جستجوی مقالات مرتبط با کلیدواژه « ERA Interim » در نشریات گروه « آب و خاک »

In recent decades, the increasing development of satellite technologies has provided access to climate data around the world with different spatial and temporal resolution. Therefore, in the present study, the goal of evaluating ECMWF datasets models is to predict climate data and drought monitoring in Qarechai basin of Markazi province. To this end, first monthly precipitation and temperature data of synoptic stations of Hamedan, Qom and Shazand in three provinces during the period of 1987-2018 were collected. Then, the mentioned data with spatial resolution of 0.125 * 0.125 degrees during 1979-2020 were extracted from the reanalysis models including ERA-Interim and ERA5 of ECMWF datasets. Statistics criteria's such as coefficient of determination (R2), nash-sutcliffe (NS), normalized square root mean square error (NRMSE) and mean oblique error (MBE) and contingency table indices consisting of POD, FAR and CSI were used to compare the data of reanalysis models with observational data. The results showed that ERA5 data were more consistent with observational data than ERA-Interim data. As the values of correlation coefficient in most areas above 0.5, mean square error in 70% of areas is very low and mean oblique error in most areas is positive and small. The values of the agreement table indices also confirm the greater compatibility of the ERA5 model.   Afterward, based on data of the selected model and observational, SPEI and SPI drought indices in selected stations were calculated. The results showed that SPEI index had higher correlation and less error with SPI than SPI. Finally, the trend based on the selected index showed that the severity of drought in the western region compared to other regions, has an increasing trend at the level of 5%.

In this study, the temporal and spatial variation of snow depth over the mountainous region of Zagros, in the western Iran, for the period 1979–2010 was investigated for the cold season when the probability of snow occurrences was high. For this purpose, daily gridded snow depth data relative to Era-Interim/land were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) and used for spatiotemporal analysis of snow in the region. Furthermore, monthly maximum, minimum and mean air temperature relative to the weather stations distributed over the region were also used to investigate the relationship between snow depth and air temperature variability in the region. In each grid point, the rate of temporal changes in the snow depth was estimated using the Sen’s slope estimator, while the modified Mann-Kendall Test was applied to assess if the change identified was statistically significant. The results showed that in almost all of the studied months, especially February and March, the snow depth was significantly reduced in the region, which was statistically significant at 5% significant level. Unlike the observed statistically significant decreasing trend in the depth snow in the region, a significant increase in the maximum, minimum and average temperature was observed for all the studied months and the stations. The result suggested that the observed decrease in the snow depth in the region was related to the increasing trend in the temperature during the study period, which could be attributed to the global warming and climate change.