Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT

Abstract The dynamic behaviour of pesticides in river systems strongly depends on varying climatological conditions and agricultural management practices. To describe this behaviour at the river-basin scale, integrated hydrological and water quality models are needed. A crucial step in understanding the various processes determining pesticide fate is to perform a sensitivity analysis. Sensitivity analysis for hydrology and pesticide supply in SWAT (Soil and Water Assessment Tool) will provide useful support for the development of a reliable hydrological model and will give insight in which parameters are most sensitive concerning pesticide supply towards rivers. The study was performed on the Nil catchment in Belgium. In this study we utilised an LH-OAT sensitivity analysis. The LH-OAT method combines the One-factor-At-a-Time (OAT) design and Latin Hypercube (LH) sampling by taking the Latin Hypercube samples as initial points for an OAT design. By means of the LH-OAT sensitivity analysis, the dominant hydrological parameters were determined and a reduction of the number of model parameters was performed. Dominant hydrological parameters were the curve number (CN2), the surface runoff lag (surlag), the recharge to deep aquifer (rchrg_dp) and the threshold depth of water in the shallow aquifer (GWQMN). Next, the selected parameters were estimated by manual calibration. Hereby, the Nash–Sutcliffe coefficient of efficiency improved from an initial value of −22.4 to +0.53. In the second part, sensitivity analyses were performed to provide insight in which parameters or model inputs contribute most to variance in pesticide output. The results of this study show that for the Nil catchment, hydrologic parameters are dominant in controlling pesticide predictions. The other parameter that affects pesticide concentrations in surface water is ‘apfp_pest’, which meaning was changed into a parameter that controls direct losses to the river system (e.g., through the clean up of spray equipment, leaking tools, processing of spray waste on paved surfaces). As a consequence, it is of utmost importance that hydrology is well calibrated while––in this case––a correct estimation of the direct losses is of importance as well. Besides, a study of only the pesticide related parameters, i.e. application rate (kg/ha), application time (day), etc., reveals that the application time has much more impact than the application rate, which has itself a higher impact than errors in the daily rainfall observations.

[1]  P. Vanrolleghem,et al.  Dealing with variability in chemical exposure modelling in rivers , 2002 .

[2]  Andrew Parker,et al.  Pesticides and other micro-organic contaminants in freshwater sedimentary environments—a review , 2003 .

[3]  J. P. Burt Prevention of water pollution by agriculture and related activities , 1993 .

[4]  I. Dubus,et al.  Sources of uncertainty in pesticide fate modelling. , 2003, The Science of the total environment.

[5]  M. Vanclooster,et al.  Sensitivity of the SWAT model to the soil and land use data parametrisation : a case study in the thyle catchment, belgium , 2005 .

[6]  M.J.W. Jansen,et al.  Review of Saltelli, A. & Chan, K. & E.M.Scott (Eds) (2000), Sensitivity analysis. Wiley (2000) , 2001 .

[7]  Matthias Liess,et al.  The significance of entry routes as point and non-point sources of pesticides in small streams. , 2002, Water research.

[8]  Feike J. Leij,et al.  The RETC code for quantifying the hydraulic functions of unsaturated soils , 1992 .

[9]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[10]  Hans-Georg Frede,et al.  Comparison of two different approaches of sensitivity analysis , 2002 .

[11]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[12]  Raghavan Srinivasan,et al.  INTEGRATION OF WATERSHED TOOLS AND SWAT MODEL INTO BASINS 1 , 2002 .

[13]  J. Wösten,et al.  Development and use of a database of hydraulic properties of European soils , 1999 .

[14]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[15]  T. Albanis,et al.  Monitoring of pesticide residues and their metabolites in surface and underground waters of Imathia (N. Greece) by means of solid-phase extraction disks and gas chromatography. , 1998, Journal of chromatography. A.

[16]  E. Ongley Control of water pollution from agriculture , 1996 .

[17]  William E. Kastenberg,et al.  A multimedia, multiple pathway exposure assessment of atrazine: fate, transport and uncertainty analysis , 1999 .

[18]  P. Vanrolleghem,et al.  Dynamic in‐stream fate modeling of xenobiotic organic compounds: A case study of linear alkylbenzene sulfonates in the Lambro River, Italy , 2004, Environmental toxicology and chemistry.

[19]  V. Novotny,et al.  Water Quality: Prevention, Identification and Management of Diffuse Pollution , 1996 .

[20]  Y. Ronen Uncertainty Analysis , 1988 .

[21]  S. A. Cryer,et al.  Regional sensitivity analysis using a fractional factorial method for the USDA model GLEAMS , 1999, Environ. Model. Softw..