Data Related Uncertainty in Near‐Surface Vulnerability Assessments for Agrochemicals in the San Joaquin Valley

Abstract

Precious groundwater resources across the United States have been contaminated due to decades-long nonpoint-source applications of agricultural chemicals. Assessing the impact of past, ongoing, and future chemical applications for large-scale agriculture operations is timely for designing best-management practices to prevent subsurface pollution. Presented here are the results from a series of regional-scale vulnerability assessments for the San Joaquin Valley (SJV). Two relatively simple indices, the retardation and attenuation factors, are used to estimate near-surface vulnerabilities based on the chemical properties of 32 pesticides and the variability of both soil characteristics and recharge rates across the SJV. The uncertainties inherit to these assessments, derived from the uncertainties within the chemical and soil data bases, are estimated using first-order analyses. The results are used to screen and rank the chemicals based on mobility and leaching potential, without and with consideration of data-related uncertainties. Chemicals of historic high visibility in the SJV (e.g., atrazine, DBCP [dibromochloropropane], ethylene dibromide, and simazine) are ranked in the top half of those considered. Vulnerability maps generated for atrazine and DBCP, featured for their legacy status in the study area, clearly illustrate variations within and across the assessments. For example, the leaching potential is greater for DBCP than for atrazine, the leaching potential for DBCP is greater for the spatially variable recharge values than for the average recharge rate, and the leaching potentials for both DBCP and atrazine are greater for the annual recharge estimates than for the monthly recharge estimates. The data-related uncertainties identified in this study can be significant, targeting opportunities for improving future vulnerability assessments.

Sensitivity and Uncertainty Analysis for the Annual Phosphorus Loss Estimator Model

“…Although the importance of explicitly accounting for uncertainty in hydrologic and water quality models has been widely acknowledged (Beck, 1987;Beven, 2006b;Harmel and Smith, 2007;Krueger et al, 2009;Loague et al, 2012;Reckhow, 1994;Refsgaard et al, 2007;Shirmohammadi et al, 2006), the use of prediction uncertainties is not standard practice in P loss modeling studies (Radcliffe et al, 2009). The reasons for this are varied but likely include the fact that some P loss models do not have the capability to generate prediction uncertainties.…”

Evaluating Oilseed Biofuel Production Feasibility in California’s San Joaquin Valley Using Geophysical and Remote Sensing Techniques

“…To supplement the SSURGO data an extensive spatial database of quantitative soils information (i.e., EC e , pH e , saturation percentage, B, available water content, LF) for the SJV exists. The supplemental data set, collected over a period of 25 years by Corwin and colleagues, is a compilation of data that appeared in publications by Bourgault et al [ 30 ], Corwin [ 31 ], Corwin and Lesch [ 8 , 9 , 10 , 32 ], Corwin et al [ 7 , 33 , 34 , 35 , 36 ], Lesch and Corwin [ 17 ], Lesch et al [ 37 , 38 , 39 ], Loague et al [ 40 ], Rhoades et al [ 41 ], Sanden et al [ 42 ], and Scudiero et al [ 26 , 29 , 43 ]. The supplemental data set consisted of edaphic property data from 83 fields within the SJV ranging in spatial extent from 0.4 to 65 ha with from 6 to 72 sample sites within a field.…”

Prioritization Approaches for Substances of Emerging Concern in Groundwater: A Critical Review

“…84,90,91 There will always be tradeoffs among complexity, accuracy, and data requirements. 117 The use of MECs is the preferred method for surface water and groundwater when adequate data are available. It is a more reliable method for representing environmental exposure especially for groundwaters where it is difficult to predict concentrations.…”

“…The level of detail required to provide realistic estimates of loading or concentrations in groundwater therefore remains unknown. For example, soil organic carbon was found to be important for sorption of ECs (e.g., refs and ), but some of the simpler approaches depend on the physicochemical properties of the EC that often do not reflect real environmental conditions. ,, There will always be trade-offs among complexity, accuracy, and data requirements . The use of MECs is the preferred method for surface water and groundwater when adequate data are available.…”