Prof. Rafael L. Bras
Massachusetts Institute of Technology
Department of Civil and Environmental Engineering
77 Massachusetts Avenue, Room 48-213
Cambridge, MA 02139
Program Manager: Dr. Jin Huang, Climate Prediction program for the Americas, NOAA
Related NOAA Strategic Plan Goal:
Goal 1. Understand climate variability and change to enhance society’s ability to plan and respond.
Goal 2. Serve society’s needs for weather and water information.
The focus of this research is to enhance our understanding of the spatial effects of rainfall and dynamic vegetation on basin hydrologic response at multiple, nested gauging stations. These spatial processes are critical to understanding catchment hydrology, with the hydrometeorology driving rapid runoff response and the vegetation modulating interstorm moisture redistribution. Current forecasting tools represent each of these spatial effects inadequately by using spatiallylumped approaches. They should be studied within the context of fully-distributed modeling on complex terrain. We seek to answer the following questions: a) what is the role that rainfall and basin feature variability have on hydrologic response; b) what are the bidirectional interactions between vegetation dynamics (transpiration, water stress) and hydrological mechanisms occurring over complex terrain? c) what are the impacts that dynamic vegetation exerts on moisture cycling in a catchment and the effects of these impacts on extreme flood events? In order to answer these questions effectively, the research couples a vegetation model that explicitly considers plant dynamics to the existing distributed hydrological model accounting for the spatial variability of water and energy fluxes. This approach overcomes two major limitations in current modeling approaches: the static nature of vegetation in most hydrology models and
the lumped or point representation in most vegetation-hydrology models.
This work was a one-year proof-of-concept effort, leading to a full scale proposal. Indeed that proposal was written and accepted by NOAA and should be starting soon. All the limited objectives of the on-year effort were accomplished. A weather generator was added to the TIN-based Real-time Integrated Basin Simulator (tRIBS), a fully-distributed physically-based hydrological model used in this project and developed by the proponents. The stochastic climate simulator of
Curtis and Eagleson  was used as the foundation of this work. The weather generator allows synthetic simulation of several hydrometeorological variables representative of a given geographic location: precipitation, total cloud cover, incoming shortwave radiation, air temperature, humidity, and wind speed. The approach captures the essential relationships among the quantities of interest, while modeling the diurnal variation of weather conditions at the hourly scale. Precipitation is considered to be the key driver of simulated hydrometeorological conditions, which leads to a consistent co-variation of the weather variables. The generator was calibrated and validated with data from three meteorological stations located in New Mexico, Arizona, and Oklahoma. The model is suitable for creating scenarios of climate regimes (e.g., dry vs. wet climates), which are a part of the implementation plan of this project. An example of simulation realization of the weather generator is shown in Figure 1.
Dynamic vegetation has also been added to tRIBS, which represents one of the essential aspects for achieving the objectives of this project. The functions of dynamic vegetation in tRIBS are built on existing schemes such as LPJ of Sitch et al. , Levis et al. , CTEM of Arora and Boer , Hybrid v3.0 of Friend et al. , DGVM of Foley et al. , BIOME3 of Haxeltine and Prentice , and other ecologic models to capture the essential characteristics of
vegetation including albedo, leaf-area index (LAI), stomatal resistance, rooting depth, and surface roughness that are important in determining water and energy balance. A number of plant functional types (PFT), e.g. deciduous/coniferous trees, C3/C4 grasses, and shrubs, are represented simultaneously via fractional weighting of the individual plant types. Each PFT is quasi-uniformly distributed in a model element where all vegetation types are under identical climate forcing and soil conditions, but respond differently as their water use strategy and tolerance to soil moisture deficit vary. Carbon pools of leaves, stems and roots are simulated for each vegetation type within a model element. Canopy of represented vegetation types is treated as two “big-leaves” (sunlit and shaded). The modeled plant physiology include:
• Photosynthesis and stomatal resistance: The Gross Primary Productivity (GPP) is computed using the biochemical model of leaf photosynthesis developed by Farquhar et al. (1980), extended to C3 and C4 plants by Collatz et al. [1991, 1992]. The process of up-scaling from leaf to canopy follows the work of Wang and Leuning . Ball et al.  stomatal resistance model is directly related to the root soil moisture as in Bonan . The Net Primary Productivity (NPP) is therefore explicitly affected by the hydrometeorological conditions through the soil water and radiation dynamics.
• Plant Respiration: Plant respiration rates are calculated based on a common approach used in many ecological models. The growth respiration is expressed as a constant fraction of NPP and maintenance respiration is estimated separately for different plant compartments based on their carbon pool size and climatic variables.
• Tissue turnover and stress-induced foliage loss: tissue turnover is estimated based on first-order kinetics reaction with a heuristic function accounting for a rapid foliage loss during water stress.
• Carbon Allocation: The net assimilated carbon is partitioned into carbon pools of leaf, stem and root. We use the allocation scheme proposed by Friedlingstein et al.  and Arora and Boer  that allocates carbon to different plant components based on resource availability (water and light).
• Phenology: Cycles of onset and offset of leaves are accounted for to define the time period during which plants influence the energy and water fluxes. The “carbon gain” parameterization proposed by Ludeke et al.  and refined by Arora and Boer  is adopted. Phenology is linked to hydrometeorological conditions through air and soil temperature and soil moisture availability.
A diagram of carbon fluxes simulated for a vegetated land unit is shown in Figure 2. A short-term simulation showing the response of a generic C4 grass to hydrometeorological conditions of semi-arid climate of New Mexico is illustrated in Figure 3.
An initial testing of the vegetation-topography interaction using synthetic catchments generated by CHILD (Tucker et al. 2001) was also carried out. The two domains represent diffusive and fluvially dominated landscapes. Figure 4 illustrates the spatial distribution of the mean annual Above-ground Net Primary Productivity obtained for a generic C4 grass on loamy soil over the 50-year simulation period.
Diffusion erosion dominated landscape is shown. The semi-arid climate of New Mexico was assumed and the simulations were driven using the series generated by the developed weather generator. The two cases shown correspond to the “base” case (panel (a)) with essentially negligible lateral transfer of water in the domain, and the “runon” case (panel (b)), where runoff produced due partially impermeable soil was allowed to re-infiltrate at downstream locations. As seen, the obtained spatial distributions of the mean annual ANPP corroborate those observed in the field spatial distribution of grass, e.g., association of higher amounts of biomass with North-facing slopes. Additional association of more favorable sites with convergent topographic locations, particularly often observed in the field, can also be clearly seen in the modeling results for the case with significant lateral transfer (panel (b)).
Ivanov, V.Y., Bras, R.L., Curtis D.C. A Weather Generator for Hydrological, Ecological, and Agricultural Applications, submitted to Water Resources Research, July 2006.
Ivanov, V.Y., Bras, R.L., Vivoni, E.R. Vegetation-Hydrology Dynamics in Complex Terrain of Semi-Arid Areas: I. A mechanistic Approach to Modeling Dynamic Feedbacks, in preparation.
SUMMARY OF INTERACTION WITH NOAA
A new proposal to continue the work was submitted and accepted by NOAA.
SUMMARY OF EDUCATION AND OUTREACH ACTIVITY
Arora, V.K., and G.J. Boer, A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Global Change Biology, 11(1), 39-59, 2005.
Ball, J. T., I. E. Woodrow, and J. A. Berry, A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Progress in Photosynthesis Research, ed. J. Biggins. M. Nijhoff Publishers, Dordrecht. 4, 221-224, 1987.
Bonan, G. B. TN-417+STR A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide, CGD, 165pp., October 1996.
Collatz, G. J., J. T. Ball, C. Grivet, and J.A. Berry, Physiological and environmental regulation of stomatal conductance, photosynthesis, and transpiration: A model that includes a laminar boundary layer, Agric. For. Meteorol., 54, 107-136, 1991.
Collatz, G. J., M. Ribas-Carbo, and J. A. Berry, Coupled photosynthesis-stomatal conductance model for leaves of C4 plants, Aust. J. Plant Physiol., 19, 519-538, 1992.
Curtis, D.C., and Eagleson, P.S. (1982). Constrained Stochastic Climate Simulation. Technical Report 274, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Ralph M. Parsons Laboratory, Cambridge, MA, USA.
Farquhar, G. D., Caemmerer S. V., Berry J. A., A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149 (1), 78-90, 1980.
Foley, J. A., I. C. Prentice, N. Ramankutty, S. Levis, D. Pollard, S. Sitch, and A. Haxeltine, An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamic, Glob. Biogeochem. Cycles, 10(4), 603-628, 1996.
Friedlingstein, P., G. Joel, C. B. Field, and I. Y. Fung, Toward an allocation scheme for global terrestrial carbon models, Global Change Bio., 5, 755-770, 1999.
Friend, A. D., A. K. Stivens, R. G. Knox, and M. G. R. Cannel, A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecological Modeling, 95, 247-287, 1997.
Haxeltine, A., and I. C. Prentice, BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochemical Cycles,10(4), 693-709, 1996.
Levis, S., G. B. Bonan, M. Vertenstein, K. W. Oleson, TN-459+IA The Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical Description and User's Guide, CGD, 60 pp. May 2004.
Ludeke, M. K. B., et al., The Frankfurt Biosphere Model: A global process oriented model of seasonal and long-term CO2 exchnage between terrestrial ecosystems and the atmosphere, I, Model description and illustrative results for cold deciduous and boreal forests, Clim. Res., 4, 143-166, 1994.
Sitch, S., B. Smith, I. C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J. O. Kaplan, S. Levis, W. Lucht, M. T. Sykes, K. Thonicke, and S. Venevsky, Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161-185. 2003.
Wang, Y. P., and R. Leuning, A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I. Model description and comparison with a multi- layered model, Agric. For. Meteorol., 91, 89-111, 1998.