Evaluating Statistical Bias Correction Techniques to Enhance Precipitation Projections in the Cachi Basin, Peruvian Andes

• Published in: RGSA : Revista de Gestão Social e Ambiental 2024-11, Vol.18 (11), p.e09204
• Main Authors: Villafuerte, Elmer Moreno, Angulo, Eleazar Chuchon
• Format: Article
• Language: English
• ISSN: 1981-982X; 1981-982X
• DOI: 10.24857/rgsa.v18n11-005

Introduction: To assess the impact of climate change on watersheds, accurate meteorological data are required. Regional Climate Model (RCM) data sets are widely used to feed ecological and hydrological models. However, in Andean regions such as the Peruvian Andes such as the Cachi basin, the scarcity of observation stations generates biases in the RCMs. This study evaluates the performance effect of a simple (Linear Scaling) and complex (Power Transformation and Distribution Mapping) statistical bias correction technique applied on the set of 10 RCM simulations with and without bias correction (BC), using historical climate data (1981-2004). The suitability of each bias correction method was evaluated by analyzing statistical indices, Taylor diagrams and error distributions that were calculated considering the observed and projected rainfall data. Based on our findings, it was observed that the methods can reduce the strong precipitation bias inherent to RCM simulations, evidenced by positive correlations of 0.8 and 0.7 compared to PISCO and RAIN4PE gridded observations, respectively. However, the data set presents an overestimation and underestimation of rainfall in the uncorrected RCM simulations. On the other hand, the power transformation (PT) exhibits solid agreement with the observed data, standing out over the DM and LS method by adjusting both the mean and the variance. In summary, different correction methods may influence the simulated change signals differently. Furthermore, BC methods should be used with caution taking into account regional climatic characteristics and specific research topics.   Objective: The objective of this study is to investigate the performance of statistical bias correction techniques applied to regional climate models (RCMs) in the Cachi River Basin, with the aim of improving the accuracy of precipitation projections. The study seeks to enhance climate impact assessments and hydrological modeling, specifically focusing on the selection of appropriate correction methods to mitigate climate change uncertainties in Andean regions.   Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Theories such as bias correction in climate models (Teutschbein & Seibert, 2013), regional climate modeling (CORDEX project), and precipitation projection techniques (Fang et al., 2015) stand out, providing a solid basis for understanding the context of the investigation. These framew