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Lake Lungo and Lake Ripasottile are two shallow (4-5 m) lakes located in the Rieti Basin, central Italy, that have been described previously as surface outcroppings of the groundwater table. In this work, the two lakes as well as springs and rivers that represent their potential source waters are characterized physio-chemically and isotopically, using a combination of environmental tracers. Temperature and pH were measured and water samples were analyzed for alkalinity, major ion concentration, and stable isotope (δ2H, δ18O, δ13C of dissolved inorganic carbon, and δ34S and δ18O of sulfate) composition. Chemical data were also investigated in terms of local meteorological data (air temperature, precipitation) to determine the sensitivity of lake parameters to changes in the surrounding environment. Groundwater represented by samples taken from Santa Susanna Spring was shown to be distinct with SO42- and Mg2+ content of 270 and 29 mg/L, respectively, and heavy sulfate isotopic composition (δ34S=15.2 ‰ and δ18O=10‰). Outflow from the Santa Susanna Spring enters Lake Ripasottile via a canal and both spring and lake water exhibits the same chemical distinctions and comparatively low seasonal variability. Major ion concentrations in Lake Lungo are similar to the Vicenna Riara Spring and are interpreted to represent the groundwater locally recharged within the plain. The δ13CDIC exhibit the same groupings as the other chemical parameters, providing supporting evidence of the source relationships. Lake Lungo exhibited exceptional ranges of δ13CDIC (±5 ‰) and δ2H, δ18O (±5 ‰ and ±7 ‰, respectively), attributed to sensitivity to seasonal changes. The hydrochemistry results, particularly major ion data, highlight how the two lakes, though geographically and morphologically similar, represent distinct hydrochemical facies. These data also show a different response in each lake to temperature and precipitation patterns in the basin that may be attributed to lake water retention time. The sensitivity of each lake to meteorological patterns can be used to understand the potential effects from long-term climate variability.