Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: Insights from an experimental catchment

• Published in: Water resources research Vol. 47; no. 5
• Main Authors: Kavetski, Dmitri, Fenicia, Fabrizio, Clark, Martyn P.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 01.05.2011; John Wiley & Sons, Inc
• Subjects: Calibration; Catchment scale; data resolution; Experimental basins; Hydrology; Hypotheses; Hypothesis testing; model identification; model time scale; numerical error; Parameter estimation; Rain; Stream flow; Time series
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2010WR009525

This study presents quantitative and qualitative insights into the time scale dependencies of hydrological parameters, predictions and their uncertainties, and examines the impact of the time resolution of the calibration data on the identifiable system complexity. Data from an experimental basin (Weierbach, Luxembourg) is used to analyze four conceptual models of varying complexity, over time scales of 30 min to 3 days, using several combinations of numerical implementations and inference equations. Large spurious time scale trends arise in the parameter estimates when unreliable time‐stepping approximations are employed and/or when the heteroscedasticity of the model residual errors is ignored. Conversely, the use of robust numerics and more adequate (albeit still clearly imperfect) likelihood functions markedly stabilizes and, in many cases, reduces the time scale dependencies and improves the identifiability of increasingly complex model structures. Parameters describing slow flow remained essentially constant over the range of subhourly to daily scales considered here, while parameters describing quick flow converged toward increasingly precise and stable estimates as the data resolution approached the characteristic time scale of these faster processes. These results are consistent with theoretical expectations based on numerical error analysis and data‐averaging considerations. Additional diagnostics confirmed the improved ability of the more complex models to reproduce distinct signatures in the observed data. More broadly, this study provides insights into the information content of hydrological data and, by advocating careful attention to robust numericostatistical analysis and stringent process‐oriented diagnostics, furthers the utilization of dense‐resolution data and experimental insights to advance hypothesis‐based hydrological modeling at the catchment scale.