Reply to the Discussion of “Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge” by Littlewood<i>et al</i>.

Wang, Yi; He, Bin; Takase, Keiji

doi:10.1080/02626667.2011.565770

Cited by 12 publications

(16 citation statements)

References 8 publications

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“…In a recent case study based on the IHACRES model, Littlewood and Croke [2008] demonstrated a strong time scale dependency of model parameters and provided a methodology to relate parameter values to the modeling time step. Similarly, Wang et al [2009] also found linear and nonlinear time scale dependencies in hydrological parameter estimates and analyzed them in the context of average rainfall intensities at different time scales.…”

Section: Introductionmentioning

confidence: 96%

“…While a general consensus exists that model parameters, simulation results, and process representations are inherently and strongly time scale dependent [e.g., Duan et al , 2006; Merz et al , 2009], there is insufficient quantitative understanding of the precise underlying causes and their mathematical representation and physical interpretation and a lack of conceptually sound and practically robust strategies to handle them. In the absence of an adequate mathematical framework explaining and predicting these dependencies and encompassing both conceptual and physically based models of different degrees of complexity, current treatments of parameter scaling are largely heuristic and empirical [ Littlewood and Croke , 2008; Wang et al , 2009]. Similarly, while it has been shown that increasingly complex models can be inferred from higher‐resolution data [ Atkinson et al , 2002; Farmer et al , 2003], the quantitative and qualitative understanding of the processes revealed by high‐resolution data remains limited, especially at subdaily time scales [ Kirchner et al , 2004].…”

Section: Introductionmentioning

confidence: 99%

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

Kavetski

Fenicia

Clark

2011

Water Resources Research

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[1] 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 dataaveraging 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 processoriented diagnostics, furthers the utilization of dense-resolution data and experimental insights to advance hypothesis-based hydrological modeling at the catchment scale.Citation: Kavetski, D., F. Fenicia, and M. P. Clark (2011), Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: Insights from an experimental catchment, Water Resour. Res., 47, W05501,

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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

Kavetski

Fenicia

Clark

2011

Water Resources Research

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“…A number of studies in the past have looked into the influence of these rainfall characteristics in simulating hydrological response (e.g., Krajewski et al 1991;Finnerty et al 1997;Littlewood and Croke 2008;Wang et al 2009;Bastola and Murphy 2013). Krajewski et al (1991) observed that the temporal aggregation of rainfall data has a profound effect on hydrological response, especially on the timing and the magnitude of peak flow, as compared to the spatial resolution of the data.…”

Section: Introductionmentioning

confidence: 98%

Sensitivity of Hydrological Simulations of Southeastern United States Watersheds to Temporal Aggregation of Rainfall

Bastola

Misra

2013

Journal of Hydrometeorology

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This study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the southeastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found to be high (;35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maximum and minimum has a detrimental effect on the hydrological simulation. This theoretical experiment resulted in the underestimation of flow, with a disproportionate volume error (of as high as 77% in some watersheds). Observations indicate that over the SEUS variations of diurnal variability of rainfall explain a significant fraction of the seasonal variance throughout the year, with especially strong fractional variance explained in the boreal summer season. The results suggest that, should diurnal variations of precipitation get modulated either from anthropogenic or natural causes in the SEUS, there will be a significant impact on the streamflow in the watersheds. These conclusions are quite robust since both observational and model uncertainties have been considered in the analysis.

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“…For example, hydrologic modellers are well-aware that model parameters can change with the temporal resolution of rainfallrunoff models (e.g. Littlewood and Croke, 2008;Wang et al, 2009;Kavetski et al, 2011). Woods and Sivapalan (1999) examined space-time variability during storm events and showed that the scaling problem could be formulated using the covariance.…”

Section: W H Lim and M L Roderick: Scaling Theorymentioning

confidence: 99%

Up-scaling short-term process-level understanding to longer timescales using a covariance-based approach

Lim

Roderick

2014

Hydrol. Earth Syst. Sci.

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Abstract. General experience in hydrologic modelling suggests that the parameterisation of a model changes over different time and space scales. As a result, hydrologists often re-parameterise their models whenever different temporal or spatial resolutions are required. Here, we investigate theoretical aspects of this issue in a search for the cause(s) of the need for re-parameterisations. Based on Taylor series expansion, we present a mathematical approach for temporal up-scaling that involves covariance-based corrections. We apply the theory using a unique database of half-hourly pan evaporation measurements (comprising 237 days) and examine how the model parameters change when integrating from half-hour to daily and then monthly integration periods. We show that the model parameters change over different integration periods because of changes in the covariance between the model variables. In our model system, we find that the covariance-based correction is highly variable from day to day but settles down to a reasonably constant value over periods longer than about 15 days. The 15 days timescale is likely to be specific to our model system, nonetheless the underlying principle that there is a characteristic timescale for the covariance-based scaling correction of a particular hydrologic process might be general. If that proved true it would open up the possibility of systematically searching for characteristic integration periods for the key covariance-based scaling terms in other key hydrologic processes. That would in turn enable the development of more generalised hydrologic closure scheme(s).

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Reply to the Discussion of “Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge” by Littlewoodet al.

Cited by 12 publications

References 8 publications

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

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

Sensitivity of Hydrological Simulations of Southeastern United States Watersheds to Temporal Aggregation of Rainfall

Up-scaling short-term process-level understanding to longer timescales using a covariance-based approach

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