Zahedeh Heidarizadi; Majid Ownegh; chooghibiram komaki
Volume 29, Issue 4 , January 2023, , Pages 542-561
Abstract
Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In ...
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Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation.
Morteza Abtahi; Abdollah Seif; Mohammad Khosroshahi
Volume 21, Issue 1 , June 2014, , Pages 1-12
Abstract
Temperature and precipitation are basic constituent components of the climate of a region. For this reason, the assessment of present and future trends of these elements have been considered by different scientists such as natural resources or environmental experts. In this study, Namak lake basin was ...
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Temperature and precipitation are basic constituent components of the climate of a region. For this reason, the assessment of present and future trends of these elements have been considered by different scientists such as natural resources or environmental experts. In this study, Namak lake basin was divided in to six sub-basins and then monthly precipitation and temperature data were collected and reconstructed from 1956 to 2005. Average precipitation, maximum and minimum temperatures of sextuple sub-basins of the lake were calculated using Thiessen method in Arch Map software. In order to evaluate the precipitation trend, Standard Precipitation Index (SPI) was used. The trend of climatic factors (temperature and precipitation) was studied by linear regression and Mann-Kendall test. No significant trend was observed in standardized precipitation of the Namak Lake and in its sub-basins except in the sub-basin of Arak in which the trend of precipitation change was decreasing and significant. According to the study of monthly precipitation, strong droughts have been observed in sub-basins of Arak, Roodshoor and Karaj during the last 50 years. The trends of maximum and minimum temperature change were increasing and significant in most basins. Increasing the temperature of the Namak lake basin may be caused by several factors, including increasing greenhouse gases especially in big cities such as Tehran, Qom, Arak, Kashan, Hamadan, and Qazvin.