Modeling / Computational Science Tag
Modeling / Computational Science is one of more than 16 discipline tags that we use to categorize and aggregate our interdisciplinary information within and across CZOs. Much of our information has been tagged with 2-3 disciplines.
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IML People - Modeling / Computational Science
9 People
GRAD STUDENT
.(JavaScript must be enabled to view this email address),
IIHR
Civil and Environmental Engineering
INVESTIGATOR
.(JavaScript must be enabled to view this email address), 217-300-7464
Systems and computational biology
.(JavaScript must be enabled to view this email address), 217-333-4688
Univ of Illinois
Hydrologic processes, including hydroclimatology, ecohydrology, geomorphology, and hydroinformatics
STAFF, Data Manager
.(JavaScript must be enabled to view this email address), 217-244-1787
Research, design, and development of data management systems & collaborative environments
.(JavaScript must be enabled to view this email address), 865-974-7836
UT Knoxville
Flow and Sediment Interaction, Landscape Processes, Hydraulic Structures, Instrumentation
GRAD STUDENT
PhD student, University of Illinois, Civil and Environmental Engineering
Univ of Illinois
Hydrology & Modeling; Engineering/Method Development
GRAD STUDENT
PhD student, University of Illinois, Civil and Environmental Engineering
Univ of Illinois
Hydrology
GRAD STUDENT
PhD student, University of Illinois, Civil and Environmental Engineering
Univ of Illinois
Hydrology, geochemistry, soil science, and modeling
INVESTIGATOR
.(JavaScript must be enabled to view this email address), 319-335-5597
IIHR
Fish Passage Facilities, Physical Modeling, River Hydraulics, Hydropower, Computational Hydraulics
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IML Data - Modeling / Computational Science
No datasets with this discipline tag have been entered yet
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IML Publications - Modeling / Computational Science
2020
Bundled Causal History Interaction. Jiang, P., and Kumar, P. (2020): Entropy
2019
The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin. Gran, K.B., Dolph, C., Baker, A., Bevis, M., Cho, S.J., Czuba, J.A., Dalzell, B., Danesh-Yazdi, M., Hansen, A.T., Kelly, S., Lang, Z., Schwenk, J., Belmont, P., Finlay, J.C., Kumar, P., Rabotyagov, S., Roehrig, G., Wilcock, P., and Foufoula-Georiou, E. (2019): Water Resources Research
2019
Using Information Flow for Whole System Understanding From Component Dynamics. Jiang, P., and Kumar, P. (2019): Water Resources Research
2018
Steering Operational Synergies in Terrestrial Observation Networks: Opportunity for Advancing Earth System Dynamics Modelling. Baatz, R., Sullivan, P.L., Li, L., Weintraub, S.R., Loescher, H.W., Mirtl, M., Groffman, P.M., Wall, D.H., Young, M., White, T., Wen, H., Zacharias, S., Kuhn, I., Tang, J., Gaillardet, J., Braud, I., Flores, A.N., Kumar, P., Lin, H., Ghezzehei, T., Jones, J., Gholz, H.L., Vereecken, H., and Van Looy, K. (2018): Earth System Dynamics
2018
Modeled Postglacial Landscape Evolution at the Southern Margin of the Laurentide Ice Sheet: Hydrological Connection of Uplands Controls the Pace and Style of Fluvial Network Expansion. Lai. J. and Anders, A.M. (2018): JGR Earth Surface
2018
Stochastic Lattice-Based Modelling of Malaria Dynamics. Le, P.V.V., Kumar, P., and Ruiz, M.O. (2018): Malaria Journal
2018
Flow Resistance Interactions on Hillslopes With Heterogeneous Attributes: Effects on Runoff Hydrograph Characteristics. Papanicolaou, A.N., Abban, B., Dermisis, D., Giannopoulos, C., Flanagan, D., Frankenberger, J., and Wacha, K. (2018): Water Resources Research
2018
Dynamic Flow Resistance Interactions between Landscape Attributes and Overland Flow: Effects on Runoff Hydrograph Characteristics. Papanicolaou, A.N., Abban, B., Dermisis, D.C., Giannopoulos, C.P., Flanagan, D.C., Frankenberg, J.R., and Wacha, K.M. (2018): Water Resources Research
2018
Flow Resistance Interactions on Hillslopes With Heterogeneous Attributes: Effects on Runoff Hydrograph Characteristics. Papanicolaou, A.N., Abban, B.K., Dermisis, D.C., Giannopoulos, C.P., Flanagan, D.C., Frankenberger, J.R., and Wacha, K.M. (2018): Water Resources Research
2017
Identification and characterization of information-networks in long-tail data collections. Elag, M.M., Kumar, P., Marini, L., Myers, J.D., Hedstrom, M., and Plale, B.A. (2017): Environmental Modelling & Software
2017
Coupling flow with nutrient dynamics via BioChemFOAM in the Mississippi River. Hernandez Murcia, O.E., Schnoebelen, D.J., Papanicolaou, A.N., and Abban, B. (2017): Journal of Applied Water Engineering and Research
2017
A service-oriented architecture for coupling web service models using the Basic Model Interface (BMI). Jiang, P., Elag, M., Kumar, P., Peckham, S.D., Marini, L., and Rui, L. (2017): Environmental Modelling & Software
2016
Watershed scale impacts of bioenergy crops on hydrology and water quality using improved SWAT model. Cibin, R. E. Trybula, I. Chaubey, S. Brouder, and J.J. Volenec (2016): Global Change Biology-Bioenergy
2016
Coupling freshwater mussel ecology and river dynamics using a simplified dynamic interaction model. Hansen, A.T., J.A. Czuba, J. Schwenk, A. Longjas, M. Danesh-Yazdi, D.J. Hornbach, and E. Foufoula-Georgiou (2016): Freshwater Science 35 (1): 200-215
2016 (In Press)
Water quality estimation of river plumes in Southern Lake Michigan using Hyperion. Tan, J, K.A. Cherkauer, I. Chaubey, C.D. Troy, and R. Essig (2016): Journal of Great Lakes Research
2015
Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters. Athira, P., K.P. Sudheer, R. Cibin, and I. Chaubey (2015): Stochastic Environmental Research and Risk Assessment 30 (4): 1131-1149
2015
A computationally efficient approach for watershed scale spatial optimization. Cibin, R., and I. Chaubey (2015): Environmental Modelling and Software 66: 1-11
2015
Information Theoretic Measures to Infer Feedback Dynamics in Coupled Logistic Networks. Goodwell, A., Kumar, P. (2015): Entropy 2015, 17, 7468-7492
2015
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty. Her, Y. and I. Chaubey (2015): Hydrological Processes 29 (19): 4220–4237
2015
Simple parallel computing methods using ‘spmd’ and ‘parfar’ for improving efficiency of optimization in hydrologic model applications. Her, Y., C. Raj, and I. Chaubey (2015): Applied Engineering in Agriculture 31 (3): 455-468
2015
Spatial optimization of six conservation practices using SWAT in tile drained agricultural watersheds. Kalcic, M.M., J. Frankenberger, and I. Chaubey (2015): Journal of the American Water Resources Association 51 (4)
2015
GPU-based high-performance computing for integrated surface–sub-surface flow modeling. Le, P.V.V., Kumar, P., Valocchi, A.J., and Dang, H.-V. (2015): Environmental Modelling & Software
2015
Optimization of a 48Ca–43Ca double-spike MC-TIMS method for measuring Ca isotope ratios (δ44/40Ca and δ44/42Ca): limitations from filament reservoir mixing. Lehn G. O. and Jacobson A. D. (2015): Journal of Analytical Atomic Spectrometry 30: 1571-1581
2015
Edaphic controls on soil organic carbon stocks in restored grasslands. O'Brien, S.L., Jastrow, J.D., Grimley, D.A., and Gonzalez-Meler, M.A. (2015): Geoderma 251-252: 117–123
2015
Landscape-scale geomorphic change detection: Quantifying spatially variable uncertainty and circumventing legacy data issues. Schaffrath, K.R., Belmont, P, and Wheaton, J.M. (2015): Geomorphology 250: 334-348
2015
Deglacial hydroclimate of midcontinental North America. Voelker, S.L., Stambaugh, M.C., Guyette, R.P., Feng, X., Grimley, D.A., Leavitt, S.W., Panyushkina, I., Grimm, E.C., Marsicek, J.P., Shuman, B., and Curry, B.B. (2015): Quaternary Research 83 (2): 336–344
2014
A Model for Knickpoint Migration in First- and Second-order Streams. Bressan, F., Papanicolaou, A.N., and Abban, B. (2014): Geophysical Research Letters, 41, 4987–4996
2014
Prediction of Saturated Hydraulic Conductivity Dynamics in an Iowan Agriculture Watershed. Elhakeem, M., Papanicolaou, A.N., Wilson, C., and Chang, Y. (2014): International Journal of Biological, Veterinary, Agricultural, and Food Engineering
2014
Emergent and divergent resilience behavior in catastrophic shift systems. Srinivasan, V. and Kumar, P. (2014): Ecological Modelling
2012
Wepp: Model use, calibration, validation. Flanagan, D.C., Frankenberger, J.R., and Ascough, J.C. (2012): Transactions of the American Society of Agricultural and Biological Engineering
2012
A graphical user interface for numerical modeling of acclimation responses of vegetation to climate change. Le, P., P. Kumar, D. Drewry, and J. Quijano (2012): Computers & Geosciences 49(0): 91-101
2010
A 1-D Morphodynamic Model for Rill Erosion. Papanicolaou, A.N., Sanford, J.T., Dermisis, D.C., and Mancilla, G.A. (2010): Water Resources Research, 46, W09541
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IML News - Modeling / Computational Science
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IML Events - Modeling / Computational Science
CZO / LTER / NEON / ISMC Joint Workshop
Modeling the Critical Zone; State of the Art, Data Integration, and Frontiers
Boulder, Colorado.
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