Dryland Ecosystem Structure and Function

Dryland water and carbon cycling

Drylands are key regions for land-atmosphere interactions and are in transition zones that are vulnerable to climate change and land degradation. Furthermore, past high-profile studies have shown that semiarid ecosystems are playing a dominant role in global C cycle inter-annual variability (IAV) and its long-term trend.

However, dryland ecosystems remain poorly understood and not well modelled.

My past phenology parameter optimization work suggested that terrestrial biosphere models may not capture vegetation dynamics well in dryland ecosystems. Models therefore need further evaluation before they can be reliably be used to estimate dryland carbon cycling and climate impacts.

The first step was to check how well the models capture water availability given that is dominant control on semi-arid ecosystem processes. In the following paper, we showed that models with a mechanistic representation of soil moisture diffusion perform well at simulating daily to seasonal evapotranspiration (ET) dynamics due to more realistic fluctuations of upper layer soil moisture. Therefore, model underestimates in net CO2 flux are likely due to deficiencies in photosynthesis and phenology related processes:

Following the testing of the hydrology modules at semi-arid sites, we then showed that at least at SW US semiarid sites the terrestrial biosphere models included in the TRENDY model intercomparison (used in the annual global C budget and in IPCC simulations) underestimate both the mean annual net CO2 fluxes and their IAV. Therefore, the role of semiarid ecosystems in global C cycle IAV could be more important than previously thought. You can see these results here!

In this study we showed that model water use efficiency (WUE) is too low: the GPP is not sensitive enough to pulses of moisture availability during the peak growing season. But we need to dig into more detail in the models to figure out what is causing discrepancies in these ecosystems. This is one subject of ongoing work as part of my NSERC grant!

Former lab member Kashif Mahmud used the data assimilation (DA) system used in my global carbon cycle DA work to optimise the C cycle related parameters at the same SW US sites. His work demonstrated that parameter optimization a dramatic improvement in the ORCHIDEE model’s ability to capture NEE, which was a cool finding! He shows that the model improvement is mainly due to constraining parameters related to photosynthesis and phenology processes. Find out more about this great work here!

My current work aims to better understand which processes and intra-annual time periods are driving net CO2  IAV in semi-arid ecosystems. Analyzing eddy covariance CO2 flux data from range of semiarid sites in the southwestern US encompassing forest, shrub- and grass-dominated ecosystems, I have shown that gross CO2 uptake during the monsoon dominates the net CO2 flux (net ecosystem exchange – NEE) IAV signal (MacBean et al., 2021). Furthermore, unlike mesic ecosystems, my analyses demonstrate that both days with peak productivity and growing season length are important controls on NEE IAV. This work is being done in collaboration with carbon flux scientists and ecohydrologists from the USDA and Ameriflux site PIs. 

Meanwhile, lab member Rubaya Pervin is further assessing TBM GPP predictions in comparison to a new ecohydrologically informed upscaled flux tower GPP machine learning-based product (“DryFlux”) (Barnes et al., 2021). Preliminary results can be found here! Rubaya is also working on improving the DryFlux product by using geospatially weighted regression of sites in the random forest model used in its derivation.

Dryland vegetation cover

Rubaya Pervin has also developed a novel remote sensing data fusion methods for detecting savanna shrub, grass, and bare soil cover:

Rubaya has also explored other algorithms for capturing shrubs vs grasses, which is crucial for accurate modelling of dryland ecosystems, as well as for monitoring of shrub encroachment and conservation management.

In the long-term we will use these high-resolution datasets to validate coarser resolution data, and to examine patterns and drivers of the shrub-grass coexistence in the SW US. We are interested in the following questions: Do shrub cover patterns in these ecosystems follow a characteristic power law distribution, and if not, why not? Are these patterns mostly regulated by “bottom-up” vegetation-moisture interactions, or “top-down” disturbance controls?

We will also use these maps to examine uncertainty in modeled carbon cycle simulations due to potentially inaccurate estimates of dryland vegetation fractional cover, which previous work (Hartley et al., 2017) has shown is a particular issue in sparsely vegetated regions.

Other papers that are relevant to the dryland research theme include:
  • Wang, L., W. Jiao, N. MacBean, M. C. Ruilli, S. Manzoni, G. Vico and P. D’Odorio (2022), Dryland productivity under a changing climate: global trends, key drivers, and uncertainties, Nature Climate Change, 12, 981-994.
  • Barnes, M. L., M. M. Farella, R. L. Scott, D. J.P. Moore, G. E. Ponce-Campos, J. A. Biederman, N. MacBean, M. E. Litvak, and D. D. Breshears (2021), Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling, Communications: Earth & Environment, 2, 248.
  • Smith, W.K., M. Dannenberg, D. Yan, S. Herrmann, M.L. Barnes, G.A. Barron-Gafford, J.A. Biederman, S. Ferrenberg, A.M. Fox, A.R. Hudson, J.F. Knowles, N. MacBean, D.J.P. Moore, P.L. Nagler, S.C. Reed, W.A. Rutherford, R.L. Scott, X. Wang and J. Yang (2019), Remote sensing of dryland ecosystem structure and function: Progress, challenges and opportunities, Remote Sensing of Environment, 233, 111401.
  • Smith et al. (2018), Evidence of a robust relationship between solar-induced chlorophyll fluorescence and gross primary productivity across dryland ecosystems of southwestern North America, Geophys. Res. Lett., 45, 748–757.
  • Liu, D., Y. Li, T. Wang, P. Peylin, N. MacBean, P. Ciais, G. Jia, M. Ma, Y. Ma, M. Shen, X. Zhang and S. Piao (2018), Contrasting responses of grassland water and carbon exchanges to climate change between the Tibetan Plateau and Inner Mongolia, Agricultural and Forest Meteorology, 249, 163-175.
  • Traore et al. (2014) 1982-2010 Trends of Light use Efficiency and Inherent Water use Efficiency in African vegetation: Sensitivity to Climate and Atmospheric CO2 Concentrations, Remote Sensing, 6, 8923-8944.