Accurate simulation of ammonia (NH3) volatilization
from fertilized croplands is crucial to enhancing fertilizer-use efficiency
and alleviating environmental pollution. In this study, a process-oriented
model, CNMM–DNDC (Catchment Nutrient Management Model–DeNitrification–DeComposition), was evaluated and modified using NH3
volatilization observations from 44 and 19 fertilizer application events in
cultivated uplands and paddy rice fields in China, respectively. The major
modifications for simulating NH3 volatilization from cultivated uplands were primarily derived from a peer-reviewed and published study. NH3 volatilization from cultivated uplands was jointly regulated by wind speed, soil depth, clay fraction, soil temperature, soil moisture, vegetation canopy, and rainfall-induced canopy wetting. Moreover, three principle modifications were made to simulate NH3 volatilization from paddy rice fields. First, the simulation of the floodwater layer and its pH were added.
Second, the effect of algal growth on the diurnal fluctuation in floodwater
pH was introduced. Finally, the Jayaweera–Mikkelsen model was introduced to
simulate NH3 volatilization. The results indicated that the original
CNMM–DNDC not only performed poorly in simulating NH3 volatilization
from cultivated uplands but also failed to simulate NH3 volatilization
from paddy rice fields. The modified model showed remarkable performances in simulating the cumulative NH3 volatilization of the calibrated and
validated cases, with drastically significant zero-intercept linear
regression of slopes of 0.94 (R2= 0.76, n= 40) and 0.98 (R2= 0.71, n= 23), respectively. The simulated NH3 volatilization from cultivated uplands was primarily regulated by the dose and type of the nitrogen fertilizer and the irrigation implementation, while the simulated NH3 volatilization from rice paddy fields was sensitive to soil pH; the dose and depth of nitrogen fertilizer application; and flooding management strategies, such as floodwater pH and depth. The modified model is acceptable to compile regional or national NH3 emission inventories and develop strategies to alleviate environmental pollution.
Introduction
Synthetic fertilizer application, as the second-largest contributor to
ammonia (NH3) emissions after livestock production, accounts for
approximately 30 % to 50 % of anthropogenic NH3 emissions
(Behera et al., 2013; Bouwman et al., 1997; Huang et al., 2012; Paulot et
al., 2014). The great quantity of NH3 volatilized from agricultural
fields contributes to low nitrogen use efficiency for crops (Chien et
al., 2009; Mariano et al., 2019; Zhu et al., 1989). The subsequent dry and
wet deposition to terrestrial ecosystems results in the acidification and
eutrophication of natural ecosystems (e.g., Anderson et al., 2008;
Bobbink et al., 1998; Li et al., 2016) and is also considered an indirect
source of nitrous oxide (Martin et al., 2004; Schjørring, 1998).
Recently, NH3 in the atmosphere has played a vital role in aerosol
formation during several haze periods, which has attracted great attention
(e.g., Felix et al., 2013; Kong et al., 2019; Liu et al., 2018; Savard et
al., 2017).
Many studies have attempted to estimate NH3 loss from fertilized
croplands using biogeochemical process models, i.e.,
DeNitrification–DeComposition (DNDC; Dubache et al., 2019; Dutta et al., 2016; Giltrap et al., 2017; Michalczyk et al., 2016), water and nitrogen management (WNMM; Park et al., 2008), and Community Earth System Model (CESM; Riddick et al., 2016;
Vira et al., 2020). However, these models do not distinguish between the
simulation modules of NH3 volatilization for cultivated uplands and
rice paddy fields but rather use the same algorithm (Cannavo et
al., 2008; Li, 2016). It is worth emphasizing that the mechanisms of
NH3 volatilization are completely different between cultivated uplands
and rice paddy fields due to the presence of floodwater over rice paddy
soils. Recent studies also indicate that estimating NH3 emissions
without considering rice cultivation results in large uncertainties
(Riddick et al., 2016; Xu et al., 2019). In particular, some studies have
shown that NH3 volatilization rates from rice paddy fields are not
lower than those of upland crops (Zhou et al., 2016), which
also indicates the different mechanisms of NH3 volatilization between
cultivated uplands and rice paddy fields. Therefore, using separate modules
to simulate NH3 volatilization from cultivated uplands and rice paddy
fields is necessary for the accurate estimation of NH3 emissions.
Given the totally different mechanisms of NH3 volatilization between
cultivated uplands and rice paddy fields, the influencing factors affecting
NH3 volatilization from cultivated uplands are different from those of
rice paddy fields. The dose, type, and application methods of nitrogen
fertilizer have been confirmed as the primary factors affecting NH3
volatilization from cultivated uplands (e.g., Liu et al., 2003; Roelcke
et al., 2002; Zhang et al., 1992). Moreover, several studies have reported
that irrigation and precipitation exert a complicated influence (stimulated
or inhibited) on NH3 volatilization from cultivated uplands (e.g.,
Han et al., 2014; Holcomb et al., 2011; Sanz-Cobena et al., 2011).
However, the depth and pH of surface floodwater, which are unique
characteristics of rice paddy fields, were found to be the major factors
influencing NH3 volatilization from rice paddy fields (Bowmer and
Muirhead, 1987; Hayashi et al., 2006; Jayaweera and Mikkelsen, 1991). A
comprehensive discussion of the influencing factors affecting NH3
volatilization from cultivated uplands and rice paddy fields is crucial for
providing suggestions to further improve the performance of process-based
biogeochemical models in simulating NH3 volatilization from cropland
soils and offer specific and pertinent policy advice for the reduction in
NH3 loss.
A previous study established a scientific algorithm for the DNDC model to
simulate NH3 volatilization from cultivated uplands, which performed
well under validation with independent cases of cultivated uplands from
China (Li et al., 2019). However, no biogeochemical model has
achieved simulations of NH3 volatilization from rice paddy fields using
a process-oriented algorithm, although a classical and extensively used
model, i.e., the Jayaweera–Mikkelsen model (J–M model), exists (Jayaweera
and Mikkelsen, 1990a; Li et al., 2008; Wang et al., 2016; Zhan et al.,
2019).
The Catchment Nutrient Management Model–DeNitrification–DeComposition
(CNMM–DNDC) model, established by coupling the core carbon and nitrogen
biogeochemical processes of DNDC (e.g., decomposition, nitrification,
denitrification, and fermentation) to the distributed hydrologic framework
of CNMM, is one of the latest versions of DNDC (Zhang et al.,
2018). The CNMM–DNDC has been gradually developing into a comprehensive and
reliable process-oriented biogeochemical model that performs well in terms
of simulating the complex hydrologic and biogeochemical processes of a
subtropical catchment with various landscapes (Zhang et al.,
2018), the nitrous oxide and nitric oxide emissions from a subtropical tea
plantation (Zhang et al., 2020), and the NO3- leaching processes
of black soils in northeastern China (Zhang et al., 2021).
However, the rationality of the CNMM–DNDC's scientific processes in
simulating NH3 volatilization from fertilized croplands is still
lacking in terms of a thorough assessment. In particular, CNMM–DNDC and
other widely used biogeochemical models (e.g., DNDC) do not consider
floodwater over rice paddy soils when simulating NH3 volatilization but
rather directly adopt the scientific processes and algorithms applied in
NH3 volatilization from cultivated uplands to predict NH3
volatilization from rice paddy fields (Li, 2016; Zhang et al., 2018).
Based on the above deficiencies, the authors hypothesized that the CNMM–DNDC
is able to simulate NH3 volatilization following the application of
synthetic nitrogen fertilizers to cultivated uplands and flooded rice paddy
fields. To test this hypothesis, this study evaluated and modified the
CNMM–DNDC's scientific processes for simulating NH3 volatilization from
cropland soils using 44 and 19 fertilizer application events from cultivated
uplands and rice paddy fields in China, respectively. The objectives of this
study were to (i) evaluate the performance of the CNMM–DNDC in simulating
the observed NH3 volatilization following synthetic nitrogen
application to cultivated uplands, (ii) introduce thoroughly tested and
validated scientific algorithms simulating NH3 volatilization from
cultivated uplands into the CNMM–DNDC, (iii) adopt widely applied
process-based algorithms (J–M model) into the modified CNMM–DNDC, (iv) assess the performance of the modified model to simulate NH3
volatilization from flooded rice paddy fields using collected reliable
observations, and (v) identify the major factors affecting NH3
volatilization from cultivated uplands and rice paddy fields to offer
suggestions for further improving the model performance.
Materials and methodsModel introduction and modificationsBrief introduction of the CNMM–DNDC
The CNMM–DNDC model was originally established by
Zhang et al. (2018). In the original CNMM–DNDC, the core
biogeochemical processes (including decomposition, nitrification,
denitrification, and fermentation) of DNDC (Li, 2016; Li
et al., 1992) were incorporated into the distributed hydrologic framework of
CNMM (Li et al., 2017). Based on comprehensive observations, the
CNMM–DNDC was initially tested in a subtropical catchment, which showed
credible performances in simulating the yields of crops, emissions of
greenhouse gases (i.e., methane and nitrous oxide), emissions of nitrogenous
pollutant gases (i.e., nitric oxide and NH3), and hydrological nitrogen
losses by leaching and NO3- discharge in streams for different
land uses (including forests and arable lands cultivated with maize, wheat,
oil rape, or rice paddy) (Zhang et al., 2018). Subsequently,
Zhang et al. (2020) modified the CNMM–DNDC by adding tea-growth-related processes that may induce a soil pH reduction, and this
modified model performed well in simulating the emissions of nitrous oxide
and nitric oxide from a subtropical tea plantation plot. Moreover, the
CNMM–DNDC performed well in simulating the NO3- leaching process
of black soils in northeastern China (Zhang et al., 2021).
However, during model preparation and operation for the simulation of
NH3 volatilization, the authors found that the present model version,
using a complicated and obscure R programming script to prepare the ARC GRID
ASCII data format of site- and plot-scale inputs, was time-consuming and
confusing. Therefore, an easy-to-operate and standardized version of the
model needed to be established.
The new standard version of the model was built without changing the
original key scientific modules; however, the complicated R programming
script was converted into a simple Excel spreadsheet to prepare the model
inputs, which is easy for beginners to use. The site-scale and
regional-scale simulations were separated. In the site-scale simulation used
in this study, the authors hypothesized a flat terrain region with a 5 × 5 grid, and thus, the solar radiation was not affected by
topography. Therefore, the simulation of any grid was the same and could be
regarded as the representative simulation results of the study region. If
the users were only interested in the simulation of a field site experiment
or could only provide the input data based on the site or plot scale, then they
would not need to provide any information about the topography and stream of
their study region which was necessary for the regional-scale simulation.
The site-scale simulation, which was used in this study, is convenient for
model validation, saves time in terms of model operation, and is easy to use
for beginners.
Modifications for simulating NH<inline-formula><mml:math id="M59" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization from cultivated uplands
In the original CNMM–DNDC model, the direct processes involved in the
calculation of NH3 volatilization from cultivated uplands included
ammonium bicarbonate (ABC) decomposition, urea hydrolysis, and NH3
volatilization (Fig. S1 in the Supplement). Among them, ABC decomposition was regulated by
soil pH and soil depth; urea hydrolysis was affected by soil temperature and
soil organic carbon; and NH3 volatilization from cultivated uplands was
simply determined by the regulating factors of wind speed, soil depth, and
soil temperature (Tables S1 and S2). Moreover, other synthetic fertilizers'
dissolution and organic manure mineralization were involved in the original
model. The modifications of the new version of the CNMM–DNDC for simulating
NH3 volatilization from cultivated uplands were mainly adapted from
Li et al. (2019). Compared to the original CNMM–DNDC, three
major modifications were conducted. First, the soil temperature parameter
(fT) of the urea hydrolysis function was recalibrated, and the effect of
soil moisture on urea hydrolysis (fSM) was newly added. Second, the regulatory effect of soil temperature on ABC decomposition
(fTs_ABC) was parameterized. Finally, the effects of the
original parameters of soil temperature and moisture on NH3
volatilization were recalibrated, and the effects of the clay fraction,
plant standing, and canopy wetting on NH3 release to the atmosphere
were newly parameterized (Fig. S1). The above-mentioned calibration and
parameterization were conducted by Dubache et al. (2019) and Li
et al. (2019). Therefore, the NH3 flux from cultivated uplands (flux_(NH3)uplands) was jointly determined by the regulating factors of wind speed (fwind, 0–1), soil temperature (ftemp, 0–1), soil moisture (fwater, 0–1), soil depth (fdepth, 0–1), clay fraction
(fclay, 0–1), vegetation canopy (fcanopy, 0–1), and
rainfall-induced canopy wetting (frain, 0–1), as shown in Eq. (1). Each factor was defined as a dimensionless fraction within 0–1. NH3(l) refers to the dissolved NH3 in the liquid phase of upland soils. Among these regulating factors, fdepth was calculated by the
number of soil layers in Li et al. (2019), where the thickness
of the soil layer was set as the value of the saturated hydraulic
conductivity. However, in the CNMM–DNDC, the simulated soil layers and their
corresponding thicknesses were set to be freely defined by users. The
algorithm of fdepth from Li et al. (2019) was inappropriate
for this study. Therefore, fdepth was revised using the thickness of the soil layer based on Eq. (2), wherein dsoil denotes the depth of the simulated soil layer. The calibration cases with fertilizer application
depth were used for the calibration of fdepth. Moreover, the time step of the CNMM–DNDC was 3 h, but the time step was 1 d in the DNDC model. So the ratio of time steps of the two models (Tlayer with the value of 8) was involved in Eq. (1). Nevertheless, the deviation of the two models derived from the different time steps existed, as shown in Table S3. To solve the deviation, a time-step parameter (fTstep) was introduced into Eq. (1), which was calculated at 0.75 in this study using the calibration cases with surface broadcast (n= 21). The zero-intercept linear regression was applied for model calibration. We provided the calibration of fTstep in Fig. S2 as an instance. Table S1 listed the algorithms of the
original and modified model in simulating NH3 volatilization from
cultivated uplands. The descriptions and units of the symbols used in Table S1 were listed in Table S2.
1flux_NH3uplands=3.6fwindftempfwaterfdepthfclayfcanopyfrainfTstepNH3(l)/Tlayer2fdepth=0.5dsoil/0.03
Modifications for simulating NH<inline-formula><mml:math id="M93" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization from rice paddy fields
The original CNMM–DNDC failed to simulate NH3 volatilization from rice
paddy fields because it lacked the capability to simulate the surface-water-flooded layer over rice paddy fields. Given the presence of floodwater
over rice paddy soils, the mechanisms of NH3 volatilization are
different between cultivated uplands and rice paddy fields. However,
CNMM–DNDC and other widely used biogeochemical models (e.g., DNDC) adopted
scientific processes and algorithms applied in simulating NH3
volatilization from fertilized cultivated uplands to calculate NH3
volatilization from rice paddy fields without considering floodwater over
soils (Cannavo et al., 2008; Li, 2016). Therefore, floodwater
over rice paddy soils was added to the modified CNMM–DNDC. To add this
component, the modified CNMM–DNDC adopted the Jayaweera–Mikkelsen model
(i.e., J–M model), based on the two-film theory of mass transfer
(Jayaweera and Mikkelsen, 1990a), which is one of the
most widely applied process-based models for simulating NH3
volatilization from rice paddy fields. The J–M model consists of two
processes (Fig. 1): (i) the chemical processes of NH4+ ions and
aqueous NH3 (NH3(aq)) equilibrium in floodwater and (ii) the
volatilization processes of NH3(aq) transfer in the form of NH3 gas (NH3(air)) across the water–air interface to the atmosphere (Reaction R1); kd (first-order; s-1) and ka (second-order; L mol-1 s-1) refer to the dissociation and association rate constants
for NH4+–NH3(aq) equilibrium, respectively; kvN (first-order; s-1) refers to the volatilization rate constant of NH3(aq).
NH4+⇌kakdH++NH3(aq)⟶kvNNH3(air)
According to the above theories, the change rate of the NH4+
concentration in floodwater ([NH4+]w, mol L-1) due to NH3 volatilization (Ra, mol L-1 s-1) can be estimated by Eq. (3) as a function of [NH4+]w, H+ concentration in floodwater ([H+]w, mol L-1), kd, ka and kvN.
Mechanism of the Jayaweera–Mikkelsen model introduced into
the modified CNMM–DNDC; kd and ka refer to the dissociation and association rate constants for NH4+–NH3 chemical equilibrium, respectively; klN and kgN refer to the exchange constants for NH3 in the liquid and gas films, respectively. ClNi and CgNi refer to the average concentrations of NH3 at the interface in the liquid and gas films, respectively. NH3(aq) and NH3(air) refer to the average concentration of NH3 in aqueous and gas phases, respectively.
Ra=-kdkvNNH4+wkaH+w+kvN
The dynamic changes in [H+]w and [NH4+]w are calculated by the CNMM–DNDC instead of the field experiment described in
Jayaweera and Mikkelsen (1990a).
In the modified CNMM–DNDC, the pH of the floodwater, which is the negative
logarithm of [H+]w, is related to the initial pH of water for flooding and that of surface soil. When the floodwater depth is less than
0.04 m, the pH of the floodwater is equal to the mean of the initial pH of
water for flooding and that of surface soil, both of which are the inputs of
the modified model. Otherwise, the pH of the floodwater is equal to the
initial pH of the water for flooding. On the one hand, [H+]w is
regulated by urea hydrolysis in floodwater, the algorithm of which was
derived from that of urea hydrolysis affecting soil pH in the model. On the
other hand, many studies have found that a marked diurnal fluctuation in
floodwater pH is associated with algal photosynthesis, which was elevated
with solar radiation (De Datta, 1995; Fillery and Vlek, 1986). Therefore,
a ratio of the daytime solar shortwave radiation effect on algal
photosynthesis (Rslr; 0–1) was established by the authors using Eq. (4) as a quadratic function of the simulation time (t; 06:00 to 21:00 LT (UTC+8) with a
3 h interval) of a day. Rslr at the other moments with no or
extremely little solar radiation in a day was set as 0. The effect of algal
growth (falg) on floodwater pH was calculated by Eq. (5), where the adjusted coefficient (kalg; 0–1) was calibrated to 0.75 or 0.6 when the floodwater depth was no more than or more than 0.04 m, respectively. The floodwater pH of (t+1) was modified by the floodwater pH of t and falg using Eq. (6), which was set as no more than 10.
4Rslr=-0.0036t2+0.1096t-0.70465falg=kalgRslrR+0.256pHt+1=pHt+falg
When fertilizers (e.g., urea) are applied to the rice paddy fields, they are
first allocated to the floodwater and soil layers according to the ratio of
the floodwater depth and the application depth of fertilizer in the modified
CNMM–DNDC. Subsequently, [NH4+]w increases with urea
hydrolysis, and ABC decomposition occurs in the floodwater. In the modified
model, the calculation of urea hydrolysis in floodwater refers to that in
the upland soils (Dubache et al., 2019) by removing the
influencing factors of soil organic carbon and soil moisture. Therefore,
urea hydrolysis in floodwater is only determined by the floodwater
temperature. To simplify the calculation, the floodwater temperature is
arbitrarily set equal to the temperature in the first soil layer in the
modified model. Given that ABC decomposition in floodwater was not involved
in the original CNMM–DNDC, this study directly adopted the algorithm of ABC
decomposition in upland soils used in Li et al. (2019), and
this process was regulated by soil temperature, pH, and the applied depth of
fertilizer. However, ABC decomposition in floodwater is different from that
in upland soils; i.e., the ABC concentration is uniformly distributed in the
floodwater, and the effect factors (i.e., temperature, pH, and depth) applied
should be those of floodwater rather than those of soil. Therefore, this
study ignored the effect of soil depth and retained the effect of floodwater
temperature and pH on ABC decomposition in floodwater.
For each simulation time step, the NH4+ in the floodwater and the
first soil layer experiences uniform mixing and exchange. Then,
NH4+ is transported in soil layers, accompanied by organic
nitrogen mineralization, consumption via plant uptake, nitrification,
volatilization of NH3, and adsorption and desorption by clay (Li, 2016;
Li et al., 1992, 2019).
The variables kd, ka, and kvN in Eq. (3) are determined by the environmental factors, i.e., the temperature and the depth of floodwater (Jayaweera and Mikkelsen, 1990a). As shown in Eq. (7),
ka is affected by its relationship with floodwater temperature
(Tf; K) based on Alberty (1983):
ka=3.8×1011-3.4×109Tf+7.5×106Tf2;kd is derived from the relationship with the equilibrium constant for NH4+–NH3(aq) (K) and ka (Eq. 8):
kd=Kka.K is calculated as a function of the floodwater temperature (Eq. 9)
derived from Jayaweera and Mikkelsen (1990a):
K=10-0.0897+(2729/Tf).
TheNH3 volatilization rate constant (kvN) is estimated by the law of conservation of mass, which is considered in the system of NH3 transfer across the air–water interface. By dimensional analysis, kvN is determined by Eq. (10), based on the ratio of the floodwater depth (d; m) and the overall mass-transfer coefficient for NH3 (KON; cm h-1):
kvN=KON/(3.6×105d).
According to the two-film theory, based on Fick's first law and Henry's
law, KON is determined by Eq. (11) using the exchange constant for NH3 in the gas and liquid phases (kgN and klN, respectively) and the non-dimensional Henry's constant (HnN). As described by Jayaweera and Mikkelsen (1990a), HnN is a function of Tf, which can be calculated by Eq. (12), whereas kgN and klN are
dependent on the wind speed measured at a height of 8 m (U8; m s-1), which can be calculated using Eqs. (13)–(14). U8 can be
determined using the model input of wind speed measured at a height of 10 m
(U10; m s-1), based on Eq. (15) derived from
Jayaweera and Mikkelsen (1990a).
11KON=HnNkgNkIN/HnNkgN+kIN12HnN=183.8e-1229/Tf/RTf13kgN=19.0895+742.3016U814kIN=12.5853/1+43.0565e-0.4417U8/1.607515U8=11.51ln10/8×105U10
Finally, the 3 h cumulative flux of NH3 volatilization (flux_(NH3)rice; kg N ha-1 3 h-1) is calculated by Eq. (16) using Ra, d, and the simulation time step based on the molar mass of N (∼ 14 g mol-1) and the conversion coefficient from square meters to hectares (1 m2= 1 × 10-4 ha).
flux_NH3rice=-1.512×109dRa
The CNMM–DNDC with the above modifications is hereinafter referred to as the
modified CNMM–DNDC.
Brief description of the field sites and treatments
Two field observation datasets of NH3 volatilization using
micrometeorological methods or wind tunnel techniques, which were measured
in cultivated uplands and flooded rice paddy fields of China, respectively,
were collected from published peer-reviewed articles. For the dataset of
cultivated uplands, the collected field observations were conducted at seven
experimental sites, including Dongbeiwang (DBW) in Beijing; Fengqiu with
cultivated uplands (FQU) in Henan; Guangchuan (GC), Luancheng (LC), and
Quzhou (QZ) in Hebei; Yanting (YT) in Sichuan; and Yongji (YJ) in Shanxi
(Fig. 2). The datasets were directly inherited from Li et
al. (2019). The upland sites involved in this study were calcareous soils
cultivated with summer maize and winter wheat. The 44 cases of synthetic-fertilizer-application events in cultivated uplands (Table S4) involved
various fertilizer types (including urea, ammonium bicarbonate (ABC),
ammonium sulfate, and complex fertilizer), a wide range of applied
fertilizer doses (60–348 kg N ha-1), and various agricultural
management practices (e.g., broadcast or deep point placement of
fertilizer(s) alone or fertilization coupled with irrigation). For the rice
paddy field dataset, field observations were collected at five experimental
sites, including Changshu (CS) and Danyang (DY) in Jiangsu, Fengqiu with
rice paddy fields (FQP) in Henan, Shenzhen (SZ) in Guangdong, and Yingtan
(YTA) in Jiangxi (Fig. 2), and these sites were cultivated with summer rice
and winter wheat or double rice (Table 1). In total, 19 (P1–P19)
synthetic-fertilizer-application events were included in these measurements,
covering different fertilizer types, including urea and ABC; fertilizer
doses in the range of 41–162 kg N ha-1; and various agricultural
management practices (e.g., broadcasting or broadcasting followed by
tillage; Tables 1 and 2). In addition, the other auxiliary variables,
e.g., temperature, pH, and ammonium (NH4+) concentration of the
floodwater, measured in the rice paddy experimental sites during the
NH3 volatilization measurement periods were also collected for model
calibration and validation.
Location of the experimental field sites involved in this
study. The sites are Changshu (CS), Danyang (DY), Dongbeiwang (DBW), Fengqiu
(FQ), Guangchuan (GC), Luancheng (LC), Quzhou (QZ), Shenzhen (SZ), Yanting
(YT), Yingtan (YTA), and Yongji (YJ).
Descriptive information of the studied experimental sites
of rice paddy fields for model evaluation, including site name, experimental
year (Year), crop rotation (Crop), fertilizer type (Type) and dose (Dose; kg N ha-1), measurement method for ammonia volatilization (Method), number of fertilization cases (Number), and reference (Ref.).
SiteaYearCropbTypeDoseMethoddNumberRef.CS2002–2003RWUrea41–135MM6Song et al. (2004)DY1984RWUrea/ABCc90MM2Cai et al. (1986)FQP1986RWUrea/ABC90MM2Zhu et al. (1989)SZ2010DRUrea41–162WT8Gong et al. (2013)YTA1992DRUrea90MM1Cai et al. (1992)
a The sites are Changshu (CS), Danyang (DY), Fengqiu with rice paddy fields (FQP), Shenzhen (SZ), and Yingtan (YTA).
b The presented crop rotation types are rice–wheat (RW) and double rice (DR).
c ABC is the abbreviation of ammonium bicarbonate.
d The presented methods for the measurement of ammonia volatilization are wind tunnel (WT) and micrometeorological technique (MM).
Observed and simulated cumulative ammonia volatilization
during the measurement periods, model biases, and management practices of
individual fertilizer application cases in the rice paddy fields.
CaseSitebPeriodOcScRMBcWaterPreeFertilizer application codeatabledTypefMethodgDosehP11DY20 to 26 June 198416.47.04-57.050ABCBFT590P2FQP21 to 30 June 198635.839.139.340.18ABCBFT590P3CS22 to 30 June 200210.39.89-4.0426.38UreaB40.5P4CS22 to 30 June 200223.119.40-16.0426.38UreaB81P5CS20 to 29 July 200220.915.04-28.0420.54UreaB54P6CS20 to 29 July 200239.831.61-20.6420.54UreaB108P7CS20 to 31 August 20027.510.0233.6423.07UreaB40.5P8CS20 to 31 August 200217.920.6815.5423.07UreaB81P91DY20 to 26 June 19847.915.4494.950UreaBFT590P101FQP21 to 30 June 198627.834.3223.740.18UreaBFT590P111SZ16 May to 4 June 201016.122.5039.87.530UreaB162.2P121SZ16 May to 4 June 201021.424.0012.27.530UreaB162.2P131SZ22 June to 11 July 20109.13.43-62.47.530UreaB40.9P141SZ22 June to 11 July 201017.29.07-47.37.530UreaB81.8P151SZ31 July to 19 August 20105.95.920.37.530UreaB40.9P161SZ31 July to 19 August 20108.04.57-42.97.530UreaB40.9P171SZ26 August to 14 September 201010.07.66-23.47.530UreaB81.8P181SZ26 August to 14 September 201013.46.79-49.37.530UreaB81.8P19YTA29 July to 6 August 199236.020.98-41.721.36UreaBFT590
a P1 to P19 encode the experimental cases following individual application events of synthetic nitrogen fertilizers; the superscript “1” marks the cases with the ammonia observations referring to the model calibration.
b The sites are Changshu (CS), Danyang (DY), Fengqiu with rice paddy fields (FQP), Shenzhen (SZ), and Yingtan (YTA).
cO and S are the cumulative NH3 volatilization
(kg N ha-1) observed and simulated by the modified CNMM–DNDC, respectively; RMB is the relative model bias (%) of the modified model, which was determined as the relative difference between the simulated and observed values.
d The depth of floodwater table (cm). For the cases with “2” and “3”, the exact depth of the floodwater table was not reported. The floodwater table depth of the cases with “2” was arbitrarily set as the traditional depth of the floodwater table of the DY site, which was located in the same region. The floodwater depths of the cases with “3” were set by model calibration.
e “Pre” denotes total rainfall (cm) during the experimental period(s).
f ABC is the fertilizer type of ammonium bicarbonate.
g The application methods are surface broadcast (B) and broadcast
followed by tillage (BFT). The figures following BFT are the depth in soil
(cm).
h Unit: kg N ha-1.
Model preparation and operationInput data formatting
The input data of the modified CNMM–DNDC used in this study included the
meteorological conditions of the study area (e.g., 3-hourly average air
temperature, Tair; precipitation, P; wind speed, W; solar radiation, R; relative humidity, RH), the necessary soil properties of individual
layers (e.g., soil clay and sand fraction; organic carbon, SOC; bulk
density, BD; pH), crop parameters (e.g., crop type; thermal degree days for
maturity, TDDs; nitrogen content; plant height; and root depth), and the
implemented management practices (e.g., plant and harvest dates and methods and/or amounts of individual management practices including fertilization,
tillage, irrigation, and flooding).
For the meteorological data inputs, the reported 3-hourly meteorological
data from the weather station at the experimental site were used. If these
data were not available, then data from the adjacent weather station in the
China Meteorological Administration (CMA; http://data.cma.cn, last access: 16 July 2020) were adapted by referring to the reported average or maximum values (Table S5, Text S1).
The necessary inputs of surface soil properties at the individual upland
sites for the modified model were derived from Li et al. (2019), whereas those at the individual rice paddy sites are shown in Table S6. If the observed surface soil properties were not available, then the
values were provided using the methods of Li et al. (2019). The
soil clay and sand fraction and pH in the deep layers were set to be
consistent with those in the surface soil. Depending on the SOC at the
surface soil, the modified CNMM–DNDC calculated the SOC in the deep layers
using the algorithms involved in Li (2016), and the BD in the deep layers
was estimated using the SOC value in the corresponding layers based on the
algorithms shown in Li (2016). Other soil properties (e.g., field
capacity, wilting point, and saturated hydraulic conductivity) were estimated
using the pedo-transfer functions of Li et al. (2019).
The CNMM–DNDC contains a library of crop parameters. However, to ensure the
normal growth of the crop(s), the model's default values for the crop TDDs at
the individual sites were adapted by the multiyear (at least 5 years)
average of the sums of daily air temperatures during the growing season.
Agricultural management practice information included in the CNMM–DNDC input
was organized on a daily scale. The management practice information for the
cases of cultivated uplands was derived from Li et al. (2019),
whereas that for the cases of rice paddy fields is listed in Table S7. It is
worth noting that the information input for the cases of rice paddy fields
required the start and end dates of the individual flooding events
accompanied by the corresponding pH and depth of floodwater as model inputs.
The default value of the initial floodwater pH at all sites was set at 7.0
due to a lack of observations. The cases in DY, FQP, and YT had reported
floodwater depth observations, and the cases of CS without floodwater depth
observations were arbitrarily set to the traditional floodwater depth (0.04 m) of the DY site, which was located in the same region. For the SZ cases without floodwater depth observations, given that no site is adjacent to the Pearl River Delta region, where SZ is located, the floodwater depth of the SZ site was calculated at 0.075 m.
Model operation
To reduce the influences of initial model inputs, the model simulation
consists of a spin-up period conducted for at least 5 years (depending on
the availability of the model inputs) and the corresponding experimental
period. The sources of the daily meteorological data for the spin-up period
and the following simulation for cultivated uplands and rice paddy field
sites were derived from Li et al. (2019) and listed in Table S8, respectively. The cases for model calibration were identified on the
basis of covering as many climate conditions, soil properties, and management
practices as possible. Therefore, for the simulation of NH3 from urea
application on cultivated uplands and rice paddy fields, 26 typical cases of
DBW, FQU, and QZ and 10 typical cases of DY, FQP, and SZ were used for model
calibration. Regarding the simulation of NH3 from the ABC application
on cultivated uplands and rice paddy fields, three typical cases of DBW and YT and one typical DY case were conducted for model calibration. The remaining 23 independent cases were provided for model validation.
Sensitivity analysis
Sensitivity analysis was adopted to investigate model inputs and improved
parameters in the modified CNMM–DNDC that simulates NH3 volatilization
following fertilizer application. U37 in QZ and P4 in CS were chosen as the
baseline cases to assess the model's behavior in simulating NH3
volatilization from cultivated uplands and rice paddy fields, respectively.
One reason for this selection was that U37 and P4 were geographically
located near the center of the region for cultivated upland and rice paddy
cases, respectively. Another reason was that the selected cases implement
general Chinese management practices. The authors altered only one item at a
time by keeping the others constant. Meteorological variables (i.e.,
3-hourly averages of Tair, R, and W, 3-hourly totals of P during measurement periods of NH3 volatilization), soil properties (i.e., soil clay fraction, pH, SOC content, and BD), and field management practices (i.e., water management (irrigation water amount or depth of floodwater) and nitrogen fertilization type, dose, and depth) were involved in the sensitivity test of model inputs. The model input items of the 3-hourly
average of W and R and 3-hourly totals of P during the measurement periods of NH3 volatilization as well as the soil clay fraction, SOC content,
nitrogen fertilization dose, and depth of floodwater were altered by a
range from -30 % to +30 % with an interval of 10 %. Soil BD and pH, with narrow amplitudes in situ, were altered within the ranges of 1.17 to 1.47 (U37) and 0.89 to 1.19 (P4) with an interval of 0.05 and within the ranges of 7.3 to 8.9 (U37) and 6.2 to 8.1 (P4) with an interval of 0.3,
respectively. The 3-hourly average Tair during the measurement period of NH3 volatilization was altered within the range of -3 to
+3 ∘C with an interval of 1 ∘C. The irrigation water
amount and nitrogen fertilization depth and type were respectively set as 0.2, 0.5, and 5 cm;
5, 10, and 15 cm; and ABC and ammonium-based nitrogen (N) fertilizers excluding ABC. The corresponding baselines and lower and upper bounds of the
above model inputs involved in the sensitivity analysis are listed in Table S9. In addition, the parameters added and revised for simulating NH3 volatilization from cultivated uplands were involved in the sensitivity analysis of improved parameters, including fSM and fT in the process of urea hydrolysis; fTs_ABC in the process of ABC decomposition; and ftemp, fclay, fwater, fdepth, fcanopy, and frain in the process of liquid NH3 volatilization.
The parameters in Eq. (3) for simulating NH3 volatilization from rice
paddy fields were involved in the sensitivity analysis of improved
parameters, including kd, ka, and kvN. And falg, a newly introduced factor effect on floodwater pH, was also involved in the sensitivity analysis of improved parameters for simulating NH3 volatilization from rice paddy fields. In each sensitivity analysis, an improved parameter was altered by a range from -30 % to +30 % by an interval of 10 %, with the others remaining constant. The sensitivity analysis of fTs_ABC was conducted for the U4 case in QZ with ABC application. The change ratios of cumulative NH3 volatilization
during the measurement periods between the lower and upper and baseline
simulations were applied as the quantitative evaluation index for the
sensitivity analysis (Abdalla et al., 2020).
Evaluation of model performance and statistical analysis
The index of agreement (IA), Nash–Sutcliffe index (NSI), and relative model
bias (RMB) as well as slope, significance level, and coefficient of
determination (R2) of the zero-intercept linear regression (ZIR) between
the observed (O) and simulated (S) values were applied to quantitatively
assess the performance of the original and modified models. The algorithms
of these statistical metrics refer to Li et al. (2019). If the
slope and R2 of the zero-intercept linear regression as well as the IA
and NSI values are closer to 1, then the model performance is better. The
SPSS Statistics 19.0 client (SPSS Inc., Chicago, USA) software package was
used for the multiple regression analysis. The Origin 8.0 (OriginLab Ltd.,
Guangzhou, China) software package was used for graph drawing.
ResultsAmmonia volatilization from cultivated uplands
The observed cumulative NH3 volatilization (CAV) in all the cases of
cultivated uplands during the measurement periods totaled 0.6–127.7 kg N ha-1 (mean: 27.5 kg N ha-1). The corresponding CAVs simulated by the original and modified CNMM–DNDC totaled 0.5–94.1 kg N ha-1 (mean: 33.2 kg N ha-1) and 0.8–115.2 kg N ha-1 (mean: 27.8 kg N ha-1), respectively (Table S4). The original CNMM–DNDC performed poorly
in simulating all the observed cumulative NH3 volatilization cases,
showing an acceptable IA (0.55), an unacceptable NSI (-1.49), and an
insignificant ZIR (slope = 1.11 and R2= 0.06) (data not shown).
Statistical indices for evaluating the performance of the
modified CNMM–DNDC in simulating daily and cumulative ammonia (NH3)
fluxes from ammonium bicarbonate (ABC) and urea (including other fertilizer
types) applications for the independent calibration (Cal) and validation
(Val) cases in uplands and rice paddy fields.
The statistical indices are the index of agreement (IA); Nash–Sutcliffe
index (NSI); and the slope, determination coefficient (R2), and
significance level (p) of the zero-intercept univariate linear regression
(ZIR) of observations against simulations. “Not available” (NA)
indicates a negative R2 and a suffering F test. “Not significant”
(ns) indicates a ZIR with p> 0.05. Num is the abbreviation of
sample number.
In this study, several modifications were conducted to improve the CNMM–DNDC
performance in simulating NH3 volatilization from cultivated uplands.
Regarding either the typically calibrated or independently validated cases,
the modified CNMM–DNDC did not perform well in simulating daily NH3
fluxes, with low IA and unacceptable NSI values (Table 3). This result was
probably because the simulated NH3 dynamic peak time could not
absolutely be matched to the observed peak time, although the modified model
captured the observed NH3 dynamic trend. For the three only typically
calibrated ABC cases, the modified model performed marginally well in
simulating CAVs, showing a good IA (0.75) but a low NSI (0.14) and an
insignificant ZIR (R2= 0.64) (Table 3). However, the modified model
showed a perfect performance in simulating CAVs of both the calibrated and
validated urea cases, with IA values (0.93 and 0.91) close to 1, acceptable
NSI values (0.73 and 0.49), and significant ZIRs (R2= 0.71 with slope = 0.94 and R2= 0.74 with slope = 1.06) (Table 3). Regarding the CAVs of all the individual cases of cultivated uplands, the modified model reported an |RMB| of 1.0–307.8 % (mean: 69.8 %; Table 2), with only 16 % (7 of 44) of cases suffering from an
|RMB| larger than 100 %.
Ammonia volatilization from rice paddy fields
Figures 3, 4, and 5 illustrated the observed and simulated NH3
volatilization and auxiliary variables (e.g., temperatures, pH, and
NH4+ concentrations of floodwater) in each rice paddy case. The
cases with the same observed variables were associated in a figure for
unified formatting. The observed CAVs in all cases of rice paddy fields (2
and 17 cases for ABC and urea applications, respectively) during the
measurement periods totaled 5.9–39.8 kg N ha-1 (mean: 18.1 kg N ha-1; Table 2), with fertilizer application doses of 40.5–162.2 kg N ha-1 (mean: 81.4 kg N ha-1). Given the lack of the capacity to
simulate the water-flooded layer over rice paddy fields, the original
CNMM–DNDC could not simulate NH3 volatilization from rice paddy
fields. The corresponding CAVs simulated by the modified CNMM–DNDC totaled
3.4–39.1 kg N ha-1 (mean: 16.2 kg N ha-1; Table 2). Regarding the CAVs of all the individual cases of rice paddy fields, the modified model demonstrated an |RMB| of 0.3 %–94.9 % (mean: 32.7 %; Table 2), and none of the 19 cases showed an |RMB| larger than 100 %. With regard to the only two ABC cases, the simulated daily NH3 fluxes generally matched the observations of the typically calibrated and independently validated cases (P1 and P2, respectively), although the simulated peak emissions of the first day for P1 were lower than the observations (Fig. 3e–f). For P1 and P2, the corresponding statistical
indices showed that IA values were 0.33 and 0.94, the NSI values were 0.02
and 0.85, and the ZIR slopes were 0.16 (R2 and p values not available,
n= 7) and 0.90 (R2= 0.71, not significant, n= 4), respectively (Table 3). The observed and simulated daily NH3 fluxes due to urea application in the individual cases are illustrated in Fig. 4c–f and
Fig. 5i–k, respectively. As the figures demonstrate, the temporal NH3
flux variation pattern simulated by the modified model generally followed
that observed in the field. Regarding the simulations of the 10 typically
calibrated and 7 independently validated urea cases, the modified model did
not show good performance in terms of the daily NH3 flux, with IA
values of 0.53 and 0.72, NSI values of -0.35 and 0.36, and ZIR slopes of
0.47 (R2= 0.04, p< 0.05, n= 176) and 0.56 (R2= 0.19, p< 0.001, n= 63), respectively (Table 3). However, the modified
CNMM–DNDC performed extremely well in simulating CAVs of the calibrated and
validated urea cases, showing good IA values of 0.88 and 0.85, acceptable
NSI values of 0.30 and 0.60, and significant ZIR slopes of 1.03 (R2= 0.68, n= 10) and 0.77 (R2= 0.65, n= 7), respectively (Table 3).
Observed and simulated pH and ammonium concentrations of
floodwater and daily ammonia volatilization from the ammonium carbonate
application for DY and FQP. The definitions of the case codes refer
to Table 2. The sites are Danyang (DY) and Fengqiu with rice paddy fields
(FQP).
Observed and simulated ammonium concentrations of floodwater
and daily ammonia volatilization from the urea application for CS and SZ.
The definitions of the case codes refer to Table 2. The sites are
Changshu (CS) and Shenzhen (SZ).
Observed and simulated temperatures; pH and ammonium
concentrations of floodwater; and daily ammonia volatilization from the urea
application for DY, FQP, and YTA. The definitions of the case codes refer to Table 2. The sites are Danyang (DY), Fengqiu with rice paddy
fields (FQP), and Yingtan (YTA).
Model performance in terms of other auxiliary variables in rice paddy fields
Table 4 lists the statistical indices used to evaluate the performance of
the modified CNMM–DNDC in the simulation of floodwater temperatures, pH
values, and NH4+ concentrations when the model was calibrated and
validated. The modified model generally captured the trends in floodwater
temperature (Fig. 5a–b), although the simulated floodwater temperatures of
several certain days for P9 were lower than the observations. The modified
CNMM–DNDC, which introduced the effect of algal growth on floodwater pH,
generally simulated the observed daily elevated floodwater pH resulting from
algal photosynthetic activity (Figs. 3a–b and 5c–e). The simulation of
calibrated (P1, P9, and P10; Figs. 3a and 5c–d) and validated cases (P2
and P19; Figs. 3b and 5e) of floodwater pH resulted in good IA values of
0.83 and 0.79, acceptable NSI values of 0.55 and 0.36, and ZIRs with
significant R2 values of 0.55 (slope = 1.00, n= 147) and 0.36 (slope = 1.01, n= 45), respectively. The simulated and observed daily NH4+ concentrations in the floodwater of the ABC and urea cases
are illustrated in Figs. 3c–d, 4a–b, and 5f–h. Compared to the
observed floodwater NH4+ concentrations of the ABC cases, the
model simulation underestimated the peak concentration on the first day
after ABC application for the P1 case but captured the peak concentration of
the P2 case (Fig. 3c–d). The modified CNMM–DNDC generally captured the
observed temporal pattern in the daily NH4+ concentrations during
the observation periods following urea application, although discrepancies
existed in the magnitudes of some cases; e.g., the model overestimated the
floodwater NH4+ concentration in the P7 and P8 cases (Fig. 4a–b)
and underestimated that in the P6 (Fig. 4b) and P19 cases (Fig. 5h).
Significant ZIRs between the simulated and observed daily floodwater
NH4+ concentrations of the typically calibrated and independently
validated cases yielded significant slopes of 1.03 (R2= 0.48, n= 24) and 0.74 (R2= 0.34, n= 55), the IA values were 0.78 and 0.68, and the NSI values were 0.48 and 0.25, respectively (Table 4).
Statistical indices for evaluating the performance of the
CNMM–DNDC in simulating the daily temperature (T), pH, and ammonium
concentration (NH4+) in floodwater for the calibration (Cal) and
validation (Val) cases.
The statistical indices are the index of agreement (IA); Nash–Sutcliffe
index (NSI); and the slope, determination coefficient (R2), and
significance level (p) of the zero-intercept univariate linear regression
(ZIR) of observations against simulations. “Not available” (NA)
indicates a negative R2 and a suffering F test. “Not significant”
(ns) indicates a ZIR with p> 0.05. “Num” is the abbreviation of
sample number. The definitions of the case codes refer to Table 2.
Summary for the performance of CNMM–DNDC in simulating NH<inline-formula><mml:math id="M483" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>
volatilization
To sum up, as Fig. 6 shows, with regard to the simulations of all 40
typically calibrated and 23 independently validated cases of cultivated
uplands and rice paddy fields by the modified model, significant
zero-intercept linear relationships between the simulated and observed CAVs
were found, with slopes of 0.94 (R2= 0.76) and 0.98 (R2= 0.71), respectively. In general, the above results indicated that the
modifications made in this study obviously improved the performance of the
CNMM–DNDC in simulating NH3 volatilization following applications of
synthetic nitrogen fertilizers to cultivated upland and rice paddy soils.
Nevertheless, the simulated CAV from cultivated upland cases with fertilizer
application depth (U6, U20, and U44) and irrigation/precipitation (U16, U22, and U26) by the modified model resulted in the RMB larger than 150 %. With regard to the cases in the rice paddy fields, the simulations of the
modified model with an absolute value of RMB larger than 50 % occurred in
the P1, P9, and P13 cases. The modified model resulted in the largest RMB of
94.9 % between the observed and simulated CAV that occurred in the urea case of P9, which was located in DY under cloudy conditions. The ABC case of P1 with RMB of -57 % suffered from a serious underestimation of NH4+ concentration in the floodwater (Fig. 3). For the cases in SZ, the modified CNMM–DNDC generally underestimated NH3 volatilization from almost all cases with low N dose but overestimated NH3 volatilization from the cases with high N dose.
Comparison between the observed and simulated cumulative
ammonia volatilization from the individual fertilization events across all calibrated and validated cases of upland
and rice paddy fields; n, p, and R2 denote the sample size, significance level, and coefficient of determination for the zero-intercept linear regression, respectively.
Sensitivity of model inputs and improved parameters in simulating
NH<inline-formula><mml:math id="M496" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization
The sensitivity analysis of model input items indicated that NH3
volatilization from cultivated uplands was primarily regulated by field
management practices (Fig. 7a). The changes in N dose, the different N types,
and the implementation of irrigation had considerable effects on NH3
volatilization from cultivated uplands. In addition, a fertilization depth
of 15 cm resulted in a -23 % change in NH3 volatilization, and the increase in irrigation amount had an inhibitory effect on NH3
volatilization. Moreover, in comparison to other soil properties, the
changes in soil SOC had a greater influence (-19 % to 16 %) on NH3 volatilization. Among all considered meteorological variables, NH3 volatilization from cultivated uplands appeared to be the most sensitive response to changes in air temperature (Fig. 7a). However, NH3
volatilization from rice paddy soils was sensitive to changes in
fertilization and floodwater management, which increased with N dosage and
decreased with the depth of fertilizer application and that of floodwater
(Fig. 7b). For all soil variables considered in the sensitivity analysis,
only the changes in soil pH had a great influence on NH3 volatilization
from rice paddy fields. In addition, NH3 volatilization from rice paddy
soils decreased with solar radiation. With regard to the sensitivity
analysis of the improved parameters (Fig. 8), NH3 volatilization from cultivated uplands showed more sensitivity to the reduction in the improved parameters than to the increase in those. Generally, as the improved parameters reduced, NH3 volatilization from cultivated uplands decreased.
Moreover, NH3 volatilization from rice paddy fields displayed
a complicated response to the change in the improved parameters involved in
the process of NH3 volatilization from rice paddy fields. For instance,
no matter whether ka increased or decreased, the NH3 volatilization decreased, while NH3 volatilization increased with increasing kd. The above results indicated that the modifications in simulating NH3 volatilization from either cultivated uplands or rice paddy fields were effective and feasible.
Sensitivity analysis of the modified CNMM–DNDC in simulating
cumulative ammonia (NH3) volatilization from uplands and rice paddy fields during the measurement periods through change input factors. The investigated input factors include 3-hourly averages of air temperature (Tair) and wind speed (W); 3-hourly totals of precipitation
(P) and solar radiation (R) during individual measurement periods of
NH3 volatilization; soil clay fraction, pH, organic carbon (SOC) content and bulk density (BD); irrigation water amount (Irri. amount) and floodwater depth (Flo. depth); and nitrogen fertilization depth, dose, and type (Fert. depth, N dose, and N type, respectively). The N types include ammonium bicarbonate (ABC) and other ammonium-based nitrogen fertilizers (Other). The legends within the frame apply to all the subfigures and all the factors without notes highlighted by arrows.
Sensitivity analysis on the improved parameters of the modified CNMM–DNDC model in simulating cumulative ammonia (NH3) volatilization from cultivated uplands and rice paddy fields. The improved parameters for simulating NH3 volatilization from cultivated uplands involved
in the sensitivity analysis include effect of soil moisture and soil
temperature on urea hydrolysis (fSM and fT); effect of soil temperature on ammonium bicarbonate decomposition (fTs_ABC); and effect of soil temperature, soil clay content, soil moisture, soil depth, dry canopy, rain wetting canopy on NH3 volatilization (ftemp, fclay, fwater, fdepth, fcanopy, and frain). The improved parameters for simulating NH3 volatilization from rice paddy fields involved in the sensitivity analysis include the effect of algal growth on floodwater pH (falg), the dissociation and association rate constants for NH4+–NH3(aq) equilibrium (kd and ka), and the volatilization rate constant of NH3(aq) (kvN).
DiscussionModel performance in simulating NH<inline-formula><mml:math id="M543" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization from cultivated uplands
The mechanism of NH3 volatilization in the modified CNMM–DNDC is mainly
inherited from that in the DNDC model modified by Li et al. (2019). The simulated rate of NH3 flux is jointly determined by the
regulating factors of wind speed, soil depth, clay fraction, soil
temperature, soil moisture, vegetation canopy, and rainfall-induced canopy
wetting. We found that the complicated management practices bring obstacles
to modeling. Across all the cases of cultivated uplands (Table S4), the
simulations of the modified CNMM–DNDC with an RMB larger than 150 %
occurred in the cases with fertilizer application depth (U6 with broadcast
followed by tillage (BFT) 20 cm, U20 with BFT 5 cm, and U44 with deep point
placement 5–10 cm) and irrigation/precipitation (U16 with 4–6 cm
irrigation, U22 with 4–6 cm irrigation, and U26 with 0.8 cm irrigation and
3.69 cm precipitation). Among all the cases with fertilizer application
depth or irrigation/precipitation (38 total cases), 16 % (6 cases) had an
RMB greater than 150 %.
This result might be because the model could not simulate well the
inhibition mechanisms of some situations of fertilization depth and
water-adding events' effect on NH3 volatilization. Moreover,
Li et al. (2019) also reported that irrigation/precipitation
during the measurement periods had a complex effect (e.g., reduction and
stimulation) on NH3 volatilization following nitrogen fertilizer
application in cultivated uplands, and determining this information is still
a considerable challenge in NH3 simulations by biogeochemical models.
At the same time, Li et al. (2019) also found that the doses
and depths of the fertilizer applications jointly accounted for 43 % (p< 0.001) of the variance in the observed CAVs. The results
demonstrated that the simulated NH3 volatilization from cultivated
uplands following nitrogen fertilizer application accompanied by deep or
mixed placement or irrigation/precipitation by the modified model still had
some deviation from the observations, and more synchronous observations of
NH3 volatilization and other auxiliary variables (e.g., soil moisture,
NH4+ concentration, and nitrogen uptake by crops) in these
situations are urgently needed to further revise the CNMM–DNDC.
Model performance in simulating NH<inline-formula><mml:math id="M554" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization from rice paddy fields
In this study, four improvements in the pH of floodwater were involved in
the modified CNMM–DNDC. First, floodwater over rice paddy soil was added,
which enabled the simulation of floodwater pH in the modified model. Second,
the modified model used the initial pH of floodwater and the pH of the
surface soil to calculate the floodwater pH. The above two improvements
allowed the introduction of the J–M model into the modified CNMM–DNDC. The
present relatively reliable biogeochemical models rarely involve floodwater
over rice paddy soil when simulating NH3 volatilization from rice paddy
fields, which is not in accordance with the natural state. Third, when urea
was applied to the surface floodwater, the subsequent urea hydrolysis
reaction could increase the floodwater pH, and this process was added to the
modified model by referring to the algorithms applied in the original model
for upland soils (Sect. 2.1.2). Finally, the effect of algal growth on
floodwater pH was introduced into the modified model by calculating the
ratio of the solar shortwave radiation effect on algal photosynthesis. In
detail, under cloudy conditions in DY (P9), only 9 % of the applied urea N was observed to be lost as NH3 from the rice paddy soil, while up to 40 % of the applied urea N was observed to be lost under high-solar-radiation conditions in YTA (P19) (Cai et al., 1992).
However, the modified CNMM–DNDC overestimated the emissions from DY but
underestimated those from YTA, which could be attributed to the
overestimation of the pH during the first 3 observation days in DY and
the underestimation of NH4+ concentrations in YTA (Fig. 5). In
addition, algal blooms only appeared on the surface of calm water; thus, a
number of factors, such as irrigation, heavy rain, strong wind, and
drainage, could hamper the growth of algae (Cao et al.,
2013). Due to the basal dressing followed by irrigation (Gong
et al., 2013), which inhibited the reproduction of algae in SZ (P11 and P12
cases), the observed NH3 emissions accounted for only 10 %–13 %
of the applied nitrogen. Unfortunately, the aforementioned factors that
reduced algal growth were not introduced into the modified CNMM–DNDC because
of limited reports, which resulted in an overestimation of NH3
emissions of 6.4 and 2.6 kg N ha-1 for P11 and P12 in SZ cases with a
high rate of urea application, respectively. More observational data on the
effect of algal growth on floodwater pH and subsequent NH3
volatilization are needed to improve the simulation of the modified model on
NH3 volatilization from rice paddy fields.
Therefore, the modified CNMM–DNDC with the introduction of a floodwater
layer, as well as the corresponding processes, into the simulation of
NH3 volatilization provided a more scientific algorithm for the
simulation of NH3 loss from rice paddy fields, thereby enabling the
simulation of the pH and NH4+ concentration of floodwater.
However, the depth of surface floodwater was kept at a constant value (such
as the average depth of the floodwater) for each flooding event in the
modified model, but this operation was inconsistent with the field states.
The floodwater depth actually changed with real-time evaporation and
precipitation. Therefore, a module for calculating the dynamics of
floodwater depth driven by real-time evaporation and precipitation is needed
to better simulate the effect of floodwater depth on NH3
volatilization. The results of this study suggest that accurate field
measurements and a corresponding reliable simulation of floodwater depth are
crucial for the simulation of NH3 volatilization by the modified
CNMM–DNDC.
Differences between NH<inline-formula><mml:math id="M568" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> volatilization from cultivated uplands and rice paddy fields
NH3 volatilization from soil–plant upland systems is an extremely
complex process (Freney and Simpson, 1983; Sommer et al., 2004). It is
obvious that soil properties play an important role in regulating NH3
volatilization from cultivated uplands, as has been reported by a great
number of studies (e.g., Duan and Xiao, 2000; Lei et al., 2017; Martens
and Bremner, 1989). In addition, NH3 volatilization from cultivated
uplands was simultaneously regulated by the complicated management
practices. As the sensitivity analysis indicated, NH3 volatilization
from cultivated uplands was primarily regulated by the dose, type, and
application depth of N fertilizer and water management (Fig. 7a).
With regard to NH3 volatilization from rice paddy fields, floodwater
pH has been considered one of the primary factors affecting NH3
volatilization from rice paddy fields (Fillery et al., 1984; Hayashi et
al., 2006; Jayaweera and Mikkelsen, 1991). As floodwater pH increases, the
equilibrium of NH4+ ions and NH3(aq) in floodwater transfers in the direction of NH3(aq) formation, which will increase the potential for subsequent NH3 volatilization (Jayaweera and
Mikkelsen, 1990a; Sommer et al., 2004). Previous studies have also shown
that the stimulation of NH3 volatilization from rice paddy fields is
affected by algal growth, which largely contributes to the elevation of
floodwater pH resulting from algal photosynthetic activity (Buresh et
al., 2008; Fillery and Vlek, 1986; Mikkelsen et al., 1978).
The addition of a suitable photosynthetic inhibitor also controlled the pH
of the floodwater, implying that the increase in pH was caused by algal
growth (Bowmer and Muirhead, 1987). In addition, many studies
have found that the depth of surface floodwater has a substantial influence
on NH3 volatilization (Fillery et al., 1984; Freney et al., 1988;
Hayashi et al., 2006). The sensitivity analysis of this study also indicated
that NH3 volatilization from rice paddy fields was sensitive to changes
in the depth of surface floodwater (Fig. 7b). Jayaweera
and Mikkelsen (1990b) demonstrated that the volatilization rate of NH3
decreases as the depth of floodwater increases despite the small difference
in meteorological factors and soil physicochemical properties. The reducing
effects might be attributed to the following mechanisms. First, with
increasing floodwater depth, the concentration of NH4+ in
floodwater decreases (Cai et al., 1986). Many studies have
found that a lower concentration of NH4+ in floodwater contributes
to the reduced potential of NH3 volatilization in paddy fields
(Bhagat et al., 1996; Hayashi et al., 2006; He et al., 2014; Liu et al.,
2015; Song et al., 2004). Observations based on wind tunnel experiments
showed that the NH3 loss decreased from 14.6 to 4.5 mg L-1 as the depth of floodwater increased from 6.4 to 21.3 cm, while
other environmental conditions were similar (Jayaweera et al., 1990). Second, a reduction in the depth of floodwater increases the volatilization rate constant of NH3(aq) (kvN), thus increasing NH3 volatilization from floodwater.
According to the above results, the regulatory factors affecting NH3
volatilization from rice paddy fields were demonstrated to be different from
those from cultivated uplands, which was also supported by previous research
(Tian et al., 2001; Zhao et al., 2009). NH3 volatilization from
cultivated uplands was primarily influenced by the regulatory factors of
soil properties and field management practices. However, given the existence
of floodwater over rice paddy field soils, NH3 volatilization from rice
paddy fields was additionally affected by flooding management strategies,
such as floodwater pH and depth. Therefore, the mechanisms and algorithms
applied in simulating NH3 volatilization from cultivated uplands are
not appropriate for simulating NH3 volatilization from rice paddy
fields. In the modified CNMM–DNDC, NH3 volatilization following
nitrogen fertilizer application in cultivated uplands was based on
first-order kinetics. However, the modified CNMM–DNDC adopted the J–M model,
which was based on the two-film theory of mass transfer, to calculate
NH3 volatilization following nitrogen fertilizer application in rice
paddy field soils. The results suggest that the application of two different
mechanisms according to the distinguished properties of cultivated uplands
and rice paddy fields to simulate NH3 volatilization is necessary for
process-based biogeochemical models, such as the CNMM–DNDC used in this
study.
Code and data availability
All of the model output used to produce the figures can be obtained from the Supplement, and all of the observed datasets used in this study were collected from published peer-reviewed articles. The code and executive program of the modified model can be obtained from
10.6084/m9.figshare.19388756.v3 (Li, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-3001-2022-supplement.
Author contributions
XZ, YL, and WZ contributed to developing the idea and methodology of this study. SL arranged the research data, improved and implemented the model simulation, and prepared the paper with contributions from all co-authors. RW, KW, and CZ contributed to collecting and maintaining the research data. SH, CL, and ZY analyzed study data and verified the results.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Financial support
This research has been supported by the Chinese Academy of Sciences (grant no. ZDBS-LY-DQC007), the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab), the National Natural Science Foundation of China (grant no. 41907280), and the China Postdoctoral Science Foundation (grant no. 2019M650808).
Review statement
This paper was edited by Ben Bond-Lamberty and reviewed by two anonymous referees.
ReferencesAbdalla, M., Song, X., Ju, X., Topp, C. F. E., and Smith, P.: Calibration and
validation of the DNDC model to estimate nitrous oxide emissions and crop
productivity for a summer maize-winter wheat double cropping system in
Hebei, China, Environ. Pollut., 262, 114199, 10.1016/j.envpol.2020.114199, 2020.
Alberty, R. A.: Physical chemistry, 6th edn., John Wiley & Sons, New York, ISBN 0-471-09284-3, 1983.Anderson, D. M., Burkholder, J. M., Cochlan, W. P., Glibert, P. M., Gobler,
C. J., Heil, C. A., Kudela, R. M., Parsons, M. L., Rensel, J. E. J.,
Townsend, D. W., Trainer, V. L., and Vargo, G. A.: Harmful algal blooms and
eutrophication: Examining linkages from selected coastal regions of the
United States, Harmful Algae, 8, 39–53, 10.1016/j.hal.2008.08.017,
2008.Behera, S. N., Sharma, M., Aneja, V. P., and Balasubramanian, R.: Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and
deposition on terrestrial bodies, Environ. Sci. Pollut. R., 20, 8092–8131,
10.1007/s11356-013-2051-9, 2013.Bhagat, R. M., Bhuiyan, S. I., and Moody, K.: Water, tillage and weed
interactions in lowland tropical rice: a review, Agr. Water Manage., 31,
165–184, 10.1016/0378-3774(96)01242-5, 1996.Bobbink, R., Hornung, M., and Roelofs, J. G. M.: The effects of air-borne
nitrogen pollutants on species diversity in natural and semi-natural
European vegetation, J. Ecol., 86, 717–738,
10.1046/j.1365-2745.1998.8650717.x, 1998.Bouwman, A. F., Lee, D. S., Asman, W. A. H., Dentener, F. J., VanderHoek, K.
W., and Olivier, J. G. J.: A global high-resolution emission inventory for
ammonia, Global Biogeochem. Cy., 11, 561–587, 10.1029/97gb02266, 1997.Bowmer, K. H. and Muirhead, W. A.: Inhibition of algal photosynthesis to
control pH and reduce ammonia volatilization from rice floodwater, Fertil.
Res., 13, 13–29, 10.1007/BF01049799, 1987.Buresh, R., Ramesh Reddy, K., and van Kessel, C.: Nitrogen Transformations in
Submerged Soils, in: Nitrogen in Agricultural Systems, edited by: Schepers, J. S. and Raun, W. R., John Wiley & Sons, Ltd., 401–436, 10.2134/agronmonogr49.c11, 2008.Cai, G., Zhu, Z., Trevitt, A., Freney, J. R., and Simpson, J. R.: Nitrogen loss from ammonium bicarbonate and urea fertilizers applied to flooded rice,
Fertil. Res., 10, 203–215, 10.1007/BF01049350, 1986.
Cai, G., Peng, G., Wang, X., and Zhu, J.: Ammonia volatilization from urea
applied to acid paddy soil in southern china and its control, Pedosphere, 2,
345–354, 1992.Cannavo, P., Recous, S., Parnaudeau, V., and Reau, R.: Modeling N dynamics to
assess environmental impacts of cropped soils, Advances in Agronomy, 97,
Academic Press, 131–174, 10.1016/S0065-2113(07)00004-1, 2008.Cao, Y., Tian, Y., Yin, B., and Zhu, Z.: Proliferation of algae and effect of
its on fertilizer–N immobilization in flooded paddy field, J. Plant Nutr.
Fertil. Sc., 19, 111–116, 10.11674/zwyf.2013.0113, 2013.Chien, S. H., Prochnow, L. I., and Cantarella, H.: Recent developments of fertilizer production and use to improve nutrient efficiency and minimize
environmental impacts, chap. 8, Advances in Agronomy, 102, Academic Press, 267–322, 10.1016/S0065-2113(09)01008-6, 2009.De Datta, S. K.: Nitrogen transformations in wetland rice ecosystems,
Fertil. Res., 42, 193–203, 10.1007/bf00750514, 1995.
Duan, Z. and Xiao, H.: Effects of soil properties on ammonia volatilization,
Soil Sci. Plant Nutr., 46, 845–852, 2000.Dubache, G., Li, S., Zheng, X., Zhang, W., and Deng, J.: Modeling ammonia
volatilization following urea application to winter cereal fields in the
United Kingdom by a revised biogeochemical model, Sci. Total Environ., 660,
1403–1418, 10.1016/j.scitotenv.2018.12.407, 2019.Dutta, B., Congreves, K. A., Smith, W. N., Grant, B. B., Rochette, P.,
Chantigny, M. H., and Desjardins, R. L.: Improving DNDC model to estimate
ammonia loss from urea fertilizer application in temperate agroecosystems,
Nutr. Cycl. Agroecosys., 106, 275–292, 10.1007/s10705-016-9804-z, 2016.Felix, J. D., Elliott, E. M., Gish, T. J., McConnell, L. L., and Shaw, S. L.:
Characterizing the isotopic composition of atmospheric ammonia emission
sources using passive samplers and a combined oxidation-bacterial
denitrifier approach, Rapid Commun. Mass Sp., 27, 2239–2246,
10.1002/rcm.6679, 2013.Fillery, I. R. P. and Vlek, P. L. G.: 4. Reappraisal of the significance of
ammonia volatilization as an N loss mechanism in flooded rice fields,
Fertil. Res., 9, 79–98, 10.1007/BF01048696, 1986.Fillery, I. R. P., Simpson, J. R., and Dedatta, S. K.: Influence of field
environment and fertilizer management on ammonia loss from flooded rice,
Soil Sci. Soc. Am. J., 48, 914–920,
10.2136/sssaj1984.03615995004800040043x, 1984.Freney, J. R. and Simpson, J. R.: Gaseous loss of nitrogen from plant-soil
systems, Dev. Plant Soil Sci., 9, 1–32, 10.1007/978-94-017-1662-8, 1983.Freney, J. R., Trevitt, A. C. F., Muirhead, W. A., Denmead, O. T., Simpson,
J. R., and Obcemea, W. N.: Effect of water depth on ammonia loss from lowland
rice, Fertil. Res., 16, 97–107, 10.1007/bf01049767, 1988.Giltrap, D., Saggar, S., Rodriguez, J., and Bishop, P.: Modelling NH3
volatilisation within a urine patch using NZ-DNDC, Nutr. Cycl. Agroecosys.,
108, 267–277, 10.1007/s10705-017-9854-x, 2017.Gong, W., Zhang, Y., Huang, X., and Luan, S.: High–resolution measurement of
ammonia emissions from fertilization of vegetable and rice crops in the
Pearl River Delta Region, China, Atmos. Environ., 65, 1–10,
10.1016/j.atmosenv.2012.08.027, 2013.Han, K., Zhou, C., and Wang, L.: Reducing ammonia volatilization from maize
fields with separation of nitrogen fertilizer and water in an alternating
furrow irrigation system, J. Integr. Agr., 13, 1099–1112,
10.1016/s2095-3119(13)60493-1, 2014.Hayashi, K., Nishimura, S., and Yagi, K.: Ammonia volatilization from the
surface of a Japanese paddy field during rice cultivation, Soil Sci. Plant
Nutr., 52, 545–555, 10.1111/j.1747-0765.2006.00053.x, 2006.He, Y., Yang, S., Xu, J., Wang, Y., and Peng, S.: Ammonia volatilization losses from paddy fields under controlled irrigation with different drainage
treatments, Sci. World J., 2014, 417605, 10.1155/2014/417605, 2014.Holcomb III, J. C., Sullivan, D. M., Horneck, D. A., and Clough, G. H.: Effect of irrigation rate on ammonia volatilization, Soil Sci. Soc. Am. J., 75, 2341–2347, 10.2136/sssaj2010.0446, 2011.Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and
Zhang, H.: A high-resolution ammonia emission inventory in China, Global
Biogeochem. Cy., 26, GB1030, 10.1029/2011gb004161, 2012.Jayaweera, G. R. and Mikkelsen, D. S.: Ammonia volatilization from flooded soil systems – a computer-model. I. Theoretical aspects, Soil Sci. Soc. Am. J., 54, 1447–1455, 10.2136/sssaj1990.03615995005400050039x, 1990a.Jayaweera, G. R. and Mikkelsen, D. S.: Ammonia volatilization from flooded soil systems – a computer-model. II. Theory and model results, Soil Sci. Soc. Am. J., 54, 1456–1462, 10.2136/sssaj1990.03615995005400050040x, 1990b.Jayaweera, G. R. and Mikkelsen, D. S.: Assessment of ammonia volatilization
from flooded soil systems, Adv. Agron., 45, 303–356,
10.1016/S0065-2113(08)60044-9, 1991.Jayaweera, G. R., Paw U., K. T., and Mikkelsen, D. S.: Ammonia volatilization
from flooded soil systems: a computer model. III. Validation of the model,
Soil Sci. Soc. Am. J., 54, 1462–1468,
10.2136/sssaj1990.03615995005400050041x, 1990.Kong, L., Tang, X., Zhu, J., Wang, Z., Pan, Y., Wu, H., Wu, L., Wu, Q., He,
Y., Tian, S., Xie, Y., Liu, Z., Sui, W., Han, L., and Carmichael, G.: Improved inversion of monthly ammonia emissions in China based on the Chinese ammonia monitoring network and ensemble Kalman filter, Environ. Sci. Technol., 53, 12529–12538, 10.1021/acs.est.9b02701, 2019.Lei, T., Guo, X., Ma, J., Sun, X., Feng, Y., and Wang, H.: Kinetic and
thermodynamic effects of moisture content and temperature on the ammonia
volatilization of soil fertilized with urea, Int. J. Agr. Biol. Eng., 10,
134–143, 10.25165/j.ijabe.20171006.3232, 2017.
Li, C.: Biogeochemistry: Scientific fundamentals and modelling approach,
Tsinghua University Press, Beijing, ISBN 978-7-302-41265-6, 2016.Li, C., Frolking, S., and Frolking, T. A.: A model of nitrous-oxide evolution
from soil driven by rainfall events. 1. Model structure and sensitivity, J.
Geophys. Res.-Atmos., 97, 9759–9776, 10.1029/92jd00509, 1992.Li, H., Han, Y., and Cai, Z.: Modeling the ammonia volatilization from common
urea and controlled releasing urea fertilizers in paddy soil of Taihu region
of China by Jayaweera–Mikkelsen model, Environ. Sci., 29, 1045–1052,
10.1007/BF01049507, 2008.Li, S.: Code and executive program_data for bg_2022, figshare [code], 10.6084/m9.figshare.19388756.v3, 2022.Li, S., Zheng, X., Zhang, W., Han, S., Deng, J., Wang, K., Wang, R., Yao,
Z., and Liu, C.: Modeling ammonia volatilization following the application of
synthetic fertilizers to cultivated uplands with calcareous soils using an
improved DNDC biogeochemistry model, Sci. Total Environ., 660, 931–946,
10.1016/j.scitotenv.2018.12.379, 2019.Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann,
C. M. B., Puchalski, M. A., Gay, D. A., and Collett Jr., J. L.: Increasing
importance of deposition of reduced nitrogen in the United States, P. Natl.
Acad. Sci. USA, 113, 5874–5879, 10.1073/pnas.1525736113, 2016.
Li, Y., Shen, J., Wang, Y., Gao, M., Liu, F., Zhou, P., Liu, X., Chen, D.,
Zou, G., Luo, Q., and Ma, Q.: CNMM: a grid-based spatially-distributed catchment simulation model, China Science Press, Beijing, ISBN 978-7-03-049714-7, 2017.Liu, J., Ding, P., Zong, Z., Li, J., Tian, C., Chen, W., Chang, M., Salazar,
G., Shen, C., Cheng, Z., Chen, Y., Wang, X., Szidat, S., and Zhang, G.: Evidence of rural and suburban sources of urban haze formation in China: a case study from the Pearl River Delta region, J. Geophys. Res.-Atmos., 123,
4712–4726, 10.1029/2017jd027952, 2018.Liu, T., Fan, D., Zhang, X., Chen, J., Li, C., and Cao, C.: Deep placement of
nitrogen fertilizers reduces ammonia volatilization and increases nitrogen
utilization efficiency in no-tillage paddy fields in central China, Field
Crops Res., 184, 80–90, 10.1016/j.fcr.2015.09.011, 2015.Liu, X., Ju, X., Zhang, F., Pan, J., and Christie, P.: Nitrogen dynamics and
budgets in a winter wheat–maize cropping system in the North China Plain,
Field Crops Res., 83, 111–124, 10.1016/S0378-4290(03)00068-6, 2003.Mariano, E., de Sant Ana Filho, C. R., Bortoletto-Santos, R., Bendassolli,
J. A., and Trivelin, P. C. O.: Ammonia losses following surface application of enhanced-efficiency nitrogen fertilizers and urea, Atmos. Environ., 203,
242–251, 10.1016/j.atmosenv.2019.02.003, 2019.Martens, D. A. and Bremner, J. M.: Soil properties affecting volatilization of ammonia from soils treated with urea, Commun. Soil Sci. Plan., 20,
1645–1657, 10.1080/00103628909368173, 1989.Martin, S. T., Hung, H.-M., Park, R. J., Jacob, D. J., Spurr, R. J. D., Chance, K. V., and Chin, M.: Effects of the physical state of tropospheric ammonium-sulfate-nitrate particles on global aerosol direct radiative forcing, Atmos. Chem. Phys., 4, 183–214, 10.5194/acp-4-183-2004, 2004.Michalczyk, A., Kersebaum, K.C., Heimann, L., Roelcke, M., Sun, Q. P., Chen,
X. P., and Zhang, F. S.: Simulating in situ ammonia volatilization losses in the North China Plain using a dynamic soil-crop model, J. Plant Nutr. Soil Sc., 179, 270–285, 10.1002/jpln.201400673, 2016.Mikkelsen, D. S., Datta, S. K. D., and Obcemea, W. N.: Ammonia volatilization
losses from flooded rice soils, Soil Sci. Soc. Am. J., 42, 725–730,
10.2136/sssaj1978.03615995004200050043x, 1978.
Park, K. D., Lee, D. W., Li, Y., Chen, D., Park, C. Y., Lee, Y. H., Lee, C.
H., Kang, U. G., Park, S. T., and Cho, Y. S.: Simulating ammonia volatilization from applications of different urea applied in rice field by WNMM, Korean J. Crop Sci., 53, 8–14, 2008.Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., and Henze, D. K.: Ammonia emissions in the United States, European Union, and China
derived by high-resolution inversion of ammonium wet deposition data:
Interpretation with a new agricultural emissions inventory
(MASAGE_NH3), J. Geophys. Res.-Atmos., 119,
4343–4364, 10.1002/2013jd021130, 2014.Riddick, S., Ward, D., Hess, P., Mahowald, N., Massad, R., and Holland, E.: Estimate of changes in agricultural terrestrial nitrogen pathways and ammonia emissions from 1850 to present in the Community Earth System Model, Biogeosciences, 13, 3397–3426, 10.5194/bg-13-3397-2016, 2016.Roelcke, M., Li, S., Tian, X., Gao, Y., and Richter, J.: In situ comparisons of ammonia volatilization from N fertilizers in Chinese loess soils, Nutr.
Cycl. Agroecosys., 62, 73–88, 10.1023/a:1015186605419, 2002.Sanz-Cobena, A., Misselbrook, T., Camp, V., and Vallejo, A.: Effect of water
addition and the urease inhibitor NBPT on the abatement of ammonia emission
from surface applied urea, Atmos. Environ., 45, 1517–1524,
10.1016/j.atmosenv.2010.12.051, 2011.Savard, M. M., Cole, A., Smirnoff, A., and Vet, R.: ä15N values of
atmospheric N species simultaneously collected using sector – based samplers
distant from sources isotopic inheritance and fractionation, Atmos.
Environ., 162, 11–22, 10.1016/j.atmosenv.2017.05.010, 2017.Schjørring, J. K.: Atmospheric ammonia and impacts of nitrogen
deposition: Uncertainties and challenges, New Phytol., 139, 59–60,
10.1046/j.1469-8137.1998.00173.x, 1998.Sommer, S. G., Schjoerring, J. K., and Denmead, O. T.: Ammonia emission from
mineral fertilizers and fertilized crops, in: Advances
in Agronomy, edited by: Sparks, D. L., Academic Press, 82, 557–622, 10.1016/S0065-2113(03)82008-4, 2004.Song, Y., Fan, X., Lin, D., Yang, L., and Zhou, J.: Ammonia volatilization from paddy fields in the Taihu lake region and its influencing factors, Acta
Pedologica Sinica, 41, 265–269, 10.11766/trxb200306090216, 2004.Tian, G., Cai, Z., Cao, J., and Li, X.: Factors affecting ammonia volatilization from a rice–wheat rotation system, Chemosphere, 42, 123–129, 10.1016/S0045-6535(00)00117-X, 2001.Vira, J., Hess, P., Melkonian, J., and Wieder, W. R.: An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2), Geosci. Model Dev., 13, 4459–4490, 10.5194/gmd-13-4459-2020, 2020.Wang, H., Hu, Z., Lu, J., Liu, X., Wen, G., and Blaylock, A.: Estimation of
ammonia volatilization from a paddy field after application of
controlled-release urea based on the modified Jayaweera-Mikkelsen model
combined with the Sherlock-Goh model, Commun. Soil Sci. Plan., 47,
1630–1643, 10.1080/00103624.2016.1206120, 2016.Xu, R., Tian, H., Pan, S., Prior, S. A., Feng, Y., Batchelor, W. D., Chen,
J., and Yang, J.: Global ammonia emissions from synthetic nitrogen fertilizer
applications in agricultural systems: Empirical and process-based estimates
and uncertainty, Global Change Biol., 25, 314–326, 10.1111/gcb.14499,
2019.Zhan, X., Chen, C., Wang, Q., Zhou, F., Hayashi, K., Ju, X., Lam, S. K.,
Wang, Y., Wu, Y., Fu, J., Zhang, L., Gao, S., Hou, X., Bo, Y., Zhang, D.,
Liu, K., Wu, Q., Su, R., Zhu, J., Yang, C., Dai, C., and Liu, H.: Improved
Jayaweera-Mikkelsen model to quantify ammonia volatilization from rice paddy
fields in China, Environ. Sci. Pollut. R., 26, 8136-8147,
10.1007/s11356-019-04275-2, 2019.Zhang, S., Cai, G., Wang, X., Xu, Y., Zhu, Z., and Freney, J. R.: Losses of
urea-nitrogen applied to maize grown on a calcareous Fluvo-Aquic soil in
North China Plain, Pedosphere, 2, 171–178, 10.1109/34.142909, 1992.Zhang, W., Li, Y., Zhu, B., Zheng, X., Liu, C., Tang, J., Su, F., Zhang, C.,
Ju, X., and Deng, J.: A process-oriented hydro-biogeochemical model enabling
simulation of gaseous carbon and nitrogen emissions and hydrologic nitrogen
losses from a subtropical catchment, Sci. Total Environ., 616, 305–317,
10.1016/j.scitotenv.2017.09.261, 2018.Zhang, W., Yao, Z., Zheng, X., Liu, C., Wang, R., Wang, K., Li, S., Han, S., Zuo, Q., and Shi, J.: Effects of fertilization and stand age on N2O and NO emissions from tea plantations: a site-scale study in a subtropical region using a modified biogeochemical model, Atmos. Chem. Phys., 20, 6903–6919, 10.5194/acp-20-6903-2020, 2020.Zhang, W., Li, S., Han, S., Xie, H., Lu, C., Sui, Y., Rui, W., Liu, C., Yao,
Z., and Zheng, X.: Less intensive nitrate leaching from Phaeozems cultivated
with maize generally occurs in northeastern China, Agr. Ecosyst. Environ., 310, 107303, 10.1016/j.agee.2021.107303, 2021.Zhao, X., Xie, Y., Xiong, Z., Yan, X., Xing, G., and Zhu, Z.: Nitrogen fate and environmental consequence in paddy soil under rice-wheat rotation in the
taihu lake region, China, Plant Soil, 319, 225–234, 10.1007/s11104-008-9865-0, 2009.
Zhou, F., Ciais, P., Hayashi, K., Galloway, J., Kim, D. G., Yang, C., Li,
S., Liu, B., Shang, Z., and Gao, S.: Re-estimating NH3 emissions from
Chinese cropland by a new nonlinear model, Environ. Sci. Technol., 50,
564–572, 10.1021/acs.est.5b03156, 2016.Zhu, Z., Cai, G., Simpson, J. R., Zhang, S., Chen, D., Jackson, A. V., and
Freney, J. R.: Processes of nitrogen loss from fertilizers applied to
flooded rice fields on a calcareous soil in north–central China, Nutr.
Cycl. Agroecosys., 18, 101–115, 10.1007/BF01049507, 1989.