Integration of nitrogen dynamics into the Noah-MP land surface model v1.1  for climate and environmental predictions

Cai, Xitian; Yang, Zong‐Liang; Fisher, Joshua B.; Barlage, Michael; Chen, F.

doi:10.5194/gmd-9-1-2016

Cited by 34 publications

(48 citation statements)

References 37 publications

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“…It has been proposed that wood density may be a useful proxy for drought tolerance in tropical tree species (Fearnside, 1997) and that it is negatively correlated with the precipitation gradient that exists across the Amazon basin (Baker et al, 2004;but see also Ter Steege et al, 2006). Moreover, in a pan-tropical synthesis of moist tropical forest species, a weak but significant negative relationship between p o and wood density was found (Christoffersen et al, 2016). Other observational studies have found that variation in stem economic traits, such as wood density, is largely independent of hydraulic traits (Baraloto et al, 2010;Mar echaux et al, 2015).…”

Section: Introductionmentioning

confidence: 83%

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Powell

Wheeler

Oliveira

et al. 2017

Global Change Biology

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Considerable uncertainty surrounds the impacts of anthropogenic climate change on the composition and structure of Amazon forests. Building upon results from two large-scale ecosystem drought experiments in the eastern Brazilian Amazon that observed increases in mortality rates among some tree species but not others, in this study we investigate the physiological traits underpinning these differential demographic responses. Xylem pressure at 50% conductivity (xylem-P 50 ), leaf turgor loss point (TLP), cellular osmotic potential (p o ), and cellular bulk modulus of elasticity (e), all traits mechanistically linked to drought tolerance, were measured on upper canopy branches and leaves of mature trees from selected species growing at the two drought experiment sites. Each species was placed a priori into one of four plant functional type (PFT) categories: drought-tolerant versus drought-intolerant based on observed mortality rates, and subdivided into early-versus late-successional based on wood density. We tested the hypotheses that the measured traits would be significantly different between the four PFTs and that they would be spatially conserved across the two experimental sites. Xylem-P 50 , TLP, and p o , but not e, occurred at significantly higher water potentials for the drought-intolerant PFT compared to the drought-tolerant PFT; however, there were no significant differences between the early-and late-successional PFTs. These results suggest that these three traits are important for determining drought tolerance, and are largely independent of wood density-a trait commonly associated with successional status. Differences in these physiological traits that occurred between the drought-tolerant and drought-intolerant PFTs were conserved between the two research sites, even though they had different soil types and dry-season lengths. This more detailed understanding of how xylem and leaf hydraulic traits vary between co-occuring drought-tolerant and drought-intolerant tropical tree species promises to facilitate a much-needed improvement in the representation of plant hydraulics within terrestrial ecosystem and biosphere models, which will enhance our ability to make robust predictions of how future changes in climate will affect tropical forests.

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Section: Introductionmentioning

confidence: 83%

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Powell

Wheeler

Oliveira

et al. 2017

Global Change Biology

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“…FUN2.0 (Brzostek et al, 2014) included two main improvements: (i) integrated trade-offs between ECM, AM, and nonmycorrhizal roots in their C costs of N acquisition and (ii) a resistance network to allow simultaneous uptake across all pathways (Fig. At extremely high levels of soil N (i.e., at the level of fertilized agricultural fields), passive and nonmycorrhizal root uptake dominates (Cai et al, 2015). In FUN2.0, when soil N is relatively abundant, plants supporting AM have the lowest C costs; when soil N is limiting, plants supporting ECM have the most optimal strategy.…”

Section: Model Descriptionmentioning

confidence: 99%

Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model

Shi

Fisher

Brzostek

et al. 2016

Global Change Biology

142

149

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Plants typically expend a significant portion of their available carbon (C) on nutrient acquisition - C that could otherwise support growth. However, given that most global terrestrial biosphere models (TBMs) do not include the C cost of nutrient acquisition, these models fail to represent current and future constraints to the land C sink. Here, we integrated a plant productivity-optimized nutrient acquisition model - the Fixation and Uptake of Nitrogen Model - into one of the most widely used TBMs, the Community Land Model. Global plant nitrogen (N) uptake is dynamically simulated in the coupled model based on the C costs of N acquisition from mycorrhizal roots, nonmycorrhizal roots, N-fixing microbes, and retranslocation (from senescing leaves). We find that at the global scale, plants spend 2.4 Pg C yr(-1) to acquire 1.0 Pg N yr(-1) , and that the C cost of N acquisition leads to a downregulation of global net primary production (NPP) by 13%. Mycorrhizal uptake represented the dominant pathway by which N is acquired, accounting for ~66% of the N uptake by plants. Notably, roots associating with arbuscular mycorrhizal (AM) fungi - generally considered for their role in phosphorus (P) acquisition - are estimated to be the primary source of global plant N uptake owing to the dominance of AM-associated plants in mid- and low-latitude biomes. Overall, our coupled model improves the representations of NPP downregulation globally and generates spatially explicit patterns of belowground C allocation, soil N uptake, and N retranslocation at the global scale. Such model improvements are critical for predicting how plant responses to altered N availability (owing to N deposition, rising atmospheric CO2 , and warming temperatures) may impact the land C sink.

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“…Two major directions for the LSM developments include improving the existing process representations and coupling new modules and functionalities [e.g., Cai et al ., ]. For example, vegetation dynamics, detailed hydrologic processes, snowpack evolution, and biogeochemical processes have been incorporated into the Noah model [ Cai et al ., ; K. Yang et al ., ]. With these rapid developments, LSMs are capable of quantitatively evaluating the contributions of different environmental factors to the changes in runoff and projecting the hydrological changes when coupled with the outputs of a general circulation model (GCM).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

et al. 2016

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The Global Land Data Assimilation System (GLDAS) project estimates long‐term runoff based on land surface models (LSMs) and provides a potential way to solve the issue of nonexistent streamflow data in gauge‐sparse regions such as the Tibetan Plateau (TP). However, the reliability of GLDAS runoff data must be validated before being practically applied. In this study, the streamflows simulated by four LSMs (CLM, Noah, VIC, and Mosaic) in GLDAS coupled with a river routing model are evaluated against observed streamflows in five river basins on the TP. The evaluation criteria include four aspects: monthly streamflow value, seasonal cycle of streamflow, annual streamflow trend, and streamflow component partitioning. The four LSMs display varying degrees of biases in monthly streamflow simulations: systematic overestimations are found in the Noah (1.74 ≤ bias ≤ 2.75) and CLM (1.22 ≤ bias ≤ 2.53) models, whereas systematic underestimations are observed in the VIC (0.36 ≤ bias ≤ 0.85) and Mosaic (0.34 ≤ bias ≤ 0.66) models. The Noah model shows the best performance in capturing the temporal variation in monthly streamflow and the seasonal cycle of streamflow, while the VIC model performs the best in terms of bias statistics. The Mosaic model provides the best performance in modeling annual runoff trends and runoff component partitioning. The possible reasons for the different performances of the LSMs are discussed in detail. In order to achieve more accurate streamflow simulations from the LSMs in GLDAS, suggestions are made to further improve the accuracy of the forcing data and parameterization schemes in all models.

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Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

Cited by 34 publications

References 37 publications

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model

Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

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