Abstract
Abstract. With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall–runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall–runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become more widespread, and there is an urgent need for hydrologists both in academia and in the field to realise the significance of the data time interval issue. It is important that more case studies in different catchments with various hydrological forecasting systems are explored in the future to further verify and improve the proposed hypothetical pattern.
Highlights
Hydrological forecasting has always been a dominant and challenging field in operational hydrology
It is still true that the increasing pattern is sharper for larger catchments without the use of autoregressive moving-average (ARMA); for exam- This paper explores the time interval of the model input ple, the optimal time intervals for the 12 h lead time increase data and its impact on the hydrological forecasting systo 90, 60, 30 and 30 min for the catchments of Bishop_Hull, tem, consisting of a conceptual rainfall–runoff model and
The results without the use of the ARMA able to measure and record hydrological variables with inmodel can be treated as a baseline to evaluate the impact creasingly higher sampling rates; for example, nowadays of the data time interval when more complicated updating rainfall data can be collected once a second by the optischemes are adopted in the forecasting system
Summary
Hydrological forecasting has always been a dominant and challenging field in operational hydrology. Han: Optimal data time interval for real-time hydrological forecasting (Freer et al, 1996; Krzysztofowicz, 1999, 2002; Tamea et al, 2005; Mantovan and Todini, 2006; Chen and Yu, 2007), as well as numerical weather prediction (NWP), which provides precipitation forecasts as the input of the forecasting system and allows for an extension of the forecast lead time (Cloke and Pappenberger, 2009; Wood and Schaake, 2008; Lin et al, 2002, 2006, 2010). When constructing the forecasting system, there is an important issue that cannot be avoided, i.e. the selection of the time interval of the model input data (e.g. the rainfall, streamflow and evaporation, etc.) used to drive the system, which is, mostly ignored by the hydrological community in operational applications. “data time interval” is seldom mentioned; “model time interval” and “model time step” are rarely mentioned
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