عنوان مقاله [English]
نویسنده [English]چکیده [English]
The lack of reliable and updated precipitation datasets is the most important limitation in the study of many climatological and hydrological subjects, including climate change and temporal variability of precipitation in many data sparse areas around the globe. This is particularly valid for Iran where vast areas of central-eastern country that host the Iranian deserts, suffer from an inadequate network of rain-gage stations, required for climatological studies. The highlands of the mountainous regions of western and northern Iran have the same problem and limited representative stations are available for high elevation areas of these regions. One of solution to overcome this obstacle is to use available gridded precipitation datasets that have proved their representativeness for many different parts of the world. Among many available precipitation datasets are the Global Precipitation Climatology Center (GPCC) and theTropical Rainfall Measuring Mission (TRMM) that have been widely used in many researches, indicating their accurate estimation of precipitation values and intera-annual variation for the regions studied. The GPCC is a gage based dataset that is routinely creating through interpolation of worldwide precipitation stations combined with satellite records, whereas the TRMM is a purely remote sensed data developed by joint collaboration between NASA and the Japan Aerospace Exploration Agency (JAXA). The representativeness and performance of the GPCC and TRMM-3B43V7 precipitation datasets in estimating precipitation amounts at the locations of 46 Iranian synoptic stations distributed across the country is herein examined. Spatial resolutions of TRMM-3B43V7 and GPCC datasets used in this study are respectively 0.25 × 0.25 and 0.5 × 0.5 latitude and longitude. For each station, the closest grid point of each of the datasets to the station coordinates were chosen for statistically comparison analysis. To evaluate the performance of these datasets in comparison with the observed precipitation records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient, RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performances of the datasets were also graphically represented through scatter plots of the established regression between the observation and each of the two used datasets. The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease revealing spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. The results revealed that both GPCC and TRMM-3B43V7 perform well in majority of the studied stations with strong correlation coefficients. However, it was found that the TRMM-3B43V7 underestimates precipitation in some stations located in the coastal areas of the Caspian Sea as well as in some stations along the Persian Gulf and the Oman seas, indicating that TRMM-3B43V7 is somewhat inefficient in adequately estimating precipitation in the coastal areas; which is very likely due to being unable to remove the effect of sea atmosphere interaction in stations nearby the seas. Contrarily, in some locations mostly situated in northwestern and northeastern mountainous areas of the country the TRMM-3B43V7 moderately over estimates the observed precipitation. Similarly, the GPCC well estimates precipitation in almost all stations with very high correlation coefficient and Nash–Sutcliffe model efficiency coefficient. Similar to TRMM-3B43V7, again it was found that the GPCC underestimates precipitation in most stations located along the coastal areas of the Caspian Sea. As for TRMM-3B43V7, the over-estimations of GPCC are mostly observed in northwestern Iran which is very likely due to not incorporating enough stations from high elevation areas of western Iran by the GPCC. On the whole, the results indicate that both datasets perform well in most locations of Iran and can be confidentially used in climatological and hydrological studies with or without the observation data. The results also indicate that the GPCC perform better in areas that share a denser network of stations with GPCC and vice versa. However, the very good results achieved with TRMM-3B43V7 that are completely independent from the observation indicates a promising future in having much improved remotely sensed precipitation records that well match the observed precipitation in very remote areas having no rain gages.