Performance Evaluation of TerraClimate Monthly Rainfall Data after Bias Correction in the Fes-Meknes Region (Morocco)

Abstract

Morocco’s meteorological observation network is quite old, but the spatial coverage is insufficient to conduct studies over large areas, especially in mountainous regions, such as the Fez-Meknes region, where spatio-temporal variability in precipitation depends on altitude and exposure. The lack of station data is the main reason that led us to look for alternative solutions. TerraClimate (TC) reanalysis data were used to remedy this situation. However, reanalysis data are usually affected by a bias in the raw values. Bias correction methods generally involve a procedure in which a “transfer function” between the simulated and corrected variable is derived from the cumulative distribution functions (CDFs) of these variables. We explore the possibilities of using TC precipitation data for the Fez-Meknes administrative region (Morocco). This examination is of great interest for the region whose mountain peaks constitute the most important reservoir of water in the country, where TC data can overcome the difficulty of estimating precipitation in mountainous regions where the spatio-temporal variability is very high. Thus, we carried out the validation of TC data on stations belonging to plain and mountain topographic units and having different bioclimatic and topographic characteristics. Overall, the results demonstrate that the TC data capture the altitudinal gradient of precipitation and the average rainfall pattern, with a maximum in November and a minimum in July, which is a characteristic of the Mediterranean climate. However, we identified quasi-systematic biases, negative in mountainous regions and positive in lowland stations. In addition, summer precipitation is overestimated in mountain regions. It is considered that this bias comes from the imperfect representation of the physical processes of rainfall formation by the models. To reduce this bias, we applied the quantile mapping (QM) method. After correction using five QM variants, a significant improvement was observed for all stations and most months, except for May. Validation statistics for the five bias correction variants do not indicate the superiority of any particular method in terms of robustness. Indeed, results indicate that most QM methods lead to a significant improvement in TC data after monthly bias corrections.