Climatology
younes nikookhesal; Ali Akbar Rasouli; Davod Mokhtari; Khalil Valizadeh Kamran
Abstract
IntroductionThe water cycle in nature is directly related to the climate of that region. Reasonable and correct use of water resources requires accurate quantitative and qualitative knowledge and collection of appropriate climate data and information. Depletion of groundwater reservoirs, drying of canals ...
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IntroductionThe water cycle in nature is directly related to the climate of that region. Reasonable and correct use of water resources requires accurate quantitative and qualitative knowledge and collection of appropriate climate data and information. Depletion of groundwater reservoirs, drying of canals and springs and even semi-deep wells and reduction of deep well discharge, change of groundwater flow direction, salinization of aquifers, salinization of soil due to irrigation with saline water, barren The emergence of fields, soil erosion, etc. has put most of the plains of the country at risk of further desertification (Tavousi, 2009: 14).Atmospheric precipitation is the main source of surface and groundwater and the study area is poor in terms of atmospheric precipitation and its amount is between 150 to 450 mm per year, which varies in plain and mountainous areas. The climate of the region is semi-arid and cold and is mostly influenced by the Mediterranean climate. Due to the fact that groundwater is the most important source of water consumption in the study area, the impact of climate change, especially precipitation on the water table of wells in the area was investigated in this study.Materials and methodsTo study the trend of groundwater level changes in Marand plain, water table data of 23 piezometric wells and data of 8 rain gauge stations during the last 16 years of 1395-1395 were used. After using the correlation matrix method to select rainfall stations and considering the complete statistical data and appropriate coverage of the area by these stations, 4 stations were selected for the study and for each station, a piezometric well was selected within the station. This research was first calculated using precipitation data and water table of piezometric wells SPI and SWI values and then NRMC values for each index, respectively, in each method are briefly referred to:Calculate SPI and plot seasonal SPI variations of selected stationsThe standardized rainfall index was provided by McKay et al. (1993, 1995) to provide a warning and help assess drought severity and is calculated by the following formula: Relation 1: SPI = (X_ij-X_im) / σIn the above relation, X_ij is the seasonal rainfall at rainfall station i, with j number of observations, X_im is the long-term average rainfall and σ is the standard deviation.Calculate SWI and plot the seasonal SWI of selected wells The standard water level index was presented in 2004 by Bui Yan et al. (2006) to monitor fluctuations in groundwater aquifers in the study of hydrological droughts, which is calculated by the following formula:Relation 2: SWI = (W_ij-W_im) / σWhere W_ij is the seasonal average of the water table of observation wells i to j, W_im is the long-term seasonal average and σ is the standard deviation.Calculate the NRMC values of each indicator and plot the normalized distribution curveIn this method, seasonal normalized distribution curves were adjusted for both SPI and SWI indices. Cumulative normalized curve is a kind of condensation diagram of a climatic or hydrological variable (such as precipitation and water table) that is extracted from the subtraction of each observation in the statistical series of the long-term average and its division by the average according to the following formula. (Rasooli, 1994)Relation 3: NRMC xi = ( (Xi-X m) / ({(Xi-X ̅m) / X ̅m}) ) * 100 In the above formula, Xi represents the amount of each rainfall observation or the amount of water table and X ̅m is the long-term average in the series of observations.Results and DiscussionInvestigation of normalized distribution curves showed a correlation between precipitation changes and groundwater level in Marand plain. This correlation has a higher significance with a delay season. Shamsipoor (2003) in Hamedan plain achieved a 9-month delay between precipitation and water table. Mohammadi et al. (2012) in Arak plain expressed the impact of groundwater resources from drought with a delay of two months. The results of the study (Rudel and Lee 2014) in the study of groundwater drought index in the United States showed that the SPI drought index with a delay of 12 and 24 months had the highest correlation with the SWI index.ConclusionConsidering the more fluctuations of the water table than the fluctuations of the rainfall, it can be concluded that human factors such as uncontrolled harvesting is an effective factor on the water level of wells. Komasi et al. (2016) stated the effect of human factors on the decrease of groundwater level before the factor of climate change in Silakhor plain. Calculations showed that the value of correlation for both SPI and SWI indices in the nonlinear multivariate equation is higher than the value of the linear equation, which indicates the effect of several other factors in addition to precipitation fluctuations on the groundwater level. According to the results of the study, it seems that the groundwater level in addition to precipitation depends on other factors such as geology, lithology, tectonic morphology, the shape of the aquifer, the distance of aquifers to the feeding site and .... And to achieve more complete results, it seems necessary to address these factors in future research.
Rural Planning
Zahra Arabi; Rezvan Ghorbani salkhord; yosef darvishi
Abstract
IntroductionDrought is one of the environmental disasters that are very common in arid and semi-arid country regions. Rainfall defects have different effects on groundwater, soil moisture, and river flow. Meteorological drought indices are calculated directly from meteorological data such as rainfall ...
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IntroductionDrought is one of the environmental disasters that are very common in arid and semi-arid country regions. Rainfall defects have different effects on groundwater, soil moisture, and river flow. Meteorological drought indices are calculated directly from meteorological data such as rainfall and will not be useful in monitoring drought in the absence of data. Therefore, remote sensing techniques can be a useful tool in measuring drought. Drought is a known environmental disaster and has social, economic, and environmental impacts. Lack of rainfall in an area for long periods is known as drought. Drought and rainfall affect the water and agricultural resources of each region. Materials & MethodsDue to the nature of the problem and the subject under study, the present study is descriptive-analytical with emphasis on quantitative methods. In this study, satellite images of Tera Sensor Modis in 2000 and 2017 were used to verify the existence of wet and drought phenomena. In the next step, by examining the rain gauge and synoptic data of the existing stations and using the standardized precipitation index model of three months (May, June, and April), the sample was selected. Next, we compared temperature status indices (TCI) and vegetation health indices (VHI) in these three months to determine the difference between these indices over the three months. Modira Terra satellite was used to study the vegetation status in the study area. Subsequently, vegetation-free areas were isolated from vegetation areas using the conditions set for the NDVI layer, the experimental method was used to determine the threshold value of this index. For this purpose, different thresholds were tested, with the optimum value of 1 being positive. NDVI is less than 1 free of positive plants and more than free of vegetation. MODIS spectral sensor images for surface temperature variables with a spatial resolution of 1 km, including 31 bands (1080/1180 bandwidth, central bandwidth / 11.017 spatial resolution of 1000 m) and 32 bands - 770/11Central Wavelength Band 032/12 Spatial Resolution Power (1000 m) Selected for months that are almost cloudless. All images are downloaded from the SearchEarthData site and edited. Total rainfall in June, April, and May for 20 years has been provided by the Meteorological Organization of Iran. ARC GIS software and geostatistical methods were used to process Excel data. Pearson correlation coefficient was also used to estimate the correlation between the data. Results & DiscussionA standard precipitation index is a powerful tool in analyzing rainfall data. This study aimed to compare the relationship between remote sensing indices and meteorological drought indices and to determine the effectiveness of remote sensing indices in drought monitoring. The correlation between the variables with the SPI index was evaluated and calculated. The results of the indicators are different, so a criterion should be used to evaluate the performance of these indicators. SPI index on a quarterly time scale (correlation with vegetation) was selected as the preferred criterion. According to the results of correlations, the TCI index with the SPI index had a strong correlation with other indices. In the short run, this index has the highest correlation with thermal indices at the level of 1%. The correlation between meteorological drought index and plant water content and thermal indices increases with increasing time intervals. The positive correlation between vegetation indices and plant water content with meteorological drought indices shows that the trend of changes is in line. Therefore, the TCI index makes the drought more accurate and is a better method to estimate drought.ConclusionThe results showed that among the surveyed fish, the most drought trend was observed in the eastern provinces and covers more than 50% of the region. The trend of changes in this slope was statistically significant. According to the results of correlations, the TCI index had a strong correlation with the SPI index with other indices. It can also be concluded that Modis images and processed indices along with climatic indices have the potential to monitor drought. The use of maps derived from drought indices can help improve drought management programs and play a significant role in reducing the effects of drought.
