Document Type : Research Article
Authors
1 University of Mohaghegh Ardabili
2 University of Birjand
Abstract
Introduction & Background: Assessment of climate change impacts on hydrology is relied on the information of climate changes in adequate scale. Due to outputs of GCMs (General Circulation Models) that are the most confident tools for simulating climate change impacts but are available in coarse resolution. Downscaling process which is classified to several methods such as transfer function, weather generator and weather typing is performed for improving of GCMs projection and using them in local scale. Meanwhile feature selection is the main essential step in downscaling with transfer function. Because the main goal of downscaling is the improvement of GCMs projections, several researches examined vary approaches for feature selection. This study aims to assess performance of downscaling daily precipitation under four different selection methods such as PCA, CA, SRA and ParCA using comprehensive comparison tests.
Materials and Methods: Measured daily rainfall for Ardebil (with cold semi-arid climate) and Birjand (arid climates) were collected for the period from 1977 to 2004. The CanESM2 (Canadian Earth System Model) outputs were used as GCM for simulating of climate change impacts on precipitation pattern. So of CanESM2 outputs (large scale predictors) and measured daily precipitation (local scale predictants) were considered as input and target for downscaling respectively. The Artificial Neural Network (ANN) which widely has been used in climate change researches was selected as downscaling method. Despite of the most of literature have used only efficiency criteria for distinguishing from different approaches in downscaling, this study reveals performance of feature selection methods based on either them or statistical tests. The comparison tests between measured and downscaled rainfall such as assessment criteria, statistics characteristics comparison, contingency table event for wet and dry series diagnostics and Violin plot were used as tools for skill assessment of feature selection approaches.
Results and Discussion: Results showed that although different methods of predictor selection had includes various subsets, predictors such as relative humidity at surface and zonal velocity component at 500-hPa pressure levels in Birjand and mean temperature at 2m, mean sea level pressure and rotation of the air in Ardebil are the most descriptive features which have more relationship with measured daily precipitation. The efficiency criteria of comparing measured and downscaled precipitation indicated that CA method is superior to other in Birjand station and SRA’s results were better than those of other in Ardebil station. Value of RMSE, R and NSE was achieved 1.2 mm/day, 0.55 and 0.25 in Birjand and 1.75 mm/day, 0.14 and 0.013 in Ardebil respectively. The examination of measured and downscaled statistical characteristics reveals that CA has the better influence on downscaling than those of others in Birjand station. In this comparative test most of downscaled statistical components such as mean, median and skewness under CA have more similarity to measured values. But in Ardebil, with cold and arid climate, performance of SRA to downscale was the same as performance of CA to it. Also both SRA and CA were better than ParCA. The skill assessment of different methods to fit measured and downscaled variability by violin plot showed that generally ParCA outperformed other method in Birjand station. The comparison of violin plots, in Ardebil, revealed that no one of predictor selection methods has acceptable accuracy for fitting measured variability. Outcomes of contingency table event showed although all feature selection methods have not remarkable capability for distinguishing from the measured wet and dry series in Ardebil station, performance of ParCA and SRA were acceptable in Birjand station. The values of CSI for ParCA and SRA were calculated 0.25 and 0.22 in Birjand and it shows that more of 20 percent of ParCA and SRA’s diagnostics was correct.
Conclusions: By assessing of results, it can be inferred that generally downscaling of daily rainfall in Birjand station is outperforming Ardebil. In other expression daily downscaling of precipitation in arid climate has better results than cold and arid climate. Also different tests have various results about feature selection methods. In Ardebil, SRA in efficiency criteria test and both SRA and CA in statistics characteristics have better performance than others. But in this region no methods have remarkable performance in violin and dry and wet tests.
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