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Surrogate modeling and risk-based analysis for solute transport simulations

  • Ernesto Arandia
  • Fearghal O’Donncha
  • Sean McKenna
  • Seshu Tirupathi
  • Emanuele Ragnoli
Original Paper
  • 94 Downloads

Abstract

This study is driven by the question of how quickly a solute will be flushed from an aquatic system after input of the solute into the system ceases. Simulating the fate and transport of a solute in an aquatic system can be performed at high spatial and temporal resolution using a computationally demanding state-of-the-art hydrodynamics simulator. However, uncertainties in the system often require stochastic treatment and risk-based analysis requires a large number of simulations rendering the use of a physical model impractical. A surrogate model that represents a second-level physical abstraction of the system is developed and coupled with a Monte Carlo based method to generate volumetric inflow scenarios. The surrogate model provides an approximate 8 orders of magnitude speed-up over the full physical model enabling uncertainty quantification through Monte Carlo simulation. The approach developed here consists of an stochastic inflow generator, a solute concentration prediction mechanism based on the surrogate model, and a system response risk assessment method. The probabilistic outcome provided relates the uncertain quantities to the relevant response in terms of the system’s ability to remove the solute. We develop a general approach that can be applied in a generality of system configurations and types of solute. As a test case, we present a study specific to salinization of a lake.

Keywords

Solute removal Surrogate model Aquatic environment Risk-based assessment Monte Carlo simulation 

Notes

Acknowledgements

This project was funded by The Jefferson Project at Lake George, which is a collaboration between Rensselaer Polytechnic Institute (RPI), International Business Machines (IBM), and The FUND for Lake George. We gratefully acknowledge the critical reviews by Steven A. Norton and Jeffrey Short on an earlier version of the manuscript.

References

  1. Blatman G, Sudret B (2010) Efficient computation of global sensitivity indices using sparse polynomial chaos expansions. Reliab Eng Syst Saf 95(11):1216–1229CrossRefGoogle Scholar
  2. Bliznyuk N, Ruppert D, Shoemaker C, Regis R, Wild S, Mugunthan P (2008) Bayesian calibration and uncertainty analysis for computationally expensive models using optimization and radial basis function approximation. J Comput Graph Stat 17(4):270–294CrossRefGoogle Scholar
  3. Borgonovo E, Castaings W, Tarantola S (2012) Model emulation and moment-independent sensitivity analysis: an application to environmental modelling. Environ Model Softw 34:105–115CrossRefGoogle Scholar
  4. Boylen C, Eichler L, Swinton M, Nierzwicki-Bauer S, Hannoun I, Short J (2014a) The state of the lake: thirty years of water quality monitoring on Lake George. ReportGoogle Scholar
  5. Boylen CW Eichler L, Swinton M, Nierzwicki-Bauer S, Hannoun I, Short J (2014b) The state of the lake: thirty years of water quality monitoring on Lake George. Technical report, Darrin Fresh Water Institute and Department of Biological Sciences, Rensselaer Polytechnic InstituteGoogle Scholar
  6. Chapra SC, Reckhow KH (1983) Engineering approaches for lake management, vol 1. Butterworth, BostonGoogle Scholar
  7. Ciriello V, Di Federico V, Riva M, Cadini F, De Sanctis J, Zio E, Guadagnini A (2013) Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer. Stoch Environ Res Risk Assess 27(4):945–954CrossRefGoogle Scholar
  8. Dugan HA, Bartlett SL, Burke SM, Doubek JP, Krivak-Tetley FE, Skaff NK, Summers JC, Farrell KJ, McCullough IM, Morales-Williams AM, Roberts DC, Ouyang Z, Scordo F, Hanson PC, Weathers KC (2017) Salting our freshwater lakes. Proc Natl Acad Sci 114:4453–4458CrossRefGoogle Scholar
  9. Ek M, Mitchell K, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley J (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res Atmos 108(D22):8851CrossRefGoogle Scholar
  10. Environment Canada (2001) Priority substances list assessment report for road saltsGoogle Scholar
  11. Esfandiar B, Porta G, Perotto S, Guadagnini A (2015) Impact of space–time mesh adaptation on solute transport modeling in porous media. Water Resour Res 51(2):1315–1332CrossRefGoogle Scholar
  12. Fang K-T, Li R, Sudjianto A (2006) Design and modeling for computer experiments. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  13. Godwin K, Hafner S, Buff M (2003) Long-term trends in sodium and chloride in the Mohawk River, New York: the effect of fifty years of road-salt application. Environ Pollut 124(2):273–281CrossRefGoogle Scholar
  14. Hamrick JM (2007) The environmental uid dynamics code: user manual. US EPA, Fairfax, VA, Version, p 1Google Scholar
  15. Hart BT, Lake P, Webb JA, Grace MR (2003) Ecological risk to aquatic systems from salinity increases. Aust J Bot 51(6):689–702CrossRefGoogle Scholar
  16. Hintz WD, Relyea RA (2017) Impacts of road deicing salts on the early-life growth and development of a stream salmonid: salt type matters. Environ Pollut 223:409–415CrossRefGoogle Scholar
  17. Ji Z (2008) Hydrodynamics and water quality: modeling rivers, lakes and estuariesGoogle Scholar
  18. Johnston L (2014) Watershed management in the Adirondacks. Technical report, Union CollegeGoogle Scholar
  19. Kaushal SS, Groffman PM, Likens GE, Belt KT, Stack WP, Kelly VR, Band LE, Fisher GT (2005) Increased salinization of fresh water in the northeastern United States. Proc Natl Acad Sci USA 102(38):13517–13520CrossRefGoogle Scholar
  20. Khu S-T, Werner MG (2003) Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modeling. Hydrol Earth Syst Sci Discuss 7(5):680–692CrossRefGoogle Scholar
  21. Leibundgut C, Maloszewski P, Külls C (2011) Tracers in hydrology. Wiley, ChichesterCrossRefGoogle Scholar
  22. Levy JK, Hall J (2005) Advances in flood risk management under uncertainty. Stoch Environ Res Risk Assess 19(6):375–377CrossRefGoogle Scholar
  23. Lund JR (2002) Floodplain planning with risk-based optimization. J Water Resour Plan Manag 128(3):202–207CrossRefGoogle Scholar
  24. Marrel A, Perot N, Mottet C (2015) Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators. Stoch Environ Res Risk Assess 29(3):959–974CrossRefGoogle Scholar
  25. Martin JL, McCutcheon SC (1998) Hydrodynamics and transport for water quality modeling. CRC Press, Boca RatonGoogle Scholar
  26. Melillo JM, Richmond T, Yohe GW (2014) Climate change impacts in the United States: the third national climate assessment. Global Change Research Program, Technical report, USGoogle Scholar
  27. O’Donncha F, Ragnoli E, Suits F (2014) Parallelization study of a three-dimensional environment flow model. Comput Geosci 64:96–103CrossRefGoogle Scholar
  28. Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R et al (2014) Climate change 2014: synthesis report. contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Technical report, IPCCGoogle Scholar
  29. Quinn FH (1992) Hydraulic residence times for the laurentian great lakes. J Great Lakes Res 18(1):22–28CrossRefGoogle Scholar
  30. Ratto M, Pagano A, Young P (2007) State dependent parameter metamodelling and sensitivity analysis. Comput Phys Commun 177(11):863–876CrossRefGoogle Scholar
  31. Razavi SB, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48(7):1–32CrossRefGoogle Scholar
  32. Riva M, Guadagnini A, Dell’Oca A (2015) Probabilistic assessment of seawater intrusion under multiple sources of uncertainty. Adv Water Resour 75:93–104CrossRefGoogle Scholar
  33. Rosenberry DO, Bukaveckas PA, Buso DC, Likens GE, Shapiro AM, Winter TC (1999) Movement of road salt to a small New Hampshire lake. Water Air Soil Pollut 109(1):179–206CrossRefGoogle Scholar
  34. Schultz MT, Small MJ, Fischbeck PS, Farrow RS (2006) Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model. Environ Model Assess 11(4):345–359CrossRefGoogle Scholar
  35. Shrestha D, Kayastha N, Solomatine D (2009) A novel approach to parameter uncertainty analysis of hydrological models using neural networks. Hydrol Earth Syst Sci 13(7):1235–1248CrossRefGoogle Scholar
  36. Simpson TW, Toropov V, Balabanov V, Viana FA (2008) Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come or not. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, vol 5, pp 10–12Google Scholar
  37. Singhal S, Aneja S, Liu F, Real LV, George T (2014) IFM: a scalable high resolution flood modeling framework. In: Silva F, Dutra I, Santos Costa V (eds) Euro-Par 2014 parallel processing 20th international conference. Springer International Publishing, pp 692–703Google Scholar
  38. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research wrf version 2. Technical report, DTIC DocumentGoogle Scholar
  39. Stewart R (1972) Contributions to the international biological program year ii. Eastern deciduous forest biome, international biological program, Oak Ridge, Tennessee. EDFB-IBP Memo Report, pp 72-71Google Scholar
  40. Storlie CB, Bondell HD, Reich BJ, Zhang HH (2011) Surface estimation, variable selection, and the nonparametric oracle property. Stat Sin 21(2):679CrossRefGoogle Scholar
  41. Tariq MAUR (2011) Risk-based planning and optimization of flood management measures in developing countries: case Pakistan. Delft University of Technology, TU DelftGoogle Scholar
  42. Teefy S (1996) Tracer studies in water treatment facilities: a protocol and case studies. American Water Works Association, Freemont, CAGoogle Scholar
  43. Volkova E, Iooss B, Van Dorpe F (2008) Global sensitivity analysis for a numerical model of radionuclide migration from the RRC “Kurchatov Institute” radwaste disposal site. Stoch Environ Res Risk Assess 22(1):17–31CrossRefGoogle Scholar
  44. Williams W (2001) Anthropogenic salinization of inland waters. Hydrobiologia 466(1):329–337CrossRefGoogle Scholar
  45. Yazdi J, Torshizi Doostparast A, Zahraie B (2016) Risk based optimal design of detention dams considering uncertain inflows. Stoch Environ Res Risk Assess 30(5):1457–1471CrossRefGoogle Scholar
  46. Zhang X, Srinivasan R, Van Liew M (2009) Approximating SWAT model using artificial neural network and support vector machine. JAWRA J Am Water Resour Assoc 45(2):460–474CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IBM ResearchDublinIreland

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