TECHNICAL PAPERS
Jun 14, 2011

Relative Importance of Input Parameters in the Modeling of Soil Moisture Dynamics of Small Urban Areas

Publication: Journal of Hydrologic Engineering
Volume 17, Issue 3

Abstract

Continuous-simulation water balance models may be used to study the soil moisture dynamics of small urban areas. These models require as input many soil-texture and land-use-related parameters. Difficulties encountered in determining the values of these input parameters warrant an investigation on their relative importance. In this study, a series of global sensitivity analyses were performed to evaluate the response of selected outputs from a continuous-simulation soil moisture model to variations of specified input parameters. Using randomly generated input parameter values representing various site conditions, the soil moisture model was run with meteorological data from Toronto, Ontario, Canada. Three output statistics, namely, average soil moisture, the standard deviation, and skewness of the output daily soil moisture distributions, were determined from each model run. Four types of sensitivity indices between the output statistics and the input parameters were calculated. Based on these sensitivity indices, it was concluded that the wilting and hygroscopic-point soil moisture levels and the soil moisture level below which plants start to endure water stress are the most important input parameters for all three output statistics. The relative importance of soil’s porosity, saturated conductivity, and the runoff curve number of the study area becomes greater and almost reaches the same level as the most important parameters when the skewness of the output daily soil moisture distributions is the output statistic of interest.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The authors would like to acknowledge the support from the Natural Sciences and Engineering Research Council of CanadaNSERC. The authors also thank Dr. Stefano Tarantola of the Joint Research Center of the European Commission for helpful suggestions and for allowing the use of SIMLAB Version 2.2, the global sensitivity analysis software.

References

Albertson, J. D., and Kiely, G. (2001). “On the structure of soil moisture time series in the context of land surface models.” J. Hydrol. (Amsterdam), 243(1–2), 101–119.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration, guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations (FAO), Rome.
Arhonditsis, G. B., and Brett, M. T. (2005). “Eutrophication model for Lake Washington (USA) Part, I. Model description and sensitivity analysis.” Ecol. Model., 187(2–3), 140–178.
Avati, K. (2009). “The effect of task duration correlations on project schedules—A study using Monte Carlo simulation.” Eight to Late (Digital Journal). 〈http://eight2late.wordpress.com〉.
Bois, B., et al. (2008). “Using remotely sensed solar radiation data for reference evapotranspiration estimation at a daily time step.” Agric. For. Meteorol., 148(4), 619–630.
Calvert, J.-C., et al. (1998). “An interactive vegetation SVAT model tested against data from six contrasting sites.” Agric. For. Meteorol., 92(2), 73–95.
Choi, M., and Jacobs, J. M. (2007). “Soil moisture variability of root zone profiles within SMEX02 remote sensing footprints.” Adv. Water Resour.,, 30(4), 883–896.
Chow, V. T., Maidment, D. V., and Mays, L. W. (1988). Applied hydrology, McGraw-Hill, New York.
Clapp, R. B., and Hornberger, G. M. (1978). “Empirical equations for some soil hydraulic properties.” Water Resour. Res., 14(4), 601–604.
D’Odorico, P. D., Ridolfi, L., Porporato, A., and Rodriguez-Iturbe, I. (2000). “Preferential states of seasonal soil moisture: The impact of climate fluctuations.” Water Resour. Res., 36(8), 2209–2219.
Douville, H., Conil, S., Tyteca, S., and Voldoire, A. (2007). “Soil moisture memory and West African monsoon predictability: Artefact or reality?” Clim. Dyn., 28(7–8), 723–742.
Francos, A., Elorza, F. J., Bouraoui, F., Bidoglio, G., and Galbiati, L. (2003). “Sensitivity analysis of distributed environmental simulation models: Understanding the model behaviour in hydrological studies at the catchment scale.” Reliab. Eng. Syst. Saf., 79(2), 205–218.
Hall, J. W., Tarantola, S., Bates, P. D., and Horritt, M. S. (2005). “Distributed sensitivity analysis of flood inundation model calibration.” J. Hydraul. Eng., 131(2), 117–126.
Hamby, D. M. (1995). “A comparison of sensitivity analysis techniques.” Health Phys., 68(2), 195–204.
Hamby, D. M., and Tarantola, S. (1999). “Exploring sensitivity analysis techniques for the assessment of an environmental transport model.” Proc. of ESREL '99—The Tenth European Conf. on Safety and Reliability, Garching, Germany.
Headrick, T. C., and Rotou, O. (2001). “An investigation of the rank transformation in multiple regression.” Comput. Stat. Data Anal., 38(2), 203–215.
Hedin, A. (2003). “Probabilistic dose calculations and sensitivity analyses using analytic models.” Reliab. Eng. Syst. Saf., 79(2), 195–204.
Helton, J. C. (2004). “Sampling-based methods for uncertainty and sensitivity analysis.” Proc. of the 4th Int. Conf. on Sensitivity Analysis of Model Output SAMO, K. M. Hanson and F. M. Hemez, eds., Los Alamos National Laboratory, Los Alamos, NM, 221–229.
Iman, R. L., and Conover, W. J. (1989). Modern business statistics, Wiley, New York.
Jacobs, J. M., Mohanty, B. P., Hsu, E.-C., and Miller, D. (2004). “SMEX02: Field scale variability, time stability and similarity of soil moisture.” Remote Sens. Environ., 92(4), 436–446.
Jacquin, A. P., and Shamseldin, A. Y. (2009). “Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models.” Hydrol. Earth Syst. Sci., 13(1), 41–55.
Jaques, J., Lavergne, C., and Devictor, N. (2005). “Sensitivity analysis in presence of model uncertainty and correlated inputs.” Proc. of the 4th Int. Conf. on Sensitivity Analysis of Model Output SAMO, K. M. Hanson and F. M. Hemez, eds., Los Alamos National Laboratory, Los Alamos, NM, 317–323.
Jones, C. A., and Kiniry, J. R. (1986). CERES-Maize: A simulation model of maize growth and development, Texas A&M Univ. Press, College Station, TX.
Kanso, A., Chebbo, G., and Tassin, B. (2005). “Application of MCMC global sensitivity analysis method for model calibration to urban runoff quality modeling.” Proc. of the 4th Int. Conf. on Sensitivity Analysis of Model Output SAMO, K. M. Hanson and F. M. Hemez, eds., Los Alamos National Laboratory, Los Alamos, NM, 17–26.
Knisel, W. G., and Davis, F. M. (1999). “LEAMS: Groundwater loading effects of agricultural management systems.” Version 3.0 User Manual. Publication No. SEWRL-WGK/FMD-050199.
Laio, F., Porporato, A., Ridolfi, L., and Rodriguez-Iturbe, I. (2001). “Plants in water-controlled ecosystems: Active role in hydrologic processes and response to water stress II. Probabilistic soil moisture dynamics.” Adv. Water Resour., 24(7), 707–723.
Liu, J. (2009). “A GIS-based tool for modelling large-scale crop-water relations.” Environ. Model. Software, 24(3), 411–422.
Mahmood, R., and Hubbard, K. G. (2003). “Simulating sensitivity of soil moisture and evapotranspiration under heterogeneous soils and land uses.” J. Hydrol. (Amsterdam), 280(1–4), 72–90.
Manache, G., and Melching, C. S. (2008). “Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water-quality model parameters.” Environ. Model. Software, 23(5), 549–562.
Manobavan, M., Lucas, N. S., Boyd, D. S., and Petford, N. (2003). “The sensitivity and response of terrestrial South American vegetation to interannual climatic variability induced by the ENSO.” J. Environ. Inform., 2(2), 1–10.
Mertens, J., Madsen, H., Kristensen, M., Jacques, D., and Feyen, J. (2005). “Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective, laboratory and in situ estimates.” Hydrol. Proces., 19(8), 1611–1633.
Mishra, S. (2004). “Sensitivity analysis with correlated inputs—An environmental risk assessment example.” Proc. of the Crystal Ball User Conf., Denver, CO.
Mishra, S., Deeds, N. E., and Rama Rao, B. S. (2003). “Application of classification trees in the sensitivity analysis of probabilistic model results.” Reliab. Eng. Syst. Saf., 79(2), 123–129.
Mohanty, B. P., and Skaggs, T. H. (2001). “Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation.” Adv. Water Resour., 24(9–10), 1051–1067.
Nishat, S., Guo, Y., and Baetz, B. W. (2007). “Development of a simplified continuous simulation model for investigating long-term soil moisture fluctuations.” Agric. Water Manage., 92(1–2), 53–63.
Nishat, S., Guo, Y., and Baetz, B. W. (2008). “Climate change and urban grass land soil moisture conditions in south-western Ontario, Canada.” J. Environ. Inf., 12(2), 105–119.
Pappenberger, F., Beven, K. J., Ratto, M., and Matgen, P. (2008). “Multi-method global sensitivity analysis of flood inundation models.” Adv. Water Resour., 31(1), 1–14.
Pastres, R., Chan, K., Solidoro, C., and Dejak, C. (1999). “Global sensitivity analysis of a shallow-water 3D eutrophication model.” Comput. Phys. Commun., 117(1–2), 62–74.
Qian, T., Dai, A., Trenberth, K. E., and Oleson, K. W. (2006). “Simulation of global land surface conditions from 1984 to 2004. Part I: Forcing data and evaluation.” J. Hydrometeorol., 7(5), 953–974.
Quader, A., and Guo, Y. (2009). “Relative importance of hydrological and sediment-transport characteristics affecting effective discharge of small urban streams of southern Ontario.” J. Hydrol. Eng., 14(7), 698–711.
Ratto, M., Young, P. C., Romanowicz, R., Pappenberger, F., Saltelli, A., and Pagano, A. (2007). “Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology.” Hydrol. Earth Syst. Sci., 11(4), 1249–1266.
Ridolfi, L., D’Odorico, P. D., Porporato, A., and Rodriguez-Iturbe, I. (2000). “Impact of climate variability on the vegetation water stress.” J. Geophys. Res., 105(D14), 18013–18025.
Rodriguez-Iturbe, I., D’Odorico, P. D., Porporato, A., and Ridolfi, L. (1999a). “Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation.” Proc. R. Soc. London, Ser. A, 455, 3789–3805.
Rodriguez-Iturbe, I., D’Odorico, P. D., Porporato, A., and Ridolfi, L. (1999b). “On the spatial and temporal links between vegetation, climate and soil moisture.” Water Resour. Res., 35(12), 3709–3722.
Roxburgh, S. H. (2006). “Create correlated random deviates.” 〈http://www.steverox.info〉.
Rudiger, C., Calvert, J.-C., Gruhier, C., Holmes, T. R. H., de Jeu, R. A. M., and Wagner, W. (2009). “An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France.” J. Hydrometeorol., 10(2), 431–447.
Ryu, D., and Famiglietti, J. S. (2005). “Characterization of footprint-scale surface soil moisture variability using Gaussian and beta distribution functions during the Southern Great Plains 1997 (SGP97) Hydrology Experiment.” Water Resour. Res., 41(12), W12433.
Saltelli, A., Chan, K., and Scott, E. M. (2000). Sensitivity analysis, Wiley, London.
Saltelli, A., Tarantola, S., and Chan, K. P.-S. (1999). “A quantitative model-independent method for global sensitivity analysis of model output.” Technometrics, 41(1), 39–56.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models, Wiley, London.
Saltelli, A., Ratto, M., Tarantola, S., and Campolongo, F. (2006). “Sensitivity analysis practices: Strategies for model-based inference.” Reliab. Eng. Syst. Saf., 91(10–11), 1109–1125.
Sieber, A., and Uhlenbrook, S. (2005). “Sensitivity analyses of a distributed catchment model to verify the model structure.” J. Hydrol. (Amsterdam), 310(1–4), 216–235.
Tingey, D. T. et al. (2007). “Elevated temperature, soil moisture, and seasonality but not CO2 affect canopy assimilation and system respiration in seedling Douglas-fir ecosystems.” Agric. For. Meteorol., 143(1–2), 30–48.
U.S. Department of Agriculture (USDA). (2008). 〈http://www.usda.gov〉 (Oct. 11, 2008).
Viessman, W. J., and Lewis, G. L. (2003). Introduction to hydrology, 5th Ed., Addison-Wesley, Boston.
Wagner, W., Naeimi, V., Scipal, K., de Jeu, R., and Martinez-Fernanadez, J. (2007). “Soil moisture from operational meteorological satellites.” Hydrogeol. J., 15(1), 121–131.
Whittrock, V., and Ripley, E. A. (1999). “The predictability of autumn soil moisture levels on the Canadian prairies.” Int. J. Climatol., 19(3), 271–289.
Zeng, X., Zeng, X., Shen, S. S. P., Dickinson, R. E., and Zeng, Q.-C. (2005). “Vegetation-soil water interaction within a dynamic ecosystem model of grassland in semi-arid areas.” Tellus, 57B(3), 189–202.

Information & Authors

Information

Published In

Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 17Issue 3March 2012
Pages: 359 - 367

History

Received: Oct 22, 2010
Accepted: Jun 10, 2011
Published online: Jun 14, 2011
Published in print: Mar 1, 2012

Permissions

Request permissions for this article.

Authors

Affiliations

Shazia Nishat [email protected]
Former Graduate Student, Dept. of Civil Engineering, McMaster Univ., Hamilton, ON, Canada L8S 4L7 (corresponding author). E-mail: [email protected]
Yiping Guo, M.ASCE [email protected]
Associate Professor, Dept. of Civil Engineering, McMaster Univ., Hamilton, ON, Canada L8S 4L7. E-mail: [email protected]
Brian W. Baetz, M.ASCE [email protected]
Professor, Dept. of Civil Engineering, McMaster Univ., Hamilton, ON, Canada L8S 4L7. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share