Process-based Biogeochemical Model Research Articles

Impacts of wildfires on boreal forest ecosystem carbon dynamics from 1986 to 2020

Wildfires significantly change boreal forest ecosystem carbon balance through both direct combustion and post-fire carbon dynamics. Affected vegetation influences soil thermal regime and carbon cycling by impacting the surface energy balance of boreal forests. This study uses a process-based biogeochemistry model to quantify carbon budget of North American boreal forests during 1986–2020 based on satellite-derived burn severity data. During the study period, burn severity generally increases. Fires remove ecosystem carbon of 2.4 Pg C and reduce net ecosystem production (NEP) from 32.6 to 0.8 Tg C yr−1, making the forest ecosystems lose 3.5 Pg C, shifting a carbon sink to a source. The canopy’s cooling effect leads to lower soil temperature and lower net primary production due to lower nitrogen mineralization and uptake. Post-fire NEP decreases from 1.6 to 0.8 Tg C yr−1. This reduction accounts for 50% of the simulated NEP when the effects of fire-affected canopy are not considered. Our study highlights the importance of wildfires and their induced-canopy changes in soil thermal and ecosystem carbon dynamics of boreal forests.

Maximizing carbon sequestration potential in Chinese forests through optimal management

Forest carbon sequestration capacity in China remains uncertain due to underrepresented tree demographic dynamics and overlooked of harvest impacts. In this study, we employ a process-based biogeochemical model to make projections by using national forest inventories, covering approximately 415,000 permanent plots, revealing an expansion in biomass carbon stock by 13.6 ± 1.5 Pg C from 2020 to 2100, with additional sink through augmentation of wood product pool (0.6-2.0 Pg C) and spatiotemporal optimization of forest management (2.3 ± 0.03 Pg C). We find that statistical model might cause large bias in long-term projection due to underrepresentation or neglect of wood harvest and forest demographic changes. Remarkably, disregarding the repercussions of harvesting on forest age can result in a premature shift in the timing of the carbon sink peak by 1–3 decades. Our findings emphasize the pressing necessity for the swift implementation of optimal forest management strategies for carbon sequestration enhancement.

Evaluating key climatic and ecophysiological parameters of worldwide tree mortality with a process-based BGC model and machine learning algorithms

Drought-induced tree mortality has been increasing worldwide under climate change; therefore, forests will become more vulnerable as warming continues. Meanwhile, carbon starvation and hydraulic failure have been proposed as main drought-induced mortality mechanisms, mostly validated through individual tree-level experiments. However, there lack of a unified way to monitor and assess tree mortality across the different biomes and climate regions. In this sense, process-based biogeochemical (BGC) modeling may be an effective tool for simulating and understanding ecophysiological processes for tree mortality at large spatial scales. In this study, a hydraulic vulnerability curve for percentage loss of conductivity (PLC) was added to the BGC-NSCs model, the modified version of the BIOME-BGC with two additional non-structural carbohydrates (NSCs) pools. And then, we simulate the model at the sites around the world where tree mortality were reported. Using sensitivity analysis and machine learning algorithms for hydraulic stress, PLC and NSCs showed a high sensitivity and significance to tree mortality within the modeling framework. The model simulations also reveal the relationship between PLC and NSCs based on mortality stress intensity, plant functional types, and climate conditions, further validated with the results of previous experiment studies at the plot scale. This study proposes a potential to estimate eocphysiological variables at the regional scale using the BGC model, and to use high sensitivity variable, such as PLC and NSCs, as effective diagnostics for hydraulic stress across different biomes and climate regions.

Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)

Abstract. Simulation models are tools commonly used to predict changes in soil carbon stocks. Prior validation is essential, however, for determining the reliability and applicability of model results. In this study, the process-based biogeochemical model MONICA (Model of Nitrogen and Carbon dynamics on Agro-ecosystems) was evaluated with respect to soil organic carbon (SOC), using long-term monitoring data from 46 German agricultural sites. A revision and parameterisation of equations, encompassing crop- and fertiliser-specific C contents and the abiotic factors of soil temperature, soil water and clay content, were undertaken and included in the model. The modified version was also used for a Morris elementary effects screening method, which confirmed the importance of environmental and management factors to the model's performance. The model was then calibrated by means of Bayesian inference, using the Metropolis–Hastings algorithm. The performance of the MONICA model was compared with that of five established carbon turnover models (CCB, CENTURY, C-TOOL, ICBM and RothC). The original MONICA model systematically overestimated SOC decomposition rates and produced on average a ∼17 % greater mean absolute error (MAE) than the other models. The modification and calibration significantly improved its performance, reducing the MAE by ∼30 %. Consequently, MONICA outperformed CENTURY, CCB and C-TOOL, and produced results comparable with ICBM and RothC. Use of the modified model allowed mostly adequate reproduction of site-specific SOC stocks, while the availability of a nitrogen, plant growth and water submodel enhanced its applicability when compared with models that only describe carbon dynamics.

The ratio of denitrification end-products were influenced by soil pH and clay content across different texture classes in Oklahoma soils

Nitrous oxide (N2O) is a potent greenhouse gas that contributes to stratospheric ozone depletion and global climate change. Soil denitrification has two potential end-products, N2O and dinitrogen (N2), and the ratio of these end-products (N2O:(N2O+N2) or the N2O ratio) is controlled by various factors. This study aims to quantify the influence of soil pH on the ratio of denitrification end-products in Oklahoma soils with different soil textures. Six natural grassland soils encompassing three distinct soil textures were incubated in the laboratory under natural and modified pH with an overall tested pH ranging from 2 to 10. Denitrification end-products were measured in the laboratory using the acetylene inhibition technique and further estimated using a process-based biogeochemical model. Both the laboratory and model results showed that soil pH and texture influenced the ratio of the denitrification end-products. Generally, as soil pH increased the N2O ratio decreased, although both lab and model results indicated that this relationship was not linear. Soil texture may have an indirect effect on the N2O ratio, as two soils of the same texture could have different N2O ratios. However, clay percentage of the soil did show a linear positive correlation with the N2O ratio, suggesting components of soil texture may be more influential than others. Overall, soil pH was a controlling factor in the ratio of denitrification end-products and the newly observed nonlinear relationship warrants further study, particularly when considering its effects in different soil textures.

Modified DNDC model to improve performance of soil temperature simulation under plastic film mulching and snow cover

Soil temperature plays a fundamental role in climatological and hydrological modeling as well as in simulating environmental processes and climate change. There are many models to simulate soil temperature, and the denitrification decomposition (DNDC) model is a widely used process-based biogeochemical model among them. However, this model does not simulate soil temperatures well under seasonal snow cover and plastic film mulching in arid and semi-arid regions. In this study, the DNDC model was calibrated and validated using a 9 years (2012–2020) soil temperature dataset from a typical semi-arid region of Gansu province, China (Yuzhong). Then, the model was tested in two other experimental sites in semi-arid regions (Dingxi and Tongwei in Gansu province, China). Model sensitivity analysis demonstrated that the simulated soil temperature was mainly related to air temperature. Model validation results indicated that the measured and simulated soil temperatures were in agreement with each other. Compared with the original model, the average RMSE of the soil temperature in the modified model decreased by 125.3 % and the average R2 increased by 52.1 % in winter under conditions of plastic film mulching and snow cover. For the test dataset, the R, R2, and RMSE of the modified DNDC model were 0.96–0.97, 0.89–0.94, and 2.01–2.76 in the Dingxi experiment site, and 0.94–0.95, 0.88–0.93, and 1.86–3.16, in the Tongwei experiment site, respectively. The performance of the modified model simulation was better than that of the original model. The modified DNDC model can be used as an effective simulation tool to predict soil temperature. The modified model also had good expandability and applicability in different experimental sites in different semi-arid regions. It can be used as an alternative scheme for future soil temperature predictions, and it can provide theoretical input for the formulation of local agricultural strategies.

Assessing the phosphorus cycle in European agricultural soils: Looking beyond current national phosphorus budgets

Phosphorus (P) is an essential nutrient for all crops, yet its excess negatively affects public health, the environment, and the economy. At the same time, rock P is a critical raw material due to its importance for food production, the finite geological deposits, and its unequal regional distribution. As a consequence, nutrient management is addressed by numerous environmental policies. Process-based biogeochemical models are valuable instruments to monitor the P cycle and predict the effect of agricultural management policies. In this study, we upscale the calibrated DayCent model at European level using data-derived soil properties, advanced input data sets, and representative management practices. Our results depicted a P budget with an average P surplus (0.11 kg P ha−1 year−1), a total soil P (2240.0 kg P ha−1), and available P content (77.4 kg P ha−1) consistent with literature and national statistics. Through agricultural management scenarios, we revealed a range of potential changes in the P budget by 2030 and 2050, influenced by the interlink of P with biogeochemical carbon and nitrogen cycles. Thus, we developed a powerful assessment tool capable of i) identifying areas with P surplus or deficit at high spatial resolution of 1 km2, (ii) pinpointing areas where a change in agricultural management would be most urgent to reach policy goals in terms of environmental pollution, food security and resource efficiency of a critical raw material, and iii) assessing the response of the P cycle to modifications in agricultural management.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking systemto examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Methane emissions from Arctic landscapes during 2000–2015: an analysis with land and lake biogeochemistry models

Abstract. Wetlands and freshwater bodies (mainly lakes) are the largest natural sources of the greenhouse gas CH4 to the atmosphere. Great efforts have been made to quantify these source emissions and their uncertainties. Previous research suggests that there might be significant uncertainties coming from “double accounting” emissions from freshwater bodies and wetlands. Here we quantify the methane emissions from both land and freshwater bodies in the pan-Arctic with two process-based biogeochemistry models by minimizing the double accounting at the landscape scale. Two non-overlapping dynamic areal change datasets are used to drive the models. We estimate that the total methane emissions from the pan-Arctic are 36.46 ± 1.02 Tg CH4 yr−1 during 2000–2015, of which wetlands and freshwater bodies are 21.69 ± 0.59 Tg CH4 yr−1 and 14.76 ± 0.44 Tg CH4 yr−1, respectively. Our estimation narrows the difference between previous bottom-up (53.9 Tg CH4 yr−1) and top-down (29 Tg CH4 yr−1) estimates. Our correlation analysis shows that air temperature is the most important driver for methane emissions of inland water systems. Wetland emissions are also significantly affected by vapor pressure, while lake emissions are more influenced by precipitation and landscape areal changes. Sensitivity tests indicate that pan-Arctic lake CH4 emissions were highly influenced by air temperature but less by lake sediment carbon increase.

Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis

Abstract. Northern peatlands have been a large C sink during the Holocene, but whether they will keep being a C sink under future climate change is uncertain. This study simulates the responses of northern peatlands to future climate until 2300 with a Peatland version Terrestrial Ecosystem Model (PTEM). The simulations are driven with two sets of CMIP5 climate data (IPSL-CM5A-LR and bcc-csm1-1) under three warming scenarios (RCPs 2.6, 4.5 and 8.5). Peatland area expansion, shrinkage, and C accumulation and decomposition are modeled. In the 21st century, northern peatlands are projected to be a C source of 1.2–13.3 Pg C under all climate scenarios except for RCP 2.6 of bcc-csm1-1 (a sink of 0.8 Pg C). During 2100–2300, northern peatlands under all scenarios are a C source under IPSL-CM5A-LR scenarios, being larger sources than bcc-csm1-1 scenarios (5.9–118.3 vs. 0.7–87.6 Pg C). C sources are attributed to (1) the peatland water table depth (WTD) becoming deeper and permafrost thaw increasing decomposition rate; (2) net primary production (NPP) not increasing much as climate warms because peat drying suppresses net N mineralization; and (3) as WTD deepens, peatlands switching from moss–herbaceous dominated to moss–woody dominated, while woody plants require more N for productivity. Under IPSL-CM5A-LR scenarios, northern peatlands remain as a C sink until the pan-Arctic annual temperature reaches −2.6 to −2.89 ∘C, while this threshold is −2.09 to −2.35 ∘C under bcc-csm1-1 scenarios. This study predicts a northern peatland sink-to-source shift in around 2050, earlier than previous estimates of after 2100, and emphasizes the vulnerability of northern peatlands to climate change.

Opposing seasonal temperature dependencies of CO2 and CH4 emissions from wetlands.

Wetlands are critically important to global climate change because of their role in modulating the release of atmospheric greenhouse gases (GHGs) carbon dioxide (CO2 ) and methane (CH4 ). Temperature plays a crucial role in wetland GHG emissions, while the general pattern for seasonal temperature dependencies of wetland CO2 and CH4 emissions is poorly understood. Here we show opposite seasonal temperature dependencies of CO2 and CH4 emissions by using 36,663 daily observations of simultaneous measurements of ecosystem-scale CO2 and CH4 emissions in 42 widely distributed wetlands from the FLUXNET-CH4 database. Specifically, the temperature dependence of CO2 emissions decreased with increasing monthly mean temperature, but the opposite was true for that of CH4 emissions. Neglecting seasonal temperature dependencies may overestimate wetland CO2 and CH4 emissions compared to the use of a year-based static and consistent temperature dependence parameter when only considering temperature effects. Our findings highlight the importance of incorporating the remarkable seasonality in temperature dependence into process-based biogeochemical models to predict feedbacks of wetland GHG emissions to climate warming.

Modeling nitrous oxide emissions from agricultural soil incubation experiments using CoupModel

Abstract. Efforts to develop effective climate mitigation strategies for agriculture require methods to estimate nitrous oxide (N2O) emissions from soil. Process-based biogeochemical models have been often used for field- and large-scale estimates, while the sensitivity and uncertainty of model applications to incubation experiments are less investigated. In this study, a process-oriented model (CoupModel) was used to simulate N2O and CO2 fluxes and soil mineral nitrogen (N) contents in a short-term (43 d) factorial incubation experiment (16 treatments). A global sensitivity analysis (GSA) approach, “Morris screening”, was applied to quantify parameter sensitivity. The GSA suggested that a higher number of sensitive parameters was associated with N2O flux estimates and that inter-treatment variations in parameter sensitivities were distinguished by soil moisture levels or NO3- content and residue types. Important parameters regarding N2O flux estimates were linked to the decomposability of soil organic matter (e.g., organic C pool sizes) and the denitrification process (e.g., Michaelis constant and denitrifier respiratory rates). After calibration, the model better captured temporal variations and magnitude of gas fluxes and mineral N in unamended soils than in residue-amended soils. Low-magnitude daily and cumulative N2O fluxes were well simulated with mean errors (MEs) close to zero, but the model tended to underestimate N2O fluxes, as observed daily values increased by over 0.1 g N m−2 d−1, in which the major mismatch was due to limited success of the model to describe the high emissions during the first few days after crop residue addition. A larger uncertainty was also seen in the magnitude of pulse emissions by the posterior simulations. We also evaluated ancillary variables regarding N cycling, which indicated that more frequent measurements and additional types of observed data such as soil oxygen content and the microbial sources of emitted N2O are required to further evaluate model performance and biases. The major challenges for calibration were associated with high sensitivities of denitrification parameters to initial soil abiotic conditions and the instantaneous residue amendment. Model structure uncertainties and improved modeling practices in the context of incubation experiments were discussed.

China’s terrestrial ecosystem carbon balance during the 20th century: an analysis with a process-based biogeochemistry model

BackgroundChina’s terrestrial ecosystems play a pronounced role in the global carbon cycle. Here we combine spatially-explicit information on vegetation, soil, topography, climate and land use change with a process-based biogeochemistry model to quantify the responses of terrestrial carbon cycle in China during the 20th century.ResultsAt a century scale, China’s terrestrial ecosystems have acted as a carbon sink averaging at 96 Tg C yr− 1, with large inter-annual and decadal variabilities. The regional sink has been enhanced due to the rising temperature and CO2 concentration, with a slight increase trend in carbon sink strength along with the enhanced net primary production in the century. The areas characterized by C source are simulated to extend in the west and north of the Hu Huanyong line, while the eastern and southern regions increase their area and intensity of C sink, particularly in the late 20th century. Forest ecosystems dominate the C sink in China and are responsible for about 64% of the total sink. On the century scale, the increase in carbon sinks in China’s terrestrial ecosystems is mainly contributed by rising CO2. Afforestation and reforestation promote an increase in terrestrial carbon uptake in China from 1950s. Although climate change has generally contributed to the increase of carbon sinks in terrestrial ecosystems in China, the positive effect of climate change has been diminishing in the last decades of the 20th century.ConclusionThis study focuses on the impacts of climate, CO2 and land use change on the carbon cycle, and presents the potential trends of terrestrial ecosystem carbon balance in China at a century scale. While a slight increase in carbon sink strength benefits from the enhanced vegetation carbon uptake in China’s terrestrial ecosystems during the 20th century, the increase trend may diminish or even change to a decrease trend under future climate change.

산림 생태계에 활용된 생지화학모형의 국내 적용성 분석

Identifying the carbon cycle in forest ecosystems is important when studying climate change. Since 2010, however, most of the suitable process-based biogeochemical models for forest sectors in South Korea have not been validated. For forest ecosystems, Korean and international research using a process-based biogeochemical model was collected and examined in this study. The applicability of models based on methodology, input/output parameters, and the availability of relevant spatio-temporal data was determined. After 2010, a total of 13 models, including one model commonly used by Koreans and foreign studies, were applied, with five originating domestically and nine originating internationally. Global, regional and temporal scales were used in each model, and a variety of targets, including agriculture, forestry and vegetation, were included. The input/output factors in most of the models were comparable. It was found that the CLM3.5-DGVM, MC1, VISIT and BIOME-BGC series models have been used most frequently in Korea and the models are highly applicable. Recent research, however, was limited by poor spatio-temporal resolution and lack of local characteristic data. Future research should include the creation of algorithms that represent forest management and the validation of modeling results using diverse models.

The climate change mitigation potential of annual grasslands under future climates.

Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process-based biogeochemical model (DayCent) at a daily time step to explore the long-term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long-term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site-specific daily climate data from two Earth system models (CanESM2 and HadGEM-ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one-time amendment of compost to rangeland acted as a slow-release organic fertilizer and increased NPP by up to 390-814 kg C ha-1 year-1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha-1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N2 O) emissions, the cumulative net emissions (in CO2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal-scale climate change mitigation goals alongside emissions reductions.

Improved global wetland carbon isotopic signatures support post-2006 microbial methane emission increase

Atmospheric concentrations of methane, a powerful greenhouse gas, have strongly increased since 2007. Measurements of stable carbon isotopes of methane can constrain emissions if the isotopic compositions are known; however, isotopic compositions of methane emissions from wetlands are poorly constrained despite their importance. Here, we use a process-based biogeochemistry model to calculate the stable carbon isotopic composition of global wetland methane emissions. We estimate a mean global signature of −61.3 ± 0.7‰ and find that tropical wetland emissions are enriched by ~11‰ relative to boreal wetlands. Our model shows improved resolution of regional, latitudinal and global variations in isotopic composition of wetland emissions. Atmospheric simulation scenarios with the improved wetland isotopic composition suggest that increases in atmospheric methane since 2007 are attributable to rising microbial emissions. Our findings substantially reduce uncertainty in the stable carbon isotopic composition of methane emissions from wetlands and improve understanding of the global methane budget.

Calibration and validation of phenological models for Biome-BGCMuSo in the grasslands of Tibetan Plateau using remote sensing data

Modeling vegetation phenology is crucial to assessing how climate change impacts carbon cycles in terrestrial ecosystems. The process-based biogeochemical model Biome-BGCMuSo is widely used for simulating carbon and water storages and fluxes of grassland ecosystems. However, the lack of accurate phenological information, such as the start of the growing season (SOS), impedes better simulations of the biogeochemical processes in the Tibetan Plateau (TP). Here, based on the snow-free satellite-derived SOS and the end of the growing season (EOS) in the TP during 1982–2018, we calibrated and validated three phenological models for SOS (i.e., the Biome-BGC phenological (BBGC) model, the heatsum growing season index (HSGSI) model, and the alpine meadow prognostic phenological (AMPP) model) and five phenological models for EOS (i.e., BBGC, HSGSI, AMPP, the low temperature and photoperiod multiplicative model induced by photoperiod (TPMP) and temperature (TPMT)) using particle swarm optimization (PSO) algorithm. For SOS, all three phenological models with calibrated parameters performed similarly and all captured the change in SOS well along gradients of aridity. The performance of BBGC, HSGSI, and AMPP models were largely improved with the calibration. The AMPP model simulated SOS with the lowest estimation errors with the mean absolute error (MAE) of 18.67 days and the Kling-Gupta efficiency (KGE) of 0.47 in validation. For EOS, the calibrated HSGSI and AMPP models, with mean MAEs of 9.85 and 9.29 days, respectively, captured the change in EOS well along the gradients of aridity and performed better than other models. The calibration significantly improved the simulation performance of all five models. Therefore, the phenological models can be calibrated and validated at a large scale with snow-free satellite-derived phenological data. Our study recommends that calibration and validation for the phenological model play a vital role in accurately simulating SOS and EOS in the regional carbon cycle simulation.

A Decreasing Trend of Nitrous Oxide Emissions From California Cropland From 2000 to 2015

Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial‐temporal dynamics of nitrous oxide (N2O) emissions. Process‐based models can quantify N2O emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was applied to quantify spatial‐temporal dynamics of direct N2O emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct N2O emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000–2015 by linking the model with a spatial‐temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct N2O emissions ranged from 3,830 to 7,875 tonnes N2O‐N yr−1, representing 0.73%–1.21% of the N inputs. N2O emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total N2O emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct N2O emissions declined by 47% from 2000 to 2015. Simulations showed N2O emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial‐temporal emission dynamics and driving factors provide knowledge toward effective N2O mitigation and highlight the importance of coupling process‐based models with high‐resolution data for characterizing the spatial‐temporal variability of N2O emissions in regions with diverse croplands.

Modeling temporal variations of non-structural carbohydrate (NSC) storages across biomes

Recent findings on non-structural carbohydrates (NSCs) highlight their importance for plant growth and survival. Process-based biogeochemical models (BGC) focusing on primary production hardly capture the lagged NSC-related effects on tree mortality and growth. This study modified BIOME-BGC by adding sugar and starch NSC pools to incorporate NSCs as a temporary carbon reservoir between production and growth. The modified BGC-NSCs were evaluated with field NSC and flux sites across different biomes. As a result, the BGC-NSCs simulated the daily variations in gross primary production (GPP), the monthly and interannual variations of starch with a mean R2 of 0.60, 0.55 and 0.62, respectively. The simulation demonstrates that the starch pool size regulates the coupling strength between production and growth, implicating the involvement of NSCs in tree growth and mortality lagging. Consequently, we proposed future modeling guidance for mapping and simulating NSC dynamics to analyze growth and die-back process under climate change.

Evaluation of LandscapeDNDC Model Predictions of CO2 and N2O Fluxes from an Oak Forest in SE England

Process-based biogeochemical models are valuable tools to evaluate impacts of environmental or management changes on the greenhouse gas (GHG) balance of forest ecosystems. We evaluated LandscapeDNDC, a process-based model developed to simulate carbon (C), nitrogen (N) and water cycling at ecosystem and regional scales, against eddy covariance and soil chamber measurements of CO2 and N2O fluxes in an 80-year-old deciduous oak forest. We compared two LandscapeDNDC vegetation modules: PSIM (Physiological Simulation Model), which includes the understorey explicitly, and PnET (Photosynthesis–Evapotranspiration Model), which does not. Species parameters for both modules were adjusted to match local measurements. LandscapeDNDC was able to reproduce daily micro-climatic conditions, which serve as input for the vegetation modules. The PSIM and PnET modules reproduced mean annual net CO2 uptake to within 1% and 15% of the measured values by balancing gains and losses in seasonal patterns with respect to measurements, although inter-annual variations were not well reproduced. The PSIM module indicated that the understorey contributed up to 21% to CO2 fluxes. Mean annual soil CO2 fluxes were underestimated by 32% using PnET and overestimated by 26% with PSIM; both modules simulated annual soil N2O fluxes within the measured range but with less interannual variation. Including stand structure information improved the model, but further improvements are required for the model to predict forest GHG balances and their inter-annual variability following climatic or management changes.