Spatio-Temporal Interpolation and Bias Correction Ordering Analysis for Hydrological Simulations: An Assessment on a Mountainous River Basin

Triggering hydrological simulations with climate change gridded datasets is one of the prevailing approaches in climate change impact assessment at a river basin scale, with bias correction and spatio-temporal interpolation being functions routinely used on the datasets preprocessing. The research object is to investigate the dilemma arisen when climate datasets are used, and shed light on which process-i.e., bias correction or spatio-temporal interpolation-should go first in order to achieve the maximum hydrological simulation accuracy. In doing so, the fifth generation of the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) temperature and precipitation products of 9 x 9 km spatial resolution, which are considered as the reference data, are initially compared with the same hindcast variables of a regional climate model of 12.5 x 12.5 km spatial resolution over a specific case study basin and for a 10-year period (1991-2000). Thereafter, the climate model's variables are (a) bias corrected followed by their spatial interpolation at the reference resolution of 9 x 9 km with the use of empirical quantile mapping and spatio-temporal kriging methods respectively, and (b) spatially downscaled and then bias corrected by using the same methods as before. The derived outputs from each of the produced dataset are not only statistically analyzed at a climate variables level, but they are also used as forcings for the hydrological simulation of the river runoff. The simulated runoffs are compared through statistical performance measures, and it is established that the discharges attributed to the bias corrected climate data followed by the spatio-temporal interpolation present a high degree of correlation with the reference ones. The research is considered a useful roadmap for the preparation of gridded climate change data before being used in hydrological modeling.

Automated summary

This study investigates the dilemma of using climate datasets for hydrological simulations and explores the impact of the order of bias correction and spatio-temporal interpolation on simulation accuracy. By comparing different datasets and conducting statistical analyses and hydrological simulations, the study finds that bias correction followed by spatio-temporal interpolation results in high correlation with reference data. This research provides a useful roadmap for preparing gridded climate change data for hydrological modeling.