TY - JOUR
T1 - Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes
AU - Muñoz, Randy
AU - Huggel, Christian
AU - Drenkhan, Fabian
AU - Vis, Marc
AU - Viviroli, Daniel
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/10
Y1 - 2021/10
N2 - Study region: Glaciated headwaters of the Vilcanota-Urubamba river basin, Southern Peru Study focus: 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. New hydrological insights for the region: 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.
AB - Study region: Glaciated headwaters of the Vilcanota-Urubamba river basin, Southern Peru Study focus: 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. New hydrological insights for the region: 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.
KW - Glacio-hydrological simulation
KW - Model complexity
KW - Multi-model comparison
KW - Scarce data
KW - Shaman model
KW - Tropical Andes
UR - http://www.scopus.com/inward/record.url?scp=85118749382&partnerID=8YFLogxK
U2 - 10.1016/j.ejrh.2021.100932
DO - 10.1016/j.ejrh.2021.100932
M3 - Article
AN - SCOPUS:85118749382
SN - 2214-5818
VL - 37
JO - Journal of Hydrology: Regional Studies
JF - Journal of Hydrology: Regional Studies
M1 - 100932
ER -