Soils are important sources of emissions of nitrogen-containing (N-containing) gases
such as nitric oxide (NO), nitrous acid (HONO), nitrous oxide (N2O),
and ammonia (NH3). However, most contemporary air quality models lack a
mechanistic representation of the biogeochemical processes that form these
gases. They typically use heavily parameterized equations to simulate
emissions of NO independently from NH3 and do not quantify emissions
of HONO or N2O. This study introduces a mechanistic, process-oriented
representation of soil emissions of N species (NO, HONO, N2O, and
NH3) that we have recently implemented in the Community Multiscale Air
Quality (CMAQ) model. The mechanistic scheme accounts for biogeochemical
processes for soil N transformations such as mineralization, volatilization,
nitrification, and denitrification. The rates of these processes are
influenced by soil parameters, meteorology, land use, and mineral N
availability. We account for spatial heterogeneity in soil conditions and
biome types by using a global dataset for soil carbon (C) and N across
terrestrial ecosystems to estimate daily mineral N availability in
nonagricultural soils, which was not accounted for in earlier parameterizations
for soil NO. Our mechanistic scheme also uses daily year-specific fertilizer
use estimates from the Environmental Policy Integrated Climate (EPIC v0509)
agricultural model. A soil map with sub-grid biome definitions was used to
represent conditions over the continental United States. CMAQ modeling for
May and July 2011 shows improvement in model performance in simulated
NO2 columns compared to Ozone Monitoring Instrument (OMI) satellite
retrievals for regions where soils are the dominant source of NO emissions.
We also assess how the new scheme affects model performance for NOx
(NO+NO2), fine nitrate (NO3) particulate matter, and ozone
observed by various ground-based monitoring networks. Soil NO emissions in
the new mechanistic scheme tend to fall between the magnitudes of the
previous parametric schemes and display much more spatial heterogeneity. The
new mechanistic scheme also accounts for soil HONO, which had been ignored
by parametric schemes.
Introduction
Global food production and fertilizer use are projected to double in this
half-century in order to meet the demand from growing populations (Frink et
al., 1999; Tilman et al., 2001). Increasing nitrogen (N) fertilization to
meet food demand has been accompanied by increasing soil N emissions across
the globe, including in the United States (Davidson et al., 2012). N
fertilizer consumption globally increased from 0.9 to 7.4 g N per
m-2 cropland yr-1 between 1961 and 2013, with the US still among
the top five N fertilizer users in the world (Lu and Tian, 2017). US N
fertilizer use increased from 0.28 to 9.54 g N m-2 yr-1 during
1940 to 2015. In the past century, hotspots of N fertilizer use have shifted
from the southeastern and eastern US to the Midwest and the Great Plains
comprising the Corn Belt region (Cao et al., 2017). Recent studies have
pointed to soils as a significant source of NOx emissions, contributing
∼20 % to the total budget globally and larger fractions
over heavily fertilized agricultural regions (Jaeglé et al., 2005;
Vinken et al., 2014; Wang et al., 2017).
Despite the significance of NOx emissions generated by soil microbes,
policies both globally and for the continental US (CONUS) have focused largely on limiting mobile
and point fossil fuel sources of NOx (Li et al., 2016). Hence, it is
incumbent to strategize for the reduction of non-point soil sources of NOx
emissions, especially in agricultural areas. Recent studies have shown
higher soil NOx, even in nonagricultural areas like forests, to
significantly impact summertime ozone in CONUS (Hickman et al., 2010; Travis
et al., 2016). Consequently, it is increasingly important to estimate both
N-fertilizer-induced and nonagricultural NH3 and NOx emissions in
air quality models.
Soil NO emissions tend to peak in the summertime, when they can contribute
15 %–40 % of the total tropospheric NO2 column in the continental
CONUS (Williams et al., 1992; Hudman et al., 2012; Rasool et al., 2016).
Summer is also the peak season for ozone concentrations (Cooper et al.,
2014; Strode et al., 2015) and the time when photochemistry is most
sensitive to NOx (Simon et al., 2014). N oxides (NOx=NO+NO2) worsen air quality and threaten human health directly and by
contributing to the formation of other pollutants. NOx drives the
formation of tropospheric ozone and contributes to a significant fraction of
both inorganic and organic particulate matter (PM) (Seinfeld and Pandis,
2012; Wang et al., 2013). Global emissions of NOx are responsible for
one in eight premature deaths worldwide as reported by the World Health
Organization (Neira, 2014). The premature deaths are a result of the
link of these pollutants to cardiovascular and chronically obstructive
pulmonary (COPD) diseases, asthma, cancer, birth defects, and sudden infant
death syndrome. These adverse health impacts have been shown to worsen with
the rising rate of reactive N emissions from soil N cycling (Kampa and
Castanas, 2008; Townsend et al., 2003). NOx indirectly impacts Earth's
radiative balance by modulating concentrations of OH radicals, the dominant
oxidant of certain greenhouse gases such as methane (IPCC, 2013; Steinkamp
and Lawrence, 2011). Nitrous acid (HONO) upon photolysis releases OH
radicals along with NO, driving tropospheric ozone and secondary aerosol
formation (Pusede et al., 2012). Soils and agriculture are the leading
emitters of N2O, a potent greenhouse gas (IPCC, 2013).
Ammonia (NH3) also contributes to a large fraction of airborne fine
particulate matter (PM2.5) (Kwok et al., 2013). Elevated levels of
PM2.5 are linked to various adverse cardiovascular ailments, such as
irregular heartbeat and aggravated asthma, that cause premature death (Pope
et al., 2009) and contribute to visibility impairment through haze (Wang et
al., 2012). NH3 gaseous emissions also influence the nucleation of new
particles (Holmes, 2007). Air quality models such as the Community Multiscale
Air Quality (CMAQ) model and GEOS-Chem represent bidirectional NH3
exchange between the atmosphere and soil–vegetation, analyzed under varied
soil, vegetative, and environmental conditions (Cooter et al., 2012; Bash et
al., 2013; Zhu et al., 2015).
NOx, NH3, HONO, and N2O are produced from both microbial and
physicochemical processes in soil N cycling, predominantly nitrification and
denitrification (Medinets et al., 2015; Parton et al., 2001; Pilegaard,
2013; Su et al., 2011). Nitrification is the oxidation of NH4+ to
NO3- whereby intermediate species such as NO and HONO are emitted
along with relatively small amounts of N2O as byproducts.
Denitrification is the reduction of soil NO3-; it produces some
NO, but predominantly produces N2O and N2 (Firestone and Davidson,
1989; Gödde and Conrad, 2000; Laville et al., 2011; Medinets et al.,
2015). The fraction of N emitted as NO and HONO relative to N2O
throughout nitrification and denitrification depends on several factors:
soil temperature; water filled pore space (WFPS), which in turn depends on
soil texture and soil water content; gas diffusivity; and soil pH. HONO is
produced during nitrification only and is a source of NO and OH after
undergoing photolysis (Butterbach-Bahl et al., 2013; Conrad, 2002; Ludwig et
al., 2001; Oswald et al., 2013; Parton et al., 2001;
Venterea and Rolston,
2000).
Whether N2O or N2 becomes dominant during denitrification depends
on the availability of soil NO3- relative to available carbon (C),
WFPS, soil gas diffusivity, and bulk density (i.e., dry weight
of soil divided by its volume, indicating soil compaction and/or aeration by
O2). Denitrification rates are quite low even at high soil N
concentrations if available soil C is absent. However, the presence of high
NO3 concentrations with sufficient available C is the inhibiting factor
for the conversion of N2O to N2, keeping N2O emissions dominant
during denitrification (Weier et al., 1993; Del Grosso et al., 2000).
Denitrification N2O emissions are also found to increase with a
decrease in soil pH in the range of 4.0 to 8.0 generally (Liu et al., 2010).
Fertilizer application and wet and dry deposition add to the soil NH4 and NO3 pools, which undergo transformation to emit soil N as
intermediates of nitrification and denitrification (Kesik et al., 2006; Liu
et al., 2006; Redding et al., 2016; Schindlbacher et al., 2004).
Soil moisture content is the strongest determinant of nitrification and
denitrification rates and the relative proportions of various N gases
emitted by each. Increasing soil water content due to wetting events such as
irrigation and rainfall can stimulate nitrification and denitrification.
Nitrification rates peak 2–3 days after wetting, when excess water has
drained away and the rate of downward water movement has decreased.
Denitrification rates substantially increase and nitrification rates become
much slower in wetter soils. This is also influenced by soil texture; for
instance, denitrification is favored in poorly drained clay soils and
nitrification is favored in freely draining sandy soils (Barton et al.,
1999; Parton et al., 2001).
WFPS is a metric that incorporates the above factors. The relative proportions
of NO, HONO, and N2O emitted vary with WFPS. Dry aerobic conditions
(WFPS ∼0 %–55 %) are optimal for nitrification, with soil NO
dominating soil N gas emissions at WFPS ∼30 %–55 % (Davidson
and Verchot, 2000; Parton et al., 2001). HONO emissions have been observed
up to WFPS of 40 % and dominate N gas emissions under very dry and acidic
soil conditions (Maljanen et al., 2013; Mamtimin et al., 2016; Oswald et
al., 2013; Su et al., 2011). Nitrification influences N2O production
within the range of 30 %–70 % WFPS, whereas denitrification dominates
N2O production in wetter soils. Denitrification N2O is limited by
lower WFPS in spite of sufficient available NO3- and C
(Butterbach-Bahl et al., 2013; Del Grosso et al., 2000; Hu et al., 2015;
Medinets et al., 2015; Weier et al., 1993). As a result, NO and HONO
emissions tend to decrease with increasing water content, whereas N2O
emissions increase subject to available NO3- and C (Parton et al.,
2001; Oswald et al., 2013).
Extended dry periods also suppress soil NO emissions by limiting substrate
diffusion while water-stressed nitrifying bacteria remain dormant, allowing
N substrate (NH4+ or organic N) to accumulate (Davidson, 1992;
Jaeglé et al., 2004; Hudman et al., 2010; Scholes et al., 1997).
Rewetting of soil by rain reactivates these microbes, enabling them to
metabolize accumulated N substrate (Homyak et al., 2016). The resulting NO
pulses can be 10–100 times background emission rates and typically last for
1–2 days (Yienger and Levy, 1995; Hudman et al., 2012; Leitner et al.,
2017).
Higher soil temperature is critical in increasing NO emission during
nitrification under dry conditions. However, N2O generated in
denitrification positively correlates with soil temperature only when WFPS
and N substrate availability in soil are not the limiting factors (Machefert
et al., 2002; Robertson and Groffman, 2007). Recently, a nearly 38 %
increase in NO emitted was observed under dry conditions (∼25 %–35 % WFPS) in California agricultural soils when soil temperatures
rose from 30–35 to 35–40 ∘C (Oikawa et al., 2015).
Temperature-dependent soil NOx emissions may strongly contribute to the
sensitivity of ozone to rising temperatures
(Romer et al., 2018). Also, some
soil NO is converted to NO2 and deposited to the plant canopy, reducing
the amount of NOx entering the atmosphere (Ludwig et al., 2001).
Mechanistic models of soil N emissions already exist and are used in the
Earth science and soil biogeochemical modeling community (Del Grosso et al.,
2000; Manzoni and Porporato, 2009; Parton et al., 2001). However,
photochemical models like CMAQ have been using a mechanistic approach only
for NH3, while using simpler parametric approaches for NO (Bash et
al., 2013; Rasool et al., 2016). Other N oxide emissions like HONO and
N2O are absent from the parametric schemes used in CMAQ
(Butterbach-Bahl et al., 2013; Heil et al., 2016; Su et al., 2011).
Variability in soil physicochemical properties like pH, temperature, and
moisture, along with nutrient availability, strongly control the spatial and
temporal trends of soil N compounds (Medinets et al., 2015; Pilegaard, 2013).
The U.S. Environmental Protection Agency (EPA) Air Pollutant Emissions Trends Data show that anthropogenic sources of
NOx (excluding fertilizers) fell by 60 % in the US since
1980, heightening the relative importance of soils. Area sources of
NOx like soils, along with less than expected reduction in
off-road anthropogenic sources, are believed to have contributed to a slowdown
in US NOx reductions from 2011–2016 (Jiang et al., 2018).
Hence, accurate and consistent representation of soil N is needed to address
uncertainties in their estimates.
The parameterized schemes currently implemented in CMAQ for CONUS, like
Yienger–Levy (YL) and the Berkeley–Dalhousie Soil NOx Parameterization
(BDSNP),
consider only NO expressed as a fraction of total soil N available, without
differentiating the fraction of soil N that occurs as organic N,
NH4, or NO3 (Hudman et al., 2012; Rasool et al., 2016;
Yienger and Levy, 1995). Moreover, these parametric schemes classify soil NO
emissions as constant factors for different nonagricultural
biomes or ecosystems compiled from reported literature and field estimates
worldwide (Davidson and Kingerlee, 1997; Steinkamp and Lawrence, 2011;
Yienger and Levy, 1995). These emission factors account for the baseline
biogenic NOx emissions in addition to sources from
deposition (all biomes) and fertilizer (agricultural land cover only) in the
latest BDSNP parameterization (Hudman et al., 2012; Rasool et al., 2016).
Despite their limitations, parameterized schemes do distinguish which biomes
exhibit low NO emissions (wetlands, tundra, and temperate or boreal forests)
from those producing high soil NO (grasslands, tropical savanna or woodland,
and agricultural fields) (Kottek et al., 2006; Rasool et al., 2016; Steinkamp
and Lawrence, 2011).
The EPA recently coupled CMAQ with
the U.S. Department of Agriculture (USDA) Environmental Policy Integrated
Climate (EPIC) agroecosystem model. This integrated EPIC–CMAQ framework
accounts for a process-based approach for NH3 by modeling its
bidirectional exchange (Nemitz et al., 2001;
Cooter et al., 2010; Pleim et
al., 2013). The coupled model uses EPIC to simulate fertilizer application
rate, timing, and composition. Then, CMAQ estimates the spatial and temporal
trends of the soil ammonium (NH4+) pool by tracking the ammonium
mass balance throughout processes like fertilization, volatilization,
deposition, and nitrification (Bash et al., 2013). Using the EPIC-derived
soil N pool better represents the seasonal dynamics of fertilizer-induced N
emissions across CONUS (Cooter et al., 2012). The coupling with EPIC reduces
CMAQ's error and bias in simulating total NH3+NH4+
wet deposition flux and ammonium-related aerosol concentrations (Bash et al.,
2013). The BDSNP parametric scheme implemented in CMAQ also uses the daily soil N
pool from EPIC (Rasool et al., 2016).
Our work builds a new mechanistic approach for modeling soil N emissions in
CMAQ based on the DayCENT (Daily version of CENTURY model) biogeochemical scheme
(Del Grosso et al., 2000; Parton et al., 2001), integrating nitrification and
denitrification mechanistic processes that generate NO, HONO, N2O,
and N2 under different soil conditions and meteorology. We compare
the NO and HONO emissions estimates and associated estimates of tropospheric
NO2 column, ozone, and PM2.5 with those obtained from CMAQ
using the YL and BDSNP parametric schemes. For agricultural biomes, our
mechanistic scheme uses daily soil N pools from the same EPIC simulations as
in Rasool et al. (2016). Unlike BDSNP, which uses a total weighted soil N,
the new mechanistic model tracks different forms of soil N as NH4,
NO3, and organic N for different soil layers and vegetation types
so that nitrification and denitrification can be represented. For
nonagricultural biomes, our new mechanistic scheme uses a global soil
nutrient dataset in an updated C and N mineralization framework. This enables
the model to track the conversion of organic soil N to NH4 and
NO3 pools on a daily scale for nonagricultural soils.
MethodologyOverview of soil N schemes
Key features of the YL and BDSNP parametric soil NO schemes and our new
mechanistic scheme for soil NO, HONO, and N2O are illustrated in
Fig. 1 and Table 1.
Flowchart of the (a) Yienger and Levy (1995) (YL),
(b) Berkley–Dalhousie Soil NOx Parameterization
(BDSNP), and (c) mechanistic schemes for soil nitrogen (N) emissions
as implemented in CMAQ.
Comparison of approaches of the parametric and mechanistic soil N
emissions models.
YL parametric modelBDSNP parametric modelMechanistic modelApproachYienger and Levy equations for NOHudman et al. (2012) equations for NODayCENT sub-model representing nitrification, denitrification, and mineralization for NO, HONO, and N2OSpecies emitted or outputNONONO, HONO, NH3, N2OBiome or land use classificationCMAQ default NLCD40Sub-grid biome classification; MODIS 24 mapped from NLCD40Sub-grid biome classification from NLCD40Soil N data sourceFertilizer N in growing season wet emission factorEPIC (fertilizer N + deposition (wet and dry) N from CMAQ)EPIC (fertilizer N + deposition (wet and dry) N from CMAQ); Xu et al. (2015) for nonagricultural soilAgricultural biomeBiome-specific NO emission factorsNO emissions derived from total EPIC NEPIC C and N pools used in DayCENT scheme nitrification NO, HONO, and N2O; denitrification NO and N2ONonagricultural biomeBiome-specific NO emission factorsBiome-specific NO emission factorsSchimel and Weintraub equations for N and C pools used in DayCENT to derive nitrification and denitrification emissionsVariables consideredSoil T, rainfall, and biome typeTotal soil N, soil T, soil moisture, rainfall, and biome typeSoil water content (irrigated and unirrigated), T, NH4+, NO3-, gas diffusivity, and labile C by soil layerPulsingfprecipitationfldry, with exponential decay with change in soil moistureSame as BDSNPCRFfLAI, SAIfLAI, meteorology, biomeSame as BDSNP
The YL scheme, based on Yienger and Levy (1995), parameterizes soil NO
emission (SNOYL, in ng N m-2 s-1) in
Eq. (1) as a function of biome-specific emission factors (Abiome)
and soil temperature (Tsoil).
SNOYL=fwdAbiome(w/d),TsoilPprecipitationCRFLAI,SAI
The emissions factor depends on whether the soil is wet (Abiome(w)) or dry
(Abiome(d)), with the wet factor used when rainfall exceeds
1 cm in the prior 2 weeks. For dry soils, YL assumes NO emissions exhibit a
small and linear response to increasing soil temperatures. For wet soils,
soil NO is zero for frozen conditions, increases linearly from 0 to
10 ∘C, and increases exponentially from 10 to 30 ∘C, after
which it is constant. In agricultural regions, YL assumes wet conditions
throughout the growing season (May–September) and assumes 2.5 % of the
fertilizer applied N is emitted as NO, in addition to a baseline NO emissions
rate based on grasslands. The pulsing term (P(precipitation)) is applied if
precipitation follows at least two dry weeks. The canopy reduction factor
(CRF) is set as a function of leaf area index (LAI) and stomatal area index
(SAI).
The Biogenic Emissions Inventory System (BEIS v3.61 used in current versions of
CMAQ v5.0.2 or higher) estimates NO emissions from soils essentially using
the same original YL algorithm as in Eq. (1), with slight updates accounting
for soil moisture, crop canopy coverage, and fertilizer application. The YL
soil NO algorithm in CMAQ distinguishes between agricultural and
nonagricultural land use types (Pouliot and Pierce, 2009). Adjustments due to
temperature, precipitation (pulsing), fertilizer application, and canopy
uptake are limited to the growing season, assumed as 1 April to 31 October,
and are restricted to agricultural areas as defined by the Biogenic Emissions
Landuse Database (BELD). Unlike the original YL, the implementation of YL in
CMAQ (CMAQ-YL) interpolates between wet and dry conditions based on soil
moisture in the top layer (1 cm). In this study, we use the Pleim–Xiu Land
Surface Model (PX-LSM) in CMAQ to compute soil temperature
(Tsoil) and soil moisture (θsoil).
Agricultural soil NO emissions are based on the baseline grassland NO
emission (Agrassland) plus an additional factor (fertilizer(t)) that starts at its peak value during the first month of the growing
season and declines linearly to zero at the end of the growing season. The
growing season is defined as April–October in CMAQ-YL, rather than being
allowed to vary by latitude (original YL) or by a satellite-driven analysis
of vegetation (original BDSNP). A summary of the modified YL algorithm is
presented below for growing season agricultural emissions (Eq. 2).
SNOCMAQ-YL,agricultural growing season=fAgrassland+fertilizert,Tsoil,θsoilPprecipitationCRFLAI,SAI
For the nongrowing season or nonagricultural areas throughout the year, soil NO
emissions are assumed to depend only on temperature and the base emissions
for different biomes (Abiome) as provided in BEIS. CMAQ still
uses the base emission for both agricultural and nonagricultural land types
with adjustments based solely on air temperature (Tair, in K) as
done in BEIS (Eq. 3). However, for the sake of simplicity we refer to “CMAQ-YL”
merely as “YL”.
SNOCMAQ-YL,nonagricultural or nongrowing
season=Abiomee0.04686⋅Tair-14.30579
The original implementation of the BDSNP scheme in CMAQ v5.0.2
was described by Rasool et al. (2016). Here, we update that code for
CMAQv5.1, but the formulation remains the same. Soil NO emissions,
SNO, are computed in Eq. (4) as the product of biome-specific
emission rates (AbiomeNavail) and
adjustment factors to represent the influence of ambient conditions. The
biome-specific emission rates have background soil NO for 24 MODIS biome
types from the literature (Stehfest and Bouwman, 2006; Steinkamp and Lawrence,
2011). Fertilizer and deposition emission rates based on an exponential decay
after the input of fertilizer and deposition N are added to background soil NO
emission rates for respective biomes. BDSNP accounts for total N from
fertilizer and deposition obtained from EPIC. EPIC provides the N available
from the crop-specific fertilizer soil N pool in different forms as
NH4, NO3, and organic N. A final weighted total soil N
pool is used by weighting the different N forms by the fraction of each crop
type in each modeling grid. The soil temperature response
f(Tsoil) is an exponential function of temperature (in K). Unlike
YL that depends solely on rainfall, BDSNP has a Poisson function gθ based on soil moisture (θ) that increases smoothly
first until a maximum and then decreases when soil becomes water-saturated.
BDSNP also differentiates between wet and dry soil conditions and provides
a more detailed representation than YL of pulsing following precipitation and
of the CRF (described in Sect. 2.5).
SNOBDSNP=AbiomeNavailfTgθPldryCRFLAI,meteorology,biome
Our new mechanistic scheme computes soil emissions of NO, HONO, and N2O by specifically representing
both nitrification and denitrification. Equations (5)–(7) provide an
overview of the mechanistic formulation. All functions are described in
greater detail in Sect. 2.6.4. In the equations, the pulsing factor Pldry follows the formulation of Rasool et al. (2016). The
canopy reduction factor CRFLAI,meteorology,biome is described in Sect. 2.5. Briefly, we note that
nitrification rates (RN in Eq. 24, kg-Nha-1s-1) depend on the available NH4 pool,
soil temperature (Tsoil), soil moisture (θsoil),
gas diffusivity (Dr), and pH adjustment factors. Meanwhile, denitrification
rates (RD in Eq. (25), kg N ha-1 s-1) depend on the available NO3 pool, relative
availability of NO3 to C, soil temperature, gas diffusivity, and
soil moisture adjustment factors.
SNO=NNOx-SHONO+DNOCRFLAI, meteorology, biome≡fNH4,Tsoil,θsoil,Dr,pHPldry+fNO3:C,Tsoil,θsoil,DrCRFLAI, meteorology, biomeSHONO=(HONOf)(NNOx)(fSWC)CRFLAI, meteorology, biome≡(HONOf)fNH4,Tsoil,θsoil,Dr,pHPldry(fSWC)CRFLAI, meteorology, biomeSN2O=NN2O+DN2O≡fNH4,Tsoil,θsoil,Dr,pH+fNO3:C,Tsoil,θsoil,Dr
In all our simulations, soil NH3 emission is calculated based on
the bidirectional exchange scheme (Bash et al., 2013) in CMAQ.
Biome classification over CONUS
CMAQ uses the National Land Cover Database with 40 classifications (NLCD40;
https://www.mrlc.gov/, last access: 22 February 2019) to
represent land cover, which is used by the YL parametric scheme. However,
Steinkamp and Lawrence (2011) provide soil NO emission factors
(Abiome′Navail) for only 24 MODIS biomes
in the BDSNP parametric scheme. Thus, the initial implementation of BDSNP in
CMAQ by Rasool et al. (2016) introduced a mapping between the MODIS 24 and NLCD40
biomes to set an emission factor for each NLCD40 biome type (see Appendix
Table A2). Factors were then adjusted using Köppen climate zone
classifications (Kottek et al., 2006). Whereas the original implementation of
BDSNP by Rasool et al. (2016) treated each grid cell based on its most
prevalent biome type, our update of BDSNP for CMAQv5.1 and our mechanistic
model use sub-grid biome classification, accounting for the fraction of each
biome type in each cell.
The latest Biogenic Emissions Landcover Database version 4 (BELD4), generated
using the BELD4 tool in the SA Raster Tools system, is used to represent land
cover types consistently across both the Fertilizer Emission Scenario Tool
for CMAQ (FEST-C v1.2;
https://www.cmascenter.org/fest-c/, last access: 22 February 2019) and the Weather Research and Forecast (WRF) meteorological model
(Skamarock et al., 2008) and CMAQ framework. BEIS v3.61 within CMAQ integrates
BELD4 with other data sources generated at 1 km resolution to provide
fractional crop and vegetation cover. US land use categories are based on the
2011 NLCD40 categories. FEST-C provides tree and crop percentage coverage for
194 tree classes and 42 crops
(https://www.cmascenter.org/sa-tools/documentation/4.2/Raster_Users_Guide_4_2.pdf, last access: 22 February 2019). For determining fractional crop cover, the 2011
NLCD–MODIS data were used for Canada and the US in the BELD4 data generation
tool of FEST-C. Tree species fractional coverage is based on 2011 Forest
Inventory and Analysis (FIA) version 5.1. MODIS satellite products are used
where detailed data are unavailable outside of the US.
N fertilizer
The YL scheme set fertilizer-driven soil NO emissions to be proportional to
fertilizer application during a prescribed growing season: May–August for
the Northern Hemisphere and November–February for the Southern Hemisphere
(Yienger and Levy, 1995) or April–October for CMAQ-YL. Our implementations
of both the BDSNP parameterization and mechanistic soil N schemes into CMAQ are
designed to enable the use of year- and location-specific fertilizer data
with daily resolution. We use FEST-C to incorporate EPIC fertilizer
application data into our CMAQ runs. EPIC estimates daily fertilizer
application based entirely on simulated idealized plant demand, with N stress
and limitations in response to local soil and weather conditions, using
linkages with WRF via FEST-C. The FEST-C interface also ensures that EPIC
simulations are spatially consistent with CMAQ's CONUS domain and resolution
through the Spatial Allocator (SA) Raster Tools system
(http://www.cmascenter.org/sa-tools/, last access: 22 February 2019).
Because EPIC covers only the US, outside the US BDSNP uses fertilizer data
regridded from Hudman et al. (2012), which scaled Potter et al. (2010) data
for fertilizer N from 1994–2001 to global fertilizer levels in 2006. Our
mechanistic scheme uses a more recently compiled and speciated soil N and C
dataset for non-US agricultural regions, regridded from Xu et al. (2015).
N deposition
N deposition serves as a significant addition to the soil mineral N
(inorganic N:NH4+ and NO3-) pool and hence
influences soil N emissions. The YL scheme does not explicitly represent N
deposition but instead sets soil emissions based on biome type. In our
implementation of both the updated BDSNP and new mechanistic soil N schemes,
hourly wet and dry deposition rates for both reduced and oxidized forms of N,
computed within the CMAQ simulation, are added to the NH4+ and
NO3- soil pools.
Canopy reduction factor (CRF)
CRF is used to calculate above-canopy NO and HONO, assuming that some
fraction of each is converted to NO2 and absorbed by leaves. Earlier
global-scale GEOS-Chem simulations with BDSNP had a monthly averaged CRF
that reduced total soil NOx by an average of 16 % (Hudman et al.,
2012).
The original YL soil NO scheme (Yienger and Levy, 1995) and the in-line BEIS
in CMAQ set CRF as a function of LAI and SAI. Recently, implementations of
BDSNP in CMAQ and GEOS-Chem implemented CRF as a function of wind speed,
turbulence, and canopy structure (Geddes et al., 2016; Rasool et al., 2016;
Wang et al., 1998).
Here, we compute CRF using equations from Wang et al. (1998) for both the BDSNP
and new mechanistic scheme using spatially and temporally variable
land-surface parameters: surface (2 m) temperature, solar radiation
(W m-2), surface pressure, snow cover, wind speed (vwind),
cloud fraction, canopy structure, vegetation coverage (LAI and canopy
resistances), gas diffusivity, and deposition coefficients. The final
reduction factor (CRF(LAI, meteorology, biome)) for primary
biogenic soil NO emissions is based on two main factors: bulk stomatal
resistance (RBulk) and the land-use-specific ventilation velocity of
NO (vvent,NO), calculated based on the parameters mentioned above
(Eq. 8).
CRFLAI, meteorology, biome=RBulkRBulk+vvent,NO
The ventilation velocity of NO (vvent,NO) is
calculated by adjusting a normalized day- and night-specific velocity from
Wang et al. (1998): 10-2 and 0.2×10-2 m s-1,
respectively. The adjustments are based on biome-specific LAI and canopy wind
extinction coefficients (CBiome). Ctropical rainforest
is the canopy wind extinction coefficient for tropical rainforests, the
biome on which most canopy uptake studies for NOx are based
(Eq. 9).
vvent,NO=vvent,NOday/nightvwind327LAICtropical rainforestCBiomeRBulk is
a combination of various canopy resistances in series and parallel: internal
stomatal resistance, cuticle resistance, and aerodynamic resistance, which
have biome-specific normalized values for the MODIS 24 biomes also available
in the dry deposition scheme of CMAQ. These normalized values of individual
resistances are subsequently adjusted and dependent on multiple conditions
for solar radiation, surface temperature, pressure, deposition coefficients,
and the molecular diffusivity of NO2 in air. The calculation of
RBulk based on Wang et al. (1998) has been documented and shared
in the open-source BDSNP code repository (canopy_nox_mod.F) for the purpose
of reproducibility (available at
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=_1351, last access: 22 February 2019).
Detailed description of the mechanistic soil N schemeOverview
Our new mechanistic soil N model tracks the NH4, NO3, and
organic C and N pools in soil separately, in contrast to the total N pool of
BDSNP, and estimates NO, HONO, and N2O rather than just NO
(Fig. 2). It uses DayCENT to represent both nitrification and denitrification.
For agricultural biomes, we use speciated N and C pools from EPIC to drive
DayCENT. For nonagricultural biomes, we use a C–N mineralization framework
(Manzoni and Porporato, 2009) to estimate the inorganic N and C pools for
DayCENT.
Schematic for N transformation to estimate soil pools of ammonium
(NH4) and nitrate (NO3) and the resultant nitrification and
denitrification N emissions in the mechanistic model.
One of the advantages of using DayCENT is its ability to simulate all types
of terrestrial ecosystems. DayCENT is one of the only biogeochemical models
that not only provides a process-based representation of soil N emissions,
but has also been calibrated and validated across an array of conditions for
crop productivity, soil C, soil temperature and water content, N2O,
and soil NO3- (Necpálová et al., 2015). Hence,
mechanistic models like DayCENT yield more reliable results by applying
validated controls of soil properties like soil temperature and moisture,
which are the key process controls to nitrification and denitrification. More
recent mechanistic models like DNDC, MicNit, ECOSYS, and COUPMODEL are quite
similar to DayCENT in their representation of the nitrification and
denitrification process. However, these models have not been as widely
evaluated and impose greater computational costs (Butterbach-Bahl et al.,
2013). DayCENT also enhances consistency in our mechanistic model by
utilizing the same C–N mineralization scheme (taken from the CENTURY model;
Parton et al., 2001) that is used in EPIC.
Most stand-alone applications of DayCENT and other mechanistic models have
focused on the biogeochemical, climate, and agricultural impacts of soil
emissions. Our linkage of DayCENT with CMAQ provides an opportunity for
the first time to estimate emissions of multiple soil N species through a
process-based approach and then assess their impact on atmospheric chemistry
in a regional photochemical model.
Agricultural regions
In agricultural regions, we use EPIC to derive organic N, NH4,
NO3, and C pools updated on a daily scale. EPIC follows the same
approach used in the CENTURY model (Parton et al., 1994), but uses an updated
crop growth model and better represents the effects of sorption on soil water
content that affect leaching losses and the surface-to-subsurface flow of N. In
contrast, CENTURY used monthly water leached below 30 cm of soil depth, annual
precipitation, and the silt and clay content of soil (Izaurralde et al.,
2006).
In EPIC, organic N residues added to the agricultural soil surface or
belowground from plant or crop residues, roots, fertilizer, deposition, and
manure are split into two broad compartments: microbial or active biomass
and slow or passive humus. Slow or passive humus is essentially recalcitrant
and nonliving in nature with very slow turnover rates ranging from centuries
to even thousands of years and makes up most of the organic matter. N uptake
by soil microbes from organic matter, also called “microbial biomass” or
“microbial–active N”, is the living portion of the soil organic matter,
excluding plant roots and soil animals larger than 5×10-3µm3. Although microbial biomass constitutes a
small portion of organic matter (∼2 %), it is central in microbial
activity: in other words, the conversion of organic N to inorganic N (Cameron et al., 2013; Manzoni and Porporato, 2009). The transformation rate of organic
N to microbial N is controlled by the relative C and N content in microbial
biomass, soil temperature and water content, soil silt and clay content,
organic residue composition enhanced by tillage in agricultural soil, bulk
density, oxygen content, and inorganic N availability. Microbial N has
quicker turnover times ranging from days to weeks compared to hundreds of
years for slow or passive organic matter (Izaurralde et al., 2006; Schimel
and Weintraub, 2003). Hence, microbial biomass is the main clearinghouse and
driver of C and N cycling in EPIC. Whether net mineralization of organic N to
NH4+ occurs or net immobilization of NO3- to
microbial N depends strongly on the relative C and N contents in microbial
biomass. Higher N content supports net mineralization, whereas higher C
content supports net immobilization. C and N can also be leached or lost in
gaseous forms (Izaurralde et al., 2012).
We then estimate gaseous N emissions by using the organic N, NH4,
NO3, and C pools provided from EPIC/FEST-C along with relevant soil
properties for agricultural biomes from the DayCENT nitrification and
denitrification sub-model, as described in Sect. 2.6.4 and illustrated in
Fig. 2.
Nonagricultural regions
We adapt the framework for linked C and N cycling from Schimel and
Weintraub (2003) for nonagricultural regions, where EPIC is not applicable.
This framework accounts for the mineralization of organic N by considering
which element is limiting based on the relative C-to-N content in microbial
biomass. If N is in excess, then the mineralization of organic N producing
NH4+ is favored. If C is in excess, it results in overflow
metabolism that results in elevated C respiration rates not
associated with microbial growth. The resultant inorganic N and C respiration
rates are then applied on a temporal and spatial scale consistent with those
for the EPIC agricultural pool.
To ensure mass balance, enzyme production (Eqs. 11–13) and recycling
mechanisms (Eqs. 14–15) to replenish microbial biomass C are crucial.
Similarly, net immobilization is assumed as was done in EPIC when we
approach C-saturated conditions with time to replenish microbial N. Without
such mechanisms, there is a danger to always incorrectly predict the N- or
C-limited state for microbes. Also, some proportion of the microbial biomass
is utilized for the maintenance of living cells (only C demand) (Eq. 14), while
the rest accounts for decay and regrowth (both C and N demands) (Eqs. 16–17,
18–19) (Schimel and Weintraub, 2003; Manzoni and Porporato, 2009). Fractions
of C and N in dying microbial biomass are recycled into the available
microbial C and N pools. Schimel and Weintraub (2003) provide values for
parameters that quantify these growth and decay processes: fraction of biome
C to exoenzymes (Ke) =0.05; microbial maintenance rate
(Km) =0.01 d-1; substrate use efficiency (SUE) =0.5;
proportion of microbial biomass that dies per day (Kt) =0.012 d-1; proportion of microbial biomass (C or N) for microbial use
(Kr) =0.85.
Rmrespiration from maintenance=Km(SMC)Rerespiration from enzyme production=((1-SUE)(EPC)/SUE)EPCenzyme production as, C loss/sink=KeSMCEPNenzyme production as N loss/sink=EPC/3(where 3 is the approximateC:Nratio
for protein)CYCrecycle from C microbial biomass=KtKrSMCCYNrecycle from N microbial biomass=CYC/Cm:NmHCC death/decay=Kt1-KrSMCHNN death/decay=HC/Cm:NmIf C is limited or N in excess:SMC<Rm+EPC/SUE+SMN-EPNCm:Nm/SUERgrespiration from growth, C limited=1-SUESMC-EPC/SUE-RmROrespiration from overflow mechanism=0NH4from net mineralization after mass balance=(SMN-EPN-(SMC-EPC/SUE-RmSUE/Cm:Nm))
We represent spatial heterogeneity in soil C and N by using the Schimel and
Weintraub (2003) algorithm with sub-grid land use fractions from NLCD40 to
estimate the different parameters for specific nonagricultural biomes in
Eqs. (10)–(20). That allows us to account for inter-biome variability in
soil properties and organic and/or microbial biomass.
Mineralized N pools generated as NH4+ in this framework are
calculated eventually as a function of microbial biomass and the aforementioned
parameters driving the net mineralization (Eqs. 18 and 21).
We map a global organic C and N pool dataset (Xu et al., 2015) onto our CONUS
domain using biome-specific fractions from 12 different biome types for
the conversion of these organic pools into microbial biomass pools (Xu et al.,
2013). We map these 12 broader biome types to the 24 MODIS biome types with the
mapping shown in Table A1. To ensure consistency with the sub-grid biome
fractions for the 40 NLCD biome types (Sect. 2.2), we map the MODIS 24
biome-specific microbial / organic C and N fractions to NLCD 40
(Cmicbiome and Nmicbiome; biome
represents the 40 NLCD categories) with the mappings shown in Tables A2 and A3.
We calculate area-weighted microbial C and N pools (SMC and SMN) using
Cmicbiome and Nmicbiome that
account for the inter-biome variability in the availability of soil microbial
biomass. Also, spatial heterogeneity in terms of vertical stratification is
crucial as emission losses from N cycling primarily happen in the top 30 cm
layer. Hence, we incorporate the Xu et al. (2015) data for the top 30 cm for
the organic nutrient pool and microbial C:N ratio (Cm:Nm) along
with other soil properties such as soil pH, θsoil, and
Tsoil. This framework (Fig. 2) enables us to estimate soil
NH4, NO3, and C pools from area-weighted microbial
biomass as consistently as possible with the pools that EPIC provides in
agricultural regions.
DayCENT representation of soil N emissions
The final part of the mechanistic framework is formed by using a
nitrification and denitrification N emissions sub-model adapted from DayCENT
along with nitrification and denitrification rate calculations adapted from
EPIC. Nitrification and denitrification rates are adapted from EPIC to
maintain consistency with the NH3 bidirectional scheme in CMAQ, which
uses the same. It should be noted that the coupled C–N decomposition module
in the EPIC terrestrial ecosystem model is similar to that of DayCENT
(Izaurralde et al., 2012, 2017; Gaillard et al., 2018).
EPIC-simulated agricultural NH4 and NO3 soil pools are
generated as described in Sect. 2.6.2, whereas the nonagricultural
NH4 and NO3 soil pools are calculated by using the methods
described in Sect. 2.6.3 (Eqs. 22–23). NH4 and NO3 soil
pools drive nitrification and denitrification as shown in Eqs. (24)–(25).
Variability in terms of the soil conditions influencing N emissions in
nitrification and denitrification is introduced through the rates at which
NH4 is nitrified (RN) and NO3 is denitrified
(RD) (Eqs. 24–25).
The nitrification rate (KN) (Eq. 26) is estimated based on
regulators from the soil water content, soil pH, and soil temperature
(Tsoil), following the approach of Williams et al. (2008),
consistent with the bidirectional NH3 scheme in CMAQ (Bash et al.,
2013). The nitrification soil temperature regulator (fT) accounts
for frozen soil with no evasive N fluxes (Eq. 27). The nitrification soil
water content regulator (fSW) accounts for soil water content at
the
wilting point and field capacity (Eqs. 28–29). The regulator terms
fT and fSW both get their dependent variables from
land-surface outputs derived from the Meteorology–Chemistry Interface Processor (MCIP) (Otte and Pleim, 2010). However, the nitrification soil pH regulator
(fpH) takes soil pH for agriculture soil from EPIC and for
nonagricultural soil from a separate global dataset (Xu et al., 2015),
available at both 0.01 and 1 m depths to maintain consistency with MCIP
(Eq. 30). The denitrification rate (KD) (Eq. 31) is regulated by soil
temperature (Eq. 34), with WFPS (Eq. 33) acting as a proxy for O2
availability and soil moisture θsoil and
the relative availability of NO3 and C (Eq. 32) determining
N2O or N2 emissions during denitrification (Williams et
al., 2008). Note that Eqs. (26) and (31) set upper limits for KN
and KD, respectively.
NO3(kg-Nha-1,after
nitrification)=NH4(1.0-e-(KNdt))NH4(kg-Nha-1,after nitrification)=NH4e-(KNdt)RN(kg-Nha-1s-1)=NH4(1.0-e-(KNdt))/dtRD(kg-Nha-1s-1)=NO3(1.0-e-KDdt)/dtKN(s-1)=min0.69,(fT)(fSW)(fpH)fTnitrification soil temperature regulator=max0.041Tsoil-278.15,0.0fSWnitrification soil water content regulator=0.1,ifθsoil≤wilting pointmax0.1,0.1+0.9θsoil-wilting pointfield capacity-wilting point,θsoil-wilting point0.25field capacity–wilting point,ifwg25>θsoil>wilting point1.0,iffield capacity>θsoil≥wg25max0.1,1.0-θsoil-field
capacityθsoilat saturation-field capacity,ifθsoil>field capacitywg25=wilting point+0.25(field capacity–wilting point)fpHnitrification soil pH regulator=0.307pH-1.269,acidic soilpH<71.0,neutral soil7.4>pH≥75.367-0.599pH,alkaline soilpH≥7.4,KDs-1=min0.01,f(WFPS,Tsoil,NO3:C)fWFPS,Tsoil,NO3:C,denitrification regulators=(fT,D)(fWFPS,D)1.4labile CNO3labile C+17NO3+83fWFPS,D=min1.0,4.8214(16/(121.39(WFPS)))fT,D=min1.0,e308.56168.02-1Tsoilin K-227.13
DayCENT partitions N emissions as NOx and N2O
based on relative gas diffusivity in soil compared to air (Dr) (Eq. 35). Dr
is calculated based on the algorithm from Moldrup et al. (2004),
which accounts for soil water content, soil air porosity, and soil type.
Dr, and hence the ratio of NOx to N2O
emissions (rNOx/N2O) being a function of Dr, also accounts for soil
texture by quantifying pore space, which is highest in coarse soil (Parton et
al., 2001; Moldrup et al., 2004). DayCENT assumes 2 % of nitrified N
(RN) is lost as N2O (Eq. 36). rNOx/N2O is
the ratio of NOx (both NO and HONO, which photolyze rapidly
to NO) to N2O, in which emissions are expressed on a g N h-1 basis. These
emissions are susceptible to pulsing after rewetting of soil in arid or
semiarid conditions (Pldry), as explained in
Sect. 2.1 (Eq. 37). Denitrification NO is also calculated using the overall
rNOx/N2O ratio (Eq. 38) but does not experience pulsing (Parton
et al., 2001). Equation (35) does quantify rNOx/N2O as a function of Dr,
but as a unitless ratio as expected.
rNOx/N2O=15.2+35.5arctan0.68π10.0Dr-1.86πNN2OnitrificationN2O,g-Nh-1=0.02(RN)(grid cell area)NNOxnitrificationNOx,g-Nh-1=rNOx/N2O(NN2O)PldryDNO(denitrification NO,g-Nh-1)=rNOx/N2O(DN2O)N2O from denitrified NO3 (RD) is calculated
using the partitioning function derived by Del Grosso et al. (2000) (Eq. 39).
The ratio of N2 to N2O emitted as an intermediate
during denitrification (rN2/N2O) is dependent on
WFPS (Eq. 42) and the relative availability of NO3 substrate
and C for heterotrophic respiration (Eqs. 40–41). The C available
for heterotrophic respiration in the surface soil layer (labile C) (Eq. 41)
is taken from EPIC for agricultural biomes and from Xu et al. (2015) for
nonagricultural biomes. fNO3:C is controlled by
variability in soil texture, accounted for by a factor k, which depends on
soil diffusivity at field capacity as estimated in Del Grosso et al. (2000).
Also, the NO3 pool is updated at each time step when
denitrification happens (Eq. 43). Equations (40)–(42) also quantify
rN2/N2O as a unitless ratio, while still
accounting for the variables influencing these ratios.
DN2O(denitrificationN2O,g-Nh-1)=(RD1.0+rN2/N2O)(grid cell area)rN2/N2O=fNO3:CfNO3:C=max0.16(k),(k)e-0.8NO3labile Cif labile C>00.16(k)if labile C∼0fWFPS=max0.1,0.015(WFPS(as fraction)-0.32)NO3kg-Nha-1,after denitrification=RNKD+NO3-RNKD(e-KDdt)
HONO is emitted as an intermediate during nitrification and has been
reported in terms of a ratio relative to NO for each of 17 ecosystems by
Oswald et al. (2013). In the mechanistic scheme, the proportions of HONO
relative to total NOx for these 17 biomes were mapped to the closest 24
MODIS-type biome categories (Table A1) and then to the NLCD 40 types
(HONOf) with the mappings in Tables A2 and A3. This allows for consistency
with sub-grid land use fractions from NLCD40. HONO emissions are further
adjusted to reflect their dependence on WFPS (Oswald et al., 2013). The
adjustment factor fSWC reflects observations that HONO emissions rise
linearly up to 10 % WFPS and then decrease until they are negligible
around ∼40 % (Su et al., 2011; Oswald et al., 2013)
(Eq. 45). Subsequently, total NO emission is a sum of nitrification NO
emission, which is a difference of NNOx and SHONO, and
denitrification NO (Eq. 46). Similarly, total N2O is a sum of
NN2O (Eq. 36) and DN2O (Eq. 39). The canopy
reduction factor (Sect. 2.1) is then applied to both SHONO and SNO
(Eqs. 44 and 46). Finally, sub-grid-scale emission rates are
aggregated for each grid cell.
SHONO=HONOf(NNOx)(fSWC)CRFLAI, meteorology, biomefSWCsoil water content adjustment factor to compute HONO=HONOfWFPS0.1,ifWFPS≤0.10(assuming linear increase up to 10 % WFPS)HONOf0.4-WFPS(0.4-0.1),ifWFPS≤0.400,ifWFPS>0.40SNO=NNOx-HONOf(NNOx)fSWC+DNOCRFLAI, meteorology, biome
Model configurations
We obtained from the U.S. EPA a base case WRFv3.7-CMAQv5.1 simulation for 2011
with the settings and CONUS modeling domain described by Appel et al. (2017),
who thoroughly evaluated its performance against observations. Here,
we simulate only May and July to test the sensitivity of air pollution to soil N
emissions during the beginning and middle of the growing season. Each
episode is preceded by a 10-day spin-up period.
Table 2 summarizes the WRF-CMAQ modeling configurations settings. The
simulations use the Pleim–Xiu Land Surface Model (PX-LSM) (Pleim and Xiu,
2003) and the Asymmetric Convective Mixing v2 (ACM2) planetary boundary
layer (PBL) model. The modeling domain for CMAQ v5.1 covers the entire CONUS
including portions of northern Mexico and southern Canada with 12 km
resolution and a Lambert conformal projection. Vertically, we use 35 vertical layers of increasing thickness extending up to 50 hPa. Boundary
conditions are provided by a 2011 global GEOS-Chem simulation (Bey et al.,
2001).
Modeling configuration used for the WRF-CMAQ simulations.
WRF/MCIP Version:ARW V3.7Shortwave radiation:RRTMG schemeHorizontal resolution:CONUS (12 km × 12 km)Surface layer physic:PX LSMVertical resolution:35 layerPBL scheme:ACM2Boundary condition:NARR 32 kmMicrophysics:Morrison double-moment schemeInitial condition:NCEP-ADPCumulus parameterization:Kain–Fritsch schemeLongwave radiation:Rapid Radiation Transfer Model Global (RRTMG) schemeAssimilation:Analysis nudging above PBL for temperature, moisture, and wind speedBDSNP Horizontal resolution:Same as WRF/MCIPEmission factor:Steinkamp and Lawrence (2011)Soil biome type:Sub-grid biome fractions from WRFv3.7Fertilizer database:EPIC 2011 based from FEST-C v1.2CMAQ Version:5.1Anthropogenic emission:NEI 2011 v1Horizontal resolution:Same as WRF/MCIPBiogenic emission:BEIS v3.61 in-lineInitial condition:Pleim–Xiu (MET) GEOS-Chem (CHEM)Boundary condition:Pleim–Xiu (MET) GEOS-Chem (CHEM)Aerosol module:AE6Gas-phase mechanism:CB-05Simulation case arrangement (in-line with CMAQ) 1. YL:WRF/MCIP-CMAQ with standard YL soil NO scheme2. BDSNP (EPIC with new biome):WRF/MCIP-BDSNP-CMAQ with EPIC and new sub-grid biome fractions3. Mechanistic scheme:WRF/MCIP–mechanistic soil N-CMAQ with EPIC (agricultural US) and Xu et al. (2015) (non-US agricultural and all nonagricultural in CONUS), new sub-grid biome fractionsSimulation time period 1–31 May and 10–31 July 2011 (10-day spin-up for each) for CMAQ simulation with in-line YL, updated BDSNP, and mechanistic modulesModel performance evaluation USEPA Clean Air Status and Trends Network (CASTNET) and Air Quality System (AQS) data for ozoneInteragency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) (Malm et al., 1994) for PM2.5 nitrate AQS and South Eastern Aerosol Research and CHaracterization (SEARCH) for NOx concentrations NASA's OMI satellite retrieval product as derived in Lamsal et al. (2014) for the tropospheric NO2 column
WRF simulations employed the same options as Appel et al. (2017) (summarized
in Table 2). WRF outputs for meteorological conditions were converted to
CMAQ inputs using MCIP version 4.2 (https://www.cmascenter.org, last access: 22 February 2019).
Gridded speciated hourly model-ready emission inputs were generated using the Sparse Matrix Operator Kernel
Emissions (SMOKE; https://www.cmascenter.org/smoke/, last access: 22 February 2019) version
3.5 program and the 2011 National Emissions Inventory v1. Biogenic emissions
were processed in-line in CMAQ v5.1 using BEIS version 3.61 (Bash et al.,
2016). All the simulations employed the bidirectional option for estimating
the air–surface exchange of ammonia. We applied CMAQ with three sets of
soil NO emissions: (a) standard YL soil NO scheme in BEIS; (b) updated BDSNP
scheme for NO (Rasool et al., 2016) with new sub-grid biome classification;
and (c) mechanistic soil N scheme for NO and HONO.
Observational data for model evaluation
To evaluate model performance for each of the three soil N cases, we
employed regional and national networks: the EPA's Air Quality System (AQS; 2086
sites; https://www.epa.gov/aqs, last access: 22 February 2019) for hourly NOx and
O3; the Interagency Monitoring of Protected Visual Environments
(IMPROVE; 157 sites; http://vista.cira.colostate.edu/improve/, last access: 22 February 2019)
and Chemical Speciation Network (CSN; 171 sites; https://www3.epa.gov/ttnamti1/speciepg.html, last access: 22 February 2019) for PM2.5 nitrate
(measured every third or sixth day); the Clean Air Status and Trends Network
(CASTNET; 82 sites; http://www.epa.gov/castnet/, last access: 22 February 2019) for hourly
O3 and weekly aerosol PM species; and SEARCH network measurements
(http://www.atmospheric-research.com/studies/SEARCH/index.html, last access: 22 February 2019)
of NOx concentrations in remote areas. NO2 was also evaluated
against tropospheric columns observed by the OMI aboard NASA's Aura satellite (Bucsela et al., 2013; Lamsal et al.,
2014).
Results and discussionSpatial distribution of soil NO, HONO, and <inline-formula><mml:math id="M301" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">N</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math></inline-formula>
emissions
Figure 3 compares the spatial distribution of soil N oxide emissions from
the three schemes. The incorporation of EPIC fertilizer in BDSNP results in
soil NO emission rates up to a factor of 1.5 higher than in YL, consistent
with the findings of Rasool et al. (2016). Hudman et al. (2012) found nearly
twice as large of a gap between BDSNP and YL in GEOS-Chem; the narrower gap
here likely results from our use of sub-grid biome classification and EPIC
fertilizer data (Rasool et al., 2016). The mechanistic scheme (Fig. 3c)
generates emission estimates that are closer to the YL scheme but with
greater spatial and temporal heterogeneity, reflecting its use of more
dynamic soil N and C pools. The agricultural plains extending from Iowa to
Texas with high fertilizer application rates have the highest biogenic NO
and HONO emission rate, with obvious temporal variability between May and
July (Fig. 3). In all of the schemes, soil N represents a substantial
fraction of total NOx emissions over many rural regions, especially in
the western half of the country (Fig. S1 in the Supplement). However, the aggregated budget
of soil NO is much less than anthropogenic NOx from non-soil-related
sources because fossil fuel use is concentrated in a limited number of
urbanized and industrial locations. The percentage contribution of soil NO
to total NOx aggregated across the CONUS domain varied for May–July
from 15 %–20 % for YL, 20 %–33 % for updated BDSNP, and 10 %–13 %
for the
mechanistic scheme.
Soil N oxide emissions on a monthly average basis for May (left) and
July (right) 2011 for (a) the YL scheme (NO),
(b) parameterized BDSNP scheme (NO), and (c) mechanistic
scheme (NO + HONO).
Direct observations of soil emissions are sparse and most were reported
decades ago. While the meteorological conditions will differ, these
observations give us the best available indicator of the ranges of
magnitudes of emission rates actually observed in the field. The sites
encompass a variety of fertilized agricultural fields and fertilized and
unfertilized grasslands (Bertram et al., 2005; Hutchinson and Brams, 1992;
Parrish et al., 1987; Williams and Fehsenfeld, 1991, 1992;
Martin
et al., 1998). For fair comparison, the peak location or site was selected across a
range of sites for a specific observation study and compared to the respective
peak modeled value across sites or grids in the same spatial domain. Also, for
comparison with natural unfertilized grassland observational studies based
in Colorado, modeled estimates from nonagricultural grids only were
selected. Overall, the YL scheme and the mechanistic scheme produce
emissions estimates that are roughly consistent with the ranges of emission
rates observed at each site (Table 3). By contrast, BDSNP tends to
overestimate soil NO compared to these observations (Table 3).
NO emission rates (ng N m-2 s-1) observed in field studies
in agricultural and grassland locations, modeled by CMAQ with the three
soil N schemes for May and July 2011. Observed and modeled values are from
peak location or site within a range of values across sites.
LocationObserved peakMechanistic YL BDSNP (study)summertime soil NOsoil NObsoil NO soil NO May 2011July 2011May 2011July 2011May 2011July 2011Iowa fertilized fields (Williams et al., 1992)18.017.113.08.211.420.141.7Montana fertilized fieldsa (Bertram et al., 2005)12.07.814.27.112.99.842.3South Dakota fertilized fields (Williams and Fehsenfeld, 1991)10.011.710.08.013.918.454.6Texas grasses and fields (both fertilized) (Hutchinson and Brams, 1992)43.052.545.015.015.954.160.3Colorado natural grasslands (Parrish et al., 1987; Williams and Fehsenfeld, 1991; Martin et al., 1998)10.07.911.59.715.318.633.2
a Derived from SCIAMACHY NO2 columns.
b Mechanistic scheme estimates are NO + HONO emission rates.
Table 3 also shows opposing trends for May and July soil NO estimates
between YL or BDSNP and the mechanistic scheme for Iowa and South Dakota
fertilized fields that make up a significant part of the Corn Belt in the US. For
these regions, soil NO tends to be higher in July than in May in YL and
BDSNP, but lower in July in the mechanistic scheme (Table 3). The US Corn
Belt has the most synthetic N fertilizer application in April (Wade et al.,
2015), which can explain the high soil NO emissions in May that decline in
July. N2O emissions have been particularly observed to be highest
during May–June after April N fertilizer application in the US Corn Belt,
with a decline thereafter (Griffis et al., 2017). This is further confirmed
in our estimates for soil N2O emissions from the mechanistic
scheme, for which
May estimates are higher than in July and the maximum emissions are observed
in the Iowa Corn Belt (Fig. 4). However, unlike NOx emissions, for
N2O no background conditions or emission inventories are in place in
CMAQ's chemical transport model, so comparisons with ambient observations
are not yet possible.
Soil N2O emissions on a monthly average basis for
May (a) and July (b) 2011 estimated from the mechanistic
scheme.
Evaluation with PM<inline-formula><mml:math id="M319" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, ozone, and <inline-formula><mml:math id="M320" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
observations
Model results with the three soil N schemes are compared with observational
data from IMPROVE and CSN monitors for the PM2.5NO3 component, AQS
monitors for NOx and ozone, and CASTNET monitors for ozone. Both YL and
the new mechanistic scheme exhibit similar ranges of bias for these
pollutants (see Figs. S2, S3, S4, S5, and S6 in the Supplement).
Use of the mechanistic scheme in place of YL changes soil N emissions by
less than 25 ng N m-2 s-1 in most regions, corresponding to
NOx concentration changes of less than 1 ppb (Fig. 5). CASTNET and
IMPROVE monitors tend to be more remote than AQS and CSN monitors, many of
which are located in urban regions.
Total NOx (NO +NO2) concentration
sensitivity (right) to changes in soil NOx emissions (left)
on a monthly average basis for May (a, c) and July (b, d)
2011 when switching from the YL scheme (NO) to the mechanistic scheme (NO +
HONO).
At AQS monitors, switching between soil N schemes changes MB for O3
by up to ∼1.5 ppb (Fig. 6), whereas the absolute MB of models versus
observations is up to ∼10 ppb (Fig. S2). For NOx, the
maximum difference in MB between soil N schemes is ∼0.4 ppb (Fig. 7)
compared to a maximum absolute MB of ∼10 ppb between model and
observations (Fig. S3). For CASTNET monitors, the differences in MB for
O3 between soil N schemes can reach a maximum of ∼1.5 ppb
(Fig. 8) compared to the 6 ppb maximum absolute MB of models versus
observations (Fig. S4). Similarly, for IMPROVE PM2.5NO3,
the maximum difference in MB between soil N schemes is ∼0.06µgm3 (Fig. 9) compared to the maximum absolute MB of 0.4 µgm3 (Fig. S5). For CSN PM2.5NO3, the maximum MB
difference between soil N schemes is ∼0.1µgm3
(Fig. 10) compared to the maximum absolute MB of ∼50µgm3
(Fig. S6). Similar trends are observed for both May and July as illustrated
in Figs. 6–10.
Overall, the mechanistic scheme tends to reduce CMAQ's positive biases for
pollutants across the Midwest and eastern US, whereas BDSNP worsens
overestimations in these regions for both May and July 2011 (Figs. 6–10).
In addition, the negative bias in the difference means less bias compared to
observations (Figs. 6–10). One reason for the differences is that the
mechanistic scheme recognizes dry conditions in unirrigated fields in these
regions, whereas the low WFPS threshold in BDSNP (θ=0.175
(m3 m-3)) treats most of these regions as wet and thus higher
emitting.
Change in average monthly mean bias (MB) of the Community Multiscale Air
Quality (CMAQ) model evaluated against the EPA Air Quality System (AQS)
O3 observations for May (a, b) and July (c, d)
2011 when switching to the mechanistic (a, c) or BDSNP (b, d) scheme from YL.
Change in average monthly MB of CMAQ evaluated against EPA AQS
NOx observations for May (a, b) and July
(c, d) 2011 when switching to the mechanistic (a, c) or
BDSNP (b, d) scheme from YL.
Change in average monthly MB of CMAQ evaluated against the EPA Clean
Air Status and Trends Network (CASTNET) O3 observations for May
(a, b) and July (c, d) 2011 when switching to
the mechanistic (a, c) or BDSNP (b, d) scheme from YL.
Change in average monthly MB of CMAQ evaluated against Interagency
Monitoring of Protected Visual Environments (IMPROVE) PM2.5NO3 observations for May (a, b) and July (c, d)
2011 when switching to the mechanistic (a, c) or BDSNP (b, d)
scheme from YL.
Change in average monthly MB of CMAQ evaluated against Chemical
Speciation Network (CSN) PM2.5NO3 observations for
May (a, b) and July (c, d) 2011 when switching to
the mechanistic (a, c) or BDSNP (b, d) scheme from YL.
Evaluation with South Eastern Aerosol Research and CHaracterization
(SEARCH) network <inline-formula><mml:math id="M361" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> measurements
We analyzed how the choice of soil NO parameterization affects NOx
concentrations in nonagricultural regions by using SEARCH network
measurements (http://www.atmospheric-research.com/studies/SEARCH/index.html, last access: 22 February 2019). Six SEARCH
sites located in the southeastern US are evaluated for May and July 2011:
Gulfport, Mississippi (GFP), an urban coastal site ∼1.5 km from the
shoreline; Pensacola, an outlying (aircraft) landing field (OLF) remote
coastal site near the gulf ∼20 km inland; Atlanta, Georgia (Jefferson
Street, JST), and North Birmingham, Alabama (BHM) – both urban inland sites;
and Yorkville, Georgia (YRK), and Centreville, Alabama (CTR) – remote inland
forest sites.
Comparison of average monthly (May and July 2011) MB for CMAQ
NOx with (a) YL, (b) BDSNP parameterized,
and (c) mechanistic schemes compared to South Eastern Aerosol
Research and CHaracterization (SEARCH) NOx observations in
nonagricultural remote regions.
Across the southeastern US during these episodes, BDSNP estimated higher
emissions than YL and the mechanistic scheme estimated lower emissions
(Fig. 3). Also, CMAQ with each scheme overestimated NOx observed at
each SEARCH site (Fig. 11). Thus, shifting from YL to BDSNP worsens mean
bias (MB) for NOx, while the mechanistic scheme reduces MB. The impacts
are most pronounced at the rural Centreville site (Fig. 11).
Evaluation with OMI satellite <inline-formula><mml:math id="M369" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> column observations
Tropospheric NO2 columns observed by OMI and available publicly at the
NASA archive (http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omno2_v003.shtml, last access: 22 February 2019;
Bucsela et al., 2013; Lamsal et al., 2014) are used to evaluate
the performance of CMAQ under the three soil NOx schemes. To enable a
fair comparison, the quality-assured and quality-checked (QA / QC) clear-sky
(cloud radiance fraction <0.5) OMI NO2 data are gridded and
projected to our CONUS domain using ArcGIS 10.3.1. CMAQ NO2 column
densities in molecules per cm2 are generated from CMAQ through vertical
integration using the variable layer heights and air mass densities in these
tropospheric layers. These NO2 column densities are then extracted for
13:00–14:00 local time across the CONUS domain to match the time of OMI
overpass measurements.
We compared CMAQ-simulated tropospheric NO2 columns with OMI data for
four broad regions that showed the highest sensitivity to the soil N
schemes. For May 2011, the mechanistic scheme produces higher estimates of
NO2 than YL in the western US and Texas, with lower estimates in the
rest of the agricultural Great Plains. In July, however, the mechanistic
scheme produces lower estimates than YL in each of these regions, but the
differences are narrower than in May (Fig. 12). Switching from YL to our
updated mechanistic scheme improved agreement with OMI NO2 columns in
the western US (for May only), Montana, North and South Dakota, North and
South Carolina and Georgia (July only), and Oklahoma and Texas (red
boundaries). However, switching from YL to the mechanistic scheme worsens
underpredictions of column NO2 in the rest of the Midwest (black
boundaries) during both May and July (Figs. 12 and 13). The mechanistic
scheme improves model performance in the southeastern US and many portions
of the central and western US (Table 4). Overestimation is exhibited for
the eastern US across all soil N schemes and can be attributed more to the
current emission inventory in CMAQ overestimating NO2 vertical column
density in this region of CONUS (Kim et al., 2016). For Texas and Oklahoma,
the mechanistic scheme performs better than YL but still underestimates OMI
observations in May and performs well in July (Fig. 13).
Impact of switching from the YL scheme to the mechanistic scheme on CMAQ
tropospheric NO2 column density at NASA's Ozone Monitoring
Instrument (OMI) overpass time (13:00–14:00 local time) on a monthly average
(May and July 2011) basis.
Statistical performance of the CMAQ modeled (with YL, updated BDSNP, and
mechanistic schemes) tropospheric NO2 column for May 2011 with OMI
NO2 observations for sensitive sub-domains for CONUS.
Underestimates of soil N in some regions with an abundance of animal farms,
such as parts of Colorado, New Mexico, north Texas, California, the
northeast US, and the Midwest, may be attributed to the lack of
representation of farm-level manure N management practices, in which manure
application can exceed the EPIC estimate of optimal crop demand. Farms in
the vicinity of concentrated animal units often apply N in excess of the
crop N requirements as part of the manure management strategy, typically
increasing the N emissions (Montes et al., 2013). The USDA has reported that
confined animal units or livestock production correlates with increasing
amounts of farm-level excess N (Kellogg et al., 2000; Ribaudo et al., 2016). Model representations of these practices are needed to
better estimate the impact of nitrogen in the environment.
Conclusions
Our implementation of a mechanistic scheme for soil N emissions in CMAQ
provides a more physically based representation of soil N than previous
parametric schemes. To our knowledge, this is the first time that soil
biogeochemical processes and emissions across a full range of nitrogen
compounds have been simulated in a physically realistic manner in a regional
photochemical model. Our mechanistic scheme directly simulates nitrification
and denitrification processes, allowing it to consistently estimate soil
emissions of NO, HONO, NH3, and N2O (Figs. 1 and 2). The
mechanistic scheme also updates the representation of the dependency of soil
N on WFPS by utilizing parameters like water content at saturation, wilting
point, and field capacity and their impact on gas diffusivity (Del Grosso et
al., 2000; Parton et al., 2001).
Overall, the magnitudes of soil NOx emissions predicted by the
mechanistic scheme are similar to those predicted by the YL parametric
scheme and smaller than those predicted by the BDSNP scheme. In dry
conditions, soil NO has been shown to be the highest compared to wet
conditions with the lowest, explained by sustained high nitrification rates due
to high gas diffusivity in dry conditions (Homyak and Sickman, 2014).
Arid soils
or dry seasons with adequate soil N due to asynchrony between soil C
mineralization and nitrification have been shown to shut down plant N uptake
through high gas diffusivity, causing NO emissions to increase (Evans and
Burke, 2013; Homyak et al., 2016). The mechanistic scheme exhibits this spatial
variability in soil NO depending on dry or wet conditions, since it accounts
for their dependence on soil moisture and gas diffusivity, as well as the C
and N cycling that leads to adequate soil N.
Spatial patterns of NOx emissions differ across the schemes and
episodes (Fig. 3), but generally show the highest emissions in fertilized
agricultural regions. During the episodes considered here, Texas experienced
severe to extreme drought, while parts of the northeast and Pacific
Northwest were unusually wet
(http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/regional_monitoring/palmer/2011/, last access: 22 February 2019). Testing for
other time periods is needed to see how results differ during different
seasons and as drought conditions vary. Model evaluation will also depend on
the meteorological model's skill in capturing dry and wet conditions.
Comparison of average monthly (May and July 2011) OMI NO2
column densities with CMAQ tropospheric NO2 column density using
YL, BDSNP, and mechanistic schemes. Regions are depicted in Fig. 12.
The lower emissions of the mechanistic scheme reduce the overprediction
biases for ground-based observations of ozone and PM nitrate that had been
reported by Rasool et al. (2016) for the BDSNP scheme (Figs. 6–10). The
mechanistic scheme reduced overpredictions of NOx concentrations at
SEARCH sites in the southeastern US (Fig. 11). However, changes in
performance for simulating satellite observations of NO2 columns were
mixed (Figs. 12–13). The underestimation of NO2 by CMAQ with the
mechanistic scheme in agricultural regions of the Midwest may be partially
attributed to neglecting manure management practices from livestock
operations. In the US, 60 % of nitrogen from manure produced on
animal feedlot operations cannot be applied back to the same land because it
is in “excess” of USDA advised agronomic rates. Most US counties with
animal farms have adequate crop acres not associated with animal operations,
but these are within the county, on which it is feasible to spread the excess manure
at agronomic rates at certain additional cost. However, 20 % of the
total US on-farm excess manure nitrogen is produced in counties with
insufficient cropland for its application at agronomic rates (Gollehon et
al., 2001). For areas without adequate land, alternatives to local land
application such as energy production (for example, biofuel) are needed. In
the absence of such a mitigation strategy, excess manure N applied on soil
contributes to reactive N emissions and leaching (Ribaudo et
al., 2003, 2012).
Although this work represents the most process-based representation of soil
N ever introduced to a regional photochemical model, limitations remain.
EPIC still lacks a complete representation of farming management practices
like excess N applied as part of a nutrient management strategy for livestock, which
can increase soil N pools and associated emissions. Developing and
evaluating these models to address management decisions is challenging as
they are often regionally specific and based on expert knowledge including
regional and global economics and biogeochemical processes that have yet to
be codified into a predictive system. Some aspects of soil N biogeochemistry
remain insufficiently understood, especially as they relate to HONO
emissions. Nevertheless, the mechanistic approach introduced here will make
it possible to incorporate future advancements in understanding C and N
cycling processes.
For future work, there is a need for more accurate representation of actual
farming practices beyond the generalizations made by the EPIC model. Model
development should be continued to better constrain N sources such as rock
weathering, which are still ignored for estimating soil N emissions.
Recently, Houlton et al. (2018) postulated that bedrock weathering can
contribute an additional 6 %–17 % to global inorganic soil N for different
natural biomes. There is also a need for more field observations of soil N
emissions to better evaluate the spatial and temporal patterns simulated by
the models.
Code availability
The modified and new source code, inputs, and sample outputs along with the
user manual giving details on implementing the new mechanistic module
in-line with CMAQ version 5.1, as used in this work, are available on the Oak
Ridge National Laboratory Distributed Active Archive Center for
Bio-geochemical Dynamics (Rasool et al., 2018; 10.3334/ORNLDAAC/1661). Source codes for CMAQ version 5.1
and FEST-C version 1.2 are both open source and available with applicable free
registration at http://www.cmascenter.org (last access: 22 February 2019). The Advanced Research
WRF model (ARW) version 3.7 used in this study is also available as a free
open-source resource at http://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 22 February 2019).
List of 24 MODIS soil-biome-based Cmic, Nmic,
and HONOf
emission factors (%) derived from Xu et al. (2013) and Oswald et al. (2013).
IDMODIS land coverKöppen mainclimate*Cmic %Nmic %HONOf %1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Water Permanent wetland Snow and ice Barren Unclassified Barren Closed shrubland Open shrubland Open shrub land Grassland Savanna Savanna Grassland Woody savanna Mixed forest Evergreen broadleaf forest Deciduous broadleaf forest Deciduous needle. forest Evergreen needle. forest Deciduous broadleaf forest Evergreen broadleaf forest Cropland Urban and built-up lands Cropland–nat. veg. mosaic– – – D, E – A, B, C – A, B, C D ,E D, E D, E A, B, C A, B, C – – C, D, E C, D, E – – A, B A, B – – –0 1.20 0 5.02 0 5.02 1.43 1.43 1.43 2.09 1.66 1.66 2.09 2.09 1.29 0.99 1.16 1.79 1.76 1.16 0.99 1.67 0 1.460 2.58 0 5.72 0 5.72 2.33 2.33 2.33 4.28 3.61 3.61 4.28 4.28 2.8 2.62 2.42 3.08 4.18 2.42 2.62 2.53 0 2.620 0 0 48 0 48 35.5 41 41 22 41 41 22 41 13 9 11 8.5 8.5 11 9 42.9 0 43.5
* A – equatorial, B – arid, C – warm temperature, D – snow, E – polar
Mapping table to create the MODIS 24 soil biome map based on NLCD40
MODIS land cover categories for updated BDSNP parameterization.
NLCD IDNLCD40 MODIS category (40)MODIS IDSoil biome category (24)1Evergreen needleleaf forest19Evergreen needleleaf forest2Evergreen broadleaf forest16 and 21Evergreen broadleaf forest3Deciduous needleleaf forest18Dec. needleleaf forest4Deciduous broadleaf forest17 and 20Dec. broadleaf forest5Mixed forests15Mixed forest6Closed shrublands7Closed shrublands7Open shrublands8 and 9Open shrublands8Woody savannas14Woody savanna9Savannas11 and 12Savanna10Grasslands10 and 13Grassland11Permanent wetlands2Permanent wetland12Croplands22Cropland13Urban and built up23Urban and built-up lands14Cropland–natural vegetation mosaic24Cropland–nat. veg. mosaic15Permanent snow and ice3Snow and ice16Barren or sparsely vegetated6Barren17IGBP water1Water18Unclassified4Barren*19Fill value5Unclassified*20Open Water1Water21Perennial ice–snow3Snow and ice22Developed open space23Urban and built-up lands23Developed low intensity23Urban and built-up lands24Developed medium intensity23Urban and built-up lands25Developed high intensity23Urban and built-up lands26Barren land (rock–sand–clay)24Cropland–nat. veg. mosaic27Unconsolidated shore24Cropland–nat. veg. mosaic28Deciduous forest16 and 21Evergreen broadleaf forest29Evergreen forest19Evergreen needleleaf forest30Mixed forest15Mixed forest31Dwarf scrub8 and 9Open shrublands32Shrub–scrub8 and 9Open shrublands33Grassland–herbaceous10 and 13Grassland34Sedge–herbaceous14Woody savanna35Lichens10 and 13Grassland36Moss10 and 13Grassland37Pasture–hay24Cropland–nat. veg. mosaic38Cultivated crops22Cropland39Woody wetlands2Permanent wetland40Emergent herbaceous wetlands2Permanent wetland
* NLCD categories 18 and 19 were mapped as MODIS category 1 (water)
in Rasool et al. (2016), which have been corrected here.
Microbial / organic biomass C and N % and HONO/NNOx %
mapped to the respective NLCD40 MODIS land cover categories based on Xu et al. (2013) estimates.
NLCD IDNLCD40 MODIS category (40)Cmic %Nmic %HONOf %1Evergreen needleleaf forest1.764.188.52Evergreen broadleaf forest0.992.6293Deciduous needleleaf forest1.793.088.54Deciduous broadleaf forest1.162.42115Mixed forests1.292.80136Closed shrublands1.432.3335.57Open shrublands1.432.33418Woody savannas2.094.28419Savannas1.663.614110Grasslands2.094.282211Permanent wetlands1.22.58012Croplands1.672.5342.913Urban and built up00014Cropland–natural vegetation mosaic1.462.6243.515Permanent snow and ice00016Barren or sparsely vegetated5.025.724817IGBP water00018Unclassified5.025.724819Fill value00020Open water00021Perennial ice–snow00022Developed open space00023Developed low intensity00024Developed medium intensity00025Developed high intensity00026Barren land (rock–sand–clay)a00027Unconsolidated shoreb00028Deciduous forest0.992.62929Evergreen forest1.764.188.530Mixed forest1.292.81331Dwarf scrub1.432.334132Shrub–scrub1.432.334133Grassland–herbaceous2.094.282234Sedge–herbaceous2.094.284135Lichens2.094.282236Moss2.094.282237Pasture–hayb0043.538Cultivated cropsb0042.939Woody wetlands1.22.58040Emergent herbaceous wetlands1.22.580
a NLCD classes 26 and 27 consisting of mostly rocks. b Cmic
and Nmic for US croplands classified under NLCD classes 37 and 38 are kept
as zero to prevent double counting, as they are accounted for by EPIC N
data.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-12-849-2019-supplement.
Author contributions
QZR developed the model code with JOB. QZR
performed the simulations and analysis. QZR prepared the paper
with extensive reviews and edits from JOB and DSC.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
NASA (grant number NNX15AN63G) provided the funding for this work. We
acknowledge Ellen Cooter from the U.S. EPA for her insights and invaluable
help with EPIC/FEST-C modeling. The views expressed in this article are
those of the authors and do not necessarily reflect the views or policies of
the U.S. Environmental Protection Agency.Edited by: Gerd A. Folberth
Reviewed by: two anonymous referees
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