Internal catchment process simulation in a snow‐dominated basin: performance evaluation with spatiotemporally variable runoff generation and groundwater dynamics

Abstract

Abstract:Hydrologic models have increasingly been used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models are also plagued by uncertainty, however, and parameter equifinality is a common concern. Physicallybased, spatially-distributed hydrologic models must therefore be tested with high-quality experimental data describing a multitude of concurrent internal catchment processes under a range of hydrologic regimes. This study takes a novel approach by not only examining the ability of a pre-calibrated model to realistically simulate watershed outlet flows over a four year period, but a multitude of spatially-extensive, internal catchment process observations not previously evaluated, including: continuous groundwater dynamics, instantaneous stream and road network flows, and accumulation and melt period spatial snow distributions. Many hydrologic model evaluations are only on the comparison of predicted and observed discharge at a catchment outlet and remain in the 'infant stage' in terms of model testing. This study, on the other hand, tests the internal spatial predictions of a distributed model with a range of field observations over a wide range of hydroclimatic conditions. Nash-Sutcliffe model efficiency was improved over prior evaluations due to continuing efforts in improving the quality of meteorological data collection. Road and stream network flows were generally well simulated for a range of hydrologic conditions, and snowpack spatial distributions were well simulated for one of two years examined. The spatial variability of groundwater dynamics was effectively simulated, except at locations where strong stream-groundwater interactions exist. Model simulations overall were quite successful in realistically simulating the spatiotemporal variability of internal catchment processes in the watershed, but the premature onset of simulated snowmelt for one of the simulation years has prompted further work in model development.

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

“…regional signatures of similarity (e.g., Yadav et al, 2007;Bloeschl et al, 2013;Hrachowitz et al, 2014), typically applied to regionalize hydrologic models for streamflow prediction in ungauged catchments; 2. objective functions or error metrics (e.g., Wagener et al, 2001;Gupta et al, 2008;van Werkhoven et al, 2008;Pfannerstill et al, 2014;Shafii and Tolson, 2015), which measure how well simulations match observations; and 3. measures of internal catchment behavior, which describe how well simulations match either observations or perceptions of the spatiotemporal variability of hydrologic behavior (e.g., Franks et al, 1998;Lamb et al, 1998;Grayson et al, 2002;Wealands et al, 2005;Kurás et al, 2011;Koch et al, 2016).…”

“…Internal catchment behavior Aggregated spatial predictions (if values are unknown) and simulations (if values are known) may be used to assess the realism of predicted internal catchment behavior (Grayson et al, 2002;Wealands et al, 2005;Kuraś et al, 2011;Koch et al, 2016;. There are many examples of distributed models that have evaluated internal catchment behavior using what Grayson et al (2002) refer to as the "many points" approach -comparing model simulations to (often, time series) observations at several locations throughout the catchment for a given process of interest (e.g., Thyer et al, 2004;Kuraś et al, 2011).…”

“…There are many examples of distributed models that have evaluated internal catchment behavior using what Grayson et al (2002) refer to as the "many points" approach -comparing model simulations to (often, time series) observations at several locations throughout the catchment for a given process of interest (e.g., Thyer et al, 2004;Kuraś et al, 2011). However, in the absence of internal observations, we suggest that internal simulations may still be incorporated into evaluations of distributed model behavior and parameter estimation.…”

“…While there are examples where different observations, including snow accumulation and melt (Thyer et al, 2004;Whitaker et al, 2003), soil moisture (Koren et al, 2008;Graeff et al, 2012), and catchment chemistry (Birkel et al, 2014), have been incorporated into the calibration and parameter set selection process, many catchments lack measurements beyond streamflow, suggesting that other sources of information are needed Grayson et al, 2002;Paniconi et and Putti, 2015). Finally, while examples exist where model simulations are compared to internal measurements of different hydrologic processes at a few points, there are fewer examples where researchers holistically evaluate the patterns of these simulated processes (Franks et al, 1998;Lamb et al, 1998;Grayson et al, 2002;Wealands et al, 2005;Koch et al, 2016).…”

Forest harvesting effects on the magnitude and frequency of peak flows can increase with return period

“…The subdued topography of this small scale, snow‐dominated watershed is unique, and snow melts nearly uniformly during the melt season with no clear topography or elevation related snowline. The model has also proven to be successful in realistically simulating spatiotemporally variable runoff generation processes in further performance evaluations [ Kuraś et al , 2011], and is believed to be a reliable tool for contributing to the ongoing debate on the effects of forest roads and different levels of harvesting on peak flow regimes in snow‐dominated watersheds.…”