Evaluating the functionality and streamflow impacts of explicitly modelling forest–snow interactions and canopy gaps in a distributed hydrologic model

Abstract

Many plot‐scale studies have shown that snow‐cover dynamics in forest gaps are distinctly different from those in open and continuously forested areas, and forest gaps have the potential to alter the magnitude and timing of snowmelt. However, the watershed‐level impacts of canopy gap treatment on streamflows are largely unknown. Here, we present the first research that explicitly assesses the impact of canopy gaps on seasonal streamflows and particularly late‐season low flows at the watershed scale. To explicitly model forest–snow interactions in canopy gaps, we made major enhancements to a widely used distributed hydrologic model, distributed hydrology soil vegetation model, with a canopy gap component that represents physical processes of snowpack evolution in the forest gap separately from the surrounding forest on the subgrid scale (within a grid typically 10–150 m). The model predicted snow water equivalent using the enhanced distributed hydrology soil vegetation model showed good agreement (R2 > 0.9) with subhourly snow water equivalent measurements collected from open, forested, and canopy gap sites in Idaho, USA. Compared with the original model that does not account for interactions between gaps and surrounding forest, the enhanced model predicted notably later melt in small‐ to medium‐size canopy gaps (the ratio of gap radius (r) to canopy height (h) ≤ 1.2), and snow melt rates exhibited great sensitivity to changing gap size in medium‐size gaps (0.5 ≤ r/h ≤ 1.2). We demonstrated the watershed‐scale implications of canopy gaps on streamflow in the snow‐dominated Chiwawa watershed, WA, USA. With 24% of the watershed drainage area (about 446 km2) converted to gaps of 60 m diameter, the mean annual 7‐day low flow was increased by 19.4% (i.e., 0.37 m3/s), and the mean monthly 7‐day low flows were increased by 13.5% (i.e., 0.26 m3/s) to 40% (i.e., 1.76 m3/s) from late summer through fall. Lastly, in practical implementation of canopy gaps with the same total gap areas, a greater number of distributed small gaps can have greater potential for longer snow retention than a smaller number of large gaps.

Impacts of Climate Change and Land Use/Cover Change on Streamflow in Beichuan River Basin in Qinghai Province, China

“…It is typically applied at high resolutions varying from 25 to 200 m at a sub-daily time scale for watersheds of up to 10,000 km 2 . The DHSVM has been widely applied in both tropical [43][44][45] and temperate catchments [10,46] in many research fields, including forest-snow interactions [47], climate change [48], landscape change [27,49], human activities [27], urbanization [10,44], stream temperature [50], water quality simulations [51], and sediment transportation [52].…”

Resolving Small‐Scale Forest Snow Patterns Using an Energy Balance Snow Model With a One‐Layer Canopy

“…Forest snow research has also substantially benefited from the increased availability of canopy structure information from a variety of remote sensing products (Ginzler & Hobi, 2015;Harpold, Guo, et al, 2014;Moeser, Morsdorf, et al, 2015;Varhola & Coops, 2013). As a consequence, many snow routines in hydrological and land surface models have been enhanced to incorporate more accurate representations of forest snow processes (Boone et al, 2017;Ellis et al, 2013;Gouttevin et al, 2015;Mahat et al, 2013;Mahat & Tarboton, 2014;Sun et al, 2018). Yet in many cases, the canopy is represented as one layer whose energy balance is coupled to that of the snowpack (Broxton et al, 2015;Mahat & Tarboton, 2012;Moeser et al, 2016;Musselman, Molotch, Margulis, Lehning, et al, 2012).…”

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

“…In the context of flood risk and water availability discussed above, physics-based energy-balance snow models have been extensively used to better understand snow processes and to project potential hydrologic response to the changing climate (Andreadis & Lettenmaier, 2006;Arheimer et al, 2017;Barnett et al, 2008;Bavay et al, 2009;Clark et al, 2015;Comola et al, 2015;Cristea et al, 2014;Hamlet et al, 2005;Hamlet & Lettenmaier, 2007;Jin & Wen, 2012;Leung & Wigmosta, 1999;Livneh et al, 2015;Marks et al, 2001;Sun et al, 2018;Thyer et al, 2004). Snow models with a wide range of complexities have been developed in the past decades .…”