Showing 7 results for Drought Index
S. Morid, S.h. Paymozd,
Volume 11, Issue 42 (1-2008)
Abstract
Application of meteorological indicators has extensive use in drought monitoring. However, hydrological indicators can also play an effective role in this task. In this research, one of the rare approaches in drought monitoring with hydrological indicators namely Chang method has been applied and assessed for the Tehran basin using daily time step. The results have been compared with the unique meteorological drought index, EDI (effective drought index) and show the capabilities of the hydrological method and its more sensivity to water resources deficit. For instance, application of these procedures for the 1998 to 2000 drought spell in Tehran province revealed that Change method declares 31.1 % of times in very severe drought whereas it is 3.7 in EDI. Because of applying different indicators (e.g. reservoir and ground storage), a combination of both procedures is an ideal approach for drought monitoring in which the water inputs to the system as well as storage and consumptions are considered. The applied methodology makes it Possible to distinguish droughts due to rainfall deficit from the ones, which are resulted from water resources miss management.
E. Rahmani, A. Khalili, A. Liaghat,
Volume 12, Issue 44 (7-2008)
Abstract
The growing season climatic parameters, especially rainfall, play the main role to predict the yield production. Therefore, the main objective of this research was to find out some possible relations among meteorology parameters and drought indexes with the yield using classical statistical methods. To achieve the objective, ten meteorological parameters and twelve drought indexes were evaluated in terms of normality and their mutual influences. Then the correlation analysis between the barley yield and the climatic parameters and drought indexes was performed. The results of this study showed that among the drought indexes, Nguyen Index, Transeau Index, Rainfall Anomaly Index and Standardized Precipitation Index (SPI24) are more effective for prediction of barely yield. It was also found that the multivariate regression is better than the univariate regression models. Finally, all the obtained regression models were ranked based on statistical indexes(R,RMSE and MBE). This study showed that the multivariate regression model including wind speed, sunshine, temperature summation more than 10, precipitation and Nguyen index is the best model for prediction yield production in Miane. Average wind speed and Nguyen index were recognized to be the most effective parameters for yield production in the model.
H. Nazaripour, Z. Karimi, M. Sedaghat,
Volume 20, Issue 75 (5-2016)
Abstract
Drought is a climatic anomaly that associates with a significant decrease (lack) of precipitation and water resources availability, which spreads on vast temporal and spatial scales, and significantly affects various aspects of life and environment. One of the most common methods of drought assessing and monitoring is calculating drought indices (DIs). Drought areal and temporal extent and its severity are determined by these indices. In this study, an aggregate drought index (Hydro-Meteorological) has been developed for the assessment of hydrological and meteorological droughts in Sarbaz river basin located in southeastern of Iran. The Aggregate Drought Index (ADI) comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. In this case, monthly values of Stream flow Drought Index (SDI) and Standardized Precipitation Index (SPI) indicators were used for four similar reference periods with principle component analysis and aggregate hydro-meteorological index was defined based on its first component. The study time span was set between 1981-82 to 2010-11, which begins of October in Iran. Results based on the aggregate drought index (ADI) revealed that a long period of hydro-meteorological drought occurred from 1999-2000 to 2005/06 in southeast of Iran, in which, 2003/04 water year has been extremely a drought year. The ADI methodology provides a clear, objective approach for describing the intensity of drought. This index is appropriately able to represent the behavior of Hydro-Meteorological droughts and recommended as an integrated index for assessing and monitoring of regional droughts. Finally, different states of hydro-meteorological drought have been extracted based on conventional regional thresholds, and have been modeled by Markov chain. This made the estimation of drought state transition frequency possible, and made the prediction of next drought state time more real. State transition frequency matrices, are the main instruments for predicting drought states in real time. Results of validation tests and conforming the predicted results with real data indicate that predicting hydrological drought state transitions in the study area using Markov chain method is valid.
S. Pourhossein, S. Soltani,
Volume 22, Issue 2 (9-2018)
Abstract
Bhalme & Mooley Drought index is one of common indices used in drought studies. Due to the fact that drought indices can have different sensitivities to different region conditions and the length of data recorded, 62 synoptic and climatological stations were selected within a homogonous region to study this index advantages and to assess the effect of climate, precipitation regime, and data record on the index. The best results were found for the humid climate. Also, this index had acceptable results for semi- mediterranean regimes regarding all different time scales,; however the situation was different for Mediterranean regimes, showing the best results for the time scales simultaneous with the precipitation period. From the data record point of view, the best results were estimated during the first 31- years of the common period which has correspondence with the results of the 36-year period.
S. Ekhtiary Khajeh, F. Negahban, Y. Dinpashoh,
Volume 23, Issue 2 (9-2019)
Abstract
In this study, drought characteristics of Arak, Bandar Anzali, Tabriz, Tehran, Rasht, Zahedan, Shiraz and Kerman stations during the statistical period of 1956 to 2015 were studied by Reconnaissance Drought Index (RDI) and Standardized Precipitation Index. Precipitation and temperature data were needed to calculate RDI. Precipitation data was also required to estimate SPI. In this study, Drinc software was used to calculate RDI, SPI and potential evapotranspiration (PET). The software calculated PET by the Thornthwaite method. One of the main challenges in drought monitoring is to determine the indicator that has a high reliability based on its monitoring purpose. Therefore, in this research, two methods used for selecting the appropriate index based on the minimum rainfall and normal distribution were evaluated. The results of the evaluation of the minimum rainfall method for selecting the appropriate index showed that most drought indices with the occurrence of minimum rainfall level indicated severe or very severe drought situations; in most cases, it could not lead to selecting an exact and unique index. Based on the results of the normal distribution method for the stations of Arak, Tabriz, Rasht, Zahedan, Shiraz and Kerman, SPI index, and for the stations of Bandar Anzali and Tehran, RDI index were selected as the most appropriate ones.
F. Saniesales, S. Soltani, R. Modarres,
Volume 25, Issue 2 (9-2021)
Abstract
Several indices are used for drought identification and quantification. In this paper, the new Standardized Palmer Drought index (SPDI) was introduced and then the drought condition of Chaharmahal-Va-Bakhtiari Province was studied using this index. For this study, 11 synoptic, climatology, and evaporation meteorology stations were selected. Essential information in this investigation includes monthly temperature, monthly precipitation, and soil moisture measurement. To estimate SPDI, moisture departure, was first calculated on a monthly time scale. Then, converted to cumulative moisture departure values in different time scales including 3, 6, 9, 12, and 24 months. The best statistical distribution (GEV) was then fitted to cumulative departure. These values were then standardized to have the SPDI. The results showed that, as soil moisture affects SPDI estimation, it will be more valid for analyzing and monitoring drought conditions, especially for agricultural drought. Also, the results showed that 2000, 2001, and 2008 years were the driest time in this Province from 1988 to 2012. Moreover, drought frequency was found out in the western half of the Province more than in the other parts.
S. Afshari, H. Yazdian, A. Rezaei,
Volume 27, Issue 3 (12-2023)
Abstract
Awareness of the types of vegetation changes and human activities in different parts has particular importance as basic information for different planning. It is very difficult and expensive to collect information about the continuous changes in vegetation cover by conventional methods. Therefore, the use of new technologies such as remote sensing is very beneficial. The objective of the present research was to introduce the appropriate vegetation index and determine the vegetation cover of the Abshar network. NDVI, EVI, SAVI, and MSAVI vegetation indices were calculated from 2000 to 2021 every year and monthly in the Google Earth Engine system using Landsat 7 satellite images of the ETM+ sensor. Also, the SPI drought index was calculated using the precipitation statistics of Kohrang station in Excel software. The results of the comparison of four indices showed the superiority and higher performance of NDVI compared to the other three indices for detecting vegetation changes. Then, vegetation changes were calculated. The results showed that the trend of agricultural development in the Abshar network is downward and has a direct relationship with precipitation and the SPI drought index. Also, the results indicated that the SPI drought index was equal to -1.73in 2008, which showed a severe drought in the region. Comparing these results with the vegetation area showed that the vegetation area was 35721 hectares in this year and the year after the drought (2009), the vegetation area was 22950 hectares. Therefore, there was a decrease in precipitation and a sharp decrease in the SPI index in 2008, which led to a sharp decrease of 35% in the vegetation area in 2009.
