Journal of Irrigation and Water Engineering
Year:2016 | Volume:6 | Issue:23|Papers Count:12

Hydraulic analysis of water networks contains determination of the distribution rate in each pipe and head of network nodes. Nowadays using meta-heuristic algorithm Simulated Annealing is increasing in solving complex engineering problems according to capabilities. Purpose of this study hydraulic analysis of water distribution network based on simulated annealing and comparing the results according to statistical indices: AE and RMSE with numerical methods: Hardy Cross, Newton-Raphson, Linear theory, Gradient and Epanet software. To determine the ability of a variety of methods to simultaneously continuity equation and energy loss network, the Objective Function is used. At the end amount of Objective Function Simulated Annealing for two loops network is 0.00016 and assessing index results indicates its accuracy in reaching problem solutions. These results prove efficiency of this method towards the other conventional methods. According the assessment can recommend Simulated Annealing as a new method and reliable in designing and analysing pressurized water networks.

Doubtlessly the first step in a river management is precipitation prediction of the watershed area. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) is extensively used as a non-linear inter-extrapolator by hydrologists. In the present study, Wavelet Analysis combined with artificial neural network and compared with Artificial Neural Network to predict the precipitation of Varayeneh station in the city of Nahavand. For this purpose, the original time series using wavelet theory decomposed to multi sub-signals. After this these sub-signals are used as input data to Artificial Neural Network to predict monthly Precipitation. The results showed that according to correlation coefficient of 0.92 and mean square error of 0.002 for the hybrid model of Wavelet- Artificial Neural Networks, the performance of this model is better than Artificial Neural Network with correlation coefficient of 0.75 and mean square error of 0.003 and can be used for short and long term precipitation prediction.

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Karoon river basin, generated during 13 years (1994-2006) monitoring of 13 parameters at 26 different sites. Hierarchical cluster analysis grouped 26 sampling sites into four clusters, i.e., relatively less polluted (LP), medium polluted (MP), highly polluted (HP) and extreme polluted (EP) sites, based on the similarity of water quality characteristics. PCA/FA, applied to the data sets of the three different groups (LP, MP and HP) obtained from CA, and Consequently effective variables reduction from PCA/FA in this case was found in VF1 and were included %Na, S.A.R, Na, Cl, Ec and T.D.S. VF1 explaining 35.89, 45.18 and 36.43% of the total variance in water quality data sets of LP areas respectively for the summer, autumn season and MP areas, for the spring season and also VF1 explaining 41.77, 44.97, 52.24, 35.14, 26.87, 38.46 and 48.38% of the total variance in water quality data sets of HP areas for the spring season and MP and HP areas, respectively for the summer, autumn and winter season.

One of the crucial information, as input in modeling of hydrological processes, is relevant to soil moisture and especially the retention characteristic information available at catchment scale. This paper presents a method to relative approximation of these quantities shown. Accordingly, at the beginning, Determination and extraction of soil texture class's USDA method by Using soil texture class proposed by the Commission of the European Communities (CEC) (these method «CEC» has the 6 texture classes with codes 0 to 5) Was conducted. In continuation of these texture classes (texture classes USDA method) to estimate and AS a result in mapping the amount of field capacity and wilting point (for the whole country) is used. In these respect, the results showed that in general, the average range of soil moisture variations in texture classes by CEC method, for permanent wilting point is exceeded field capacity So that the changes for permanent wilting point than the average 6.77 to 10.9 and for the field capacity of 6.03 to 9.3 percent Is has the variations. Also in this study, according to expert opinion and utilizing scientific resources attempted to investigate the interaction between the effects of land use and slope of watershed in order to assess the information and the resulting the depth map of the surface layer of soil moisture retention. In this direction to assess and evaluate the applicability of the approach taken comply with the physics of the catchment, attempted to utilizing it (Effective depth map of the surface layer the soil moisture retention) along with wetting properties Maps of field capacity and permanent wilting point that was achieved the National Soil Map (the first stage of the research), In order to estimate and prepare a map of available moisture in areas of study there, Nishapur, Attaiyeh, Qaleh Meydan and Sabzevar as the regions chosen for study were used. The results indicate that the resulting map is proportional to the physics of the studied areas in order to achieve total available water and the role of the interaction basin slope, land use And well as the impact the moisture field capacity and permanent wilting point that was extracted from the soil map On Total available water is well evident. Accordingly, due to the integrated and comprehensive characteristics of moisture maps (field capacity and permanent wilting) for the whole country, this amount could be used as a primary domain of soil moisture characterized along with the method described in the effective depth of surface soil moisture retention on Hydrological modeling, particularly Understanding the phenomenon of a small amount of influence and power of water retention in the soil, the estimated components of water balance, etc. in order to prevent land degradation and soil and water resources management, to be used.

Snow is one of the huge sources of water around the world, and estimation of the equivalent water of snowmelt is regarded as one of the most crucial activities of the hydrologists. Through snow melting, the soil moisture, underground water reservoirs, lakes and rivers water reservoirs are supplied; and the more readily, the snowmelt runoff is regarded as a main factor for controlling the flow regime in mountains and high basins. Therefore, the estimation of the snow cover and its runoff is very important issue in the mountainous basins. But, this effort is very difficult to be done due to lack of adequate land information in this regard. Using snowmelt hydrological models and satellite imagery are very useful to cope with this problem. In this study, it was tried to simulate and evaluate the daily runoff of snowmelt by the use of a snowmelt runoff model (SRM) and the information extracted from MODIS eight-day images, for Nahava d watershed wh ch s o e of the Karkheh r ver bas ’s parts located o wester Ira. The model was run and calibrated for the water years from 2003 to 2010; and it was validated for the water years 2011 to 2013. The highest correlation coefficients and volume difference for the model in the calibration period were 0.75 and -3.62, respectively; and 0.79 and 26.67 for the validation period, respectively. A sensitivity analysis was done for degree day, snow runoff coefficient and rain runoff factors and its results shows that a lower sensitivity of model to increased or decreased of rain runoff coefficient among the other parameters. In general, obtained results show a high level of accuracy of this model by the use of satellite imagery as an input for estimating the snowmelt runoff for the studied watershed and the capability of this model for other similar basins as well.

Correct estimation of reference evapotranspiration (ETo) needs to estimate accurate solar radiation values. There are many models to estimate solar radiation so that one of these relationships is Angstrom-Prescott (A-P) equation. The accuracy of Angstrom-Prescott equation in estimating solar radiation value depends on the estimation accuracy of a and b coefficients of the mentioned equation. The purpose of the present research was to investigate the accuracy of models for estimating coefficients in A-P equation and their effects in the solar radiation and reference evapotranspiration values. To do this purpose, the meteorological data of four synoptic stations including Urmia, Takab, Mahabad and Salmas in the Urmia lake watershed in the daily scale from 1998 to 2008 were used. The models which used for the determination of Angstrom-Prescott equation coefficients were included models based on the annual average of relative sunshine hours (n/N), elevation and (n/N), elevation and latitude and (n/N), elevation and latitude, annual average of air temperature, elevation. The obtained coefficients were used to compute the solar radiation values in Angstrom-Prescott equation and then reference evapotranspiration was calculated by FAO-Penman-Monteith equation. To evaluate the models accuracy, statistical indices including RMSE, MBE, MPE and R2 were used. The evaluation results of the models showed that the model based on annual average of relative sunshine hours (n/N), (Rietveld model) had the best performance in determining the coefficients of Angstrom-Prescott equation. The average values of RMSE, MBE, MPE and R2 indices for actual reference evapotranspiration and reference evapotranspiration obtained from coefficients of mentioned model were found 0.33 and 0.25 mm/day, 6.27% and 0.9977, respectively. For Angstrom-Prescott equation coefficients from selected model, a=0.26 and b=0.43 were suggested in the studied stations in the Urmia lake watershed.

Ekbatan dam in Hamedan is located on Abshine river. The purpose of this dam is to supply potable and agriculture water for Hamedan. With respect to water quality importance of reservoirs the study aims to investigate eutrophication state of Ekbatan Dam Lake. In this research a dry season (summer) was chosen to study the quality of water behind the dam as temperature and light is received more by the environment.12 stations were selected in different points of Ekbatan Dam lake and sampling was accomplished. In this study pH, temperature secchi disk, nitrate, phosphate and chlorophyll-a were measured. Then using Calson index the rate of eutrophication was estimated for all points. Also quality zonation was done using GIS.Carlson index varies between 35.7 and 50.7 in different stations of the lake. Based on the estimated values of this index, stations located at the entrance of the lake show more eutrophic situation. Also phosphorous is the limiting nutrient in this system. Results show that Carlson index at station one which is near to dam body is lower in comparison with other points and it is recognized as oligotrophic. Station 11 which is located on the left branch of the lake input is eutrophic. This research showed that Ekbatan reservoir has the potential of eutrophication.

The challenges of drought and groundwater shortage in arid and semi-arid countries like Iran are natural heritage. Almost 92% of the water use in Iran accounts for agriculture overexploiting groundwater aquifers. Therefore, cross-sector management is necessary to prevent unconstrained use of the aquifers and implement sustainable agriculture practices. This study used linear programming (LP) model to optimize the groundwater use and cultivation pattern in Sarkhoun plain. Therefore, the drought state of Sarkhoun plain was assessed in the study area based on SPI and GRI indices for a 25-year period (1988-1989 to 2013-2014) using the records of precipitation and groundwater level. Then, LP models were developed to maximize the net benefits of water use. So, three scenarios of current cropping pattern, irrigation efficiency, and population growth were used to predict the water supply impacts on cropping pattern in the Sarkhoun plain. The results showed that groundwater level of Sarkhoun plain has dropped 6 meters since 2008 following by severe and very severe drought events since 2000. Optimizing cropping pattern outputted to save 2 ´ 106 m3 water and increase the net benefits by %711 for the planning year (2011).

Extraction of groundwater system using qanats has been used for many years as it invented in the past, with minimum changes. No fundamental change has been performed to reconstruct of qanats along with the advancement of technology. On the other hand, the considerable sums from public funds are spending in order to restore and repair qanats every year. However due to lack of adequate information in this regard, qanats have been restored and repaired that have not been in the reconstruction priority. In this research, the reconstruction of qanats was investigated both in managerial and technical perspective. In any perspective, 10 qanats in the plain's group and 10 qanats in mountain's group are prioritized by using Network Analysis Process (ANP) and Super Decisions software. The used indices for qanats are discharge, length, depth, chemical water quality, precipitation and distance from the river, population and cultivation area. In this study, a new scientific method is introduced to prioritize the reconstruction of qanats. Also by the mentioned method used in this research, qanats that are in the prioritization of reconstruction can reach the desired discharge with allocation costs. In this research, in the ANP method, Boshrooyeh qanat in the plain group from two managerial and technical perspective with a weightage of 0.159 and 0.164 respectively and Darmian qanat in the mountain group from two managerial and technical perspective with a weightage of 0.247 and 0.202 respectively are in the prioritization of reconstruction.

Based on the importance of water quality and its effects on human, in this study the quality of drinkable waters in south of Fars province was evaluated. In this study the parameters such as Ca, Cl, Mg, TH, Na, EC and SO4 in 28 points as the primary and entrance data were used to determine the water quality. Because of the abnormality of the data, the IDW method that has the least errors against other methods was used in GIS. In order to homogenization of data to prepare the final map of water quality, the Fuzzy method was used. Fuzzy Membership functions were created based on the standard domains of water quality (World Health Organization). In order to Preparation of the final map of water quality with different assurance level, ordered weighting average method (OWA) was used. Results showed that in spite of the reduction of risk taking (without trade-off), most of the study area don’t have any problems in water quality, so that the areas with class4 and 5 that have the moderate and good water quality, have more surface than the other classes. By means of the significant level increase and the risk taking decrease, the unsuitable areas based on the water quality will be increased, so that the most area of study are categorized in class 1 with unsuitable water quality.

Every kind of climate change on the terrestrial earth is the beginning of a chain of reactions whose effect can be directly observed on hydrological processes. Qara Su basin catchment, located in Kermanshah Province, is one of the important sub– basins of Karkheh River that, in terms of agriculture and food security, is considered as an important area in the west of Iran. In this regard, achieving reliable methods of predicting the river flows in order to plan proper operation of water resources has become increasingly significant. In this study, first, Statistical Downscaling Model (SDSM) was used to measure the large -scale data of the atmospheric general circulation model (HadCM3) in the local climate scenario, A2, which considers both the rapid economic development for the future world, the remarkable technological advances, and excessive use of the fossil fuels in future. These are measured for the meteorological parameters of temperature and precipitation in the basin of Qara Su. Then, considering the data of rainfall, temperature and flow rate during the baseline (1971-2000) and the outputs of the fine- scale model in the period (2011-2040) as well as using an artificial neural network in MATLAB 2012 the river’s discharge during the future period was simulated. Ultimately, the allocation condition of water resources and the required water for agricultural sector were simulated using the water evaluation and assessment programming model, WEAP. The results of the climate model indicated a rise in temperature and a decrease in rainfall within the desired course rather than the base period (1997-2000), so that it was followed by a 1.6ºC rise in temperature and a 1.77% decrease in rainfall all over the area. Consequently, after this increase in temperature and decrease in rainfall, a 32.62% decrease of discharge rate is estimated for Q ra Su River. The result analysis of WEAP model, under the climate scenarios and given the existing under- cultivation level, indicates an increased unmet need for agriculture in the studied region for future period.

In recent years human activities induced increases in atmospheric carbon dioxide (CO2) which caused global warming and climate change. Temperature increasing resulting from climate change lead to increase the evapotranspiration which play key role in determination of crop water requirement. Potential evapotranspiration (ETo) was calculated using Ref-ET model and FAO56 formula to assess the climate change impacts on ETo for the baseline (1971-2000) and future (2016-2045 and 2070-2099) periods in Shahrood city. Hence, climate change scenarios were generated from 13 coupled atmosphere-ocean general circulation models (AOGCM) under two greenhouse gases emission (GHG) scenarios (A2 and B1) and then was downscaled using LARS-WG model. The calculated ETo for different climate change scenarios were compared with those of baseline period. Result showed that in first future period (2016-2045), mean of ETo is increased about 4.5 percent for both A2 and B1 scenarios. In 2070-2099 period, mean of ETo is increased about 15 and 8 percent for A2 and B1 scenarios, respectively. It was also found that ETo is increased more in warmer months in comparison with cold months.