جستجوی مقالات مرتبط با کلیدواژه « bias correction » در نشریات گروه « جغرافیا »

The outputs of general circulation models (GCMs) usually have a bias compared to observational data, and some corrections must be made before using them to develop future climate scenarios. The bias correction methods are the standard statistical methods for processing the output of climate models. In this research, the effect of five bias correction methods on the projected precipitation of the GFDL-ESM4 model in the Lake Urmia basin has been evaluated. The methods used in this research include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and delta change factor (DC). Statistical metrics such as the correlation coefficient, root mean square error (RMSE) and percentage bias (PBias) have been used to evaluate the accuracy of the corrected data in the period of 1990-2014 compared to the observational data and to choose the best method for correcting the data of future scenarios. research results showed that the delta change method significantly improved the raw estimates after correction; Therefore, this method was used to correct the data of scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5. In addition, the projection of the mean annual precipitation shows a decrease between 2 and 9 percent in SSP1-2.6, between 5 and 17 percent in SSP2-4.5, and between 8 and 26 percent in SSP2-8.5 compared to the observed data.

The Intergovernmental Panel on Climate Change (IPCC) has observed that climate variable changes associated with global warming are affecting different sector of human society. Changes in precipitation and temperature are anticipated as direct driving factors because they are the main factors that impact regional hydrological processes.The general circulation models (GCMs) are the most important tool for predicting the future climate change, which can reproduce important processes about global and continental scale atmosphere and project future climate under the different scenarios.The use of climate model simulations is now widespread, but there's still a risk in using these data, given the existing biases as well as the inability of these models to represent regional topography and land-sea contrast properly due to the coarse resolution of GCMs which makes local climate projection a huge challenge. Teutschbein and Seibert (2012) state that simulations of temperature and precipitation using GCMs often show significant biases due to systematic model errors or discretization and spatial averaging within grid cells, which hampers the use of simulated climate data as direct input data for climate change studies. Bias correction procedures are used to minimize the discrepancy between observed and simulated climate variables on a daily time step so that the corrected simulated climate data match simulations using observed climate data reasonably well. Many bias correction methods, ranging from simple scaling techniques to the rather more sophisticated distribution mapping techniques, have been developed to correct biased GCM outputs.This study aims to select the best model and bias correction method based on different statistical metrics to downscale the daily precipitation and Maximum and Minimum temperature (Tax and Thin) outputs of selected CMIP6 models for Mashhad and Goldmkan synoptic stations, which are located in the Kashaf Rroud basin.

For this study, daily observations of precipitation, minimum temperature and maximum temperature during 1989-2019 of two synoptic stations in the Kashaf Roud basin were used. Over the last few years, the Copernicus database has been of great help to researchers in the field of climate change studies for the preparation and aggregation of climate data, observational data, satellite data, etc. This database has enabled researchers to pre-select models as well as specify the desired time and place. The historical CMIP6 data have been download from the Copernicus Climate Data Store (CDS).First, the precipitation and temperature time series were qualified. RHtests-dlyPrcp and RHtestsV4 packages in the R software environment were then used to test the homogeneity of the precipitation and temperature daily time series.CMhyd (Climate Model data for hydrologic modeling) is a software that has been used to extract and bias-correct data obtained from the selected global climate models using three statistical methods e.g., linear scaling (LS), distribution mapping (DM) and delta change (DC). The bias, RMSE, Pearson, Spearman and Kendall correlation coefficients and the Taylor diagram were used to determine the accuracy of the results of each model and to select the best method to downscale the daily and monthly outputs of GCMs. There are two sets of data in the CMIP6 simulations:2-1. Historical data Historical data of CMIP6 covers the period of 1850-2014, which can be used as a reference period to compare and verify the performance of each GCM.2-2. Scenarios dataSSP scenarios provide different pathways for future climate forcing. They typically cover the period 2100-2006.

In this study, daily precipitation and temperature time series from two synoptic stations, Mashhad and Golmakan, located in the Kashaf Roud basin, were first used as primary quality control. Then the homogeneity of these data was tested. Maximum and minimum temperatures and precipitation in Golmakan were homogeneous. Mashhad was homogeneous for precipitation and minimum temperature time series, but for maximum temperature it had a change point on 09/30/1994, which was homogenized in the series and then used. The large-scale data of precipitation and temperature of three models have been scaled down to the level of the stations using three statistical methods and have been corrected for bias. Root-mean-square error (RMSE), correlation coefficients (Pearson, Spearman and Kendall) and bias (RB) were calculated to determine the accuracy and performance of each model and each exponential downscale method. In a further step, the daily time series of these data were converted to monthly data, then all validation process were done for monthly data in order to make a better and more comprehensive decision based on these results.Among downscale results of rainfall and temperature using LS, DM and DC methods; in all models and at both stations for daily and monthly rain; LS method showed better results. However, the DM method gives better results for the maximum and minimum temperature data (except for the minimum temperature in the ACCESS-CM2 model, where the LS method is slightly better for both Mashhad and Golmakan). With a difference of 1.6 mm in the DM method, the largest bias in the monthly precipitation data for Mashhad is observed in the MIROC6 model.

The results of this research show that LS for precipitation and DM for temperature will have higher accuracy in the Kashaf Roud basin. MRI model performed better for precipitation and maximum temperature using LS method, however ACCESS performed better for minimum temperature in these stations. Exponential down scaling using the DM method gives better results for precipitation in Mashhad for the MRI model and in Goldmkan for the ACCESS model. MRI gives better results at Mashhad and MIRO at Goldmkan. The DM method also simulates more accurate results at both stations of the MRI model for the minimum temperature. Therefore, the MRI model is ranked first and ACCESS and MIRO are ranked second and third, respectively, if we want to select a comprehensive model among these three models that has the best simulations of precipitation and temperature.

This study identifies the impact of climate change on precipitation in watersheds of Tehran water supply in the horizon 2025-2050 under the scenarios of CMIP6. Therefore, first, the changes in precipitation trends in the base period were calculated using the precipitation data of the study area's 33 synoptic and rain gauge stations for the period 1989-2019. Then changes soon based on four models, CanESM5, CNRM-CM6-1, MIROC6, and MRI-ESM2-0, and under two scenarios, SSP2-4.5 and SSP5-8.5 were projected. Due to the large scale of atmospheric general circulation models, two methods of linear scaling (LS) and distribution mapping (DM) were used for downscaling selected GCMs. Finally, the DM method was chosen to produce climatic scenarios due to its higher accuracy after calculating the validation indices of the models. The trend tests showed that in a significant part of the study basin, an increasing trend (with confidence levels of 0.95 and 0.99) in autumn and a decreasing trend in winter are observed during the observation period. In spring, the eastern and northeastern regions show a decreasing trend, and the northern and northwestern regions show an increasing precipitation trend. According to the output of GCM models, spring precipitation under the SSP2-4.5 scenario increases in all stations and decreases in 17 stations according to SSP5-8.5. Changes in summer precipitation are not significant in the present and future conditions, and winter and autumn are somewhat consistent with the changes in the observation period. Thus, in winter, precipitation, according to both scenarios, is less than in the current situation and is more in autumn under the SSP5-8.5 scenario. The effect of climate change on the amount of water in the watersheds also showed that the maximum water volume in the current conditions is related to the Karaj watershed. Between 2025 and 2050, the water content of this watershed increases by 8.9% in the CNRM-CM6-1 model according to the SSP2-4.5 But in MIROC6, MRI-ESM2-0, and CanESM5 models, it decreases by 5.3, 6.3, and 59.6 percent, respectively.

In this research, the performance of CMIP6 general circulation models has been evaluated in the simulation of temperature and precipitation in the catchment area of Lake Urmia. The models used are MPI, CMCC, INM and MRI with a spatial resolution of 100 km. The performance of the models was evaluated before and after the bias correction. To correct the bias of the models, the statistical method of the delta factor change and the data of seven synoptic stations at the basin level have been used. For this purpose, the studied period (1990-2013) was divided into two twelve-year periods for calibration and verification. In this way, the accuracy of the corrected data of the models was evaluated using the delta factor change for the period of 2002-2013 compared to the observational data by using R2, RMSE, NRMSE indicators and Taylor and scatter diagrams. The results showed that for the entire basin, there is a strong linear relationship between the corrected data of models and station observations for temperature and a weak linear relationship for precipitation. The RMSE and NRMSE indices indicate the high accuracy of the models in simulating the monthly temperature and relatively weaker accuracy in simulating the monthly rainfall of the basin, and among the models, CMCC had the highest error. The spatial distribution maps of NRMSE for the total monthly averages showed that all four models have simulated temperature with high accuracy, but the simulation of precipitation in some areas of the basin does not have acceptable accuracy. Therefore, the performance of the studied models was evaluated as good in temperature simulation and poor in precipitation simulation, especially in the CMCC model.

This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these data was performed using 5 quantitative mapping methods based on observational data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance.

Bias correction usually used for the outputs of the climatic model before use as an input of environmental models in the climate change effects studies. In this research, GCM outputs obtained from ESGF dataset with the RcgCM4-4 climate model in the South Asia CORDEX domain with horizontal resolution about 50 km. Precipitation, maximum and minimum temperature data of 41 synoptic stations with the closest distance to model cells in Iran domain obtained from Iran Meteorological Organization. Then the accuracy of the outputs of the cells corresponding compared to the observational data with correlation and normalized standard deviation methods evaluated by Taylor diagram. Then, the model bias with the least error corrected for the precipitation outputs with fitQmapRQUANT method and for the maximum and minimum temperature outputs with the linear scanning bias correction. Results showed that the bias correction methods used for the temperature outputs improved the error of data but for precipitation outputs, it was not effective due to the large difference between observation and model data. The model to estimating the maximum temperature in these regions had less bias than in those at low latitudes. The CCCma model's monthly minimum temperature outputs overestimated this variable, especially in hot seasons, compared to station data in southern Iran. At most stations located at high latitudes, this variable is corrected or underestimated. The bias correction of the model outputs for this variable corrected the biases in the cells corresponding to the observation stations.