Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications

Abstract

Accurate rainfall datasets with high temporal and spatial resolutions are crucial for most hydrological applications. One potentially valuable source of rainfall data that has consistent spatial and temporal resolutions are atmospheric reanalysis products. However, while such data sets can provide a physically consistent representation of rainfall over large spatial and temporal extents, they are generally less accurate than observed datasets at daily scales. In contrast, while the gauge measurements are accurate source of rainfall data, sub-daily observations are spatially sparse and are of shorter length than daily observations. While the temporal resolution of daily observations can be enhanced using temporal disaggregation methods, they are often applied stochastically with a focus on capturing the fine-scale statistical properties rather than generating a best estimate time series useful for hindcasting purposes. The increasing availability of high resolution regional reanalysis products prompts the question whether they can be used to temporally disaggregate daily observations to derive high-resolution estimates of sub-daily rainfalls suitable for hydrologic applications. This study investigates the efficacy of a simple disaggregation approach to temporally disaggregate daily rainfalls to hourly values using a regional reanalysis at moderate spatial resolutions. The approach is tested on attributes relevant to a wide range of hydrological applications. The selected performance metrics include the distribution and frequency of various sub-daily rainfall accumulations, statistics characterising the sequencing and central tendency of sub-daily rainfalls, and the efficacy of areal estimates of sub-daily rainfalls for simulating catchment streamflows. Categorical evaluation shows that the disaggregated rainfalls reduce the frequency of false alarms and improves the probability of detection compared to the use of raw reanalysis estimates. However, a mixed performance in capturing fine-scale statistical characteristics suggests that the disaggregation approach is less robust for applications that rely solely on high-resolution rainfall characteristics. In hydrological evaluation, when compared to estimates based on raw reanalysis or uniformly disaggregated daily observations, the disaggregated catchment rainfalls improve the simulation of the magnitude and timing of peak flows, and the accuracy of derived flood frequency estimates. The proposed disaggregation method is easily applied to any high-resolution dataset and has the potential to be used in hydrological applications that rely heavily on sub-daily characterisation, with a varying performance across the target applications.