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Sensitivity and Uncertainty Analysis of the L-THIA-LID 2.1 Model

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Abstract

Sensitivity analysis of a model can identify key variables affecting the performance of the model. Uncertainty analysis is an essential indicator of the precision of the model. In this study, the sensitivity and uncertainty of the Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model in estimating runoff and water quality were analyzed in an urbanized watershed in central Indiana, USA, using Sobol′‘s global sensitivity analysis method and the bootstrap method, respectively. When estimating runoff volume and pollutant loads for the case in which no best management practices (BMPs) and no low impact development (LID) practices were implemented, CN (Curve Number) was the most sensitive variable and the most important variable when calibrating the model before implementing practices. When predicting water quantity and quality with varying levels of BMPs and LID practices implemented, Ratio_r (Practice outflow runoff volume/inflow runoff volume) was the most sensitive variable and therefore the most important variable to calibrate the model with practices implemented. The output uncertainty bounds before implementing BMPs and LID practices were relatively large, while the uncertainty ranges of model outputs with practices implemented were relatively small. The limited observed data in the same study area and results from other urban watersheds in scientific literature were either well within or close to the uncertainty ranges determined in this study, indicating the L-THIA-LID 2.1 model has good precision.

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References

  • Abbott MB, Bathurst JC, Cunge JA, O'Connell PE, Rasmussen J (1986) An introduction to the European Hydrological System—Systeme Hydrologique Europeen,“SHE”, 2: Structure of a physically-based, distributed modelling system. J Hydrol 87(1):61–77

    Article  Google Scholar 

  • Ahiablame, LM (2012) Development of methods for modeling and evaluation of low impact development practices at the watershed scale. Doctoral dissertation, PURDUE UNIVERSITY, West Lafayette, IN, USA

  • Ahiablame LM, Engel BA, Chaubey I (2012) Representation and evaluation of low impact development practices with L-THIA-LID: An example for site planning. Environ Pollut 1(2):p 1

    Article  Google Scholar 

  • Ahiablame LM, Engel BA, Chaubey I (2013) Effectiveness of low impact development practices in two urbanized watersheds: Retrofitting with rain barrel/cistern and porous pavement. J Environ Manage 119:151–161

    Article  Google Scholar 

  • Andersson JCM, Pechlivanidis IG, Gustafsson D, Donnelly C, Arheimer B (2015) Key factors for improving large-scale hydrological model performance. European Water 49:77–88

    Google Scholar 

  • Baird, C, Jennings, M, Ockerman, D, Dybala, T (1996) Characterization of nonpoint sources and loadings to the Corpus Christi bay national estuary program study area; Corpus christi national estuary program, Corpus Christi, TX

  • Beaulac MN, Reckhow KH (1982) An examination of land use-nutrient export relationships1. J Am Water Resour As 18(6):1013–1024

    Article  Google Scholar 

  • Bedan ES, Clausen JC (2009) Stormwater runoff quality and quantity from traditional and low impact development watersheds. J Am Water Resour As 45:998–1008. doi:10.1111/j.1752–1688.2009.00342.x

    Article  Google Scholar 

  • Benaman J, Shoemaker CA (2004) Methodology for analyzing ranges of uncertain model parameters and their impact on total maximum daily load process. J Environ Eng 130(6):648–656

    Article  Google Scholar 

  • Castaño S, Sanz D, Gómez-Alday JJ (2013) Sensitivity of a Groundwater Flow Model to Both Climatic Variations and Management Scenarios in a Semi-arid Region of SE Spain. Water Resour Manag 27(7):2089–2101

    Article  Google Scholar 

  • Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrol Process 24(9):1133–1148

    Article  Google Scholar 

  • CWP and CSN (Center for Watershed Protection and Chesapeake Stormwater Network) (2008) Technical memorandum: the runoff reduction method. Ellicott City, MD. www.chesapeakestormwater.net

  • D’Agostino DR, Scardigno A, Lamaddalena N, El Chami D (2014) Sensitivity analysis of coupled hydro-economic models: quantifying climate change uncertainty for decision-making. Water Resour Manag 28(12):4303–4318

    Article  Google Scholar 

  • Debnath S, Adamala S, Raghuwanshi NS (2015) Sensitivity analysis of FAO-56 Penman-Monteith method for different agro-ecological regions of India. Environmental Processes 2(4):689–704

    Article  Google Scholar 

  • Dietz ME, Clausen JC (2008) Stormwater runoff and export changes with development in a traditional and low impact subdivision. J Environ Manage 87(4):560–566

    Article  Google Scholar 

  • Duan Q, Gupta HV, Sorooshian S, Rousseau AN, Turcotte R (2003) Calibration of watershed models. Am Geophys Union 6:1–345

    Google Scholar 

  • Efron B (1979) Bootstrap methods: another look at the jackknife. The annals of. Statistics 7:1–26

    Article  Google Scholar 

  • Efron, B, Tibshirani, RJ (1993) An introduction to the Bootstrap, Chapman & Hall, New York

  • Ellis JB, Mitchell G (2006) Urban diffuse pollution: key data information approaches for the Water Framework Directive. Water Environ J 20(1):19–26

    Article  Google Scholar 

  • Engel BA, Choi JY, Harbor J, Pandey S (2003) Web-based DSS for hydrologic impact evaluation of small watershed land use changes. Comput Electron Agr 39(3):241–249

    Article  Google Scholar 

  • Gaur A, Simonovic SP (2015) Towards reducing climate change impact assessment process uncertainty. Environ Process 2(2):275–290

    Article  Google Scholar 

  • GC and WWE (Geosyntec Consultants and Wright Water Engineers) (2011) International Stormwater Best Management Practices (BMP) Database Technical Summary: Volume Reduction, 27. www.bmpdatabase.org (accessed November, 2012)

  • Gitau MW, Chen J, Ma Z (2016) Water Quality Indices as Tools for Decision Making and Management. Water Resour Manag 30(8):2591–2610

    Article  Google Scholar 

  • Haan CT, Storm DE, Al-Issa T, Prabhu S, Sabbagh GJ, Edwards DR (1998) Effect of parameter distributions on uncertainty analysis of hydrologic models. Transactions of the ASAE 41(1):65–70

    Article  Google Scholar 

  • Hall, JW, Tarantola, S, Bates, PD, Horritt, MS (2005) Distributed sensitivity analysis of flood inundation model calibration. J Hydraul Eng 131(2):117–126

  • Hanel M, Mrkvičková M, Máca P, Vizina A, Pech P (2013) Evaluation of simple statistical downscaling methods for monthly regional climate model simulations with respect to the estimated changes in runoff in the Czech Republic. Water Resour Manag 27(15):5261–5279

    Google Scholar 

  • Harbor JM (1994) A practical method for estimating the impact of land-use change on surface runoff, groundwater recharge and wetland hydrology. J Am Plann Assoc 60(1):95–108

    Article  Google Scholar 

  • Helton JC (1993) Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliab Eng Syst Safe 42(2):327–367

    Article  Google Scholar 

  • Holvoet K, van Griensven A, Seuntjens P, Vanrolleghem PA (2005) Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT. Physics and Chemistry of the Earth, Parts A/B/C 30(8):518–526

    Article  Google Scholar 

  • Kanakoudis V, Tsitsifli S, Samaras P, Zouboulis A, Demetriou G (2011) Developing appropriate performance indicators for urban water distribution systems evaluation at Mediterranean countries. Water utility. Journal 1:31–40

    Google Scholar 

  • Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method. Water Resour Manag 29(13):4863–4883

    Article  Google Scholar 

  • Li H, Davis AP (2009) Water quality improvement through reductions of pollutant loads using bioretention. J Environ Eng 135(8):567–576

    Article  Google Scholar 

  • Lim KJ, Engel BA, Muthukrishnan S, Harbor J (2006) Effects of initial abstraction and urbanization on estimated runoff using CN technology1. J Am Water Resour As 42(3):629–643

    Article  Google Scholar 

  • Liu, Y (2015) Improvement of simulating BMPs and LID practices in L-THIA-LID model. Doctoral dissertation, Purdue University, West Lafayette, IN, USA

  • Liu Y, Ahiablame LM, Bralts VF, Engel BA (2015a) Enhancing a rainfall-runoff model to assess the impacts of BMPs and LID practices on storm runoff. J Environ Manage 147:12–23. doi:10.1016/j.jenvman.2014.09.005

    Article  Google Scholar 

  • Liu Y, Bralts VF, Engel BA (2015b) Evaluating the effectiveness of management practices on hydrology and water quality at watershed scale with a rainfall-runoff model. Sci Total Environ 511:298–308. doi:10.1016/j.scitotenv.2014.12.077

    Article  Google Scholar 

  • Liu Y, Cibin R, Bralts VF, Chaubey I, Bowling LC, Engel BA (2016a) Optimal selection and placement of BMPs and LID practices with a rainfall-runoff model. Environ Modell Softw 80:281–296

    Article  Google Scholar 

  • Liu Y, Theller LO, Pijanowski BC, Engel BA (2016b) Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: An application to the Trail Creek Watershed, Indiana. Sci Total Environ 553:149–163

    Article  Google Scholar 

  • Machado AR, Wendland E, Krause P (2016) Hydrologic simulation for water balance improvement in an outcrop area of the Guarani Aquifer system. Environ Process 3(1):19–38

    Article  Google Scholar 

  • Migliaccio KW, Chaubey I (2008) Spatial distributions and stochastic parameter influences on SWAT flow and sediment predictions. J Hydrol Eng 13(4):258–269

    Article  Google Scholar 

  • Muleta MK, Nicklow JW (2005) Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. J Hydrol 306(1):127–145

    Article  Google Scholar 

  • Muthukrishnan S, Harbor J, Lim KJ, Engel BA (2006) Calibration of a simple rainfall-runoff model for long-term hydrological impact evaluation. Urisa-Washington Dc 18(2):35

    Google Scholar 

  • Narsimlu B, Gosain AK, Chahar BR, Singh SK, Srivastava PK (2015) SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environ Process 2(1):79–95

    Article  Google Scholar 

  • NRCS (Natural Resources Conservation Services) (1986) Urban hydrology for small watersheds. Technical Release 55, USDA Natural Resources Conservation Services

  • Patil A, Deng Z (2010) Analysis of uncertainty propagation through model parameters and structure. Water Sci Technol 62(6)):1230–1239

    Article  Google Scholar 

  • Reinelt LE, Horner RR (1995) Pollutant removal from stormwater runoff by palustrine wetlands based on comprehensive budgets. Ecol Eng 4(2):77–97

    Article  Google Scholar 

  • Sample, DJ, Heaney, JP, Wright, LT, Koustas, R (2001) Geographic information systems, decision support systems, and urban storm-water management. J Water Res Pl 127(3):155–161

  • Sharifi A, Dinpashoh Y (2014) Sensitivity analysis of the Penman-Monteith reference crop evapotranspiration to climatic variables in Iran. Water Resour Manag 28(15):5465–5476

    Article  Google Scholar 

  • Sinclair Knight Merz Pty. Ltd (1999) Duck River stormwater management plan, Final, St Leonards, NSW

  • Sobol′ IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exper 1(4):407–417

    Google Scholar 

  • Sobol′ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simulat 55:271–280

    Article  Google Scholar 

  • Sreekanth J, Datta B (2014) Stochastic and robust multi-objective optimal management of pumping from coastal aquifers under parameter uncertainty. Water Resour Manag 28(7):2005–2019

    Article  Google Scholar 

  • Strecker, EW, Quigley, MM, Urbonas, B, Jones, J (2004) Analyses of the expanded EPA/ASCE International BMP Database and potential implications for BMP design. Proceedings of the World Water and Environmental Resources Congress 2004, Salt Lake City, Utah

  • Tang Z, Engel BA, Pijanowski BC, Lim KJ (2005) Forecasting land use change and its environmental impact at a watershed scale. J Environ Manage 76(1):35–45

    Article  Google Scholar 

  • Tang T, Reed P, Wagener T, Van Werkhoven K (2006) Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrol Earth Syst Sci Discuss 3:3333–3395

    Article  Google Scholar 

  • Tang Y, Reed P, Van Werkhoven K, Wagener T (2007) Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis. Water Resour Res 43:W06415. doi:10.1029/2006WR005813

  • Theodossiou N, Fotopoulou E (2015) Delineating well-head protection areas under conditions of hydrogeological uncertainty. A case-study application in northern Greece. Environ Process 2(1):113–122

    Article  Google Scholar 

  • Tsakiris G, Spiliotis M (2004) Fuzzy linear programming for problems of water allocation under uncertainty. European Water 7(8):25–37

    Google Scholar 

  • Valdez MC, Adler I, Barrett M, Ochoa R, Pérez A (2016) The Water-Energy-Carbon Nexus: Optimising Rainwater Harvesting in Mexico City. Environ Process 3(2):307–323

    Article  Google Scholar 

  • Weeks, CR (1982) Pollution in urban runoff. In: Hart, B.T. (Ed.), Water quality management monitoring programs and diffuse runoff Water Studies Centre, Chisholm Institute of Technology and Australian Society of Limnology, Melbourne, pp. 121–140.

  • Wilson C, Weng Q (2010) Assessing surface water quality and its relation with urban land cover changes in the Lake Calumet Area, Greater Chicago. Environ Manage 45(5):1096–1111

    Article  Google Scholar 

  • Wright, TJ, Liu, Y, Carroll, NJ, Ahiablame, LM, Engel, BA (2016) Retrofitting LID practices into existing neighborhoods: Is it worth it? Environ Manage 57(4):856–867

  • Zhang Q, Qi T, Singh VP, Chen YD, Xiao M (2015) Regional Frequency Analysis of Droughts in China: A Multivariate Perspective. Water Resour Manag 29(6):1767–1787

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Agricultural and Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN, 47907-2093, USA

    Yaoze Liu, Indrajeet Chaubey, Vincent F. Bralts & Bernard A. Engel

  2. Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907-2051, USA

    Indrajeet Chaubey

  3. Department of Agronomy, Purdue University, 915 West State Street, West Lafayette, IN, 47907-2054, USA

    Laura C. Bowling

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  1. Yaoze Liu
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  2. Indrajeet Chaubey
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  4. Vincent F. Bralts
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  5. Bernard A. Engel
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Correspondence to Bernard A. Engel.

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Liu, Y., Chaubey, I., Bowling, L.C. et al. Sensitivity and Uncertainty Analysis of the L-THIA-LID 2.1 Model. Water Resour Manage 30, 4927–4949 (2016). https://doi.org/10.1007/s11269-016-1462-z

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  • DOI: https://doi.org/10.1007/s11269-016-1462-z

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