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\begin{document}

\title{Impacts of subgrid elevation bands on hydrological portrayals: insights
from a suite of hydroclimatically diverse mountainous catchments}



\author[1]{Octavio Murillo}%
\author[1]{Pablo A. Mendoza}%
\author[1]{Nicolás Vásquez}%
\author[2]{Naoki Mizukami}%
\author[3]{Álvaro Ayala}%
\affil[1]{Universidad de Chile}%
\affil[2]{National Center for Atmospheric Research}%
\affil[3]{Centre for Advanced Studies in Arid Zones (CEAZA)}%


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  \date{June 15, 2024}


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\begin{abstract}
The implementation of elevation bands is a common strategy to account
for vertical heterogeneity in hydrology and land surface models;
however, there is no consensus guidelines for their delineation. We
characterize hydrological implications of this choice by configuring the
Variable Infiltration Capacity (VIC) model in nine mountainous basins of
the Andes Cordillera, central Chile, using six different setups: no
elevation bands (benchmark model), and elevation bands with vertical
discretizations of 1000, 750, 500, 200 and 100 m. The analyses are
conducted in a wet period (April/1982-March/1987), dry period
(April/2010-March/2015) and a climatological period
April/1982-March/2015). The results show that adding elevation bands
yield little variations in simulated monthly or daily streamflow;
however, there are important effects on the partitioning of
precipitation between snowfall and rainfall, snowmelt, sublimation, and
the spatial variability in September 1 SWE, suggesting a model-structure
equifinality. Incorporating elevation bands generally yields less
basin-averaged snowmelt, and more (less) catchment-scale sublimation
across water-limited (energy-limited) basins. Further, the implications
of elevation bands vary with the analysis period: fluxes are more
affected during the wet period, while variations in September 1 SWE are
more noticeable during the dry period. In general, the effects of adding
elevation bands are reduced with increasing vertical discretization, and
can differ among catchments. Finally, the grid cells that yield the
largest sensitivities to vertical discretization have relatively lower
mean altitude, elevation ranges \textgreater{}1000 m, steep slopes
(\textgreater{}15\selectlanguage{ngerman}°) and annual precipitation amounts \textless{}1000 mm,
with large intra-annual variations in the water/energy budget.%
\end{abstract}\selectlanguage{ngerman}%



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