Long-term isotopic monitoring in Southeast region of Brazil

Observations on the variability of the isotopic composition of rainfall have been used to understand the effects of climate change, but there is a gap in this type of analysis in tropical regions. Studies in tropical regions are extremely important to assess the influence of meteorological parameters on the isotopic composition of rainfall, as these regions have a unique climate that greatly influences the distribution of rainfall regimes. The aim of this study is to evaluate the historical series of isotopic data from the GNIP station at Rio Claro, located in Southeast region of Brazil, since the climate in this area is directly influenced by a variety of atmospheric systems that impact the variation in isotopic composition of rainfall. This study presents the analysis of a 10-year dataset of daily isotopic precipitation data (δ¹⁸O, δ²H, and d-excess). The isotopic signatures for δ¹⁸O ranged from -21.74‰ to 9.09‰ VSMOW, and for δ²H the variation was from -158.45‰ to 44.9‰ VSMOW, determining the Local Meteoric Water Line (LMWL) of Rio Claro as δ²H = 7.76 * δ¹⁸O + 10.97. The values of the LMWL are close to the Global Meteoric Water Line (GMWL), demonstrating a balance between evaporation and vapor recirculation processes. The rainy season LMWL (δ²H = 7.72 * δ¹⁸O + 10.3) is also close to the LMWL for Rio Claro. However, when considering data from the dry season, which represents 24% of the rainfall data, the LMWL indicates stronger vapor recirculation processes (δ²H = 7.7 * δ¹⁸O + 12.12), caused by rainfall initiated by cold fronts. Trend analysis using the Mann-Kendall test revealed a decreasing trend for δ²H and d-excess, while rainfall showed an increasing trend over the study period. These findings highlight the significance of determining the LMWL for Rio Claro, as it provides a valuable reference for isotopic studies in the region. Moreover, the analysis offers a comprehensive overview of the isotopic dataset, which can be further expanded and refined through the integration of synoptic meteorological data.