ASSESSMENT OF VARIOUS BIAS CORRECTION METHODS ON PRECIPITATION OF REGIONAL 3 CLIMATE MODEL AND FUTURE PROJECTION

: 9 The application of regional climate model simulations (RCMs) in climate change impact studies is 10 challengeable due to the risk of possible biases. Some sort of correction needs to be done prior to the application of 11 RCM simulations. This study attempts to assess the performance of a simple (linear scaling and Delta Change 12 method) and complex correction technique (Local intensity scaling, Power transformation and Distribution 13 mapping) on CORDEX(Coordinated Regional Climate Downscaling Experiment)simulated precipitation series for 14 the Thanjavur district. The performance at annual resolution is evaluated using various statistical parameters such as 15 Correlation, Root Mean Square Error and Bias against the observed precipitation data. The raw RCM estimates were 16 improved significantly after the bias correction with all methods. However, Power transformation exhibits good 17 agreement with the observed data at the district level than other methods because it corrects both the mean and 18 variance. The future climate was projected from 2021 to 2100 for RCP 4.5 and RCP 8.5 scenarios. The temporal 19 distribution of future precipitation clearly shows that most of the years will receive heavy precipitation; rather, some 20 years will receive low and average precipitation. The spatial distribution pattern indicates that the northeast 21 monsoon will dominate over all the ranges and places. This study has provided clear information on future 22 precipitation to the environmentalist, urban planners, and policymakers to take appropriate mitigation measures 23 towards agriculture and disaster management. Rainwater harvesting, recharging the aquifers, afforestation, and 24 redirecting the excess amount of water to the river through proper channels is the plausible actions suggested 25 overcoming excessive precipitation in the future. This employed the daily of by the data is available in

geographical area of 3,602.86 sq. km has been studied. The region possesses three rainy seasons such as mm of normal rainfall) plays a significant role in feeding the river Cauvery, the primary source of irrigation of and other vegetation. However, in recent times, agriculture seems to get destabilized due to uncertain climatic 108 conditions. The study area map shows 14 blocks with 17 well-distributed rain gauge stations around the district

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The four steps methodology has adopted in this study (Figure 2).

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In this study, the daily precipitation data were collected for 30 years (1976 -2005)

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(https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/) were collected, and the annual mean compared 118 against the observed data. In that, APHRODITE data has shown a high correlation of 0.73 with the observed data.

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Thus, observed IMD data was primarily used in the present study, while the data gaps were filled with 120 APHRODITE data.

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(2) that exceeds the threshold will be adjusted based on the number of days the observed precipitation was determined.

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The number of precipitation events for control and scenario run is corrected by applying the calibrated RCM 150 precipitation threshold (Pth, control) using Equations (3) and (4), respectively. This approach virtually eliminates the 151 drizzle effect because excessive drizzly days are frequently added to the RCM outputs.

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A scaling factor s is then calculated using equation (5) to confirm that the mean of corrected precipitation is equal to 156 observed data.

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Finally, both control and scenario precipitations are corrected using equation (6) and (7), respectively.   186 Where Γ(.) is the Gamma function, α is the shape parameter, and β is the scale parameter. Before the DM 187 method, the LOCI method is applied to determine the wet days using the specific threshold. Subsequently, RCM 188 outputs were corrected in terms of the Gamma cumulative distribution function (Fγ) and its inverse function (F -1 γ) as

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After bias correction, the annual mean was calculated for both observed and bias-corrected control datasets.

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Subsequently, a correlation was computed amongst them, and the same is shown in Figure 3. The figure shows that 214 the DC method adjusted control data has an absolute agreement with observed data. The agreement is since the amongst the other methods, while the LOCI method underestimated the inter-annual variability in all locations.

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On the contrary, the DM method has shown a varied result. It has overestimated the inter-annual variability 218 at many locations, while in a few places, it has underestimated and in some areas demonstrated a fair agreement.

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Thus, all the bias correction methods can improve the spatial statistics of simulated mean precipitation is inferred.

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The Root Mean Square Error (RMSE) and biases were calculated (

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The biases in RCP 4.5 and 8.5 scenario data were corrected using the PT method. Subsequently, grouped Initially, the PT method's performance was evaluated by carrying out statistical analysis between the 245 observed and adjusted control data. The result shows that the model underestimates the intensity of the precipitation 246 until 1985 and then after exhibits both overestimation and good agreement with the observed data collectively the intensity of precipitation in the 30-year time series. However, though the frequency of negative deviation is 250 higher, the intensity of precipitation it underestimates is lesser than the overestimation (Table 2).  (Table 2).

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The spatial distribution of annual mean precipitation for both scenarios in the near range is represented in 263 figure 6 and figure 7. In RCP 4.5, most of the area receives precipitation of around 1150 mm. However, a decrease 264 in the west and increases towards the northeast is also noticed. The RCP 8.5 exhibits a similar spatial pattern; 265 however, most places have shown relatively increased precipitation of about ~1275 mm (Figure 6). From the wind 266 rose diagram (Figure 7), it is witnessed that Manjalar head and Lower anaicut area receives higher precipitation (~ 267 1600 mm) whereas Grand anaicut and Thirukattupalli area with lower precipitation (~ 1000 mm).

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Overall, higher precipitation of RCP 8.5 is attributed to radiative forcing. But the frequency of occurrence 269 seems to be similar for both RCP scenarios as far observed and adjusted control data is concerned. The same is   Overall, it is found that the mid-range of RCP 4.5 receives higher precipitation than RCP 8.5. Thus, it is 286 inferred that the radiative forcing does not influence the intensity of the precipitation.  (Table 2).

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The spatial distributions of annual mean precipitation for both scenarios in the far range are represented in

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Overall in all the ranges, RCP 8.5 receives high precipitation when compared to RCP 4.5. The intensity of