Daily and Seasonal Variation of the Temperature Lapse Rate and Freezing Level Location in the Andes Mountains

Quantifying the relationship between temperature and altitude is essential for understanding and modeling a wide range of processes taking place in high-elevation environments. In particular, good estimations of the surface temperature lapse rate (TLR) and the elevation of the freezing level are fundamental for the hydrological modeling of mountainous basins. Although the TLR is commonly considered to be constant in hydrological modeling (with values ranging between -5 to -7 °C/Km), it is known to change in time and space, especially in complex terrains with highly variable orography. This assumption can lead to errors when estimating contributing areas, and snowpack accumulation and melting processes, which in turns can impact the modeling and assessment of flash-floods and their consequences. In this work we study the spatial and temporal behavior of the TLR and freezing level location in the Andes Mountains in central Chile, next to where its capital Santiago is located. For this purpose, we use data collected every 10 minutes by a spatially dense network of sensors implemented in a small catchment in the area, with elevations ranging between 700 and 3200 m. The resulting values and their spatial and temporal dynamics are compared against values calculated from other sources, including publicly available data recorded nearby, as well as atmospheric sounding data. We show that the surface TLR varies significantly in space and through time, and is highly correlated with the occurrence of precipitation. In general, values of -4.5°C/Km and -3.5 °C/Km are the best estimator of surface TLR for rainy and dry days, respectively. Moreover, we use a simple rainfall-runoff model to assess the significant hydrological implications of the results and the estimators of the freezing level elevation obtained from the different sources. Overall, the monitoring network presented in this work proves to be extremely valuable for a better understanding and modeling of hydrological processes in mountainous areas, and for the potential implementation of early warning flood systems in the study area.