Abstract
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 × 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 × 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 × 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.
Highlights
Gridded dataset products emerged from the ICTs revolution of the last few decades are unique assets which foster hydrological research
Applying the combinations of the proposed bias correction showed that the mean precipitation of the ERA5 and CCLM data is 864 mm and 1092 mm reand spatio-temporal kriging interpolation method, the mean annual precipitation of the spectively, Figure 2b
The literature small number of scholars mainly focused on the climate variables level, such as the research demonstrates a small number of scholars mainly focused on the climate variables level, of Rabiei and Haberlandt [45] that concluded that the merging of rain gauge and radar such as the research of Rabiei and Haberlandt [45] that concluded that the merging of rain data through interpolation techniques gauge and radar data through interpolation techniques
Summary
Gridded dataset products emerged from the ICTs revolution of the last few decades are unique assets which foster hydrological research. Due to discrepancies among data sources and processing algorithms, gridded products, and those focusing on precipitation, usually have different performance [3,4,5,6]. Both by the European Centre for Medium-Range Weather Forecasts (ECMWF), are produced by numerical weather prediction models that assimilate observed atmosphere and surface data, to reconstruct land surface, oceans, and atmosphere state variables of the past [15]. Reprocessed datasets, e.g., the Goddard Institute for Space Studies Surface Temperature Analysis dataset (GISTEMP) [16], the Climatic Research Unit Temperature, v.4 dataset (CRUTEM4) [17], and the European gridded dataset (E-OBS) [18] are produced by spatial interpolation techniques applied on gauges for producing nested variables at different spatial scales [19,20]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
