Journal of Water Resources Engineering
Year:2011 | Volume:4 | Issue:8|Papers Count:8

Water table elevation (WTE) prediction is of utmost importance in planning and implementation of irrigated agriculture, especially where installing deficit irrigation systems is considered in water-short areas. Furthermore, this information is vital in installing drainage systems and preventing land inundation and soil salinization. Using the WTE data from 20 piezometers maintained at least for 17 years, artificial intelligence neural networks, neurofuzzy system, and genetic programming were used to develop predictive tools to forecast WTE in the Shabestar Plain, Province of East Azarbaijan, northwest of the I.R. of Iran. The nonlinear behavior of WTE was ascertained when genetic programming was employed. The neurofuzzy system was proved to be the best predictor of the WTE, however, the other 2 systems performed satisfactorily. The neurofuzzy system was the best predictor based on the previous 3-month data, the genetic and artificial neural models occupied the 2nd and 3rd ranking in predictability. Explicit solutions that show the relationships between the input and output variables are presented based on genetic programming. This adds to the superiority of genetic programming over the other two models.

Knowledge of the relationships between the watertable depth and evaporation rate from the bare soil is of immense importance in arid and semi arid area. The rise in watertable, land inundation, and soil salinization are inevitable aftermath of over-irrigation in such environments. Evaporative flux was measured in 200 mm inside diameter PVC columns filled with the sandy loam, loam and clay loam soils, maintaining the watertable in them at a depth of 40, 60 and 80 cm. Evaporation from both the bare soil and exposed water surface, soil water content, and the maximum and minimum air temperatures were measured daily for 74 consecutive days. The TDR technique was used for the soil water content determination. Several nonlinear models were developed using benefiting from the gamma test (the Win Gamma program), including the local linear regression, the 2-layer back propagation, conjugate gradient descent, and the BFGS neural network. Validity of these models was ascertained using the root mean square error, the mean absolute error, and the larger coefficient of determination. The models satisfactorily predicted the measured evaporative flux.

Prediction of reference crop potential evapotranspiration (ETo), and other effective climate parameters, are importance of in planning and management of water resources. Our objectives in this study were to: i) detect the probable trends in the parameters that effect decide the ETo rate, and ii) predict ETo for the different climatic zones of Iran using the multi-differential regressions. The meteorological stations, which had collected long-time reliable data (1951-2005) were: Mashhad, Shiraz, Tabriz, Kermanshah, Khorramabad and Orumieh, all of these stations are located in the semi-arid areas. The least mean square error technique was used to show the trends. The first 5- year data (L-5) were used to derive the coefficients of the multi-variable functions. The last 5-year data were used to predict the ETo, and to evaluate regression equations. The results indicated that the maximum and minimum temperatures, and the ETo increased, and the relative humidity and rainfall decreased in most stations. Using the multi-variable regression technique revealed the presence of a good agreement between the amount of ETo by our calculations and those obtained using the FAO-Penman-Monteith method. The relative errors had low levels, these were 2.4-4.1 mm for the Mashhad Station, -1.6 to 0.9 mm for the Shiraz Station, -0.5 to 2.4 mm for the Tabriz Station, -2.3 to - 0.7 mm for the Kermanshah Station, -0.1 to 2.9 mm for the Khorramabad Station, and -2.3 to 2.8 mm for the Orumieh Station. Therefore, our developed regression equations satisfactorily predicted the ETo.

The standard precipitation index (SPI) was used to analyze 32 years (1974-2006) of precipitation data collected at 119 rain gauging stations throughout the I.R. of Iran to determine drought frequencies in different ecological zones of that country. The results revealed that the droughts at the shorter time intervals were more frequent than those with the longer time intervals. The study also showed that drought occurrence is a climatic property happening at certain time intervals. The assessments indicated that drought has been more frequent, yet of less intensity, during the recent years. Furthermore, the frequency of these droughts was higher in the central, southern, southeastern, and eastern parts of the country. Drought zoning was achieved using the kriging method with spherical, exponential, Gaussian, and power variograms. The results showed that the spherical and power variograms were the best predictors of 12- and 3-month frequencies, respectively. The values of root mean square error (RMSE), mean absolute error (MAE), and medium error (MDE) were 0.857, 0.672 and 0.31 mm, respectively, for a 12-month period, they were 0.941, 0.714 and -0.002 mm for a 3-month period. The zoning analysis revealed that the central, southern and some regions of eastern Iran were more susceptible to droughts than the other region of the country. It further indicated that on an annual basis, most regions experience droughts of low intensity. Severe and very severe droughts were not detected in our analysis. Overall, 61% of the area of Iran suffered from droughts of different severities.

Nitrate is one of the most widespread non-point source pollutants of groundwater resources. Although nitrogenous fertilizers are the well known culprits, sewage, feed lots, and chemical industries are also among the major contributors. As the Zydoun Plain is a thriving agricultural area, and the N-polluted groundwater was a cause of concern, this study was implemented to survey the affected areas, and the time of the year when pollution is prevalent. Groundwater sampling was performed during the wet and dry periods, the latter was the growing season when irrigation is practiced. The data were analyzed both graphically and statistically using the ArcGIS and the SPSS software. It was observed that groundwater nitrate concentration was higher during the summer months due to the application of N-fertilizers to the farm fields, particularly to the rice paddies. The groundwater in the northern and central parts of the plain showed higher concentrations of nitrate due to an abundance of agricultural activities in those areas. Reduction of N-fertilization in the affected areas is highly recommended.

The multivariate time series models as the linear stochastic models were used to forecast monthly average of the Persian Gulf surface temperature (PGST). The time series data of temperature for the period 1854-2007 for six sea grid points were considered as the input file of multivariate time series. Vector autoregressive processes were employed to perform multivariate time series. The auto-correlation function plot of the residuals for the fitted model, based on the Akaike's information criterion, has indicated that the residuals were uncorrelated. The monthly values of the PGST from January 2008 to December 2009 as the test data have been forecasted using the fitted models. The results have indicated that the correlation coefficient between the observed and predicted values for each grid point was about 0.99. The root mean square prediction error for each grid point was less than 0.7 degrees Celsius.

The application of appropriate technology to increase water use efficiency (WUE) is of crucial importance in water-short regions. Therefore, sprinkler irrigation systems (SISs) are installed in such areas. However, poor design and inadequate maintenance substantially decrease WUE. As it was believed that the SISs in the Mahabad region do not perform satisfactorily, 3 solid sets were selected randomly and their performance was evaluated during 3 field visits. Meteorological characteristics, soil and water quality information, types of agronomic crops, and specifications of the SISs, including pressure and discharge rate at nozzle heads, were collected, and the following criteria were determined: Christiansen’s uniformity coefficient (CUC), distribution uniformity (DU), application efficiency of low quarter (AELQ), potential application efficiency of low quarter (PELQ), application efficiency (AE), and combined efficiency. The results showed that the overall average of the systems for CU, DU, AELQ and PELQ were 66.4, 52.2, 45.8 and 45.8 %, respectively. Furthermore the wind drift and evaporation losses (WDEL), deep percolation (DP), and overall average losses were 12.2, 13.4 and 25.6 %, respectively. The overall average of application efficiency and combined efficiency were 74.4 and 75.8 %, respectively. The main reason for the low distribution uniformity and unsatisfactory application efficiency in these systems was poor design, especially with regard to the velocity of wind.

The 7500 km2 Kerman Plain is badly suffering from water shortage due to over-exploitation of its groundwater resources. Therefore, replacing the traditional irrigation methods with the pressurized systems is in order to highly increase water use efficiency. This necessitates finding the most suitable sites for installing sprinkler and drip irrigation systems. The GIS technology along with the Boulian model facilitated locating the farm fields intended for this purpose. Of the surveyed terrains, 5 percent were judged suitable for sprinkler irrigation, and 25 percent were found to be suitable for drip irrigation.