نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم و مهندسی آب، دانشکده کشاورزی و محیط زیست، دانشگاه اراک، اراک، ایران
2 دانشیار، دانشگاه اراک، تخصص: مدیریت منابع آب، بهینه سازی
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Recently, with the advancement of technology, extensive facilities have been provided for monitoring climate data with different resolution. Therefore, the aim of this research is to investigate the combination of precipitation databases and simultaneous monitoring of meteorological and hydrological droughts on a monthly time scale using the Combined Drought Index (CDI) at sub basins of Iran. To this end, observational data (100 synoptic stations on a daily scale) and global precipitation and runoff databases including ERA5, MRRRA2, GRUN, GLDAS and TERRA (with different spatial resolution on a monthly scale) were collected and extracted during the period of 1987-2019. Then, based on the aridity index, the climatic classification of the stations and sub basins has been done and the entropy weight method (EW) was employed to combine databases and indices. The accuracy of the datasets was assessed based on Kling Gupta (KGE) and Standardized Mean Square Error (NRMSE) metrics. The results showed that the combination of precipitation databases in the Hyper-arid, Arid, Semi-arid and Humid climates reduced the error index by 32, 10, 24 and 26%, respectively, compared to individual databases. In the 3-month scale, the sub basin of Sefidroud, Lake Namak, and Talesh, and in the 12-month scale, the sub basin of Lake Urmia, Lake Namak, Sefid Rud, Hamon Jazmourian, Hamon Mashkel, South Baluchistan, and parts of Aras River have faced with drought with more severity, duration, and peak values of drought. It should be said that, on average, in both scales, the middle part of the country has less drought severity and duration. The extent of drought in the river basins of Hyper-arid, Arid and Semi-arid climates in the scale of 3 and 12 months on average is 45 and 53%, and 70 and 40% in Humid climates. Based on these findings, it can be concluded that the combining precipitation databases enhances the accuracy compared to using global single databases. Additionally, drought in the humid climate shows higher intensity, duration, and extent in the short-term scale compared to the long-term scale.
کلیدواژهها [English]
Evaluation of drought characteristics based on combined global precipitation and runoff products datasets across Iran's sub basins
EXTENDED ABSTRACT
Due to the high cost of installation and maintaining gauges, specific locations worldwide may have limited or no weather stations, and the available records might be incomplete or cover shorter time periods. To address the data scarcity in developing regions, various institutions have developed alternative sources of global gridded datasets. However, selecting an appropriate database and index for drought monitoring poses challenges and underscores the significance of combining databases and indices. The objective of this research is to assess global products, their composite nature, and their effectiveness in monitoring drought characteristics such as severity, duration, peak, and extent, utilizing the compound drought index.
For this purpose, the daily meteorological data were collected from 100 synoptic stations around the Iran during 1987-2019. Thiessen polygons method was used to extend the monthly time series of precipitation and potential evapotranspiration in synoptic stations to sub basin scale (to classify them based on Aridity index). Then, the five products including ERA5، MRRRA2، GRUN, GLDAS، TERRA with 0.5×0.5 spatial resolution were used to extract monthly precipitation and runoff. Then, for each pixel, precipitation (four products) and runoff (four products) datasets were composed. In the other words, one data as representative of precipitation and runoff was obtained that the non-parametric standardized precipitation and runoff index were calculated based on them. Then, the composite drought index (CDI) was developed and characterized drought at the short and long time scale (3 and 12-month scale). The Entropy weight method was used to compose datasets and indices. To extract drought characteristics, theory run was used and the drought extent was obtained based on the number of pixel located in each sub basin. To assess the performance of products, across Iran's sub basins on monthly scale, Kling-Gupta efficiency (KGE) and Normalized Root Mean Square Error (NRMSE), of each product were calculated at the sub basin scale.
The results indicate that the accuracy of each dataset varied over years and climates. However, both ERA5 and TERRA datasets exhibited high performance in all climates. Furthermore, combining multiple datasets demonstrated improved performance across all climates, particularly in the Hyper-arid climate. As the time scale increased, drought characteristics such as severity and duration increased, too. In the Hyper-arid, Arid, and Semi-arid climates, the severity and duration were approximately 1.7, while in the humid climate, it was around 0.8 (long-term to short-term timescale). The peak of drought did not show significant changes. Mild and extreme drought levels accounted for approximately 14% of drought occurrences during the study period across all climates. The variation in drought extent indicated an average increase in the Hyper-arid, Arid, and Semi-arid climates (from 45% to 53%) with an increase in the time scale.
In summary, this study presented an evaluation of both individual and combined precipitation datasets. The research findings demonstrate the applicability of utilizing precipitation data from appropriate global products, taking into account temporal and spatial considerations, and characterizing drought using a composite drought index. Based on the results of this study, it is recommended to utilize these datasets, their combination, and the composite index for drought monitoring purposes.
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