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Showing 2 results for Time Series.

A. Taheri Tizro, H. Nozari, H. Alikhani,
Volume 20, Issue 76 (8-2016)
Abstract

To procure the status of groundwater level fluctuations in arid and semi-arid areas, it is necessary to obtain accurate forecast of fluctuations data. Time series as a linear model have been utilized to generate synthetic data and predict future groundwater level. Minitab17 software and monthly depth of groundwater level data of 20 years (1991-2011) for 25 piezometric wells of plain were used. Time series models of each well were selected and 5 years temporal forecasting was accomplished. The predicted depth of groundwater level data was converted to Groundwater level data using ARCGIS10 and GS+5.1.1 software. Ordinary kriging with a spherical variogram was selected for interpolation of groundwater level. Five years spatial forecasting was done and spatial forecasting and groundwater level drop forecasting maps were prepared. Forecasting results of groundwater level show that over the next 5 years, the area covered by two intervals of groundwater level, 1100-1140 m and 1140-1180 m, will increase and the area covered by three ranges of 1180 -1220 m, 1220-1260 m, and 1260-1300 m, will decline. Also, according to the 5-year groundwater level drop forecasting map of the plain, the highest level of groundwater level drop, more than 16 meters for Qasemabad bozorg areas, located in North East and central of the plain, and the lowest level of the groundwater level drop, about 0.5 m for Mohammad Abad Afkham Aldoleh Lands, located in outlet area of the plain, have been predicted.


H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (6-2018)
Abstract

Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The  percentages of the model in estimating the region's  annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.


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