 for C-Q relationships in four cases representing
four different land uses). In each simulation case, the model outputs of
concentrations and discharge were used to calculate b values and
the annual average Csw, Cdw, and
Cratio (Figure S8). Model results from the 500 cases
(overlapping gray circles, Figure 6) collapse to an “S” shaped curve
(\(b=\frac{\delta_{b}\ C_{\text{ratio}}}{C_{\text{ratio},\ 1/2}\ +\text{\ C}_{\text{ratio}}\ }+b_{\min}\))
that illustrates the consistent dependence of b values on
Cratio, where \(\delta_{b}=1.66\) is the difference
between maximum b (\(b_{\max}=0.73\)) and minimum b(\(b_{\min}=-0.93\)) and \(C_{\text{ratio},\ 1/2}=0.80\) is the
concentration ratio when b = ½ (\(b_{\max}+b_{\min}\)). This
modeled “S” curve explained the b values from 81 sites with
available Cratio data. The curve shows that most
agricultural and mixed lands have high b values whereas urban and
undeveloped lands have lower b values.