Big data is popular in the areas of computer science, commerce and bioinformatics, but is in an early stage in hydroinformatics. Big data is originated from the extremely large datasets that cannot be processed in tolerable elapsed time with the traditional data processing methods. Using the analogy from the object-oriented programming, big data should be considered as objects encompassing the data, its characteristics and the processing methods. Hydroinformatics can benefit from the big data technology with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper provides a timely review on big data with its relevance to hydroinformatics. A further exploration on precipitation big data is discussed because estimation of precipitation is an important part of hydrology for managing floods and droughts, and understanding the global water cycle. It is promising that fusion of precipitation data from remote sensing, weather radar, rain gauge and numerical weather modelling could be achieved by parallel computing and distributed data storage, which will trigger a leap in precipitation estimation as the available data from multiple sources could be fused to generate a better product than those from single sources.

The rainfall outputs from the latest convection-scale Weather Research and Forecasting (WRF) model are shown to provide an effective means of extending prediction lead times in flood forecasting. In this study, the performance of the WRF model in simulating a regional sub-daily extreme rainfall event centred over Beijing, China is evaluated at high temporal (sub-daily) and spatial (convective-resolving) scales using different domain configurations and spin-up times. Seven objective verification metrics that are calculated against the gridded ground observations and the ERA-Interim reanalysis are analysed jointly using subjective verification methods to identify the likely best WRF configurations. The rainfall simulations are found to be highly sensitive to the choice of domain size and spin-up time at the convective scale. A model run covering northern China with a 1 : 5 : 5 horizontal downscaling ratio (1.62 km), 57 vertical layers (less than 0.5 km), and a 60 h spin-up time exhibits the best performance in terms of the accuracy of rainfall intensity and the spatial correlation coefficient (R′). A comparison of the optimal run and the initial run performed using the most common settings reveals clear improvements in the verification metrics. Specifically, R′ increases from 0.226 to 0.67, the relative error of the maximum precipitation at a point rises from −56 to −11.7 %, and the root mean squared error decreases by 33.65 %. In summary, re-evaluation of the domain configuration options and spin-up times used in WRF is crucial for improving the accuracy and reliability of rainfall outputs used in applications related to regional sub-daily heavy rainfall (SDHR).