Noah-MP improves over the Noah LSM (Niu et al., 2011; Yang et al., 2011) by including a short-term dynamic vegetation scheme (Dickinson et al., 1998), a modified two-stream radiation transfer scheme (Yang and Friedl, 2003; Niu and Yang, 2004), TOPMODEL-based runoff and groundwater schemes (Niu et al., 2005, 2007), and a more permeable frozen soil scheme (Niu and Yang, 2006).
Noah-MP hosts multiple parameterization options for several major physical processes, including vegetation phenology, canopy stomatal resistance, runoff, groundwater, and soil moisture limitation to transpiration (Niu et al., 2011; Yang et al., 2011). In order to incorporate nitrogen dynamics into Noah-MP, the dynamic vegetation option [instead of commonly used static climatological leaf area index (LAI) values] has to be enabled to update plant biomass and LAI based on photosynthesis. Other options are the same as the settings used in the site-evaluation experiment by Cai et al. (2016), which are recommended options in this version of Noah-MP [shown in Table S1 in electronic supplementary material (ESM)]. Nitrogen availability constrains the rate of photosynthesis. The constraint is induced by a foliage nitrogen factor parameter (Running and Coughlan, 1988) and is currently static in Noah-MP (the version without the dynamic nitrogen module). The foliage nitrogen factor adjusts the calculation of the maximum rate of carboxylation (Vmax), a factor controlling the total carbon assimilation rate, shown in Eq. (1):
where Vmax25 is the maximum carboxylation rate at 25°C (μmol CO2 m−2 s−1), αvmax is a temperature-sensitive parameter, f(Tv) is a function that mimics the thermal breakdown of metabolic processes, and β is the soil moisture controlling factor. The term fNIT, less than 1, is a foliage nitrogen factor, which is always set as a constant (0.67), indicating that a 33% reduction in Vmax is due to the limitation of nitrogen.
The static nitrogen constraint of Noah-MP is augmented by incorporating plant nitrogen dynamics based on the Fixation and Uptake of Nitrogen (FUN) model (Fisher et al., 2010) and soil nitrogen dynamics based on the Soil and Water Assessment Tool (SWAT) (Neitsch et al., 2011). The resultant Noah-MP-CN (Cai et al., 2016) is different from CLM-CN in that the former focuses on short-to-medium-range weather and environmental forecasts, while the latter targets long-term climate assessments. The FUN model was adopted in Noah-MP-CN because it uses the advanced carbon cost theory in plant nitrogen uptake, and SWAT was chosen for its strength in agricultural management and water-quality modeling. Specifically, FUN models plant nitrogen uptake and its carbon cost (NPP reduction), and there are four plant nitrogen uptake pathways: passive (advection with soil moisture being the medium) and active soil nitrogen uptake, symbiotic biological nitrogen fixation, and leaf nitrogen retranslocation. SWAT separately models the mineral and organic forms of soil nitrogen through the interactions between them via processes such as atmospheric deposition and leaching. The model flow chart and detailed mathematical formulations of the processes can be found in Cai et al. (2016).
Model parameters are the same as those used in Cai et al. (2016), except the following differences adjusted for regional simulations (shown in Table S2 in the ESM). The scaling factor of the symbiotic biological nitrogen fixation process and the threshold value of the soil water factor for denitrification to occur have been found to be two sensitive parameters (Cai et al., 2016). In this study, these two parameters are set for each land-use/land-cover type, and their values are shown in Table 1 to represent our best guesses based on the literature suggesting the default values of these parameters and our own experience. Some input parameter datasets, whose sources are shown in Table 2, are spatially varying (i.e., values vary based on location or by certain soil types or land-use/land-cover types), and they were collected by this study. Specifically, the soil-texture-related parameters, provided by Wei et al. (2014), were downscaled from their original resolution to 0.25° to be consistent with the model. The slope steepness and length factors of the Universal Soil Loss Equation (ULSE) were obtained from the National Science & Technology Infrastructure of China with interpolation. Based on the classification of the global land-cover database for the year 2000 (GLC 2000), we obtained the USLE support practice factor. According to the equation from Sharpley et al. (1993), the ULSE erodibility factor was calculated based on the soil texture attributes.
Parameter s γsw,thr Cropland −40 0.91 Forest −6.25 0.91 Grassland −30 0.85 Savannas −40 0.85 Shrublands −6.25 0.85 Others −6.25 0.85
Table 1. Major sensitive parameters based on our own experience. s is the scaling factor. γsw,thr is the threshold value of soil water factor for denitrification to occur.
Definition Source Soil bulk density Wei et al. (2014) Available water capacity of the soil layer Wei et al. (2014) Organic carbon content Wei et al. (2014) Clay content Wei et al. (2014) Silt content Wei et al. (2014) Sand content Wei et al. (2014) Rock content Wei et al. (2014) Fraction of porosity from which anions are excluded Default: 0.15 USLE slope steepness (S) factor National Science and Technology Infrastructure of China USLE slope length (L) factor National Science and Technology Infrastructure of China USLE support practice (P) factor GLC2000 USLE soil erodibility (K) factor Sharpley et al. (1993)
Table 2. Soil input data.
The nitrogen module requires two inputs: atmospheric nitrogen deposition and nitrogen fertilizer use. The atmospheric deposition is divided into wet and dry deposition, which are calculated by empirical formulae (Neitsch et al., 2011). The global fertilizer dataset, including the amount of nitrogen fertilizer of a year, provides the model inputs. The dataset does not have the fertilizer application date. We chose 1 June as the fertilizer application date according to previous point-scale work (Cai et al., 2016) because it is in the middle of the growing season. Note that this choice might be a source of uncertainty because the fertilizer application date varies spatially according to crop type. More comprehensive fertilizer-use datasets with detailed application dates are desirable in future work.
2.2. Noah-MP-CN for regional applications
LAI is defined as the one-sided green leaf area per unit ground surface area. LAI is a controlling factor of major biophysical processes including photosynthesis, respiration, ET, and the carbon cycle.
Figure 2 compares the multi-year mean LAI between simulations and GLASS. Both Noah-MP and Noah-MP-CN capture the observed spatial pattern, which exhibits an obvious gradient increasing from northwest to southeast. Noah-MP overestimates LAI in most regions, especially in the southern areas. This assessment is consistent with that by Ma et al. (2017) over the continental United States. The overestimation is reduced to some extent in Noah-MP-CN. The improvements from considering nitrogen constraints on carbon are more obvious in terms of temporal correlation. Similar to the assessment of the multi-year mean values, the improvements in temporal correlation are also mainly located in southeastern China, where both Noah-MP and Noah-MP-CN show relatively reasonable correlations with GLASS. Including the limitation of nitrogen on carbon assimilation also reduces the RMSE of the simulated LAI, and these RMSE decreases are observed in almost the entire domain.
Figure 2. Spatial distribution of multi-year averaged LAI (m2 m−2) during 2001−14 for (a) BNU_GLASS, (b) Noah-MP-CN, and (c) Noah-MP. Bias of LAI during 2001−14 (d) between Noah-MP-CN and BNU_GLASS, (e) between Noah-MP and BNU_GLASS, and (f) Noah-MP-CN minus Noah-MP in the relative absolute LAI biases. Relative absolute LAI biases are defined as the absolute biases between modeled LAI and GLASS LAI, divided by GLASS LAI. Correlation coefficients of LAI during 2001−14 (g) between Noah-MP-CN and BNU_GLASS, (h) between Noah-MP and BNU_GLASS, and (i) Noah-MP minus Noah-MP-CN. RMSE of LAI during 2001−14 for (j) Noah-MP-CN, (k) Noah-MP, and (l) Noah-MP-CN minus Noah-MP.
Figure 3 compares the simulated multi-year mean LAI from Noah-MP and Noah-MP-CN in different climatic zones, which are characterized by multi-year-averaged temperature and annual precipitation, two important climatological factors in determining vegetation distribution and growth. Figures 3g and h show that Noah-MP captures the dependency of LAI on temperature and precipitation well, but with a positive bias. Noah-MP-CN reduces the biases of Noah-MP. Figures 3a-f show that the bias reduction of Noah-MP-CN mainly exists in the relatively humid and warm regions, where the multi-year-averaged annual precipitation is more than 800 mm and the temperature is over 281 K.
Figure 3. Distribution of multi-year averaged LAI (m2 m−2) in China relative to the variations in temperature and precipitation: (a) GLASS observation; (b) Noah-MP; (c) Noah-MP-CN; (d) Noah-MP-CN minus Noah-MP; (e) Noah-MP minus GLASS; (f) Noah-MP-CN minus GLASS. Sensitivities of LAI to (g) temperature and (h) precipitation.
We further evaluate the performances of Noah-MP-CN for cropland, forest, grassland, savanna, and shrubland, which are five major land-cover types in the study domain by comparing regionally averaged LAI values. After introducing nitrogen dynamics, all simulations are more consistent with observations than the original Noah-MP. As shown in Table 3, the RMSEs/biases of all five types are reduced, with the values ranging from 0.04/0.10 (forest) to 0.23/0.26 (cropland). Four of the five types also present better correlations, except for forest.
LAI (units: m2 m−2) GPP (units: gC m-2 d−1) Noah-MP Noah-MP-CN Noah-MP Noah-MP-CN R Bias RMSE R Bias RMSE R Bias RMSE R Bias RMSE Cropland 0.93 0.83 0.88 0.94 0.57 0.65 0.97 3.01 3.33 0.98 2.5 2.95 Forest 0.84 0.50 0.87 0.83 0.39 0.83 0.95 1.32 1.51 0.95 0.98 1.23 Grassland 0.65 0.66 0.87 0.67 0.47 0.67 0.85 0.33 0.77 0.88 0.04 0.57 Savannas 0.9 1.14 1.23 0.92 0.98 1.07 0.98 1.05 1.14 0.99 0.66 0.76 Shrublands 0.58 0.24 0.50 0.61 0.13 0.41 0.78 −0.04 0.57 0.8 −0.18 0.57
Table 3. Correlation coefficients (R), bias, and root-mean-square error (RMSE) between regionally averaged simulation and observation for LAI and GPP of different land-use types in China.
As the gross amount of dry organic matter, GPP is not only the key regulator for the whole ecosystem but also the main factor for the carbon sink of terrestrial ecosystems. Noah-MP overestimates multi-year mean GPP in eastern China, whereas the overestimation is reduced to some extent by Noah-MP-CN, especially in cropland regions (Fig. 4), which is also shown in Table 3 in the comparison of regionally averaged values. Future investigations on the parameters of simulated biomass-related variables are desirable to resolve the negative GPP bias and positive LAI bias over shrubland from Noah-MP-CN. Four of the five types also present better biases, except for shrublands, whose GPP is already underestimated in the default Noah-MP with the dynamic vegetation option; the nitrogen limitation in Noah-MP-CN worsens the GPP simulations in terms of bias.
Figure 4. As in Fig. 2 except for GPP.
Despite the above-mentioned issues, Noah-MP-CN does improve the correlation in most regions, when both simulations exhibit relatively high correlation values. Additionally, reduced RMSE values are found in almost the whole domain, especially in cropland regions, which shows promise for using Noah-MP-CN in regional simulations.
As one of the most important land-cover types, cropland directly influences the terrestrial nitrogen cycle by accepting almost half of the total nitrogen input to the soil through fertilizer application (Fowler et al., 2013). Here, we consider three aspects directly related to coupling nitrogen dynamics for cropland: the carbon cycle, water cycle, and fertilizer application amount.
Figure 5a compares the LAI time series from Noah-MP, Noah-MP-CN, and the validation data. Both Noah-MP-CN and Noah-MP capture the variations of LAI. When coupled with nitrogen dynamics, the simulation is closer to the observation, with a 0.01 increase in R and 0.23 decrease in RMSE for the whole period (Table 3). Noah-MP-CN brings the most obvious decrease in LAI in winter, suggesting a tighter constraint of nitrogen on carbon due to the low temperature in winter.
Figure 5. Comparison of Noah-MP, Noah-MP-CN and observation on cropland for (a) LAI, (b) GPP, (c) NPP and (d) NEE, noting that observations only exist in the comparison of (a) LAI and (b) GPP. The right-hand column shows the corresponding multi-year mean seasonal cycle and the average of a month.
Figure 5b compares the monthly GPP variations from Noah-MP and Noah-MP-CN. Both models capture the slightly increasing trend of GPP during 2001−13. Specifically, Noah-MP-CN shows a similar variation to Noah-MP both in terms of trends and amplitudes, with the same R and 11.49 gC m−2 decrease in RMSE. Noah-MP-CN slightly decreases the GPP estimates from 177.02 to 161.55 gC m−2, with the largest difference occurring in winter, as directly influenced by the variation of LAI.
Figure 5c shows that Noah-MP-CN brings a large decrease in NPP (from 97.65 to 68.58 gC m−2), which means less carbon could be used by the plant, and directly results in a decrease in LAI (from 2.181 to 1.924 m2 m−2). The smaller decrease in LAI than NPP can possibly be explained by the decreased leaf turnover in the model, i.e., leaves senesce and fall, which is down-regulated by the limitation of nitrogen, reducing the amount of leaf biomass to loss. As for net ecosystem exchange (NEE), the annual mean is reduced by 29.68 gC m−2 (from 368.35 to 338.67), meaning that the carbon sink is decreased when taking the impact of nitrogen into consideration.
As shown in Fig. 6a, both Noah-MP-CN and Noah-MP capture the temporal variation of ET well. The differences between Noah-MP and Noah-MP-CN are insignificant. Noah-MP-CN slightly outperforms Noah-MP insofar as it produces smaller RMSE values and better temporal correlation (Table 4), despite the larger bias. The improvement is mainly attributable to the reduction in ET peaks, which is caused by the smaller LAI.
R RMSE Bias Noah-MP Noah-MP-CN Noah-MP Noah-MP-CN Noah-MP Noah-MP-CN ET (mm month−1) 0.93 0.95 14.84 14.65 0.38 −3.03 Soil moisture(mm month−1) 0.41 0.43 0.047 0.043 0.043 0.039
Table 4. Correlation coefficients (R), and root-mean-square error (RMSE), and bias between regionally averaged simulation and observation for ET and soil moisture of cropland in China.
Figure 6. As in Fig. 5 except for (a) ET, (b) transpiration, (c) root-zone soil moisture, (d) soil evaporation, (e) surface soil moisture, and (f) runoff, noting that observations only exist in the comparison of (a) ET and (e) surface soil moisture.
Figures 6b-d show the impacts of nitrogen dynamics on transpiration, root-zone (0−1 m) soil moisture and soil evaporation at different time scales. Noah-MP-CN differs from Noah-MP in partitioning total ET between transpiration and soil evaporation. Note, however, that the decrease in LAI contributes directly to the decrease in transpiration, resulting in a monthly reduction of 2.3 mm, subsequently leading to more simulated root-zone (0−1 m) soil moisture (from 0.291 to 0.294) than in the original model. The decrease in LAI also leads to more exposure of the land surface, which directly increases the amount of soil evaporation (from 18.17 to 20.03 mm), subsequently reducing the surface (0−0.1 m) moisture (from 0.251 to 0.247).
Figure 6e also shows that both Noah-MP-CN and Noah-MP reproduce the variation in surface soil moisture to some extent, although the evaluation data were derived from relatively few stations (14 sites). Noah-MP-CN slightly outperforms Noah-MP in terms of a higher temporal correlation (0.43 versus 0.41) and a slightly smaller bias (0.039 versus 0.043) and RMSE (0.043 versus 0.047), as shown in Table 4.
The most notable discrepancies of Figs. 6b and d mainly occur in summer, with the contribution provided by the relatively high temperature. With the decrease in ET and soil moisture, runoff (shown in Fig. 6f) shows an increase, from 27.88 to 29.85 mm, especially during summer.
Overall, Noah-MP-CN only shows slight improvements from Noah-MP in water-related variables, as compared to carbon-related variables, which is reasonable considering the impact of nitrogen on the water cycle occurs mainly via indirect effects. Nonetheless, the slight improvement in simulating the water cycle is promising for incorporating nitrogen dynamics to improve the performances of LSMs.
The coupled Noah-MP-CN describes fertilizer inputs and their connections to carbon/water cycles through physical parameterizations, and thus it allows us to investigate the utilization efficiency and environmental benefit of fertilizer application.
To examine the impact of fertilizer application, we use water-use efficiency (WUE) as a metric to reflect the impact of the environment on plant growth. WUE is defined as the carbon gain at the cost of unit water loss, and here we use the ratio of GPP to transpiration to define WUE.
Based on the difference of the WUE spatial distribution in the seasonal cycle (Fig. 7), we find that using nitrogen fertilizer can generally increase WUE in most regions throughout the whole year, especially in winter because nitrogen fertilizer can play an important role in supporting crop growth during this season. WUE is also higher by applying the actual amount in spring, autumn, and winter, compared to applying only half the amount of fertilizer. Interestingly, although there is a general increase in WUE in most regions in FT2 compared to FT1 (Fig. 7), in summer, FT3 exhibits the lower WUE compared to FT2 in most cropland areas. Figure 8 further shows that, with an increasing amount of fertilizer, both GPP and transpiration increase in all months. GPP shows a decreasing trend before May, while obvious increasing trends for both GPP and transpiration are seen after June in FT2 versus FT1 (when fertilizer is applied), indicating this amount of fertilizer supplements the consumption of nitrogen used by cropland in the period before June to improve WUE. However, the increasing rate of WUE in FT3 versus FT2 exhibits a steep drop starting in February, which even reaches negative values in May, indicating that this amount of fertilizer may be beyond the nitrogen demands of cropland in this period, the beginning of the growing season. After some consumption of fertilizer, along with more fertilizer needed in the summer, the increasing rate of WUE returns to positive but still keeps a relatively slight growth. In other words, this high level of fertilizer application might be too much to maintain a high WUE. After the peak nitrogen consumption in summer, the surplus nitrate ensures a large increase in WUE in the following seasons, causing it to peak in spring. Numerically, compared to the halved fertilizer use, the actual fertilizer use increases GPP by only 1.97% at the cost of near-zero (0.43%) water consumption elevation for the whole cropland area.
Figure 7. Differences in multi-year (2001−14) mean WUE (units: g kg−1) between FT1 and FT2 (left-hand column) and between FT2 and FT3 (right-hand column) for spring, summer, autumn, and winter.
Figure 8. Mean seasonal cycle of percentage change for (a) GPP, (b) transpiration, and (c) WUE between FT1 and FT2 and between FT2 and FT3 in cropland during 2001−14.
Overall, our modeling results suggest that appropriate fertilizer use can increase the grain yield and WUE, while excessive use will reduce WUE, which means a waste of resources. Only when we apply fertilizer properly (in terms of amount), can we make the best of it. These modeling results clearly demonstrate such effects by scrutinizing the nitrogen dynamics in cropland regions of China, demonstrating the model’s suitability for a range of future applications involving agricultural decision-making.
Leaching, as the main indicator of water pollution, is chosen as an indicator to further investigate the impacts of nitrogen fertilizer application on the environment. The combination of the nitrate transported to rivers via surface and subsurface runoff, and to groundwater through percolation from the soil bottom, is used as the total amount of nitrate leaching in this study.
All three experiments share a similar spatial pattern of annual leaching, and the overall magnitudes of FT3 are reasonable (Fig. 9), which is consistent with previous studies (Manevski et al., 2016). High values are seen in northern China, especially in the croplands of the Loess Plateau, Huang-Huai-Hai Plain, and Songliao Basin, where relatively high amounts of fertilizer have been applied. These regions also show an obvious increase in nitrogen leaching between different amounts of fertilizer. The increase in the amount of leaching of FT3 relative to FT2 (5.35%, based on FT2) is almost twice that of FT2 to FT1 (5.91%, based on FT1), indicating a strong contribution of fertilization application to leaching when it reaches a certain threshold. These results point to the potential of Noah-MP-CN as a tool in managing cropland fertilizer use to enhance productivity while minimizing pollution.