Eco-informatics uses computer science and data analysis tools to aid in the observation, understanding and ultimately prediction of environmental system behavior.
Our lab group specializes in eco-informatics related to understanding the terrestrial environment and how it processes water, energy, nutrients, and carbon. We specialize in the design and application of dynamic simulation models and linking these models with multiple data sources (from remote sensing to on the ground measurements).
While many models of ecosystem process and hydrology exist, we seek to improve on these approaches by:
- expanding the capacity of models to integrate an increasingly wide range of available data sets that vary in type, scale, resolution and accuracy;
- improve how we visualize the multi-dimensional outputs from models and tailor these visualizations for different audiences – from the public to field-based researchers
- integrate new understanding of ecosystem and hydrologic processes that emerges from ongoing science investigation – so that models become libraries of state-of-the art mechanistic understanding
Well-known computer scientist, Jim Gray coined the phrase “fourth paradigm” to describe an era where data-driven science unifies “theory, experiment and simulation”. Our group moves ecohydrology into this era by combining existing simulation and theory, with ongoing natural science experiments and data collection in a virtual observatory.
RHESSys: The Regional Hydro-Ecologic Simulation System
A core lab product is RHESSys (Regional Ecohydrologic Simulation System): a community based tool that simulations multiple environmental processes (eg water cycling, vegetation growth, urban runoff, fire, carbon sequestration, nutrient export, climate change impacts) over space and time: We use state-of-the art software engineering techniques to evolve this tool.
We use RHESSys as a framework for exploring and testing advances in data-model integration, looking at data-assimilation of temporal and spatial data, efficient calibration techniques representing uncertainty improving model structure. Here are some papers that describe software and model development strategy, testing and innovation.
Some current areas of model development are:
•Improved process representation
•plant carbon allocation
•hot-spot-hot-moments in terrestrial biogeochemical cycling
•models of storm water best management practices
•fire effects on terrestrial ecosystems, nutrient cycling and hydrology
•Integrating RHESSYs with R visualization tools
•Collaborations with artists to represent model results
•Assimilation of new snow remote sensing products
•Use of LiDar and other remote to initialize vegetation carbon stores
•Use of geophysics data and remote sensing of vegetation to parameterize subsurface flow properties
•Modeling software development and training
•Automated testing of model components
•Evaluation against observed data sets
•Flexible user interface development
•Training and education in the design and application of models
•Community based software development
- Adam, J.C., Stephens, J.C., Chung, S.H., Brady, M.P., Evans, R.D., Kruger, C.E., Lamb, B.K., Liu, M.L., Stöckle, C.O., Vaughan, J.K., Rajagopalan, K., Harrison, J.A., Tague, C.L., Kalyanaraman, A., Chen, Y., Guenther, A., Leung, F.Y., Leung, L.R., Perleberg, A.B., Yoder, J., Allen, E., Anderson, S., Chandrasekharan, B., Malek, K., Mullis, T., Miller, C., Nergui, T., Poinsatte, J., Reyes, J., Zhu, J., Choate, J.S., Jiang, X., Nelson, R., Yoon, J.H., Yorgey, G.G., Johnson, K., Chinnayakanahalli, K.J., Hamlet, A.F., Nijssen, B., Walden, V. (2015) BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management, Climatic Change 129(3-4): 555-571. doi: 0.1007/s10584-014-1115-2.
- Liu, M., Rajagopalan, K., Chung, S.H., Jiamng, X., Harrison, J., Nergui, T., Guenther, A., Miller, C., Reyes, J., Tague, C., Choate, J., Salathe, E.P., Stockle, C.O., Adam, J.C. (2014) What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?, Biogeosciences 11: 2601-2622. doi:10.5194/bg-11-2601-2014.
- Mullis, T., Liu, M., Kalyanaraman, A., Vaughan, J., Tague, C., Adam, J. (2014) Design and implementation of Kepler workflows for BioEarth, Procedia Computer Science 29: 1722-1732.
- Garcia, E.S., Tague, C.L., Choate, J.S. (2013) Influence of spatial temperature estimation method in ecohydrologic modeling in the Western Oregon Cascades, Water Resources Research 49(3): 1611–1624. doi:10.1002/wrcr.20140.
- Tague, C. (2009) Assessing climate change impacts on alpine stream-flow and vegetation water use: mining the linkages with subsurface hydrologic processes, Invited Commentary, Hydrological Processes 23: 1815-1819.
- Tague, C. (2009) Modeling hydrologic controls on denitrification: sensitivity to parameter uncertainty and landscape representation, Biogeochemistry 93(1-2): 79-90.
- Song, C., Katul, G., Oren, R., Band, L., Tague, C., Stoy, P., McCarthy, H. (2009) Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data, Journal of Geophysical Research 114: G04021. doi:10.1029/2009JG000951.