
NAME:
SOWI - HS 1
BUILDING:
SOWI
FLOOR:
0
TYPE:
Lecture Hall
CAPACITY:
160
ACCESS:
Only Participants
EQUIPMENT:
Beamer, PC, WLAN (Eduroam), Overhead, Flipchart, Blackboard, Sound System, Handicapped Accessible, Microphones
High-Andean wetlands or bofedales play a crucial role in the hydrological functioning of the tropical Peruvian water towers. Their capacity to buffer dry season streamflow positions them between the high mountain climate change and the downstream water users. However, these ecosystems still lack a detailed model representation that can estimate the influence of bofedales within the catchment water balance and assess the partitioning of blue, green and white water fluxes. This study applies the Tethys-Chloris model to quantify the ecohydrological processes governing bofedales, which vary seasonally and are influenced by climate variability and cryosphere dynamics that regulate water inflows and outflows. We applied the model for a 14-year period over a headwater of the Cordillera Vilcanota, Peru. Model outputs are confirmed against in-situ stream flow, snow cover from trail cameras, soil characteristics and glacier mass balance measurements. We analyse the contributing factors to the water recharging capacity of bofedales happening at the end of the precipitation season. This generates a buffering capacity during the dry season with the support of glacier meltwater. Prior to the start of the next precipitation season, the seasonal hydrological state of bofedales is affected, as evidenced by the reduction of both the water table level and the saturated areas, in response to decreased water inputs. We present an assessment of the glacier contributions and the role of short-lived, ephemeral snow processes in influencing both the catchment-wide water flux partitioning and the sensitivity of bofedales to climatic fluctuations. These insights contribute to efforts to provide a holistic understanding of the role of bofedales in the high mountain hydrological cycle in the Peruvian Andes by applying a combination of data and mechanistic modeling.
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