6 June, 2020 (Trautmann, HESS, TWS variation across scales) Trautmann, T., Koirala, S., Carvalhais, N., Eicker, A., Fink, M., Niemann, C., et al. (2018). Understanding terrestrial water storage variations in northern latitudes across scales. Hydrology and Earth System Sciences, 22, 4061–4082. Why did I read this paper? This paper contains the description and use of a hydrological model that a group I will join deals with. What they did? The hydrological model is relatively economic, or parsimonious, given small number of internal water storages (soil moisture, snowpack, and land run off), input variables (air temperature, precipitation, and net radiation), and parameters (10 params.
20 May, 2020 (Guo, NPH, SAM->iso/ansiohydry) Guo, J.S., Hultine, K.R., Koch, G.W., Kropp, H. and Ogle, K. (2020), Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub. New Phytol, 225: 713-726. doi:10.1111/nph.16196 A key reference for applying the SAM framework I have reviewed a paper in which the SAM framework was applied to fluxtower NEE data. I read the paper because I used a similar type of data (i.
1 May, 2020 (Liu, ELE, SAM->NEE across biomes) Liu, Y., Schwalm, C.R., Samuels‐Crow, K.E. and Ogle, K. (2019), Ecological memory of daily carbon exchange across the globe and its importance in drylands. Ecol Lett, 22: 1806-1816. doi:10.1111/ele.13363 Why did I read this paper? To apply the SAM framework for my paper, I needed a reference. Professor Ogle recommended this paper for me as I and Liu et al. have the same type of data.
Intro Kallestinova E. D. (2011). How to write your first research paper. The Yale journal of biology and medicine, 84(3), 181–190. Confused by now with encountering the start of a new research, I read this paper to seek for avenues. The author, Elena D. Kallestinova, provided some pragmatic rules with the relevant explanation. Rather than procastinating, it might be more helpful for my writing to dive directly into my data, models, and codes.
27 February, 2020 (Holgate, RSE, SMC) Holgate, C.M., De Jeu, R.A., van Dijk, A.I.J.M., Liu, Y.Y., Renzullo, L.J., Dharssi, I., Parinussa, R.M., Van Der Schalie, R., Gevaert, A., Walker, J., 2016. Comparison of remotely sensed and modelled soil moisture data sets across Australia. Remote Sensing of Environment 186, 479–500. Summary This paper compared time series pattern and temporal anomaly of diverse soil moisture content (SMC) products with in-situ measurements. The methodologies included the Pearson correlation coefficient, an anomaly index and the cluster analysis.
22 February, 2020 (Eller, NPH, JULES-SOX) Eller, C.B., Rowland, L., Mencuccini, M., Rosas, T., Williams, K., Harper, A., Medlyn, B.E., Wagner, Y., Klein, T., Teodoro, G.S., Oliveira, R.S., Matos, I.S., Rosado, B.H.P., Fuchs, K., Wohlfahrt, G., Montagnani, L., Meir, P., Sitch, S. and Cox, P.M. (2020), Stomatal optimization based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate. New Phytol. doi:10.1111/nph.16419 Summary Many researchers, including me, who use ecosystem models have recognized that the beta function approach for representing the change in plant photosynthesis to soil moisture stress needs improvement.
15 February, 2020 (Ogle, CHANCE, SAM) Kiona Ogle & Jarrett J. Barber (2016) Plant and Ecosystem Memory, CHANCE, 29:2, 16-22, DOI: 10.1080/09332480.2016.1181961 Why did I read this paper? It is often easier to get knowledge (i.e., model, concept, equation, and so forth) not from the original descriptive paper, but from other papers using the knowledge because they introduce the knowledge after masticating it, not as it is. This paper masticates the stochastic antecedent modelling (SAM) framework for beginners.
7 February, 2020 (Reichstein, Nature, Deep learning) Reichstein, M., Camps-Valls, G., Stevens, B. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019). https://doi.org/10.1038/s41586-019-0912-1 Question: How have machine learning algorithms, especially deep learning, been applied to Earth system science and what is the future direction of the application? Context: Our ability to produce a deluge of data outpaces our ability to assimilate the information.
4 February, 2020 (Jung, Nature, Compensatory) Jung, M., Reichstein, M., Schwalm, C. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516-520 (2017). https://doi.org/10.1038/nature20780 Question: How do changes in temperature and water availability effect on gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and net ecosystem exchange (NEE) at local and global scales? Context: Despite temperature is known to contribute to the inter annual variation (IAV) of the terrestrial carbon cycle, several studies propose that water availability play an important role in the carbon cycle relates such as the carbon balance of semi-tropics and the sensitivity to IAV.
21 January, 2020 (Brugnera, GCB, Liana) di Porcia e Brugnera, M, Meunier, F, Longo, M, et al. Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests. Global Change Biology, 2019; 25: 3767– 3780. https://doi.org/10.1111/gcb.14769 Question: How much does integrating lianas into the ED2 influence the estimates of forest carbon cycle? The authors hypothesized that the impact of lianas on carbon uptake by forest would be larger in the secondary forests than in the old-growth forests since lianas showed high density in young forests.
11 January, 2020 (Piao, GCB, Phenology) Piao S, Liu Q, Chen A, et al. Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 2019;00:1–19. https://doi.org/10.1111/gcb.14619 Piao et al. (2019) reviewed the current understanding of leaf phenological processes. They suggested that four key factors are driving the phenological processes: 1) temperature, 2) photoperiod, 3) nutrient and water availability, and 4) interseasonal phenological correlations (i.e. the positive spring and autumn phenological intercorrelation).
18 December, 2019 (Fisher, NPH, JULES-ED) Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P., Huntingford, C., Meir, P. and Ian Woodward, F. (2010), Assessing uncertainties in a second‐generation dynamic vegetation model caused by ecological scale limitations. New Phytologist, 187: 666-681. doi:10.1111/j.1469-8137.2010.03340.x Question: How demographic processes of two-dimensional spatial scales influence simulations of community structure, and responses of ecosystems to climate change? Context:
17 December, 2019 (Smith-Martin, NPH, Liana) Smith‐Martin, C.M., Xu, X., Medvigy, D., Schnitzer, S.A. and Powers, J.S. (2019), Allometric scaling laws linking biomass and rooting depth vary across ontogeny and functional groups in tropical dry forest lianas and trees. New Phytologist. doi:10.1111/nph.16275 Question: Do mature lianas invest less biomass in stems compared to trees? The authors tried to compare the investment strategy between lianas and trees. Do juveniles follow the same allocation patterns as mature individuals?