تحلیل روابط تغییرات بارش و سطح آبهای زیرزمینی دشت مرند با روش NRMC (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
با توجه به فقدان رودخانه های دائمی به جز رودهای با دبی کم در قسمت جنوب غربی مرند ، تامین آب منطقه اعم از شرب ، کشاورزی ، صنعت و دامپروری وابسته به آبهای زیرزمینی است. از طرفی اقتصاد مردم منطقه بیشتر وابسته به کشاورزی و دامپروری بوده و از آنجایی که آب مورد نیاز در این بخش ، حجم عمده ای از آب مصرفی منطقه را تشکیل می دهد ، لذا لازم است عوامل موثر بر سطح آبهای زیرزمینی منطقه بررسی شود تا ضمن مطالعه تاثیرات این عوامل ، راههای مقابله با کم آبی و افت سطح آبهای زیرزمینی جدی گرفته شود. در این پژوهش روابط بین تغییرات بارش و سطح آبهای زیرزمینی دشت مرند در بازه زمانی 16 ساله از سالهای 1380 تا 1395، با استفاده از روش NRMC مورد بررسی قرار گرفته است . در این روش ضمن محاسبه مقادیر NRMC برای دو شاخص SPI و SWI در منطقه ، منحنی توزیع نرمال شده برای هر دو شاخص در ایستگاههای باران سنجی منتخب و چاه های پیزومتری منتخب رسم شد و معادله رگرسیون خطی و چند متغیٍره محاسبه شد . نتایج نشان داد که بارش و سطح آبهای زیرزمینی در سالهای مورد بررسی نوسانات زیادی داشته است که البته با توجه به نوسانات بیشتر آبهای زیرزمینی نسبت به بارش ، عوامل انسانی از جمله برداشت بی رویه از چاه ها می تواند عامل تاثیر گذار بر سطح آبهای زیرزمینی دشت مرند باشد. محاسبات نشان داد که مقدار همبستگی برای دو شاخص SPI و SWI در معادله چند متغیره غیر خطی بیشتر از مقدار معادله ی خطی است که بیانگر تاثیر عوامل متعدد دیگر علاوه بر نوسانات بارش بر سطح آبهای زیرزمینی است .Analysis of the relationship between precipitation changes and groundwater level in Marand plain with NRMC method
Introduction
The 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 methods
To 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 stations
The 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) / σ
In 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) / σ
Where 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 curve
In 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 Discussion
Investigation 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.
Conclusion
Considering 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.
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