731
Views
0
CrossRef citations to date
0
Altmetric
Articles

Simpler models in environmental studies and predictions

, , &
Pages 1669-1712 | Published online: 13 Dec 2017

References

  • Ajami, N.K., Gupta, H., Wagener, T., and Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. J. Hydrol., 298(1–4), 112–135.
  • Altman, S.J., Arnold, B.W., Barnard, R.W., Barr, G.E., Ho, C.K., McKenna, S.A., and Eaton, R.R. (1996). Flow calculation for Yucca mountain groundwater travel time (GWTT-95). Technical Report SAND-96-0819, Albuquerque, NM: Sandia National Laboratories.
  • Asgharbeygi, N., Langley, P., Bay, S., and Arrigo, K. (2006). Inductive revision of quantitative process models. Ecol. Model., 194(1–3), 70–79.
  • Atanasova, N., Todorovski, L., Džeroski, S., and Kompare, B. (2006). Constructing a library of domain knowledge for automated modelling of aquatic ecosystems. Ecol. Model., 194(1–3), 14–36.
  • Babendreier, J.E., and Castleton, K.J. (2005). Investigating uncertainty and sensitivity in integrated, multimedia environmental models: tools for FRAMES-3MRA. Environ. Modell. Softw., 20(8), 1043–1055.
  • Babendreier, J.E., Matott, L.S., Hameedi, J., Dennis, R.L., Knightes, C.D., Mathur, R., Mohamoud, Y.M., Johnston, J.M., West, C.C., Laniak, G.F., Gaber, N., Pascual, P., and Araujo, R. (2007). Managing multimedia pollution for a multimedia world. (the magazine for environmental managers). Air Waste Manage. Assoc., 12, 6–11.
  • Bai, Y., Wagener, T., and Reed, P. (2009). A top-down framework for watershed model evaluation and selection under uncertainty. Environ. Model. Softw., 24(8), 901–916.
  • Baker, A. (2007). Occam's Razor in science: a case study from biogeography. Biol. Phil., 22, 193–215.
  • Barlas, Y. (2007). Leverage points to march “upward from the aimless plateau”. Syst. Dynam. Rev., 23(4), 469–473.
  • Bau, D.A., and Mayer, A.S. (2006). Stochastic management of pump-and-treat strategies using surrogate functions. Adv. Water Resour., 29(12), 1901–1917.
  • Beven, K. (1989). Changing ideas in hydrology: the case of physically-based models. J. Hydrol., 105, 157–172.
  • Beven, K. (2000). Uniqueness of place and the representation of hydrological processes. Hydrol. Earth Syst. Sci., 4, 203–213.
  • Beven, K. (2001). How far can we go in distributed hydrological modeling?. Hydrol. Earth Syst. Sci., 5, 1–12.
  • Beven, K. (2002). Towards a coherent philosophy for modeling the environment. Proc. R. Soc. A., 458(2026), 2465–2484.
  • Bhattacharya, B., and Solomatine, D.P. (2005). Neural networks and M5 model trees in modelling water level – discharge relationship. Neurocomputing, 63, 381–396.
  • Bigelow, J.H., and Davis, P.K. (2003). Implications for model validation of multiresolution, multiperspective modeling (Mrmpm) and exploratory analysis. Santa Monica, CA: RAND.
  • Blanning, R.W. (1975). The construction and implementation of metamodels. Simulation, 24, 177–184.
  • Bliznyuk, N., Ruppert, D., Shoemaker, C., Regis, C., Wild, S., and Mugunthan, S. (2008). Bayesian calibration and uncertainty analysis for computationally expensive models using optimization and radial basis function approximation. J. Comput. Graph. Stat., 17(2), 270–294.
  • Bolte, J.P., Hulse, D.W., Gregory, S.V., and Smith, C. (2006). Modeling biocomplexity – actors, landscapes and alternative futures. Environ. Modell. Softw., 22, 570–579.
  • Borgonovo, E., Castaings, W., and Tarantola, S. (2012). Model emulation and moment-independent sensitivity analysis: An application to environmental modelling. Environ. Modell. Softw., 34, 105–115.
  • Brooks, R.J., and Tobias, A.M. (1996). Choosing the best model: Level of detail, complexity, and model performance. Math. Comput. Model., 24(4), 1–14.
  • Bugmann, H.K.M., Yan, X., Sykes, M., and Martin, P. (1996). A comparison of forest gap models: Model structure and behaviour. Clim. Chang., 34(2), 289–313.
  • Campo-Bescós, M. A., Muñoz-Carpena, R., Southworth, J., Zhu, L., Waylen, P. R., & Bunting, E. (2013). Combined spatial and temporal effects of environmental controls on long-term monthly NDVI in the southern Africa Savanna. Remote Sensing, 5(12), 6513–6538.
  • Castelletti, A., Galelli, S., Ratto, M., Soncini-Sessa, R., and Young, P.C. (2012a). A general framework for Dynamic Emulation Modelling in environmental problems. Environ. Model. Softw., 34, 5–18.
  • Castelletti, A., Pianosi, F., Quach, X., and Sonicini-Sessa, R. (2012b). Assessing water reservoir management and development in Northern Vietnam. Hydrol. Earth Syst. Sci., 16, 189–199.
  • Caughlin, D., and Sisti, A.F. (1997). Summary of model abstraction techniques. Proc. SPIE 3083, Enabling Technol. Simul. Sci., 22–13.
  • Childs, S.W., and Hanks, R.J. (1975). Model of soil salinity effects on crop growth. Soil Sci. Soc. Am. J., 39, 617–621.
  • Chu, H.J., and Chang, L.C. (2009). Optimal control algorithm and neural network for dynamic groundwater management. Hydrol. Process., 23(19), 2765–2773.
  • Chwif, L., Barretto, M., and Paul, R. (2000). On simulation model complexity. In A. Joines, R.R. Barton, K. Kang, and P.A. Fishwick (Eds.), Proceedings of the 32nd Conference on Winter Simulation, pp. 449–455.
  • Clark, M.P., Kavetski, D., and Fenicia, F. (2011). Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resour. Res., 47, W09301.
  • Clark, M.P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., and Woods, R. A. (2015a). A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resour. Res., 51(4), 2498–2514.
  • Clark, M.P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., and Woods, R. A. (2015b). A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resour. Res., 51(4), 2515–2542.
  • Clark, M.P., Slater, A.G., Rupp, D.E., Woods, R.A., Vrugt, J.A., Gupta, H.V., Wagener, T., and Hay, L.E. (2008). FUSE: A modular framework to diagnose differences between hydrological models. Water Resour. Res., 44, W00B02.
  • Clement, T. P. (2011). Complexities in hindcasting models—when should we say enough is enough? Groundwater, 49(5), 620–629.
  • Come, G., Beon, O., Albrecht, A., Gallerand, M.O., Little, R.H., and Strenge, D.L. (2004). “Evaluating multimedia exposure codes” the BIOSCOMP exercise. In G. Whelan (Ed.). Brownfields: multimedia modeling and assessment. Billerica, MA, USA: Computational Mechanics, Inc., and Southampton, UK: WIT Press.
  • Cox, G.M., Gibbons, J.M., Wood, A.T.A., Craigon, J., Ramsden, S.J., and Crout, N.M.J. (2006). Towards the systematic simplification of mechanistic models. Ecol. Model., 198(1–2), 240–246.
  • Cramer, M.D., and Verboom, G.A. (2017). Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness. J. Biogeogr., 44(3), 579–591.
  • Crawley, M.J. (2005). Statistics: An introduction using R. Statistical modelling (Chapter 7). Sussex, UK: John Wiley & Sons Ltd.
  • Cunge, J.A. 2003. Of data and models. J. Hydroinformatics, 5, 75–98.
  • David, O., Ascough II, J.C., Lloyd, W., Green, T.R., Rojas, K.W., Leavesley, G.H., and Ahuja, L.R. (2013). A software engineering perspective on environmental modeling framework design: the object modeling system. Environ. Modell. Softw., 39, 201–213.
  • Davis, P.K., and Bigelow, J.H. (2003). Motivated metamodels: Synthesis of cause–effect reasoning and statistical metamodeling. Santa Monica, CA: RAND.
  • Decoursey, D.G. (1992). Developing models with more detail: Do more algorithms give more truth? Weed Technol., 6(3), 709–715.
  • Denton, C.M., and Sklash, M.G. (2006). Contaminant fate and transport in the courtroom. Volume 10. In E.J. Calabrese, P.T. Kostecki, and J. Dragun (Eds.), Contaminated Soils, Sediments and Water Volume 10. Successes and Challenges (pp. 81-–18). New York: Springer.
  • Doherty, J., and Christensen, S. (2011), Use of paired simple and complex models to reduce predictive bias and quantify uncertainty. Water Resour. Res., 47, W12534, doi:10.1029/2011WR010763.
  • Dooge, J.C.I., Bruen, M., and Parmentier, B. (1999). A simple model for estimating the sensitivity of runoff to long-term changes in precipitation without a change in vegetation. Adv. Water Resour., 23, 153–163.
  • Dortch, M.S., Fant, S., and Gerald, J.A. (2007). Modeling fate of RDX at demolition area 2 of the Massachusetts military reservation. J. Soil. Sediment. Contam., 16(6), 617–635.
  • Dortch, M.S., and Gerard, J.A. (2004). Recent advances in the Army Risk assessment modeling system. In G. Whelan (Ed.), Brownfields: Multimedia modeling and assessment. Billerica, MA, USA: Computational Mechanics, Inc., and Southampton, UK: WIT Press.
  • Dunker, A.M. (1980). The response of an atmospheric reaction-transport model to changes in input functions. Atmos. Environ., 14(6), 671–679.
  • Elith, J., Leathwick, J.R., and Hastie, T. (2008) A working guide to boosted regression trees. J. Anim. Ecol., 77, 802–813.
  • Erdal, D., Neuweiler, I., and Wollschläger, U. (2014). Using a bias aware EnKF to account for unresolved structure in an unsaturated zone model. Water Resour. Res., 50(1), 132–147.
  • Eriksson, O., and Tegnér, J. (2016). Modeling and model simplification to facilitate biological insights and predictions. In L. Geris and D. Gomez-Cabrero (Eds.), Uncertainty in biology: A computational modeling approach. New York, Heidelberg Dordrecht London: Springer, Cham.
  • Esche, E., Müller, D., Kraus, R., and Wozny, G. (2014). Systematic approaches for model derivation for optimization purposes. Chem. Eng. Sci., 115, 215–224.
  • Estes, L.D., Bradley, B.A., Beukes, H., Hole, D.G., Lau, M., Oppenheimer, M.G., Schulze, R., Tadross, M.A., and Turner, W.R. (2013). Comparing mechanistic and empirical model projections of crop suitability and productivity: Implications for ecological forecasting. Glob. Ecol. Biogeogr., 22, 1007–1018.
  • Farina, A. (2006). Principles and methods in landscape ecology: Towards a science of landscape. Dordrecht, The Netherlands: Springer.
  • Fen, C.S., Chan, C.C., and Cheng, H.C. (2009). Assessing a response surface-based optimization approach for soil vapor extraction system design. J. Water Resour. Plan. Manage., 135(3), 198–207.
  • Fernandes, L.C., Paiva, C.M., and Filho, O.C.R. (2012). Evaluation of six empirical evapotranspiration equations- case study: Campos Dos Goytacazes/RJ. Rev. Bras. Meteorol., 27(3), 272–280.
  • Field, M.S., and Pinsky, P.F. (2000). A two-region nonequilibrium model for solute transport in solution conduits in karstic aquifers. J. Contam. Hydrol., 44, 329–351.
  • Fienen, M.N., Nolan, B.T., and Feinstein, D.T. (2016). Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model. Environ. Model. Softw., 77, 95–107.
  • Fishwick, P.A. (1995). Simulation model design and execution: Building digital worlds. Englewood Cliffs, NJ: Prentice Hall.
  • Foglia, L., Mehl, S. W., Hill, M. C., and Burlando, P. (2013). Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland. Water Resour. Res., 49, 260–282.
  • Frantz, K.F. (1995). A taxonomy of model abstraction techniques. In K. Alexopoulos, K. Kang, W.R. Lilegdon, and D. Goldsman (Eds.). Proceedings of the 1995 Winter Simulation Conference, pp. 1413–1420. Catonsville, MD: INFORMS.
  • Fraser, C.E., McIntyre, N., Jackson, B.M., and Wheater, H.S. (2013). Upscaling hydrological processes and land management change impacts using a metamodeling procedure. Water Resour. Res., 49, 5717–5833.
  • Gaber, N., Laniak, G.F., and Linker, L. (2008). Integrated modeling for integrated environmental decision making. Washington, DC: U.S. Environmental Protection Agency, <https://www.epa.gov/sites/production/files/2015-02/documents/im4iedm_white_paper_final_epa100r08010_0.pdf >(last accessed 14.09.16.).
  • Ge, Q., Ciuffo, B., and Menendez, M. (2015). Combining screening and metamodel-based methods: An efficient sequential approach for the sensitivity analysis of model outputs. Reliab. Eng. Syst. Saf., 134, 334–344.
  • Ghavidelfar, S., Alvankar, S.R., and Razmkhah, A. (2011). Comparison of the lumped and quasi-distributed runoff models in simulating flood hydrographs on a semi-arid watershed. Water Resour. Manage., 25, 1775–1790.
  • Grace, J.B., Anderson, T.M., Seabloom, E.W., Borer, E.T., Adler, P.B., Harpole, W.S., … & Bakker, J.D. (2016). Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature, 529(7586), 390.
  • Guber, A.K., Pachepsky, Y.A., Yakirevich, A.M., Shelton, D.R., Sadeghi, A.M., Goodrich, D.C., and Unkrich, C.L. (2011). Uncertainty in modelling of faecal coliform overland transport associated with manure application in Maryland. Hydrol. Process., 15, 2393–2404.
  • Gupta, H., Wagener, T., and Liu, Y. (2008). Reconciling theory with observations: Elements of a diagnostic approach to model evaluation. Hydrol. Process., 22, 3802–3813.
  • Gupta, H.V., Clark, M.P., Vrugt, J.A., Abramowitz, G., and Ye, M. (2012). Towards a comprehensive assessment of model structural adequacy. Water Resour. Res., 48(8). https://doi.org/10.1029/2011WR011044.
  • Håkanson, L. (1999). On the principles and factors determining the predictive success of ecosystem models, with a focus on lake eutrophication models. Ecol. Model., 121, 139–160.
  • Harpham, Q., Cleverley, P., & Kelly, D. (2014). The FluidEarth 2 implementation of OpenMI 2.0. J. Hydroinformatics, 16(4), 890–906.
  • Hannemann-Tamás, R., Gábor, A., Szederkényi, G., & Hangos, K.M. (2013). Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming. Comput. Math. Appl., 65(10), 1575–1595.
  • Haydon, S., and Deletic, A. (2009). Model output uncertainty of a coupled pathogen indicator-hydrologic catchment model due to input data uncertainty. Environ. Model. Softw., 24, 322–328.
  • Hecht-Nielsen, R. (1990). Neurocomputing. Boston, MA: Addison-Wesley.
  • Hill, C., DeLuca, C., Balaji, V., Suarez, M., and da Silva, A. (2004). The architecture of the earth system modeling framework. Comput. Sci. Eng., 6(1), 18–28.
  • Hill, M.C. (1998). Methods and guidelines for effective model calibration, Water Resources Investigations Report 98–4005, Denver, CO: U. S. Geological Survey.
  • Hill, M.C. (2006). The practical use of simplicity in developing ground-water models. Ground Water, 44(6), 775–781.
  • Hill, M. C., Kavetski, D., Clark, M., Ye, M., Arabi, M., Lu, D., Foglia, L., Mehl, S. (2016). Practical use of computationally frugal model analysis methods. Groundwater, 54(2), 159–170, doi: 10.1111/gwat.12330.
  • Horowitz, A., Meisel, W.S., and Collins, D.G. (1973). The Application of repro-modeling to the analysis of a photochemical pollution model (EPA-650/4-74-001). Washington DC: U.S. Environmental Protection Agency.
  • Hussain, M.S., Javadi, A.A., Ahangar-Asr, A., and Farmani, R. (2015). A surrogate model for simulation–optimization of aquifer systems subjected to seawater intrusion. J. Hydrol., 523, 542–554.
  • Innis, G., and Rextad, E. (1983). Simulation model simplification techniques. Simulation, 41, 7–15.
  • Iooss, B., and Lemaître, P. (2015). A review on global sensitivity analysis methods. In Uncertainty management in simulation–optimization of complex systems (pp. 101–122). New York, Heidelberg Dordrecht London: Springer.
  • Jakeman, A.J., and Hornberger, G.M. (1993). How much complexity is warranted in a rainfall-runoff Model?. Water Resour. Res., 29(8), 2637–2649.
  • Jakeman, A.J., and Letcher, R.A. (2003). Integrated assessment and modelling: features, principles and examples for catchment management. Environ. Modell. Softw., 18(6), 491–501.
  • Johnson, V.M., and Rogers, L.L. (2000). Accuracy of neural network approximators in simulation–optimization. J. Water Resour. Plan. Manage., 126(2), 48–56.
  • Johnston, J.M., McGarvey, D.J., Barber, M.C., Laniak, G.F., Babendreier, J.E., Parmar, R., Wolfe, K., Kraemer, S.R., Cyterski, M., Knightes, C., Rashleigh, B., Suarez, L., and Ambrose, R. (2011). An integrated modeling framework for performing environmental assessments: application to ecosystem services in the Albemarle–Pamlico basins (NC and VA, USA). Ecol. Modell., 222(14), 2471–2484.
  • Karas, T.H. (2004). Modelers and policymakers: Improving the relationships. Oak Ridge, TN: U.S. Department of Energy.
  • Kavetski, D., and Clark, M.P. (2011). Numerical troubles in conceptual hydrology: Approximations, absurdities and impact on hypothesis testing. Hydrol. Proc., 25(4), 661–670.
  • Keating, E.H., Doherty, J., Vrugt, J.A., and Kang, Q.J. (2010). Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality. Water Resour. Res., 46, W10517.
  • Kekkonen, T.S., and Jakeman, A.J. (2001). A comparison of metric and conceptual approaches in rainfall runoff modeling and its implications. Water Resour. Res., 34, 2345–2352.
  • Kelson, V.A., Hunt, R.J., and Haitjema, H.M. (2002). Improving a regional model using reduced complexity and parameter estimation. Ground Water, 40(2), 132–143.
  • Kishné, A.S., Yimam, Y.T., Morgan, C. L. S., and Dornblaser, B.C. (2017). Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model. Geoderma, 285, 247–259.
  • Koch, S., and Flühler, H. (1993). Solute transport in aggregated porous media: Comparing model independent and dependent parameter estimation. Water Air Soil Pollut., 68, 275–289.
  • Kourakos, G., and Mantoglou, A. (2009). Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Adv. Water Resour., 32(4), 507–521.
  • Kourakos, G., and Mantoglou, A. (2013). Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. J. Hydrol. 479, 13–23.
  • Krawczyk, H. and Proficz, J. (2012). Real-Time multimedia stream data processing in a supercomputer environment. In I. Deliyannis (Ed.), Interactive multimedia. Rijeka, Croatia: InTechOpen.
  • Krueger, O., and von Storch, J.S. (2011). A simple empirical model for decadal climate prediction. J. Clim., 24, 1276–1283.
  • Krugman, P. (2000). How complicated does the model have to be?. Oxford Rev. Econ. Pol., 16(4), 33–42.
  • Landeras, G., Ortiz-Barredo, A., and Lo´pez, J.J. (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric. Water Manage., 95, 553–565.
  • Laniak, G.F., Droppo, J.G., Faillace, Jr., E.R., Gnanapragasam, E.K., Mills, W.B., Strenge, D.L., Whelan, G., and Yu, C. (1997). An overview of a multimedia benchmarking analysis for three risk assessment models: RESRAD, MMSOILS, and MEPAS. Risk Anal., 17(2), 203–214.
  • Laniak, G.F., Olchin, G., Goodall, J., Voinov, A., Hill, M., Glynn, P., Whelan, G., Geller, G., Quinn, N., Blind, M., Peckham, S., Reaney, S., Gaber, N., Kennedy, R., and Hughes, A. (2013). Integrated environmental modeling: A vision and roadmap for the future. Environ. Modell. Softw., 39, 3–23.
  • Li, G., Rabitz, H., Yelvington, P.E., Oluwole, O.O., Bacon, F., Kolb, C.E., and Schoendorf, J. (2010). Global sensitivity analysis for systems with independent and/or correlated inputs. J. Phys. Chem. A., 114(19), 6022–6032.
  • Licznar, P., and Nearing, M. (2003). Artificial neural networks of soil erosion and runoff prediction at the plot scale. Catena, 51(2), 89–114.
  • Liong, S.Y., Khu, S.T., and Chan, W.T. (2001). Derivation of Pareto front with genetic algorithm and neural network. J. Hydrol. Eng., 6(1), 52–61.
  • Loague, K.M., and Freeze, R.A. (1985). A comparison of rainfall-runoff modeling techniques on small upland catchments. Water Resour. Res., 21, 229–248.
  • Masket, S, Dibike. Y.B., Jonoski, A., and Solomatine, D.P. (2000). Groundwater model approximation with ANN for selecting optimum pumping strategy for plume removal. In O. Schleider and A. Zijderveld (Eds.), Proc. 2nd Joint Workshop “Artificial Intelligence in Civil Engineering” (pp. 67–80).
  • McLaren, R.G., Forsyth, P.A., Sudicky, E.A., Vanderkwaak, J.E., Schwartz, F.W., and Kessler, J.H. (2000). Flow and transport in fractured tuff at Yucca Mountain: Numerical experiments on fast preferential flow mechanisms. J. Contam. Hydrol., 43(3–4), 211–238.
  • MEA (Millennium Ecosystem Assessment). (2005). Ecosystems and human well-being: Synthesis. Washington, DC: Island Press.
  • Meisel, W.S., and Collins, D.C. (1973). Repro-Modeling: An approach to efficient model utilization and interpretation. IEEE Trans. Syst., Man, Cybern., Syst., 3(4), 349–358.
  • Merritt, W. S., Letcher, R. A., and Jakeman, A. J. (2003). A review of erosion and sediment transport models. Environ. Model. Softw., 18(8), 761–799.
  • Messina, J.P., Evans, T.P., Manson, S.M., Shortridge, A.M., Deadman, P.J., and Verburg, P.H. (2008). Complex systems models and the management of error and uncertainty. J. Land. Use. Sci., 3(1), 11–25.
  • Meyer, P.D., Rockhold, M.L., Gee, G.W., and Nicholson, T.J. (1997). Uncertainty analyses of infiltration and subsurface flow and transport for SDMP sites. Washington, DC: U.S. Nuclear Regulatory Commission.
  • Michaud, J., and Sorooshian, S. (1994). Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed. Water Resour. Res., 30(3), 593–605.
  • Mikailsoy, F., and Pachepsky Y. (2010). Average concentration of soluble salts in leached soils inferred from the convective-dispersive equation. Irrigation Sci., 28(5), 431–434.
  • Mills, W.B., Cheng, J.J., Droppo, J.G., Faillace, E.R., Gnanapragasam, E.K., Johns, R.A., Laniak, G.F., Lew, C.S., Strenge, D.L., Sutherland, J.F., Whelan, G., and Yu, C. (1997). Multimedia benchmarking analysis for three risk assessment models: RESRAD, MMSOILS, and MEPAS. Risk Anal., 17(2), 187–202.
  • Mohanty, B.P., Bowman, R.S., Hendrickx, J.M.H., and van Genuchten, M.Th. (1997). New piecewise-continuous hydraulic functions for modeling preferential flow in an intermittent flood-irrigated field. Water Resour. Res., 33, 2049–2063.
  • Mugunthan, P., and Shoemaker, C.A. (2006). Assessing the impacts of parameter uncertainty for computationally expensive groundwater models. Water Resour. Res., 42, W10428.
  • Mugunthan, P., Shoemaker, C.A., and Regis, R.G. (2005). Comparison of function approximation, heuristic, and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models. Water Resour. Res., 41, W11427.
  • Muhanna, W.A., and Pick, R.A. (1994). Meta-Modeling concepts and tools for model management: A systems approach. Manag. Sci., 40(9), 1093–1123.
  • Muñoz-Carpena, R., Zajac, Z., & Kuo, Y. M. (2007). Global sensitivity and uncertainty analyses of the water quality model VFSMOD-W. Trans. ASABE, 50(5), 1719–1732.
  • Naorem, N., and Devi, Th.K. (2014). Estimation of potential evapotranspiration using empirical models for Imphal. IJITEE, 4(7), 119–123.
  • NAS. (2009). Science and decisions: advancing risk assessment. Washington DC: The National Academies Press.
  • Neuman, S.P., Wierenga, P.J., and Nicholson, T.J. (2003). A comprehensive strategy of hydrogeologic modeling and uncertainty analysis for nuclear facilities and sites. Washington, DC: U.S. Nuclear Regulatory Commission.
  • Nolan, B., Malone, R., Gronberg, J., Thorp, K., and Ma, L. (2012). Verifiable metamodels for nitrate losses to drains and groundwater in the Corn Belt, USA. Environ. Sci. Technol., 46, 901–908.
  • Oreskes, N. (2003). The role of quantitative models in science. In C.D. Canham, J.J. Cole, and W.K. Lauenroth (Eds.), Models in ecosystem science ( Chapter 2, pp. 13–31). NJ, USA: Princeton University Press.
  • Oreskes, N., and Belitz, K. (2001). Philosophical issues in model assessment. In M.G. Anderson and P.D. Bates (Eds.), Model validation: Perspectives in hydrological science (pp. 23–41). Chichester, UK: Wiley.
  • Ostfeld, A., and Salomons, S. (2005). A hybrid genetic-instance based learning algorithm for CE-QUAL-W2 calibration. J. Hydrol., 310, 122–142.
  • Pachepsky, Y.A., Guber, A.K., Jacques, D., Simunek, J., Van Genuchten, M.T., Nicholson, T., and Cady, R. (2006a). Information content and complexity of simulated soil water fluxes. Geoderma, 134(3–4), 253–266.
  • Pachepsky, Y.A., Guber, A.K., Van Genuchten, M.T., Nicholson, T.J., Cady, R.E., Simunek, J, and Schaap, M.G. (2006b). Model abstraction techniques for soil-water flow and transport. Washington, DC: U.S. Nuclear Regulatory Commission.
  • Pachepsky, Y.A., Guber, A.K., Van Genuchten, M.T., Simunek, J., Jacques, D., Nemes, A., Nicholson, T.J., and Cady, R.E. (2007). The multiplicity of flow and transport models in unsaturated zone – curse or blessing. In ZNS'07 (Vol. VIII, pp. 9–17), Vadose Zone Hydrol.
  • Pachepsky, Y.A., Radcliffe, D.E., and Selim, H.M. (2003). Scaling methods in soil physics. Boca Raton, FL, USA: CRC Press.
  • Pachepsky, Y.A., and Rawls, W. (2004). Development of pedotransfer functions in soil hydrology. Amsterdam: Elsevier B.V.
  • Pachepsky, Y.A., Rawls, W. J., and Timlin, D. J. (1999). The current status of pedotransfer functions: Their accuracy, reliability, and utility in field- and regional-scale modeling. In D.L. Corwin, K. Loague, and T.R. Ellsworth (Eds.), Assessment of non-point source pollution in the vadose zone. Washington, DC: American Geophysical Union.
  • Pachepsky, Y., Gish, T., Guber, A., Yakirevich, A., Kouznetsov, M., Van Genuchten, M., Nicholson, T., and Cady, R. (2011). Application of model abstraction techniques to simulate transport in soils. NUREG/CR-7026. Office of Nuclear Regulatory Research. Washington DC: U.S. Nuclear Regulatory Commission.
  • Pan, F., Pachepsky, Y.A., Guber, A.K., and Hill, R.L. (2011). Information and complexity measures applied to observed and simulated soil moisture time series. Hydrol. Sci. J., 56(6), 1027–1039.
  • Park, Y., Pachepsky, Y.A., Cho, K.H., Jeon, D. J., and Kim, J.H. (2015). Stressor-response modeling using the 2D water quality model and regression trees to predict chlorophyll-a in a reservoir system. J. Hydrol., 529(P3), 805–815.
  • Parker, P., Letcher, R., Jakeman, A., Beck, M.B., Harris, G., Argent, R.M., Hare, M., Pahl-Wostl, C., Voinov, A., Janssen, M., Sullivan, P., Scoccimarro, M., Friend, A., Sonnenshein, M., Barker, D., Matejicek, L., Odulaja, D., Deadman, P., Lim, K., Larocque, G., Tarikhi, P., Fletcher, C., Put, A., Maxwell, T., Charles, A., Breeze, H., Nakatani, N., Mudgal, S., Naito, W., Osidele, O., Eriksson, I., Kautsky, U., Kautsky, E., Naeslund, B., Kumblad, L., Park, R., Maltagliati, S., Girardin, P., Rizzoli, A., Mauriello, D., Hoch, R., Pelletier, D., Reilly, J., Olafsdottir, R., and Bin, S. (2002). Progress in integrated assessment and modelling. Environ. Modell. Softw., 17(3), 209–217.
  • Peckham, S.D., Hutton, E., and Norris, B. (2013). A component-based approach to integrated modeling in the geosciences: the design of CSMDS. Comput. Geosci., 53, 3–12.
  • Pennington, D.D. (2007). Exploratory modeling of forest disturbance scenarios in central Oregon using computational experiments in GIS. Ecol. Inform., 2(4), 387–403.
  • Perlis, A.J. (1982). Epigrams on programming. ACM SIGPLAN Notices, 17(9), 7–13.
  • Perrin, C., Michel, C., and Andre, V. (2001). Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. J. Hydrol., 242, 275–301.
  • Pianosi, F., Beven, K., Freer, J., Hall, J.W., Rougier, J., Stephenson, D.B., and Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ. Modell. Softw., 79, 214–232.
  • Pianosi, F., Sarrazin, F., and Wagener, T. (2015). A Matlab toolbox for global sensitivity analysis. Environmental Modell. Softw., 70, 80–85.
  • Pohlman, K.F., Hassan, A.E., and Chapman, J.B. (2002). Modeling density-driven flows and radionuclide transport at an underground nuclear test: Uncertainty analysis and effect of parameter correlation. Water Resour. Res., 385, 1059.
  • Poli, R. (2013). A note on the difference between complicated and complex social systems. Cadmus, 2(1), 142.
  • Popper, K. (1992). Simplicity. The logic of scientific discovery. London: Routledge.
  • Qiu, H., Xu, Y., Gao, L., Li, X., and Chi, L. (2016). Multi-stage design space reduction and metamodeling optimization method based on self-organizing maps and fuzzy clustering. Expert. Syst. Appl., 46, 180–195.
  • Rabitz, H. (1989). Systems analysis at the molecular scale. Science, 246(4927), 221–226.
  • Ratto, M., Castelletti, A., and Pagano, A. (2012). Emulation techniques for the reduction and sensitivity analysis of complex environmental models. Environ. Modell. Softw., 34, 1–4.
  • Razavi, S., Tolson, B.A., and Burn, D.H. (2012a). Numerical assessment of metamodelling strategies in computationally intensive optimization. Environ. Modell. Softw., 34, 67–86.
  • Razavi, S., Tolson, B.A., and Burn. D.H. (2012b). Review of surrogate modeling in water resources. Water Resour. Res., 48, W07401.
  • Salt, J.D. (1993). Keynote address: Simulation should be easy and fun!, In G.W. Evans, M. Mollaghasemi, E.C. Russell, and W.E. Biles (Eds.), Proceedings of the 1993 winter simulation conference. Los Angeles, CA: Institute of Electrical and Electronics.
  • Semenov, M.A., and Brooks, R.J. (1999). Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Clim. Res., 11, 137–148.
  • Skerjanec, M., Atanasova, N., Cerepnalkoski, D., Dzeroski, S., and Kompare, B. (2014). Development of a knowledge library for automated watershed modeling. Environ. Modell. Softw., 54, 60–72.
  • Sober, E. (1981). The principle of parsimony. Brit. J. Phil. Sci., 32, 145–56.
  • Stephens, P.A., Frey-Roos, F., Arnold, W., and Sutherland, W.J. (2002). Model complexity and population predictions. The alpine marmot as a case study. J. Anim. Ecol., 71(2), 343–361.
  • Stillman, R.A., McGrorty, S., Goss-Custard., J.D., and West, A.D. (2000). Predicting mussel population density and age structure: The relationship between model complexity and predictive power. Mar. Ecol. Prog. Ser., 208, 131–145.
  • Stillman, R. A., Railsback, S. F., Giske, J., Berger, U., and Grimm, V. (2015). Making predictions in a changing world: The benefits of individual-based ecology. BioScience, 65(2), 140–150.
  • Tress, B., and Fry, G. (2005). Clarifying integrative research concepts in landscape ecology. Landsc. Ecol., 20(4), 479–493.
  • Uusitalo, L., Lehikoinen, A., Helle, I., and Myrberg, K. (2015). An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ. Modell. Softw., 63, 24–31.
  • Van der Heijden, S., and Haberlandt, U. (2015). A fuzzy rule based metamodel for monthly catchment fate simulations. J. Hydrol., 531, 863–876.
  • van Ittersum, M.K., Ewert, F., Heckelei, T., Wery, J., Olsson, J.A., Andersen, E., Bezlepkina, I., Brouwer, F., Donatelli, M., Flichman, G., Olsson, L., Rizzoli, A.E., van der Wal, T., Wien, J.E., and Wolf, J. (2008). Integrated assessment of agricultural systems e a component-based framework for the European Union (SEAMLESS). Agr. Syst., 96, 150–165.
  • Van Ness, E.H., and Scheffer, M. (2005). A strategy to improve the contribution of complex simulation models to ecological theory. Ecol. Model., 185, 153–164.
  • Villa-Vialaneix, N., Follador, M., Ratto, M., and Leip, A. (2012). A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops. Environ. Modell. Softw., 34, 51–66.
  • Voinov, A., and Bousquet, F. (2010). Modelling with stakeholders. Environ. Modell. Softw., 25(11), 1268–1281.
  • Vollenweider, R.A. (1975). Input–output models with special reference to the phosphorus loading concept in limnology. Schweiz. Zeitsch. Hydrol., 37, 53–84.
  • Wainwright, J., and Mulligan, M. (2004). Environmental modelling: Finding simplicity in complexity. London: John Wiley & Sons, Ltd.
  • Walke, R.C., Longworth, J.K., Little, R.H., Venter, A., and Miller, B.W. (2004). The practical application of the AMBER software tool to support environmental decision making. In: Whelan, G. (Ed.). Brownfields: Multimedia modeling and assessment. Billerica, MA, USA: Computational Mechanics, Inc., and Southampton, UK: WIT Press.
  • Wang, C.G., and Jamicson, D.G. (2002). An objective approach to regional wastewater treatment planning. Water Resour. Res., 38(3). doi:10.1029/2000WR000062
  • Ward, S.C. (1989). Arguments for constructively simple models. J. Oper. Res. Soc., 40, 141–153.
  • Washington, W.M., Buja, L., and Craig, A. (2009). The computational future for climate and earth system models: on the path to petaflop and beyond. Phil. Trans. R. Soc. A., 367(1890), 833–846.
  • Watson, T. A., Doherty, J.E., and Christensen, S. (2013), Parameter and predictive outcomes of model simplification. Water Resour. Res., 49, 3952–3977, doi:10.1002/wrcr.20145.
  • Whelan, G., Kim, K., Pelton, M.A., Castleton, K.J., Laniak, G.F., Wolf, K., Parmar, R., Galvin, M., and Babindreier, J. (2014a). Design of a component-based integrated environmental modeling framework. Environ. Modell. Softw., 55, 1–24.
  • Whelan, G., Kim, K., Pelton, M.A., Soller, J.A., Castleton, K.J., Molina, M., Pachepsky, Y., Ravenscroft, J., and Zepp, R. (2014b). An integrated environmental modeling framework for performing quantitative microbial risk assessments. Environ. Modell. Softw., 55, 77–91.
  • Whelan, G., and Laniak, G.F. (1998). A risk-based approach for a national assessment. In C.H. Benson, J.N. Meegoda, R.B. Gilbert, and S.P. Clemence (Eds.), Risk-based corrective action and Brownfields restorations, Geotechnical Special Publication Number 82. Reston, VA: American Society of Civil Engineers.
  • Whelan, G., Wurstner, S.K., and Taira, R.Y. (2002). Comparing semi analytical and numerical groundwater contaminant transport modeling. In G.H. Leavesley, and D. Frevert (Eds.), Proceedings of the Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada ( PNNL-SA-36519), July 28–August 1, 2002. Richland, Washington, DC: Pacific Northwest National Laboratory.
  • Wood, E.F., Roundy, J.K., Troy, T.J., van Beek, L.P.H., Bierkens, M.F.P., Blyth, E., de Roo, A., D¨oll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaff´e, P.R., Kollet, S., Lehner, B., Lettenmaier, D.P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P. (2011). Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. Water Resour. Res., 47, W05301. doi:10.1029/2010WR010090.
  • Yan, S.Q., and Minsker, B. (2006). Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour. Res., 42, W05407.
  • Yilmaz, L., and Ören, T.I. (2004). Dynamic “model updating in simulation with multimodels: A taxonomy and a generic agent-based architecture,” In Proceedings of SCSC 2004 – Summer Computer Simulation Conference (pp. 3–8).
  • Zartarian, V., Xue, J., Glen, G., Smith, L., Tulve, N., and Tornero-Velez, R. (2012). Quantifying children's aggregate (dietary and residential) exposure and dose to permethrin: application and evaluation of EPA's probabilistic SHEDSmultimedia model. J. Expo. Sci. Environ. Epidemiol., 22(3), 267–273.
  • Zeigler, B. (1976). Theory of modeling and simulation. New York, NY: Wiley and Sons.
  • Zeigler, B.P., Kim, T.G., and Praehofer, H. (1998). Theory of modeling and simulation (2nd ed.), New York, NY: Academic Press.
  • Zhou, Y., and Li, W. (2011). A review of regional groundwater flow modeling. Geosci. Front., 2(2), 205–214.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.