Long‐term ecosystem nitrogen limitation from foliar δ 15 N data and a land surface model

Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem and roots) determines C residence time and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and Leaf Area Index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a Land Surface Model (LSM), the Community Land Model (CLM4.5). We ran CLM for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i) Dynamic C allocation scheme (named "D-CLM") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual Net Primary Production (NPP). ii) An alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i) C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem and coarse roots iii–iv) Two fixed C allocation schemes, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM generally overestimated Gross Primary Production (GPP) and ecosystem respiration, and underestimated Net Ecosystem Exchange (NEE). In D-CLM, initial aboveground biomass in 1980 was largely overestimated (between 10527 and 12897 g Cm-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g Cm-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C-LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem/Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass, and aboveground NPP for deciduous forests by D-CLM. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. That could be done by addressing some of the current model assumptions about C allocation and the associated parameter uncertainty. Our results highlight the importance of using aboveground biomass data to evaluate and constrain the C allocation scheme in the model, and in particular, the sensitivity to the stem turnover rate. Revising these will be critical to improving long-term C processes in LSMs, and improve their projections of biomass accumulation in forests.

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Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

Geoscientific Model Development ◽

10.5194/gmd-10-3499-2017 ◽

2017 ◽

Vol 10 (9) ◽

pp. 3499-3517 ◽

Cited By ~ 16

Author(s):

Francesc Montané ◽

Andrew M. Fox ◽

Avelino F. Arellano ◽

Natasha MacBean ◽

M. Ross Alexander ◽

...

Keyword(s):

Primary Production ◽

Aboveground Biomass ◽

Land Surface ◽

Land Surface Model ◽

Biomass Accumulation ◽

Deciduous Forests ◽

Surface Model ◽

Allocation Scheme ◽

Tree Ring Data

Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m−2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m−2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the observed leaf C–LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem ∕ Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.

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The AquiFR hydrometeorological modelling platform as a tool for improving groundwater resource monitoring over France: evaluation over a 60-year period

Hydrology and Earth System Sciences ◽

10.5194/hess-24-633-2020 ◽

2020 ◽

Vol 24 (2) ◽

pp. 633-654 ◽

Cited By ~ 4

Author(s):

Jean-Pierre Vergnes ◽

Nicolas Roux ◽

Florence Habets ◽

Philippe Ackerer ◽

Nadia Amraoui ◽

...

Keyword(s):

Resource Management ◽

Land Surface ◽

Groundwater Resources ◽

Land Surface Model ◽

Groundwater Resource ◽

Surface Model ◽

Seasonal Forecasts ◽

Resource Monitoring ◽

Aquifer Systems

Abstract. The new AquiFR hydrometeorological modelling platform was developed to provide short-to-long-term forecasts for groundwater resource management in France. This study aims to describe and assess this new tool over a long period of 60 years. This platform gathers in a single numerical tool several hydrogeological models covering much of the French metropolitan area. A total of 11 aquifer systems are simulated through spatially distributed models using either the MARTHE (Modélisation d'Aquifères avec un maillage Rectangulaire, Transport et HydrodynamiquE; Modelling Aquifers with Rectangular cells, Transport and Hydrodynamics) groundwater modelling software programme or the EauDyssée hydrogeological platform. A total of 23 karstic systems are simulated by a lumped reservoir approach using the EROS (Ensemble de Rivières Organisés en Sous-bassins; set of rivers organized in sub-basins) software programme. AquiFR computes the groundwater level, the groundwater–surface-water exchanges and the river flows. A simulation covering a 60-year period from 1958 to 2018 is achieved in order to evaluate the performance of this platform. The 8 km resolution SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) meteorological analysis provides the atmospheric variables needed by the SURFEX (SURFace EXternalisée) land surface model in order to compute surface runoff and groundwater recharge used by the hydrogeological models. The assessment is based on more than 600 piezometers and more than 300 gauging stations corresponding to simulated rivers and outlets of karstic systems. For the simulated piezometric heads, 42 % and 60 % of the absolute biases are lower than 2 and 4 m respectively. The standardized piezometric level index (SPLI) was computed to assess the ability of AquiFR to identify extreme events such as groundwater floods or droughts in the long-term simulation over a set of piezometers used for groundwater resource management. A total of 56 % of the Nash–Sutcliffe efficiency (NSE; Ef) coefficient calculations between the observed and simulated SPLI time series are greater than 0.5. The quality of the results makes it possible to consider using the platform for real-time monitoring and seasonal forecasts of groundwater resources as well as for climate change impact assessments.

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Disentangling the long-term foliar 15N signal using a land surface model

10.5194/egusphere-egu2020-9702 ◽

2020 ◽

Author(s):

Silvia Caldararu ◽

Tea Thum ◽

Richard Nair ◽

Sönke Zaehle

Keyword(s):

Nutrient Limitation ◽

Land Surface ◽

Nitrogen Availability ◽

Land Surface Model ◽

Ecosystem Dynamics ◽

Surface Model ◽

Terrestrial Vegetation ◽

Climate System Model ◽

Foliar N

Terrestrial vegetation growth is hypothesised to increase under elevated atmospheric CO2, a process known as the CO2 fertilisation effect. However, the magnitude of this effect and its long-term sustainability remains uncertain. One of the main limitations to the CO2&#160; fertilisation effect is nutrient limitation to plant growth, in particular nitrogen (N) in temperate and boreal ecosystems. Recent studies have suggested that decreases in observed foliar N content (N%) and &#948;15N indicate widespread nitrogen limitation with increasing CO2&#160; concentrations. However, the factors driving these two variables, and especially the foliar &#948;15N values, are complex and can be caused by a number of processes. On one hand, if the observed trends reflect nutrient limitation, this limitation can be caused by either CO2 or warming driven growth. On the other hand, it is possible that nutrient limitation does not occur to its full extent due to plant plastic responses to alleviate nutrient limitation, causing a decrease in N%, but changes in the anthropogenic N deposition 15N signal cause the observed &#948;15N trend. In reality, it is likely that all these factors contribute to the observed trends. To understand ecosystem dynamics it is important to disentangle the processes behind these signals which is very difficult based on observational datasets only.We use a novel land surface model to explore the causes behind the observed trends in foliar N% and &#948;15N. The QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) model&#160; has the unique capacity to track ecologically relevant isotopic composition, including 15N in plant and soil pools. The model also includes a realistic representation of plant plastic acclimation processes, specifically a representation of nitrogen allocation to and inside the canopy in response to nitrogen availability, so implicitly to changes in CO2&#160; concentrations. We test the different hypotheses above behind the observed changes in N% and &#948;15N separately and quantify the contribution of each of the factors towards the observed trend. We then test the different hypotheses against existing observations of N% and &#948;15N from the ICP Forests database and other published datasets such as the global dataset of Craine et al. 2018.Our study showcases the use of an isotope-enabled land surface model in conjunction with long-term observations to strengthen our understanding of the ecosystem processes behind the observed trends.

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Long-term ecosystem nitrogen limitation from foliar δ15N data and a land surface model

10.1101/2021.07.16.452605 ◽

2021 ◽

Author(s):

Silvia Caldararu ◽

Tea Thum ◽

Lin Yu ◽

Melanie Kern ◽

Richard Nair ◽

...

Keyword(s):

Land Surface ◽

Nitrogen Limitation ◽

Land Surface Model ◽

N Deposition ◽

Spatial And Temporal Variation ◽

Surface Model ◽

Terrestrial Carbon ◽

N Content ◽

N Limitation ◽

Leaf N

The effect of nutrient availability on plant growth and the terrestrial carbon sink under climate change and elevated CO2 remains one of the main uncertainties of the terrestrial carbon cycle. This is partially due to the difficulty of assessing nutrient limitation at large scales over long periods of time. Consistent declines in leaf nitrogen (N) content and leaf δ15N have been used to suggest that nitrogen limitation has increased in recent decades, most likely due to the concurrent increase in atmospheric CO2. However, such datasets are often not straightforward to interpret due to the complex factors that contribute to the spatial and temporal variation in leaf N and isotope concentration. We use the land surface model QUINCY, which has the unique capacity to represent N isotopic processes, in conjunction with two large datasets of foliar N and N isotope content. We run the model with different scenarios to test whether foliar δ15N isotopic data can be used to infer large scale nitrogen limitation and if the observed trends are caused by increasing atmospheric CO2, changes in climate or changes in sources of anthropogenic N deposition. We show that while the model can capture the observed change in leaf N content and predicts widespread increases in N limitation, it does not capture the pronounced, but very spatially heterogeneous, decrease in foliar δ15N observed in the data across the globe. The addition of an observed temporal trend in isotopic composition of N deposition leads to a more pronounced decrease in simulated leaf δ15N. Our results show that leaf δ15N observations should not, on their own, be used to assess global scale N limitation and that using such a dataset in conjunction with a land surface model can reveal the drivers behind the observed patterns.

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CNRD v1.0: a high-quality natural runoff dataset for hydrological and climate studies in China

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-20-0094.1 ◽

2021 ◽

pp. 1-57

Author(s):

Jiaojiao Gou ◽

Chiyuan Miao ◽

Luis Samaniego ◽

Mu Xiao ◽

Jingwen Wu ◽

...

Keyword(s):

Parameter Uncertainty ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Comprehensive Model ◽

Analysis Framework ◽

Model Parameter Uncertainty ◽

Parameter Uncertainty Analysis ◽

Climate Studies

Capsule summaryA long-term spatiotemporally continuous naturalized runoff record, CNRD v1.0, is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model.

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Multisource Estimation of Long-Term Terrestrial Water Budget for Major Global River Basins

Journal of Climate ◽

10.1175/jcli-d-11-00300.1 ◽

2012 ◽

Vol 25 (9) ◽

pp. 3191-3206 ◽

Cited By ~ 125

Author(s):

Ming Pan ◽

Alok K. Sahoo ◽

Tara J. Troy ◽

Raghuveer K. Vinukollu ◽

Justin Sheffield ◽

...

Keyword(s):

Water Balance ◽

Error Analysis ◽

Land Surface ◽

Water Budget ◽

Large Scale ◽

Land Surface Model ◽

Data Sources ◽

Surface Model ◽

Terrestrial Water

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

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Global Reach-level 3-hourly River Flood Reanalysis (1980-2019)

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-20-0057.1 ◽

2021 ◽

pp. 1-49

Author(s):

Yuan Yang ◽

Ming Pan ◽

Peirong Lin ◽

Hylke E. Beck ◽

Zhenzhong Zeng ◽

...

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Flood Events ◽

Spatial And Temporal Scales ◽

Level 3 ◽

Global Coverage ◽

River Modeling ◽

Global Reach

AbstractBetter understanding and quantification of river floods for very local and flashy events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-year period of 1980-2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-year return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. This data record, referred as Global Reach-level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.

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Towards long-term satellite root-zone soil moisture: 40-year Soil Water Index dataset from ESA CCI COMBINED Soil Moisture product.

10.5194/egusphere-egu2020-15066 ◽

2020 ◽

Author(s):

Adam Pasik ◽

Bernhard Bauer-Marschallinger ◽

Wolfgang Preimesberger ◽

Tracy Scanlon ◽

Wouter Dorigo ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

Surface Model ◽

Water Index ◽

Global Coverage ◽

T Values

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.&#160; Here we present the first long-term SWI dataset from ESA CCI Soil Moisture v04.5 COMBINED product, covering a 40-year period between 1978 and 2018. The ESA CCI dataset is unique because of its long-term global coverage based on merged observations from both active and passive sensors. The SWI is calculated for eight T-values (1, 5, 10, 15, 20, 40, 60, 100), where T-value is a temporal length ruling the infiltration; depending on the soil characteristics it translates into different soil depths. Primary results show promise for pursuing development of an operational SWI product. Here, we present the results of SWI validation against data from the International Soil Moisture Network (ISMN) using the QA4SM framework, as well as results of the attempt to establish relationship between T-values and particular soil depths.

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Variability of Basin-Scale Terrestrial Water Storage from a PER Water Budget Method: The Amazon and the Mississippi

Journal of Climate ◽

10.1175/2007jcli1639.1 ◽

2008 ◽

Vol 21 (2) ◽

pp. 248-265 ◽

Cited By ~ 37

Author(s):

Ning Zeng ◽

Jin-Ho Yoon ◽

Annarita Mariotti ◽

Sean Swenson

Keyword(s):

Time Scales ◽

Land Surface ◽

Seasonal Cycle ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Terrestrial Water Storage ◽

Basin Scale ◽

Terrestrial Water

Abstract In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin-scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P − R) minimizes the uncertainties originating from estimated evaporation. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics. TWS was diagnosed using the PER method for the Amazon (1970–2006) and the Mississippi basin (1928–2006) and compared with the MCR method, land surface model and reanalyses, and NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100–200 mm, but multidecadal changes can be as large as 600–800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003–06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and interannual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.

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