Diagnosing a distributed hydrologic model for two high‐elevation forested catchments based on detailed stand‐ and basin‐scale data

Abstract

[1] This study evaluates the performance and internal structure of the distributed hydrology soil vegetation model (DHSVM) using 1998-2001 data collected at Upper Penticton Creek, British Columbia, Canada. It is shown that clear-cut snowmelt rates calculated using data-derived snow albedo curves are in agreement with observed lysimeter outflow. Measurements in a forest stand with 50% air crown closure suggest that the fraction of shortwave radiation transmitted through the canopy is 0.18-0.28 while the hemispherical canopy view factor controlling longwave radiation fluxes to the forest snowpack is estimated at 0.81 ± 0.07. DHSVM overestimates shortwave transmittance (0.50) and underestimates the view factor (0.50). An alternative forest radiation balance is formulated that is consistent with the measurements. This new formulation improves model efficiency in simulating streamflow from 0.84 to 0.91 due to greater early season melt that results from the enhanced importance of longwave radiation below the canopy. The model captures differences in canopy rainfall interception between small and large storms, tree transpiration measured over a 6-day summer period, and differences in soil moisture between a dry and a wet summer. While the model was calibrated to 1999 snow water equivalent (SWE) and hydrograph data for the untreated control basin, it successfully simulates forest and clear-cut SWE and streamflow for the 3 other years and 4 years of preharvesting and postharvesting streamflow for the second basin. Comparison of model states with the large array of observations suggests that the modified model provides a reliable tool for assessing forest management impacts in the region.

Advances in Canadian Forest Hydrology, 2003-2007

“…(VR) harvesting that differed markedly between tree species. These stand-level studies are useful for estimating hydrologic responses to harvesting and the subsequent rates of recovery for different processes, while also providing "internal validation" of model predictions (Thyer et al, 2004). This builds confidence in a model's ability to reproduce hydrologic behaviour at different scales.…”

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

“…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.…”

“…Together, these results broadly suggest that not all observations will reduce equifinality. Other studies have questioned the empirical equations used to calculate SWE accumulation and melt, and have found improvements in the prediction of SWE accumulation and melt by altering hardcoded parameters within the model framework (Thyer et al, 2004;Jost et al, 2009). …”

“…Distributed model predictions of internal behavior are often performed by matching model predictions of different catchment variables (e.g., snowmelt, Thyer et al, 2004;groundwater table dynamics, Kurás et al, 2011) to multiple observations at discrete points across a given catchment, though several studies have also shown how patterns of hydrologic processes may be directly incorporated into evaluating and applying complex environmental models (Grayson et al, 2002;Wealands et al, 2005;Koch et al, 2016). In the absence of time series observations of other hydrologic processes, simulated patterns may be evaluated using expert knowledge, understanding of process controls across landscapes, and other information knowledge and experience about model output realism (e.g., Franks et al, 1998).…”

“…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).…”