Aliakbar Noroozi; Morteza Miri; Davoud Nikkami; Tayeb Razi; Amir Sarreshtehdari; Ziaedin Shoaei
Abstract
The purpose of this study was to investigate the oak forest dieback with respect to drought occurrence, soil moisture changes and dust occurrences factors in Ilam, Kermanshah, Lorestan and Chaharmahal and Bakhtiari provinces of Iran. The data used were field surveys collected through GPS, MODIS satellite ...
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The purpose of this study was to investigate the oak forest dieback with respect to drought occurrence, soil moisture changes and dust occurrences factors in Ilam, Kermanshah, Lorestan and Chaharmahal and Bakhtiari provinces of Iran. The data used were field surveys collected through GPS, MODIS satellite imagery, GLDAS Soil Moisture, dust and precipitation data of the meteorological stations of the provinces during an 18-years period (2000-2017). The results of the study of greenness values of the forests in the study area showed that the first decline occurred in 2005 and repeated more severely with much wider spatial extent in 2008. Investigation of the relationship between drought events and its spatial and temporal variations with the changes in forests greenness of the study area showed that the reduction in precipitation amount is one of the main reasons for forest greenness reduction in the study area. The increased frequency of periods of rainfall shortage and drought duration, especially at 9 and 12-month time scales, showed a significant relationship between drought occurrences and forests greenness in the study area. The results indicated that by decreasing precipitation drought periods increased, soil moisture decreased, and dust storm occurrences increased. As a result, in most of the years, with decreasing soil moisture and increasing dust storms, the forests greenness of the study area has decreased and vis versa. Therefore, there is a direct relationship between soil moisture and forest greenness while an inverse relationship exists between dust and forest greenness.
Saeed Jahanbakhsh asl; Behroz Sari Sarraf; Tayeb Raziei; Akram Parandeh khouzani
Abstract
In order to monitor spatiotemporal variability of snow in mountainous areas such as Zagros in Iran, long-term records of snow observations with high spatial resolution are required. However, no such data are either observed or available for the stations of the Zagros region. Therefore, in this study, ...
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In order to monitor spatiotemporal variability of snow in mountainous areas such as Zagros in Iran, long-term records of snow observations with high spatial resolution are required. However, no such data are either observed or available for the stations of the Zagros region. Therefore, in this study, the Era-Interim/Land snow depth data for the period 1979-2010 were used in order to investigate the spatiotemporal variability of snow season length and the associated starting and ending dates in the Zagros region. To do so, for each hydrological year starting from October and ending in September, the first and last snow dates with snow depth equal to or greater than one centimeter were defined as the first and last day of observed snow on the ground and the time period between these two dates was considered as the snow season length. For each grid points over the study area, the time series of snow start and end dates, as well as the length of the snow season, were extracted and the rate of their temporal changes was estimated using the Sen Slope estimator and were examined using the Mann-Kendal trend test to test if they are statistically significant. Moreover, the considered time period was divided into three different sub-periods and the mean values of these parameters (i.e., first and last snow dates and snow season length) in the three sub-periods were also compared. The links between these parameters and the latitude, longitude, and altitude of the grid points were also examined. Results indicated that the spatial pattern of the first and last snow dates and snow season length fairly follow the geographical features of the study area and thus have a statistically significant relationship with the latitude, longitude, and altitude. Time variability of the considered parameters over all the studied grid points revealed that the date of the first snow in the most proportion of the study area retreated towards the late autumn and January and the date of the last snow also retreated towards March and February, thus, resulting in the shorter winter season in recent years. The observed statistically significant decreasing trend in the time series of the last snow dates towards March and February has the most contribution in shortening the length of the snow season.
Tayeb Raziei
Abstract
In this research, the target area were regionlized into few distinctive homoginious sub-regions by applying principal component alalysis to the SPI time series at 3-, 6- and 12-months time scales and the resultant PC scores were considered as the regional SPI time series for drought forecasting using ...
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In this research, the target area were regionlized into few distinctive homoginious sub-regions by applying principal component alalysis to the SPI time series at 3-, 6- and 12-months time scales and the resultant PC scores were considered as the regional SPI time series for drought forecasting using time series modellingineach identified sub-region. The probability of occurences of dry, normal and wet events were also predicted for all the considered stations using Markov chain model and the results were spatially mapped and analysed. The expected drught numebr and drught length of the prediceted drought events were also estimated and mapped to spatially display their results in order to ease their spatial variability comparrison. Furthermore, different time series models were fitted to the Regional SPI series (PC scores) to identify the best fitted model for each region in order to use for drought forcasting. The result shows that the ARMA is the best fitted model for SPI time series at 3- and 6-months time scales while for the 12-months time scales the SARIMA model is the best fitted model. Using the identified models the magnitude of the SPI was forcasted for the leading times. The result shows that the time series models can favorably forcast SPI values for three months ahead, wherease the predicted results for more than three months ahead is not reasonably accurate.
