عنوان مقاله [English]
Introduction: Drought is a creeping and gradual phenomenon that can cause irreparable damage in many fields such as agriculture, food security, water resources management and the economy. It is essential to accessing a tool that can measure agricultural and hydrological droughts based on the dynamics of the relationship between land surface-atmosphere -not necessarily precipitation and air temperature- and provide early awareness for managerial decision-making. What makes drought monitoring important to us is the impacts on the agricultural sector (agricultural productivity, access to food security, agricultural and livestock insurance), water supply management and economic and social impacts. The concept of evaporative demand drought index (EDDI) reflects thirst for the atmosphere and is easily and realistically available in near-real-time based on physical climatic drivers of air temperature, wind speed, solar radiation and humidity, providing comprehensive information on drought dynamics. The use of an indicator that can indicate the dynamics of drought in the shortest possible time will help to make managerial decisions and different levels of policy making to announce early operational warnings in the field of agriculture and reduce the social and economic consequences of this phenomenon. In this regard, there is a need for gridded data that can provide the required data set and compensate for non-uniform network data gaps or satellite data limitations. near-real-time networked data such as ERA-Interim is also useful in regional and extensive drought monitoring.
Material and methods: In this study, the ERA-Interim reanalysis data from ECMWF database was used to estimate the evaporative demand drought index in different climatic conditions of Iran and its ability in drought monitoring was investigated. Using probabilistic methods, the ASCI-PM method to estimate atmospheric evaporation demand, the EDDI index was calculated as the index of hydrological drought in Iran in different time scales during 1979- 2017. Also, the EDDI index was evaluated against the SPI and SPEI common drought indices.
Result and discussion: The results of evaporation demand estimates in the country show that seasonal variation and climate variability are factors that change the rate of evaporation demand. As a result of the interaction of the governing climatic factors, different climatic zones are created throughout the country and each region experiences different evaporation rates throughout the year. The EDDI index compensates for the gap between the theory of drought and operational drought management in determining and monitoring persistent drought as soon as possible between the occurrence of the phenomenon and the available data available. Significant correlation coefficients at monthly, seasonal and annual scales between EDDI and SPEI index indicate the important role of evapotranspiration in drought monitoring at arid and semi-arid regions so can offset the weakness of SPI in low rainfall areas. It has the ability to monitor short, medium and long term droughts earlier than other common indices, such as SPI and SPEI. The EDDI is capable of reporting a variety of persistent droughts without the need for precipitation data. Rapid response to environmental drying and humidification, processes caused by interactions between the atmosphere and the Earth's surface, making this index more flexible and advanced than other common indices. The longer the cumulative period of drought, the greater the time the indicator progresses.
Conclusion: The EDDI indicator is an easy tool for operational early warning, fire hazards, seasonal to seasonal drought prediction and long-term hydrological drought Monitoring at any time scale (e.g. seasonally). It can also be used to predict longer periods (annual or multi-year), so compensates the gap between sub-seasonal to seasonal forecasts. An important advantage of using networked data in calculating the EDDI index is that applicable at all times of the year - on cloudy days or for areas with snow cover, to complete the data due to satellite transit times and Delay in data access - no restrictions. Since atmospheric evaporation demand in the EDDI index is considered to be the cause of the drought, when combined with satellite data, EDDI and ESI (Green Water Index) combine to show real drought stress. Give. It is also recommended that MODIS, LANDSAT and ALEXI products be used to evaluate transpiration values and compare the EDDI index with satellite data and compare the results. The EDDI index is capable of decomposing to atmospheric governing factors such as radiation and advection components. By sensitivity analysis, it is possible to determine the main governing factors such as temperature, wind, short wavelength radiation and specific humidity on drought in each region, Determining and provided additional understanding of the dynamics and assessment of drought.