Carbon–nitrogen coupling under three schemes of model representation: a traceability analysis

Abstract. The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C–N coupling differs greatly among models, and how these diverse representations of C–N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varied with three different commonly used schemes of C–N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C–N models (i.e., TECO-CN, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, downregulation of photosynthesis, and the pathways of N import. We incorporated the three C–N coupling schemes into the C-only version of the TECO model and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all three of the C–N schemes caused significant reductions in steady-state C storage capacity compared with the C-only version with magnitudes of −23 %, −30 %, and −54 % for SM1, SM2, and SM3, respectively. This reduced C storage capacity was mainly derived from the combined effects of decreases in net primary productivity (NPP; −29 %, −15 %, and −45 %) and changes in mean C residence time (MRT; 9 %, −17 %, and −17 %) for SM1, SM2, and SM3, respectively. The differences in NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue C : N ratio, downregulation of photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant vs. microbe competition strategy and the plant N uptake, combined with the flexible C : N ratio in vegetation and soils, led to a notable spread in MRT. These results highlight the fact that the diverse assumptions on N processes represented by different C–N coupled models could cause additional uncertainty for land surface models. Understanding their difference can help us improve the capability of models to predict future biogeochemical cycles of terrestrial ecosystems.

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Carbon-nitrogen coupling under three schemes of model representation: Traceability analysis

10.5194/gmd-2018-41 ◽

2018 ◽

Cited By ~ 1

Author(s):

Zhenggang Du ◽

Ensheng Weng ◽

Jianyang Xia ◽

Lifen Jiang ◽

Yiqi Luo ◽

...

Keyword(s):

Land Surface ◽

Storage Capacity ◽

N Uptake ◽

C:N Ratio ◽

Land Surface Models ◽

Plant N Uptake ◽

Coupled Models ◽

C Storage ◽

C Cycle ◽

Surface Models

Abstract. The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C-N coupling differs greatly among models, and how these diverse representations of C-N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varies with three different commonly-used schemes of C-N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C-N models (i.e., TECO-CN 2.0, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, pathways of N import, and the competition between plants and microbes for soil mineral N. We incorporated them into the C-only version of TECO model, and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all of the three C-N schemes resulted in significant reductions in steady-state C storage capacity compared with the C-only version, but the magnitude varied with −23 %, −30 % and −54 % for SM1, SM2, SM3, respectively. The reduced C storage capacity is the combination of decreases in net primary productivity (NPP) by −29 %, −15 % and −45 % with changes of mean C residence time (MRT) by 9 %, −17 % and −17 % for SM1, SM2, and SM3, respectively. The divergent NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue C:N ratio, down-regulation photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant and microbe competition strategy and the plant N uptake, combining with the flexible C:N ratio in vegetation and soils, led to a notable spread MRT. These results highlight that the diverse assumptions on N process representation among different C-N coupled models could cause additional uncertainty to land surface models. Understanding their difference can help us to improve the capability of models to predict future biogeochemical cycles on land.

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A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models

Earth System Science Data ◽

10.5194/essd-12-2365-2020 ◽

2020 ◽

Vol 12 (4) ◽

pp. 2365-2380

Author(s):

Xavier Morel ◽

Birger Hansen ◽

Christine Delire ◽

Per Ambus ◽

Mikhail Mastepanov ◽

...

Keyword(s):

Soil Carbon ◽

Greenhouse Gas ◽

Land Surface ◽

High Water Content ◽

Land Surface Models ◽

Carbon And Nitrogen ◽

Soil Carbon And Nitrogen ◽

C Cycle ◽

Surface Models ◽

Carbon And Nitrogen Stocks

Abstract. Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon because their high water content limits decomposition rates to levels below their net primary productivity. Their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration. Nuuk fen is a well-instrumented Greenlandic fen with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2) and is of particular interest for testing and validating land-surface models. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To address this issue, we measured, for the first time, soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland (64∘07′51′′ N, 51∘23′10′′ W), colocated with the greenhouse gas measurements. Measurements were made along two transects, 60 and 90 m long and with a horizontal resolution of 5 m and a vertical resolution of 5 to 10 cm, using a 4 cm diameter gouge auger. A total of 135 soil samples were analyzed. Soil carbon density varied between 6.2 and 160.2 kg C m−3 with a mean value of 50.2 kg C m−3. Mean soil nitrogen density was 2.37 kg N m−3. Mean soil carbon and nitrogen stocks are 36.3 kg C m−2 and 1.7 kg N m−2. These new data are in the range of those encountered in other arctic peatlands. This new dataset, one of very few in Greenland, can contribute to further development of joint modeling of greenhouse gas emissions and soil carbon and nitrogen in land-surface models. The dataset is open-access and available at https://doi.org/10.1594/PANGAEA.909899 (Morel et al., 2019b).

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A new dataset of soil Carbon and Nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models

10.5194/essd-2019-225 ◽

2020 ◽

Author(s):

Xavier Morel ◽

Birger Ulf Hansen ◽

Christine Delire ◽

Per Lennart Ambus ◽

Mikhail Mastepanov ◽

...

Keyword(s):

Soil Carbon ◽

Greenhouse Gas ◽

Land Surface ◽

High Water Content ◽

Land Surface Models ◽

Complete Evaluation ◽

Carbon And Nitrogen ◽

Soil Carbon And Nitrogen ◽

C Cycle ◽

Surface Models

Abstract. Arctic and boreal peatlands play a major role in the global carbon (C) cycle. They are particularly efficient at sequestering carbon due to their high-water content which makes primary productivity exceed decomposition rates. Though, their future in a climate-change context is quite uncertain in terms of carbon emissions and carbon sequestration. Nuuk-fen site is a well-instrumented greenlandic site of particular interest for testing and validating land-surface models with monitoring of soil physical variables and greenhouse gas fluxes (CH4 and CO2). But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models, as soil carbon is one of the primary drivers of CH4 and CO2 soil emissions. To tackle this issue, we measured for the first time soil carbon and nitrogen density, profiles and stocks in the Nuuk peatland, at the exact location of fluxes monitoring. Measurements were made along two transects. Measurements horizontal resolution is 5 meter, vertical resolution ranges from 5 to 10 cm. Mean soil carbon density is 50.2 kgC.m−3. These new data are in the range of those encountered in other arctic peatlands. This new dataset can contribute to further develop joint modelisation of greenhouse gas emissions and soil carbon in land-surface models. The dataset is open-access and available at https://doi.org/10.1594/PANGAEA.909899 (Morel et al., 2019b).

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ON THE ROLE OF INTEGRATED GROUNDWATER-LAND SURFACE MODELS FOR GROUNDWATER SUSTAINABILITY ASSESSMENTS

10.1130/abs/2019am-338177 ◽

2019 ◽

Author(s):

Hoori Ajami ◽

◽

Matthew F Mccabe ◽

Jason P. Evans ◽

Simon Stisen

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Groundwater Sustainability ◽

Surface Models ◽

Sustainability Assessments

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Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

New Phytologist ◽

10.1111/nph.17558 ◽

2021 ◽

Author(s):

Sandy P. Harrison ◽

Wolfgang Cramer ◽

Oskar Franklin ◽

Iain Colin Prentice ◽

Han Wang ◽

...

Keyword(s):

Land Surface ◽

Land Surface Models ◽

Surface Models ◽

Evolutionary Optimality

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The CarboEurope Regional Experiment Strategy

Bulletin of the American Meteorological Society ◽

10.1175/bams-87-10-1367 ◽

2006 ◽

Vol 87 (10) ◽

pp. 1367-1380 ◽

Cited By ~ 92

Author(s):

A. J. Dolman ◽

J. Noilhan ◽

P. Durand ◽

C. Sarrat ◽

A. Brut ◽

...

Keyword(s):

Land Surface ◽

Major Product ◽

State Variables ◽

Land Surface Models ◽

Quality Metric ◽

Key Characteristics ◽

Surface Models ◽

Global Soil ◽

Standard Deviations ◽

Model Agreement

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

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On the utility of land surface models for agricultural drought monitoring

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3451-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3451-3460 ◽

Cited By ~ 42

Author(s):

W. T. Crow ◽

S. V. Kumar ◽

J. D. Bolten

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Cross Correlation ◽

Root Zone ◽

Vegetation Indices ◽

Agricultural Drought ◽

Drought Monitoring ◽

Global Evaluation ◽

Land Surface Models ◽

Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations

Water Resources Research ◽

10.1002/2013wr014633 ◽

2014 ◽

Vol 50 (3) ◽

pp. 2679-2692 ◽

Cited By ~ 110

Author(s):

Gholamreza Joodaki ◽

John Wahr ◽

Sean Swenson

Keyword(s):

Middle East ◽

Land Surface ◽

Groundwater Depletion ◽

Land Surface Models ◽

Grace Data ◽

Surface Models

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Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0053.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 897-915 ◽

Cited By ~ 20

Author(s):

Jennifer L. Jefferson ◽

Reed M. Maxwell ◽

Paul G. Constantine

Keyword(s):

Land Surface ◽

Stomatal Resistance ◽

Hydrologic Model ◽

Surface Model ◽

Land Surface Models ◽

Common Land Model ◽

Surface Models ◽

Input Parameters ◽

Rate Of Photosynthesis ◽

Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

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