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Zahra Hejazizadeh, Parisa Sadat Ashofteh, Ebrahim Fatahi, Zahra Gholampour,
Volume 15, Issue 38 (4-2015)


In this study, the predicted monthly temperature and rainfall data from HadCM3 model (base period, ۱۹۷۲-۲۰۰۱) and next period (۲۰۱۱-۲۰۴۰) under A2emission scenario were used to investigate the impacts of climate change on runoff variations in the Kor river basin. HadCM3 model output was downscaled based on a temporal downscaling approach (Change Factor) and spatial downscaling approach (Proportional) for the basin. The time series of monthly temperature and precipitation were produced in future period. The results showed that temperature and rainfall will increase and decrease in the future period relative to the base period, respectively. Then, the calibrated IHACRES model was used to model rainfall-runoff relationships in the Kor river basin. Then, the downscaled monthly temperature and rainfall time series in next period were employed to rainfall-runoff model and the monthly runoff time series were simulated for period ۲۰۱۱- ۲۰۴۰. The comparison of future period runoff relative to observed period showed that the annual runoff in the basin for period ۲۰۱۱-۲۰۴۰ will decrease about ۹٫۴۳% with respect to base period.

Mohammad Hossein Nasserzadeh, Zahra Hejazizadeh, Zahra Gholampour, Bohloul Alijani,
Volume 20, Issue 57 (6-2020)

The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental.  The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1  of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level  in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.

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