1 Hit in 0.039 sec

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

Christa Kelleher, Brian McGlynn, Thorsten Wagener
2016 Hydrology and Earth System Sciences Discussions  
Distributed catchment models are widely used tools for predicting hydrologic behaviour. While distributed models require many parameters to describe a system, they are expected to simulate behaviour that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several 'behavioural' sets that fit observations (typically streamflow). In this study, we investigate the extent to which
more » ... t to which equifinality impacts a typical distributed modelling application. We outline a hierarchical approach to reduce the number of behavioural sets based on regional, observation-driven, and expert knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of 'behavioural' parameter sets and increased certainty in spatio-temporal simulations, simulating a well-studied headwater catchment, Stringer Creek, MT using the distributed hydrology-soil-vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10,000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series further reduced the number of behavioural parameter sets, but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioural parameter sets can reduce equifinality and bolster more careful application and simulation of spatio-temporal processes via distributed modelling at the catchment scale.
doi:10.5194/hess-2016-642 fatcat:jgugiktgwbfm7kwzotv2eo6esu