Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes
Randy Muñoz, Christian Huggel, Fabián Drenkhan, Marc Vis, Daniel Viviroli
Abstract
Glaciated headwaters of the Vilcanota-Urubamba river basin, Southern Peru A pivotal question is if robust hydrological simulation of streamflow in data-scarce and glaciated catchments can be achieved using parsimonious or more complex models. Therefore, a multi-model assessment of three glacio-hydrological models of different complexity was conducted thoroughly analyzing model performance, flow signatures and runoff components. In data-scarce catchments, such as in the tropical Andes, parsimonious glacio-hydrological models can provide more robust results than complex models. While the overall performance of all models was reasonably good (R2: 0.65–0.70, Nash-Sutcliffe: 0.65–0.73, Nash-Sutcliffe-ln: 0.73–0.78), with increasing data scarcity more complex models involve higher uncertainties. Furthermore, complex models require substantial understanding of the underpinning hydrological processes and a comprehensive calibration strategy to avoid apparently high model performance driven by inadequate assumptions. Based on these insights we present a framework for robust glacio-hydrological simulation under data scarcity. This stepwise approach includes, among others, a multi-model focus with a comprehensive assessment of flow signatures and runoff components. Future modeling needs to be further supported by alternative data collection strategies to substantially improve knowledge and process understanding. Therefore, the extension of sensor and station networks combined with the integration of co-produced knowledge represents a meaningful measure to robust decision-making for climate change adaptation and water management under high uncertainty.