The Role of Soil Water Storage in Catchment Hydrology and Biogeochemistry Across the Andes-Amazon Transition

Abstract:

The Madre de Dios region of Peru hosts the transition from Andean tropical montane cloud forests (TMCFs) to the Amazon rainforest. These tropical ecosystems play globally important roles in the carbon cycle and in sustaining high biodiversity. TMCFs are generally immersed in clouds and as such, cloud water is an important component of their ecosystem health. Previous work in the headwaters of the Madre de Dios watershed has shown that cloud water is an important contributor to streamflow and postulates that continued release of water in soil or groundwater reservoirs sustains streamflow throughout the dry season (Clark et al, 2014).

In early 2016, we began an intensive hydrological monitoring campaign of five small catchments spanning the elevation and slope gradients from the Andean TMCF to the Amazon rainforest. Twice-monthly river, soil and precipitation waters are collected for major ion and hydrogen and oxygen isotope analyses, and plant stems are collected for xylem water analysis. Very few data sets of this scope exist for tropical systems, making these data invaluable in understanding themes of chemical weathering, nutrient fluxes, water transit times and plant water sourcing.

Here, we seek to focus specifically on the role of soil water storage and fluxes in stream hydrology and biogeochemistry. Preliminary data from the first year of our field campaign will be used to make initial constraints on the timescale of soil water storage (using seasonal variations in precipitation water isotopes) and soil-derived nutrient fluxes (using major element data from soil and stream waters). We aim to address the questions: do soil water isotopes reflect the same seasonal variation as precipitation? is soil water comprised of water that has predominantly fallen in the wet season? and what is the role of soil water flux in sustaining streamflow and contributing to stream nutrient load? through mixing models and other analyses of biogeochemical data.