Evaluating the Impact of the Spatial Distribution of Land Management Practices on Water Erosion: Case Study of a Mediterranean Catchment
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 6
Abstract
The spatial distribution of land management practices (LMPs), such as the use of vegetated filters, may have a strong impact on their efficiency in trapping sediments and pollutants. Distributed water erosion models help managers, planners, and policymakers optimize the efficiency of these LMPs regarding their location relative to water and sediment pathways. In this work, the authors analyzed the impact of the spatial distribution of LMPs using an existing distributed model and sensitivity analysis procedures. The distributed model that was used is a distributed single-event physically based water erosion model developed to calculate erosion rates and sediment flow for small (less than ) agricultural catchments. To measure the impact of the spatial distribution of LMPs, the authors developed a stochastic model that generates LMP locations over the entire catchment. The stochastic model has three input parameters: the density of LMPs, their downslope/upslope location probability, and the probability density function shape controller. Because of its ability to account for the cross effects between parameters, the variance-based Sobol method was used to calculate the sensitivity of the soil loss ratio of a typical Mediterranean agricultural catchment (Roujan, southern France) to the LMP location model parameters. Three measurement points (two subcatchment outlets and the main outlet) were used to examine the spatially distributed effects of the LMP locations. The simulation results indicated that 70% of the variation of the net erosion is explained by variations in LMP density for the main outlet catchment, making LMP density the most sensitive parameter. However, the total Sobol sensitivity indices indicate a strong interaction among the three parameters when the density values are low (few LMPs are applied). Thus, although the density of the LMPs is the most sensitive parameter, their location may influence their global trapping efficiency in (real) cases where few LMPs are applied.
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Acknowledgments
This work was made possible by the financial support of the National Research Agency (ANR-VMC 2006) through Research Project MESOEROS21. The first author is grateful for the support of the National Council for Technological and Scientific Development (CNPQ-Brazil).
References
Boardman, J. (2006). “Soil erosion science: Reflections on the limitations of current approaches.” CATENA, 68(2–3), 73–86.
Cammeraat, L. H., and Imeson, A. C. (1999). “The evolution and significance of soil-vegetation patterns following land abandonment and fire in Spain.” CATENA, 37(1–2), 107–127.
Cerdan, O., Le Bissonnais, Y., Couturier, A., and Saby, N. (2002). “Modelling interrill erosion in small cultivated catchments.” Hydrol. Processes, 16(16), 3215–3226.
Cheviron, B., et al. (2011). “Comparative sensitivity analysis of four distributed erosion models.” Water Resour. Res., 47(1), W01510.
Cheviron, B., Gumiere, S. J., Le Bissonnais, Y., Raclot, D., and Moussa, R. (2010). “Sensitivity analysis of distributed erosion models—Framework.” Water Resour. Res., 46(8), W08508.
Colin, F., Moussa, R., and Louchart, X. (2012). “Impact of the spatial arrangement of land management practices on surface runoff for small catchments.” Hydrol. Processes, 26(2), 255–271.
Courant, R., Friedrichs, K., and Lewy, H. (1928). “Uber die partiellen Differenzengleichungen der mathematischen Physik.” Math. Ann., 100(1), 32–74.
Deletic, A. (2001). “Modelling of water and sediment transport over grassed areas.” J. Hydrol., 248(1–4), 168–182.
Deletic, A. (2005). “Sediment transport in urban runoff over grassed areas.” J. Hydrol., 301(1–4), 108–122.
Deletic, A., and Fletcher, T. D. (2006). “Performance of grass filters used for stormwater treatment—A field and modelling study.” J. Hydrol., 317(3–4), 261–275.
Doherty, J. (2004). “PEST: Model-independent parameter estimation.” User manual, 5th Ed., Watermark Numerical Computing, Brisbane, Australia.
Fitzjohn, C., Ternan, J. L., and Williams, A. G. (1998). “Soil moisture variability in a semi-arid gully catchment: Implications for runoff and erosion control.” CATENA, 32(1), 55–70.
Frey, H. C., and Patil, S. R. (2002). “Identification and review of sensitivity analysis methods.” Risk Anal., 22(3), 553–578.
Fryirs, K. A., Brierley, G. J., Preston, N. J., and Kasai, M. (2007). “Buffers, barriers and blankets: The (dis)connectivity of catchment-scale sediment cascades.” CATENA, 70(1), 49–67.
Gumiere, S. J., et al. (2011a). “MHYDAS-Erosion: A distributed single-storm water erosion model for agricultural catchments.” Hydrol. Process, 25(11), 1717–1728.
Gumiere, S. J., Le Bissonnais, Y., and Raclot, D. (2009). “Soil resistance to interrill erosion: Model parameterization and sensitivity.” CATENA, 77(3), 274–284.
Gumiere, S. J., Le Bissonnais, Y., Raclot, D., and Cheviron, B. (2011b). “Vegetated filter effects on sedimentological connectivity of agricultural catchments in erosion modelling: A review.” Earth Surf. Process. Landforms, 36(1), 3–19.
Hall, J. W., Tarantola, S., Bates, P. D., and Horritt, M. S. (2005). “Distributed sensitivity analysis of flood inundation model calibration.” J. Hydraul. Eng., 117–126.
Helton, J. C. (1993). “Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal.” Reliab. Eng. Syst. Saf., 42(2–3), 327–367.
Iooss, B., Ribatet, M., and Marrel, A. (2009). “Global sensitivity analysis of stochastic computer models with joint metamodels.” 〈http://hal.archives-ouvertes.fr/hal-00232805/en〉 (Jan. 13, 2009).
Lagacherie, P., Rabotin, M., Colin, F., Moussa, R., and Voltz, M. (2010). “Geo-MHYDAS: A landscape discretization tool for distributed hydrological modeling of cultivated areas.” Comput. Geosci., 36(8), 1021–1032.
Lecomte, V. (1999). “Transfers of pesticides by the run-off and erosion from plot to catchment. Spatial modeling.” Ph.D. thesis, ENGREF et INRA-Orléans.
Lee, P., Smyth, C., and Boutin, S. (2004). “Quantitative review of riparian buffer width guidelines from Canada and the United States.” J. Environ. Manage., 70(2), 165–180.
Lilburne, L., and Tarantola, S. (2009). “Sensitivity analysis of spatial models.” Int. J. Geog. Inf. Sci., 23(2), 151–168.
Marrel, A., Iooss, B., Da Veiga, S., and Ribatet, M. (2010). “Global sensitivity analysis of stochastic computer models with joint metamodels.” 〈http://hal.archives-ouvertes.fr/hal-00525489/fr/〉 (May 22, 2012).
Marrel, A., Iooss, B., Van Dorpe, F., and Volkova, E. (2008). “An efficient methodology for modeling complex computer codes with Gaussian processes.” Comput. Stat. Data Anal., 52(10), 4731–4744.
Morel-Seytoux, H. J. (1978). “Derivation of equations for variable rainfall infiltration.” Water Resour. Res., 14(4), 561–568.
Moussa, R. (1996). “Analytical Hayami solution for the diffusive wave flood routing problem with lateral inflow.” Hydrol. Processes, 10(9), 1209–1227.
Moussa, R., and Bocquillon, C. (1996). “Algorithms for solving the diffusive wave flood routing equation.” Hydrol. Processes, 10(1), 105–123.
Moussa, R., Voltz, M., and Andrieux, P. (2002). “Effects of the spatial organization of agricultural management on the hydrological behaviour of a farmed catchment during flood events.” Hydrol. Processes, 16(2), 393–412.
Observatoire Méditerranéen de l’Environnement Rural et de l’Eau (OMERE). (2009). “OMERE: Long-term hydro-meteorological observatory.” 〈http://www.umr-lisah.fr/omere〉 (Dec. 9, 2009).
R Development Core Team. (2010). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria.
Saltelli, A. (2002). “Making best use of model evaluations to compute sensitivity indices.” Comput. Phys. Commun., 145(2), 280–297.
Saltelli, A., et al. (2008). Global sensitivity analysis: The primer, Wiley, Chichester, U.K.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S. (2010). “Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index.” Comput. Phys. Commun., 181(2), 259–270.
Saltelli, A., Chan, K., and Scott, E. M. E. (2000). Sensitivity analysis, Wiley, London.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity analysis in practice. A guide to assessing scientific models, 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.
Sobol, I. M. (1993). “Sensitivity estimates for nonlinear mathematical models.” Math. Model. Comput. Exp., 1, 407–417.
Spear, R., Grieb, T. M., and Shang, N. (1994). “Parameter uncertainty and interaction in complex environmental models.” Water Resour. Res., 30(11), 3159–3169.
Van Nieuwenhuyse, B. H. J., Antoine, M., Wyseure, G., and Govers, G. (2011). “Pattern-process relationships in surface hydrology: Hydrological connectivity expressed in landscape metrics.” Hydrol. Processes, 25(24), 3760–3773.
Wuertz, D., et al. (2010). “fOptions: Basics of option valuation.” 〈http://cran.r-project.org/package=fOptions〉 (Jun. 24, 2013).
Young, P. C. (1978). Modeling, identification and control in environmental systems, G. C. Vansteenkiste, ed., North Holland, Amsterdam, Netherlands, 103–135.
Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 6 • June 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Mar 12, 2014
Accepted: Jul 24, 2014
Published online: Oct 16, 2014
Discussion open until: Mar 16, 2015
Published in print: Jun 1, 2015
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