Chronic exposure to arsenic (As) in food and water is a significant public health problem. Person-specific aggregate exposure is difficult to collect and modeling based on limited food As residue databases is of uncertain reliability. Two cross-sectional population exposure studies, the National Human Exposure Assessment Survey-Arizona and Arizona Border Survey, had a combined total of 252 subjects with diet, water, and urinary As data. Total As was measured in 24-h duplicate diet samples and modeled using 24-h diet diaries in conjunction with several published food surveys of As. Two-stage regression was used to assess the effects of dietary As on urinary total As (uAs): (1) generalized linear mixed models of uAs above versus below the limit of detection (LOD); and (2) restricted models limited to those subjects with uAs>LOD, using bootstrap sampling and mixed models adjusted for age, sex, body mass index, ethnicity, current smoking, and As intake from drinking and cooking water. In restricted models, measured and modeled estimates were significant predictors of uAs. Modeled dietary As based on Total Diet Study mean residues greatly underestimated the dietary intake. In households with tap water As ≤10 p.p.b., over 93% of total arsenic exposure was attributable to diet.