Metaldehyde prediction by integrating existing water industry datasets with the soil and water assessment tool

WATER RESEARCH

卷:183

Article

Purnell, Sarah¹  Kennedy, Rebecca²  Williamson, Elin²  Remesan, Renji³ 

[1] Univ Brighton, Ctr Aquat Environm, Sch Environm & Technol, Environm & Publ Hlth Res & Enterprise Grp, Cockcroft Bldg,Lewes Rd, Brighton BN2 4GJ, E Sussex, England

[2] Southern Water Serv Ltd, Southern House,Yeoman Rd, Worthing BN13 3NX, W Sussex, England

[3] Indian Inst Technol Kharagpur, Sch Water Resources, Kharagpur 721302, W Bengal, India

关键词: SWAT;    Metaldehyde;    Pesticide;    Management;    Water framework directive;   

DOI  :  10.1016/j.watres.2020.116053

来源: Elsevier

【 摘 要 】

Metaldehyde (a synthetic aldehyde pesticide used globally in agriculture) has been internationally identified as an emerging contaminant of concern. This study aimed to integrate existing water industry, publicly available and purchased licensed datasets with the open-access Soil and Water Assessment Tool (SWAT), to establish if these datasets could be used to effectively model metaldehyde in river catchments. To achieve the study aim, a SWAT model was developed and calibrated for the River Medway catchment (UK). The results of calibration (1994-2004) and validation (2005-2016) of average daily streamflow (m(3)/s) showed that the SWAT model could simulate water balance well (P-factor 0.68-0.85 and R-factor 0.54-0.82, NSE 0.42-0.60). Calibration (P-factor 0.72 and R-factor 1.35, NSE 0.31) and validation (P-factor 0.49 and R-factor 1.37, NSE 0.16) for daily soluble metaldehyde (mg active ingredient) load was also satisfactory. The most sensitive pesticide parameters for metaldehyde simulation included the timing and amount of pesticide (kg/ha) applied to the hydrological response units, the pesticide percolation coefficient and pesticide application efficiency. Outputs from this research demonstrate the potential application of SWAT in large complex catchments where routine monitoring is in place, but isn't designed explicitly for the purpose of predictive modelling. The implications of this, are significant, because they suggest that SWAT could be applied universally to catchments using existing water industry datasets. This would allow more efficient use of historical datasets and would be applicable in situations where resources are not available for additional targeted monitoring programmes. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

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