Lapse rate-adjusted bias correction for CMIP6 GCM precipitation data: An application to the Monsoon Asia Region.

Bias correction (BC) of General Circulation Models (GCMs) variables is a common practice when it is being used for climate impact assessment studies at regional scales. The present study proposes a bias correction method (LR-Reg) that first adjusts the original GCM precipitation for local lapse rate corrections and later bias corrects the lapse rate-adjusted GCMs precipitation data with linear regression coefficients. We evaluated LR-Reg BC method in comparison to Linear Scaling (LS) and Quantile Mapping (QMap) BC methods, and NASA's downscaled NEX data for Monsoon Asia region. This study used Coupled Model Intercomparison Project Phase 6 (CMIP6)-based MIROC6 GCM precipitation with historical and projected shared socio-economic pathways (SSP) scenarios (SSP245 and SSP585) datasets. The BC comparison results show that the relative percentage reduction in mean absolute error (MAE) values of LR-Reg over LS-BC was up to 10-30% while this relative reduction in MAE values of LR-Reg was 30-50% over QMap-BC and 75-100% over NASA's NEX-data. The future projected precipitation over Monsoon Asia during dry season shows more decreased precipitation by up to 100% mostly in the south Asia while during wet season shows more increased precipitation by up to 50% mostly in the northeastern China and in the Himalayan belts with respect to the baseline condition (1970-2005). The results on the average precipitation per 0.25 degree increase in latitude analysis shows that the maximums of average monsoon precipitation during baseline period occur at 0 and 25 degree latitudes while the projected monsoon precipitation during both SSP scenarios occurs at 10 and 20 degree latitudes which clearly shows an inward shift in the latitude axis for the projected precipitation in the Monsoon Asia.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor.

Potential evapotranspiration (PET) is a crucial component of the hydrological cycle and energy balance. Although the Penman-Monteith (PM) model is the most widely used method to estimate daily PET, it requires temperature, relative humidity, solar radiation, and wind speed. In Thailand, the number of potential weather stations to provide the required data is limited, which resulted in the absence of some input variables in many locations. The objective of this study is to develop the revised potential evapotranspiration (RPET) model to estimate daily PET using Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data. The multiple linear regression analysis was used to develop and validate the RPET model. The performance of the RPET model along with the Global Land Evaporation Amsterdam Model (GLEAM v3.2 b) and the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5-Land) products was investigated using the PM model. The results revealed that the RPET model showed a strong correlation with the PM model (r = 0.85, RMSE = 0.97 mm day-1, RSR = 0.53, NSE = 0.72) under limited meteorological inputs. The RPET model performance was superior when compared to GLEAM and ERA5-Land (r = 0.80, RMSE = 1.06 mm day-1). Therefore, the proposed model is greatly suitable for daily PET estimation with only required GNSS-PWV and temperature data, and this can be implemented for drought assessment and water resources management.