نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانش آموخته کارشناسی ارشد، مهندسی آب،دانشگاه رازی،کرمانشاه ، ایران
2 گروه مهندسی آب دانشکده کشاورزی، دانشگاه رازی کرمانشاه، ایران
3 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Today, due to global warming and decreasing precipitation, long-term precipitation forecasts are essential for planning and proper use of available water resources. Accordingly, precipitation forecasts for the period 2020 to 2039 were extracted using the KIOSTESM and CANESM5 models from the IPCC's sixth report at 13 synoptic stations in Kermanshah province. Given that synoptic stations are not located in all locations and it is not possible to estimate precipitation for some parts of the region, in order to determine the best satellite for each synoptic station, data from CHRIPS, ERA5, PERSIAN_CDR, GPM, GSM, TRMM, TERRA satellites were used for the period 2000 to 2019 for these 13 stations. The results showed that for the stations of Islamabad, Harsin, Gilan Gharb, Javanroud, Kermanshah, Ravansar, Sonqor and Sarpol Zahab, the TERRA satellite has the best estimate, for the stations of Somar and Tazeabad the ERA5 dataset, and for Qasr Shirin, Kangavar and Sararood the TRMM satellite has the best estimate. The results showed that the CANESM model has calculated the precipitation less than the observations in most of the stations. The largest difference between this model and the observational and satellite data is 52 and 50 percent more than the total precipitation for Kangavar, respectively, and the smallest difference is 12 and 9 percent less for the Sararood station, respectively. The results showed that without having meteorological station data and relying only on the selected satellite data in this study, appropriate climate forecasts can be estimated for each region of Kermanshah province.
کلیدواژهها [English]
Today, due to global warming and decreasing rainfall, long-term precipitation forecasting is essential for planning and proper use of available water resources. Also, considering that synoptic stations are not available in all parts of the province, satellite precipitation data has been of interest to engineers and hydrological scientists.
In this study, precipitation data from CHRIPS, ERA5, PERSIAN_CDR, GPM, GSM, TRMM, TERRA satellites were extracted in the Google Earth Engine environment and compared with data from 13 synoptic stations in Kermanshah province on a monthly scale. The best satellite was selected in each region of Kermanshah province, and then precipitation in the future period was compared with observational data and the selected satellite using the delta change coefficient downscaling method with bias correction.
Kermanshah province, which is located between latitudes 33 and 35 degrees north and longitudes 45 and 48 degrees east in western Iran. This province with an area of 24,640 square kilometers has four watersheds: Sirvan, Abrizgah or Gaumasi, Ghare Su and Alvand.
The measurements used to evaluate the accuracy of the models include the correlation coefficient (CC), mean bias error (MBE), root mean square error (RMSE) and coefficient of variation of root mean square error (CV-RMSE), mean absolute error (MAE) and Pearson correlation coefficient (PCC). High CC and PCC and low values of MAE, CV-RMSE and RMSE have high accuracy in the estimates. Probability indices: false alarm ratio (FAR), probability of detection (POD), critical success index (CSI) and relative bias (Rbias). POD, FAR, CSI, RBias are one, zero, one and zero for the best case, respectively.
The IPCC prepares reports every few years in which changes in climate variables are presented by GCM models as numerical models that represent physical processes of the atmosphere in the base and future periods. The latest report of this center, the sixth report CMIP6, is better than the fifth report and the CMIP5 scenarios. This report is based on socio-economic scenarios and a combination of energy forcing at the surface. In this study, SSP5-8.5 was used, which is a combination of socio-economic development 5 and radiative forcing 8.5, which is the worst case compared to other scenarios.
TERRA satellite precipitation for the stations of Sonqor, Islamabad, Harsin, Gilan Gharb, Sarpol Zahab, Javanroud, Ravansar and Kermanshah, TRMM satellite for Kangavar, Qasr-e Shirin and Sararoud stations, and ERA5 satellite for Taze Abad and Somar stations have more appropriate estimates. In this study, the CANESM5 climate model predicted future rainfall and used observed and selected satellite data for downscaling. The results showed that the largest difference of this model for Kangavar is a 52% increase, which is reduced by observational data and is 50% higher. The same conditions have the smallest difference with the average rainfall of the Sar-Aroud station, with a decrease of 12 and 9%. Also, for most stations, the average total rainfall predicted using satellite data is close to the observed data, except for Somar and Taze Abad stations, where the difference between the two is approximately 6%. The largest decrease is related to the Javan-Roud station, which has a decrease of 30% in both observational and satellite data.
The results of comparing the precipitation data of CHRIPS, ERA5, PERSIAN_CDR, GPM, GSM, TRMM, TERRA satellites with the data of 13 synoptic stations of Kermanshah in the statistical period from 2000 to 2020 on a monthly scale showed that, according to the criteria of long-term annual averaging and the criteria of total error of the stations. In Islamabad, West Gilan, Harsin, Javanrud, Sonqor, Ravansar and Sarpol Zahab and Kermanshah, the TERRA satellite had a better estimate than other satellites. The TRMM satellite has the best estimate for the Qasr-e-Shirin, Sarrud and Kangavar stations. Based on the observation and forecast of precipitation data in the central parts of the north and northwestern parts of the province and also in the south, it was more in stations such as Javanrud and Ravansar than in other parts of the province. The lowest amount was in the stations of Harsin, Qasr-e-Shirin and Somar.According to the results of climate change with the SSP8.5 scenario, among the CANESM5 and KIOSTESM models, the CANESM5 model has calculated less precipitation than the observations at most stations. According to the results without having climate stations, predictions made using satellite data can be used instead of predictions using observations.
Conceptualization, methodology, validation, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, foroozanpayfeshordeh, supervision, project administration, Maryam Hafezparast Mavaddat, Counseling, Seyed Ehsan Fatemi.
Data available on request from the authors.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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