Masks are strongly encouraged especially in indoor settings.
B2 Earth System Modeling Lab
We are developing a Terrestrial Integrated Modeling System (TIMS; Figure 1). TIMS is a coupled systems model that focuses specifically on the interactions between hydrological, microbial, geochemical, geomorphological and ecological processes at the Earth’s land surface.
1. TIMS takes advantage of existing state-of-the-art community models, for instance, CATHY (CATchment Hydrological model; Camporese et al., 2010) and Noah-MP (multiple options of physics) (Niu et al., 2011), and couples states and fluxes between these different models to study interactions and feedbacks between soil moisture, soil development, erosion and sedimentation, and ecosystems response;
2. TIMS is developed using an experimentation-model learning cycle, so that new data derived from Biosphere 2 model ecosystems (Landscape Evolution Observatory (LEO) and the rain forest) and critical zone observatory (CZO) can help us improve our understanding and parameterizing fundamental processes.
1. To build a bridge between Biosphere 2 and the outside real world (e.g., critical zones) to transfer the fundamental knowledge and understanding obtained in Biosphere 2 and critical zones to solutions of challenges facing our society under a warming climate;
2. To enhance our understandings of the interactions between the soil, plants, and air and their integrated behavior at hillslope and catchment scales through iterations of computer modeling and experimental studies;
3. To fill the gap between plot-scale experimental studies and global-scale Earth System Model (ESM) developments;
4. To serve as a predictive tool to project the impacts of climate change on water resources, ecological processes, and landscape evolution in many environmental contexts, and across multiple spatial scale
TIMS aims to numerically simulate various physical and chemical processes that occur over the Earth’s terrestrial surface, e.g., exchanges and flows of energy, water, carbon and other chemicals between/within the soil, plants, and air. TIMS is coupling process-based surface atmospheric, hydrological, ecological, geomorphic, geochemical models. It is being compiled from existing models that have arisen from individual scientific communities, including
1. A surface energy, water and carbon exchange scheme, Noah-MP (Niu et al., 2011)
2. A 3-dimensional water and solute transport model, CATHY (Camporese et al., 2010)
3. A geochemical model (Steefel, 2001)
4. A landscape evolutional model (McGuire et al., 2013)
5. A vegetation dynamics model (Peters, 2002)
By evaluating and improving TIMS, we will develop a fully-coupled model system that will truly exceed the sum of its parts. TIMS will be evaluated against experimental measurements in LEO and other model ecosystems and used to integrate state-of-science techniques in data assimilation and forward modeling. TIMS will also be assessed against data from natural system environmental measurement campaigns (e.g., critical zone observatories) in order to validate its use in complex terrain where measurements are less dense and made without experimental control.
Achievements & New Features
1. Currently, TIMS coupled the 3-dimesional water model and the surface energy and mass (water and carbon) transfer scheme and have been tested against measurements of snow, streamflow, surface energy, water, and carbon fluxes exchanging with the atmosphere over energy limited (Niu et al., 2014a) and water-limited catchments (Niu et al., 2014b).
2. TIMS has been integrated with a newly developed, microbial enzyme based soil organic carbon decomposition and gas transport model. The model was demonstrated to be able to successfully reproduce the pulse response of microbial respiration to pulsed wetting (or the Birch effect) (Figure 2; Zhang et al., 2014).
Figure 2. (a) Observed half-hourly volumetric soil moisture (in m3 m–3) and temperature (in K) at 10 cm. Measured half-hourly CO2 efflux at the soil surface (in μmol m–2 s– 1) compared with those modeled by the (b) 4C_NOSM, (c) 4C, (d) 5C_NOSM, (e) 5C, and (f) 6C models. The shaded area (in red) represents the 95% credible interval, while the green line is for the best realization. In the legends, εbest stands for model efficiency and Rbest for the correlation coefficient of the best realization.
3. TIMS has been integrated with the vegetation dynamics model and is now being tested over Kendall catchment of the Walnut Gulch to simulate the climatic impacts and hydrological controls on invasive species (Figure 3).
Figure 3. Comparison of simulated and observed vegetation change at the Kendall catchment (WGEW). The modeled shift in the composition of vegetation from the individual based vegetation dynamics model (solid line) are compared to the observed (dashed line). Following a dry period the abundance of Lehmann lovegrass (Eragrostis lehmanniana) dramatically increased at the site while native plant diversity declined, and remained low even after wet years. In the simulation Lehmann lovegrass has higher seed survival and establishment probability during dry years relative to the native bunchgrass.
TIMS is being further linked to a reactive transport model (CrunchFlow; Steefel, 2001) and tested against Biosphere 2 LEO measurements. In addition, TIMS is being developed to incorporate a colloid transport model to simulate the feedback mechanisms of soil development to flow.
Camporese M., C. Paniconi, C. M. Putti, S. Orlandini, 2010: Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resour. Res., 46: W02512, doi:10.1029/2008WR007536.
Niu G.-Y., Z. L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, E. Rosero, M. Tewari, Y. L. Xia, 2011: The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. J. Geophys. Res., 116: D12109, doi:10.1029/2010JD015139.
Niu, G.-Y., C. Paniconi, P. A., Troch, X. Zeng, M. Durcik, and T. Huxman, 2013a: An integrated modeling framework of catchment-scale ecohydrological processes: 1. Model description and tests over an energy-limited watershed. Ecohydrolog, doi: 10.1002/eco.1362.
Niu, G.-Y., P. A. Troch, C. Paniconi, R. L. Scott, M. Durcik, X. Zeng, T. Huxman, D. Goodrich, and J. Pelletier 2013b: An integrated modeling framework of catchment-scale ecohydrological processes: 2. the role of water subsidy by overland flow on vegetation dynamics. Ecohydrology. doi: 10.1002/eco.1405.
McGuire, L. A., J. D. Pelletier, J. A. Go ́ mez, and M. A. Nearing (2013), Controls on the spacing and geometry of rill networks on hillslopes: Rain splash detachment, initial hillslope roughness, and the competition between fluvial and colluvial transport, J. Geophys. Res. Earth Surf., 118, 241–256, doi:10.1002/jgrf.20028
Steefel, C.I. (2001) GIMRT, version 1.2: Software for modeling multicomponent, multidimensional reactive transport. User’s Guide, UCRL-MA-143182. Livermore, California: Lawrence Livermore National Laboratory.
Peters, D. P. C. 2002: Plant species dominance at a grassland-shrubland ecotone: an individual-based gap dynamics model of herbaceous and woody species. Ecological Modelling, 152(1): 5-32.
Zhang, X., G.-Y. Niu, A. S. Elshall, M. Ye, G. A. Barron-Gafford, and M. Pavao- Zuckerman (2014), Assessing five evolving microbial enzyme models against field measurements from a semiarid savannah — What are the mechanisms of soil respiration pulses?, Geophys. Res. Lett., 41, 6428–6434, doi:10.1002/2014GL061399.