Predicting the temporal transferability of model parameters through a hydrological signature analysis
- 18 Downloads
Attention has recently increased on the use of hydrological signatures as a potential tool for assessing the fidelity of model structures and providing insights into the transfer of model parameters. The utility of hydrological signatures as model performance/reliability indicators in a calibration-validation testing scenario (i.e., the temporal transfer of model parameters) is the focus of this study. The Probability Distributed Model, a flexible conceptual hydrological model, is used to test the approach across a number of catchments included in the MOPEX data set. We explore the change in model performance across calibration and validation time periods and contrast it to the corresponding change in several hydrological signatures to assess signature worth. Results are explored in finer detail by utilizing a moving window approach to calibration and validation time periods. The results of this study indicated that the most informative signature can vary, both spatially and temporally, based on physical and climatic characteristics and their interaction to the model parameterization. Thus, one signature could not adequately illustrate complex watershed behaviors nor predict model performance in new analysis periods.
Keywordsstreamflow hydrological signature validation testing model calibration
Unable to display preview. Download preview PDF.
We acknowledge the National Oceanic and Atmospheric Administration for making available the Model Parameter Estimation Experiment (MOPEX) data that was used for this study and S. Razavi for providing free access to the VARS toolbox. We thank the two anonymous reviewers for their contributions toward the improvement of this manuscript. Funding for this research was provided through a graduate scholarship from Clarkson University awarded to D. Jayathilake.
- Blöschl G, Sivapalan M, Savenije H, Wagener T, Viglione A, eds. (2013). Runoff Prediction in Ungauged Basins: Synthesis Across Processes, Places and Scales. Cambridge University PressGoogle Scholar
- Duan Q, Schaake J, Andréassian V, Franks S, Goteti G, Gupta H V, Gusev Y M, Habets F, Hall A, Hay L, Hogue T, Huang M, Leavesley G, Liang X, Nasonova O N, Noilhan J, Oudin L, Sorooshian S, Wagener T, Wood E F (2006). Model parameter estimation experiment (MOPEX): an overview of science strategy and major results from the second and third workshops. J Hydrol (Amst), 320(1–2): 3–17CrossRefGoogle Scholar
- Grayson R, Blöschl G (2001). Spatial patterns in catchment hydrology: observations and modelling. CUP ArchiveGoogle Scholar
- Hrachowitz M, Savenije H H G, Blöschl G, McDonnell J J, Sivapalan M, Pomeroy J W, Arheimer B, Blume T, Clark M P, Ehret U, Fenicia F, Freer J E, Gelfan A, Gupta H V, Hughes D A, Hut R W, Montanari A, Pande S, Tetzlaff D, Troch P A, Uhlenbrook S, Wagener T, Winsemius H C, Woods R A, Zehe E, Cudennec C (2013). A decade of predictions in ungauged basins (PUB)—a review. Hydrol Sci J, 58(6): 1198–1255CrossRefGoogle Scholar
- Prudhomme C, Haxton T, Crooks S, Jackson C, Barkwith A, Williamson J, Kelvin J, Mackay J, Wang L, Young A, Watts G (2013). Future flows hydrology: an ensemble of daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain. Earth Syst Sci Data, 5(1): 101–107CrossRefGoogle Scholar
- Zhang Y Q, Viney N R, Chiew F H S, Van Dijk A I J M, Liu Y Y (2011). Improving hydrological and vegetation modelling using regional model calibration schemes together with remote sensing data. In: Proceedings of the 19th International Congress on Modelling and Simulation (MODSIM’11): 3448–3454Google Scholar