Assessment of rainfall bias correction techniques for improved hydrological simulation

• Published in: International journal of climatology Vol. 39; no. 4; pp. 2386 - 2399
• Main Authors: Ghimire, Uttam, Srinivasan, Govindarajalu, Agarwal, Anshul
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.03.2019; Wiley Subscription Services, Inc
• Subjects: Bias; bias correction; Chindwin River basin; Climate models; Computer simulation; ensemble; Global climate; Global climate models; Hydrologic models; Hydrology; improved hydrological simulation; Interpolation; Interpolation methods; Mapping; Myanmar; Rain; Rainfall; Rainfall simulators; River basins; Rivers; Robustness (mathematics); Scaling; Seasonal variations; Seasonality; Simulation; Splines; Statistical methods; Transfer functions
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.5959

Eight rainfall bias correction techniques were compared over the Chindwin River basin in Myanmar to improve hydrological simulation at multiple timescales using two approaches, viz. monthly and annual. The techniques included linear scaling, parametric quantile mapping using linear, scale, power and exponential assymptotic transfer functions and nonparametric quantile mapping using empirical, robust regression and smoothing splines interpolation methods. Three global climate models (GCMs), wet, near‐normal and dry in nature to estimate mean rainfall at the country and the basin scales were selected from a set of 13 GCMs. The rainfall bias correction factors for each GCM were generated from the control period 1981–1999 and verified over 2000–2005. Application of bias correction techniques resulted in reduction of biases and improved the flow simulations. These techniques showed better performance statistics in simulating daily, monthly and seasonal flows under the monthly approach, where correction factors were generated and applied separately for different months. The inconsistencies in magnitude and seasonality of flows were addressed under the monthly approach while only the biases related to magnitude were corrected under the annual approach. Linear scaling followed by parametric (linear and power transformation) and nonparametric empirical quantile mapping methods yielded a very good hydrological performance at all temporal scales when applied under the monthly approach. Parametric quantile mapping with scaling function yielded least efficiency under the annual approach for all temporal scales. These results are expected to be valid for other river basins in the region showing similar strong rainfall seasonality. The ability of global climate models (GCM) to simulate observed rainfall characteristics decrease from the country to basin and sub‐basin levels, making them unreliable for hydrological assessments at basin scales. Generation of individual bias correction factors accounting the monthly rainfall characteristics and applying them to daily GCM rainfall yielded better hydrological representation of seasonally dominant basin compared to a single correction factor generated from the pool of entire year.