Document Type : Research Paper
Department of Water Science and Engineering, Arak University, Arak, Iran
water resources-Arak university
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.
Material and Methods
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 was used to extract monthly precipitation and runoff. Then, for each pixel, precipitation (four products) and runoff (four products) datasets was 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 characteristics drought, theory run was used and extent drought 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 also increased. 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.