1. The relevance of mechanisms forced by mountain orography for the desiccation of Lake Urmia
(Validation of ERA5 Dataset for monitoring extreme droughts in Urmia lake basin)
Maral Habibi1; Wolfgang Schöner1 , Iman Babaeian2
1Department of Geography and Regional Science, University of Graz, Austria,
2Climate Research Institute, Mashhad, Iran
2019
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Results
Conclusion
Abstract
Due to the fact that there is not long-term observational data in most mountainous areas like Urmia lake basin which is located in northwest of Iran, The necessity of using gridded data like ERA5 dataset instead of observation data should be taken into account, In this paper we evaluated SPI from ERA-5 dataset compared with observational data between period over the study area.
With the SPI obtained from gridded data, we figured out the year 1997 is a dry year (figure2)while it has not observed as a dry year base on observational data, and the fact is,2017 is more dry year than 1997.
It seems that gridded data have more validity to calculate SPI in more dry years compares to more wet years.
By comparing the observational calculated SPI and ERA5 calculated SPI in Urmia station, we concluded that the ERA5 SPI shows more severe droughts in the period of 25 years in this station, While in Tabriz station the observational SPI indicates more severe droughts, overall there is an acceptable correlation between observational SPI and ERA5 SPI (figure7).
(Table 3)shows calculated bias for both stations.
Since for calculating SPI, the model's ability to simulate fluctuations of data is more important than the data itself, the existence of bias in gridded data is less important than correlation. We should mention that other datasets such as APHRODITE-2, AgMERRA should be reviewed for future research over the study area.
Grid ERA5 SPI for the basin
Comparison diagram for entire basin
Figure2: comparison drought monitoring maps with gridded data
Introduction
By comparing the SPI which obtained from the entire basin with two observational and gridded data in common 25-year period, we have found that in most of the years, approximately the values of calculated SPI are the same(figure3).
The purpose of the current research is to investigate the capability of gridded data ERA5 in order to monitoring drought and finally applying this data to calculate SMRI which is the standardized melt and rainfall Index.
Figure3:Comparison between observed and ERA5 SPI
SPI for accumulation period
Figure 1: Topographic map of Urmia lake basin
SPI for 3,6 and 12 months
We calculated 3,6,12 months SPI for the Urmia station between 1993 to 2017 with the observational dataset, in figure 4, It is noticeably obvious that SPI for the periods of 12 months represents more severe droughts, and the calculated SPI for 3 months periods represents normal periods.(figure4)
the calculation SPI base on ERA5 dataset shows completely different results ,the SPI for the periods of 12 months represents normal periods and SPI for 3 months periods represents more severe droughts.(figure5),
This approach is motivated by the high percentage (ca. 65%) of mountains (figure1) as part of the Urmia catchment and consequently goes clearly beyond previous studies.
Figure4:Urmia station SPI time series for the 3-6 and 12-month time scales with observation data
Data and method
Standardized Precipitation Index (SPI) is a probability (ie: statistical) index that gives a representation of abnormal wetness and dryness.(table1).
ERA-5 data: ERA-5 represents a fifth-generation reanalysis dataset of (ECMWF). In this study monthly ERA-5 total precipitation data is used.
Observational data:
Hourly precipitation
Data from 25 synoptic
Stations for period of
25 years.
Figure7:Comparative plot of SPI values for Urmia and Tabriz station between 1993 to 2018
Station
AVG SPI_ERA5
AVG SPI_OBS
Bias
Bias%
Urmia
-18%
Tabriz
-5%
Table3:bias values of calculated SPI between observational and ERA5 dataset
Figure5:Urmia station SPI time series for the 3-6 and 12-month time scales with observation data
References
McKee, T. B., N. J. Doesken, and J. Kliest, 1993: The relationship of drought frequency and duration to time scales. Proc. Eighth Conf. of Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–184.
Hejabi.s,Mendicino.G et al,2019:Climate conditions and drought assessment with the Palmer Drought Severity Index in Iran: evaluation of CORDEX South Asia climate projections (2070–2099)
Comparing observation and Era5 SPI for 2 stations
Two sample stations (Tabriz and Urmia) were selected (figure6) then by using the mean of neighboring grid points, we created two hypothetical stations and we calculated the SPI for them, the result shows that there is a remarkable resemblance between gridded data SPI and observational SPI. (table2)
SPI
SPI category
Very wet
1.50 to 1.99
Moderately wet
1.00 to 1.49
Near normal
0.99 to -0.99
Moderately dry
1.00 to -1.49
Severely dry
to -1.99
Extremely dry
and less
Acknowledgements
We would like to thanks all the Data providers. Data were provided by Iran Meteorological Organization and the European Centre for Medium-Range Weather Forecasts (ECMWF).
Station
Spi_97(era5)
Spi_97(obs)
Spi_2017(era5)
Spi_2017(obs)
Urmia
Tabriz
Table1:Classification of a drought expressed with the SPI Drought category
Figure6: location of Urmia and Tabriz station with statistic observational and ERA5 SPI
Table2:Comparing observational and Era5 SPI for Tabriz and Urmia