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Showing 4 results for سطح آب زیرزمینی
Evaluation of Deep Percolation Rates at a Transect of Paddy Fields During Crop Season
S. M. Mousavi, A. Hoshmand, S. Bromandnasab, M. Yazdani,
Volume 16, Issue 60 (7-2012)
Abstract
The common method of irrigating rice in paddy fields of Iran, like most countries, is flooded irrigation. The water required in this method is too much. However, because of water shortage in recent years, and malfunctioning of irrigation systems, it is needed to use water in a reasonable way and increase water use efficiency. Therefore, it is necessary to know water loss amounts at the paddy fields. The deep percolation (DP) was measured by closed- and open-bottom rings in 4 locations, and 7 sites at each location, of paddy fields in Somae-Sara city, Guilan province. These locations were selected on the base of different physiographic units. The average DP of these locations was also monitored during plant growth season. The measurements were performed twice a week. Results showed that the rate of DP varied during the season, and could take a positive or negative value. The most important factors of these variations were the lateral seepage (from surrounding rice fields) and the high perched groundwater table in paddy fields.
Spatio-Temporal Water Levels Forecasting by Time Series-Geostatistics as a Hybrid Model in Hashtgerd Plain-Alborz Province
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.


Simulating and Optimizing the Conjunctive Use of Surface and Groundwater Resources Using the System Dynamics Approach (A Case Study: Dashte-Abbas Irrigation Network)
H. Alizadeh, A. Hoseini, M. Soltani,
Volume 24, Issue 3 (11-2020)
Abstract
The construction of irrigation network and the water transfer from Karkheh Dam to Dashte-Abbas, due to neglecting the groundwater resources has increased groundwater level and waterlogging of the agricultural land in the recent years. The aim of this study was, therefore, to optimize the conjunctive use of surface and groundwater resources in Dashte-Abbas to minimize waterlogging problems and achieve the maximum net income. For this purpose, the behavior of groundwater was simulated using the system dynamics (SD) approach. The conjunctive use of surface and groundwater resources was then optimized using the Vensim multi-criteria optimization method with the objective function of maximizing the net income of the plain. The SD model calibration was done using climatic, hydrological, agricultural, and environmental data from the 2001-2009 time period; then it was validated based on the information from the 2009-2016 period. Evaluation of the developed SD model showed that the model had high accuracy in simulating key variables such as groundwater levels (ME=60cm, R2=97%, RMSE=47cm) and groundwater salinity (RMSE=100μS/cm, R2=74%, and ME=123μS/cm). Furthermore, the results of the optimization model showed that the optimum use of surface and groundwater resources for the agricultural demand was 65% and 35%, respectively. To sum up, it could be concluded that with the optimization of the conjunctive use of surface and groundwater resource, s about 10 MCM of water consumption could be annually saved to irrigate almost 800 ha of the new lands.

Prediction of Groundwater Level Using Artificial Neural Networks Based on Efficient Input Variables Selection by Partial Mutual Information Algorithm
F. Zarif, A. Asareh, M. Asadiloor, H. Fathian, D. Khodadadi Dehkordi,
Volume 26, Issue 2 (9-2022)
Abstract
An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.

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