bahram choubin

Floods cause financial losses and countless lives in the world every year. Identifying flood-prone areas is one of the basic steps in flood management. In the past, careful observation and note-taking of the mechanism of occurrence and the natural course of the cause and effect of the effective factors that led to the final occurrence of the phenomenon in a chain manner helped to understand the pattern and process of the occurrence to some extent. Because the processes of creating floods are numerous and affected by various factors and the flood phenomenon is multidimensional and dynamic, all-natural, human, and organizational-management factors affect the occurrence, intensity, extent, and continuity of floods. So far, many efforts have been made to use data-mining models and artificial intelligence in the spatial prediction of floods. The models try to better and more accurately estimate the distribution of the flood phenomenon by examining the relationships between each flood event (dependent factor) and the set of underlying and stimulating factors (independent factors) and fitting them to educational evidence. Since the applicability of the flexible discriminant analysis model has not been fully investigated in the field of flood susceptibility prediction, this research quantitatively evaluated its performance using real-world flood data.

Based on the availability of periodic history of flood events, the Zarrineh-Rood Watershed of Kurdistan Province, Iran, was chosen as the study area. It was tried to select the factors based on different criteria such as familiarity with the process of flood inundation, ease of data preparation, having the most spatial variability at the regional level (not uniform), and containing the most information for the model should be selected to separate areas with different levels of flood susceptibility. Thirteen diverse geo-environmental factors including elevation, aspect, slope percent, land use, drainage density, lithology, plan curvature, profile curvature, mean annual precipitation, soil texture, stream power index, distance from the stream, and topographic wetness index were used as independent variables. The maps of elevation, aspect, slope percent, plan and profile curvatures, stream power index, and topographic wetness index were produced using a digital elevation model. Hydrological layers including distance from the stream and drainage density were produced using the stream network layer. The location of the flooding events was also collected as the dependent variable. The spatial data of flooding were randomly divided into two groups of training and validation with a ratio of 70:30. After running the model (i.e., Flexible Discriminant Analysis) based on the training group, the flood susceptibility map was produced. The validation of the model results was conducted using the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS) metrics.

The results indicated that the FDA model with the value of AUROC= 0.96 and TSS= 0.86 efficiently and accurately produced the flood susceptibility map. The flexible nature of the model in the selection of regression equations, as well as the possibility of weighting, and determining the priority of the evidence of presence over the evidence of absence, are among the special capabilities of the FDA model, which many machine learning models lack. Using probability distribution estimation algorithms in the model is very important and can not only extract the hidden spatial pattern of occurrence from a set of data but also help to predict flood-prone areas in data-scarce Watersheds. Based on the results, about 14% (62 thousand ha) of the study area was categorized in the high and very high flood susceptibility zones, which include the northern, northwestern, and southeastern areas. Spatial analysis of the flood susceptibility map showed that in total 25897 ha (18.12%) of agricultural lands, 343 ha (50.91%) of garden lands, and 2126 ha (39.93%) of residential areas located in high and very high susceptible zones. Considering the successfulness of the FDA model in goodness-of-fit and validation phases, the flood susceptibility map can be used as a basis for planning flood control and management measures.

The findings of this study proved that the flexible discriminant analysis model provides the possibility of processing diverse and big geo-environmental data to predict the flood susceptibility of Watersheds and it had a high efficiency in this context. There is a lot of spatial correspondence between the vegetation status map and the flood susceptibility map; in such a way that the places that had a high flood susceptibility degree, their upstream areas were generally destroyed in terms of vegetation. The results of this research showed that a significant area of the Zarineh-Rood Watershed had a high and very high flood potential, which was characterized by the interaction of low slope and flat areas, formations and soils with low penetration and dense drainage network, and more importantly, flood-prone areas located in the northern, northwestern and southeastern parts of the Watershed. The situation of the flood probability of the Zarineh-Rood Watershed has been determined and managers and decision-makers must put the critical areas in the priority of flood management programs. More flood-driver factors are suggested to be used as predictor variables in flood susceptibility modeling in future studies. On the other hand, it is very important to determine the role of predictor variables in the flood susceptibility degree at the Watershed scale, which can be investigated in future research.

Floods cause loss of life and financial losses every year, and their management is one of the essential elements of watershed management. In this research, an attempt is made to determine the flood susceptibility of the Sirwan watershed and finally the importance of various environmental factors in flood susceptibility based on historical flood events.

In this research, the maximum entropy model along with 13 topographical, hydrological, morpho-hydrological, geological, and environmental flood-affecting factors were used to model the flood susceptibility of the Sirwan watershed and determine the importance and percentage of participation of various factors in the state of flooding potential. A cellular computing unit (pixel) was chosen as the criterion for preparing the predictive factors and flood susceptibility maps. A total of 123 historical flood inundation events detected in the last decade were used as target variables in the model, of which 70% were considered for learning and the remaining 30% for validating the model results. To evaluate the performance of the model, the criterion of the area under the receiver operating characteristic curve was also used.

The results indicate that the accuracy of learning and validation were 98.2% and 97.3%, respectively, indicating the excellent performance of the model. Based on the visual interpretation of the flood susceptibility map, streams with a higher order near the watershed outlet, which are the conduits for the passage of the flow with a larger volume and are located in lower areas, often have a higher proneness to flood inundation. Based on the results of the relative importance test, the four factors of distance from the stream, topographic wetness index, drainage density, and land use were introduced as the most important factors in the modeling flood susceptibility, with of 17, 13, 12 and 10% participation, respectively. These results show that natural hydrological, morpho-hydrological and environmental factors (both natural and man-made) have a mutual effect in increasing flooding susceptibility. Based on the quantitative analysis of modeling, about 0.76% (5600 hectares) of the studied area is in the high and very high flood susceptibility class, which requires planning and flood management.

The high classification of flood susceptibility classes in the Sirwan watershed of Kurdistan province and the determination of the importance of environmental factors in the event of flooding make it possible for managers to take an effective preventive approach by planning relief facilities and infrastructure. To reduce the risk of flooding, flood crisis management in the Sirwan watershed should be defined based on the four main factors identified in this study. Application of the maximum entropy model in flood susceptibility analysis is suggested for flood management of watersheds.

In recent years, problems caused by human interference in the Salmas Plain, such as an increase in the irrational use of surface water resources, an increase in the process of groundwater discharge, and a change in land use have caused pressure on the groundwater resources of the region; hence, land subsidence is at least a natural response to the decrease in groundwater level. Therefore, it is important to study and identify subsidence areas in the region.

In this research, groundwater information and Sentinel 1 radar images were used. Groundwater information was obtained from the regional water company of West Azarbaijan province. In the present research, the DInSAR algorithm was used to measure the ground surface displacement, and the phase difference of the SAR signals was determined using the repeated-pass interference methods. Finally, by using the manual of differential radar interferometry with synthetic aperture and 52 even numbers of Sentinel 1 image, the subsidence in the Salmas Plain was determined between 2014 and 2015 and 2018 and 2019. Validation of the radar interferometry method using Qareqeshlaq Geodynamic station data, as well as groundwater level changes, underground water drop and field observations was conducted.

The results of the subsidence study using the DInSAR method showed that the average annual displacement rate is about 5 cm. Investigation of groundwater changes also indicated a decreasing trend in most of the wells. The wells located in the eastern and southeastern regions (e.g., Kangarlu, Qareqeshlaq, Yushanlu, etc.) have the highest amount of groundwater drop. In the study period (2014 to 2019), where the wells with an increase in groundwater level were affected by their proximity to the Zola dam reservoir and the water supply channels. The wells of Qezeljeh, East Qareqeshlaq, and Maidan Doab have the highest water level reductions, but they do not have sufficient water supply from the dam. Mahlam, West Tazeshahr, and Ian wells, which are in the western part of the plain and close to the dam lake, respectively, have a suitable increase. Checking the subsidence maps of the Salmas plain showed that most of the subsidence is concentrated in the southern areas, especially in the southeast of the plain, in comparison to other parts of the plain the depth of alluvium is also greater in these areas. This subsidence trend continued from 2014 to 2019 and this trend is also prevalent in the final map. During this period, the average water level changes in the Salmas plain aquifer had a downward trend, which was in harmony with land subsidence. By reviewing the groundwater drop map, it was found that the areas with the highest drop coincided with the subsidence areas. More drop in underground water indicated more water extraction. Therefore, the discharge of underground water in the region has caused subsidence.

Before adopting any operational plan and policy for implementation, it is the most important task to identify the exact location of land subsidence in any area, in particular residential areas. Sentinel1 radar images are sufficiently capable of solving this problem at all stages; however, a longer observation time would be more useful for decision making and implementation. Land subsidence in the Salmas plain is fully confirmed by the change of the underground water level and field visits. According to the DInSAR approach, the average displacement rate due to subsidence in the region was 5 cm, which can be fully verified in terms of observations of changes in the groundwater level. The piezometric data show a reduction of in the water level in most wells compared to the past, which depicts the occurrence of excessive extraction of groundwater. In addition, field surveys have been conducted in different areas of the Salmas Plain and the subsidence areas have been recorded. The process of subsidence in the studied area is ongoing, and the attention and care of the relevant authorities is needed to reduce the damage caused by this phenomenon and adjust the possible damages. It is recommended that people should be aware of the consequences of excessive harvesting and recommend alternative crops with a low annual demand for water and early yields.

Users prepare accurate data of the amount of rainfall by using rain gauge stations. However, an interpolation of rainfall data is difficult due to temporal and spatial variability. Therefore, rain gauge stations are not well distributed in many areas, especially in mountainous areas. In a mountainous area, understanding the interaction between the resolution of the Digital Elevation Model (DEM) and climate variables is necessary for accurate spatial interpolation of average rainfall in many areas, and on the other hand, the need for accurate information in hydrological modeling and many environmental studies and it is climatic. One of the problems that exists in many hydrological studies is that rainfall maps are always prepared using interpolation or available DEM, regardless of rainfall, which have an estimated rainfall error.

In this study, four DEMs with spatial resolutions of 30, 90, 1000, and 10000 m, which are the most common DEMs in studies, were used to introduce the best elevation digital model for extracting the rainfall gradient map from the data of 11 meteorological stations in Kermanshah province. A rainfall map for Kermanshah province was prepared using a linear regression model fitted between the height of each station and the 20-year average rainfall. The best DEM for rainfall estimation was then determined on the basis of error evaluation criteria.

The results of this research showed that in estimating rainfall, DEMs with cell sizes of 1000 and 10000 m (R2 = 0.76, 0.81) were more accurate than DEMs with spatial accuracy of 30 and 90 m (R2 = 0.75). In the examination of the Nash–Sutcliffe coefficient (NS), compared to other digital height models of accuracy, DEM with a spatial resolution of 1000 m (one km) with a Nash–Sutcliffe coefficient of 0.76, a significance level of 0.01, and a correlation coefficient of 0.81 was found to have greater accuracy.

The results of the present study can be used to estimate and generalize rainfall in areas that do not have stations and to prepare rainfall maps in areas where the number of stations is limited. In addition, it should be used in univariate interpolation methods that do not have proper accuracy because spatial distances are not considered. In addition, due to the complex topography of the earth and the non-uniformity of meteorological stations on the earth’s surface, high-resolution models with higher spatial resolution are required for the estimation of rainfall, which increases the accuracy of digital models in the evaluation of rainfall studies by removing topographical levels that cause errors.

The phenomenon wherein precipitation increases as altitude ascends is referred to as the precipitation gradient, that significant importance in the field of hydrological forecasting in mountain basins and management of water resources. The present study aims to investigate the changes in the topography of the earth on the formation and movement pattern of precipitation, the average rainfall of the IMERG V06 satellite and the dem of the 0.1 degree elevation of the precipitation gradient for eight main directions or coding in the Python environment of the Alborz Mountains were extracted. Then, in order to determine the movement pattern of precipitation, longitudinal and latitude profiles were used. The study findings indicate that in the northeast region of Golestan province (positive gradient), there exists a positive gradient of precipitation, which amounts to 0. 44 mm.m elevation. Furthermore, it is worth noting that in the southeastern region of Semnan, there exists a negative gradient of 1. 91 mm.m reduction in precipitation of elevation gain. Additionally, it was found that the precipitation gradient was significantly influenced by both the north and south directions, accounting for 16. 8% and 25.4% of the effect, respectively, at a significance level of 0. 01. On the northern slopes, a negative correlation has been observed between the amount of precipitation and latitude, wherein higher latitudes are associated with lower precipitation levels. Conversely, a positive correlation has been noted between the amount of precipitation and latitude in other regions, where higher latitudes correspond to increased levels of precipitation. The intricate nature of precipitation in mountainous regions presents a challenge in establishing a comprehensive pattern due to the multifaceted influence of anthropogenic, climactic, and terrestrial variables, nevertheless, comprehending precipitation patterns in the context of decision-making can benefit water resource management.

The availability of precipitation data plays an important role in many meteorological, hydrological and applications.

In this study, to improve precipitation maps and increase the accuracy of precipitation maps, linear regression, multivariate, and Kriging subsets were used. The data from 14 meteorological stations and IMERG images in the period of 20 years (2001 to 2020), digital elevation model, Latitude and Longitude maps were used. At first, based on regression in Minitab software, the relationship between air and ground parameters was taken. Finally, with the interpolation methods and based on the error coefficients, the best equations for predicting precipitation were determined and the spatial distribution of precipitation was obtained.

According to the results, six out of 13 models were selected because of low RMSE and high R2, R, and NS. In regression models where only one climatic or edaphic parameter was used, forecast accuracy was reduced. But in the models that were used in the regression elevation, Longitude, Latitude and IMERG parameters in combination with interpolation methods, the extracted data matched the real data with a slight difference. In this study, instead of the average of the input parameters, the maps of each parameter were used, increasing the accuracy of the forecast model to R2=0.8.

results showed that combining satellite precipitation products with interpolation methods led to a more accurate estimate of precipitation in the points without recording data will be precipitated and the multivariate regression method will be more accurate than the linear gradient.

In addition to the increase in prone areas to wind erosion processes in western Azarbaijan province due to the drying of Lake Urmia, there is a growing concern regarding the intensification of dust storms. Considering the impacts of this phenomenon on the environment, public health, agriculture, and transportation sections, more detailed studies on its source, severity, and frequency is necessary. Therefore, this study was designed to assess the temporal and spatial changes of dust storm events using satellite data, to detect the dust source, and to analyze the factors affecting the occurrence of dust storms in western Azarbaijan province. Analysis of the number of dusty days using the MODIS-AOD product indicated that the years 2003, and 2008 to 2012 were the dust storm peaks during the period 2000-2020 at western Azarbaijan province. Most dust storm events have occurred in summer and spring seasons, and monthly changes indicate the maximum dust storm between March and October. Trend analysis indicated that there was no significant trend in AOD and dust storm events. Among vegetation and climate variables, there is a significant relationship between AOD and wind speed (i.e., the correlation is about 64%). Spatially, dust storms occur more frequently along the shores of Urmia Lake (mostly in the east and south and slightly in the west).  Southern areas of the province reported a higher frequency of dust event than the central and northern parts of the province.  Due to the direction of the prevailing wind, it seems to be most affected by dust storms coming from the neighboring country of Iraq, however, the areas around the Lake Urmia are the inner source of dust storms.

Integrated watershed management and regeneralization of available information to ungauged basin needs to recognize homogeneous watersheds. The hydrologic similarity of watersheds is caused by the hydroclimate and physical behaviors. In present study, hydroclimate and physical indices were used for indicating the homogeneous sub-watersheds in Karkheh Watershed and then results were compared. Factor analysis to reduce in the dimension of variables was conducted, separately for climatic, hydrological and physical parameters. Finally, using Fuzzy c-means (FCM) and hierarchical clustering analysis (HCA) homogeneous sub- watersheds were indicated by hydro-climate and physical parameters. Factor analysis results showed that indices of CSDI (cold spell duration index), GSL (growing season length), RX5day (monthly maximum consecutive 5-day precipitation), TX90p (percentage of days when daily maximum temperature is greater than 90th percentile) and TMAXmean (mean of maximum temperature) of climate indices, and indices of percent of hydrologica group D and flood potential index of hydrologic indices were selected. Also indices of basin area, basin elongation, average length of drainage and total topography between the physical parameters were selected. Comparing the results of the FCM and HCA indicated that the optimal number of clusters is same, but the results of watershed classification are not same in each cluster of two ways. Classification by hydroclimate and physical indices highlights that 25 and 27 basins were classified similar, respectively in two methods of clustering. Clustering by hydroclimate parameters produced different results compared with physical parameters, so that the 17 and 19 sub-basins are in the same classes in the FCM and HCA, respectively. However, there is no preference to accept watershed classification by hydroclimate or physical parameters, thus a combination of hydroclimate and physical parameters were used for classification. Watershed classification by the combination of hydroclimate and physical parameters indicated that 33 sub-basins were classified similar in both FCM and HCA methods. Classification in Karkheh basins based on the combination of physical and hydroclimate parameters revealed that the northern areas are in group 3, central regions (from east to west) are in group 2 and southern areas are in group 1. Inherently, these basins are similar in each group and demand the same protection operations in view of watershed management.

Management of watersheds requires understanding of watershed conditions both in gauged and ungaugedbasins. The classification of watersheds by similarcharacteristics for the implementation of coordinated watershed operations and flood control as well as giving priority to sub-basinsis of great importance. The need for a classification framework in hydrology is not an entirely new subject. In fact, this subject has long been discussed and several studies have also attempted to advance this idea. So far, no acceptedcomprehensive protocol has been presented for the classification of watersheds,and questions can be raised regarding why this has not happened. More efforts must be made in order to develop such a classification.Previous studies have used hard clustering methods more, for the classification of watersheds but, the present study used fuzzy approach as asoft method. In general, the purpose of this research is to focus on the characteristics of the watersheds including morphological characteristics, soil and land use for the identification of similar watersheds. These parameters can facilitate the watershed classification scheme and our understanding ofthe watershed conditions. The dataset for this study includes is base maps (sub-watersheds boundary, streams and rivers, digital elevation model (DEM), soil and landuse which have been collected from Iran Water Resources Management Company. To classify the Karkheh watershed, 35 spatio-physical indices including topographic, morphological, landuse characteristics and soil parameters were considered. These indices have been calculated for each watershed. The dimension reduction of the variables was an important part, because 35 indices were quite large for the classification of 38 watersheds. Therefore, factor analysis for each group of indices wasusedseparately to reduce the number of variables.
After reducing the variables and selecting the final indices, the fuzzy clustering approach was conducted to classify the watersheds into homogenous groups. The number of optimal clusterswas determined through trial and error and the functions of partition coefficient and partition entropy evaluation. Kaiser-Meyer-Olkin (KMO) test statistics for each group of the morphological, landuse and soil indices were 0.71, 0.69 and 0.76 respectively, indicating that the data was suitable for factor analysis. Factor analysis was conducted using Principle Component Analysis (PCA) method and the results revealed that among 35 spatio-physical indices, 9 indices (4 morphological indices, 3 land use indices and 2 soil parameters) had a higher load factor than other indices. Therefore, indices of the watershed surface, basin elongation, average length of drainage network and total topographic indexamong the morphological indices;percentage indices of rangelands, agricultural lands and wastelandsamong the land use indices; and indices of water holding capacity in the soil layer and saturated hydraulic conductivity among the soil parameters were selected as the ultimate criteria for grouping the watersheds.
Theselectedfactors were normalized between zero and one before the classification. Then, sub-watersheds were classified using fuzzy C-mean (FCM) approach. The trial and error method was used to find thenumber of optimum clusters. The maximum amount of evaluation function of partition coefficient equal to 0.76 and the minimum amount of partition entropy function equal to 0.49 occurred in three clusterstherefore,the number of optimum clusters equal to 3 clusters was determined through trial and error.The results of classification indicated that the triple groups included the sub-watersheds of the northeastern regions and parts of central regions of the Karkheh watershed (group 1), the northwestern- southeasternalong with the southern regions of Karkheh watershed (group 2) and the central regions and parts of southwestern regions of Karkheh watershed (group 3). Watershed classification with similar characteristics can be used as a method for watershed management, flood control and giving the priority to critical sub-basins. However, watershed classification is only completedwhenit is understood why some catchments belong to certain groups of hydrological behavior, so as to be possible to classify gauged and ungaugedwatersheds through it.
Finally, it is important to remember that classification of watersheds is not the “be-all and end-all” of research on watersheds, but rather only a means towards achieving broader aims of planning and management of our ecosystems, environment, water resources, and other relevant earth systems and resources. However, watershed classification certainly allows us to study catchments more effectively and efficiently and develop more appropriate strategies in terms of simplification in models/model development, generalization in our modeling approach, and improvement in communication both within the hydrologic community and across disciplines, as much as possible.

One of the techniques to eliminate groundwater resources crisis is to carry out artificial recharge projects, which cause infiltration of water from the surface into the aquifer and balance of the water table. An appropriate site selection for artificial recharge is one of the most important steps in implementation of these projects, itself. In this study, capability of gridding technique and AHP method was evaluated for zonation of artificial recharge potential. Accordingly, a grid, with cell size 0.1 km2, was defined for rangelands of Chamshor watershed and geological parameters, slope, thickness of unsaturated layer, electrical conductivity and transmissivity were selected for entry into the model. Finally, potential zonation map of artificial recharge project, using gridding technique, Analytical Hierarchy Process (AHP) and weighting linear combination methods were produced for implementation of artificial recharge. In order to assess the model, the project of artificial recharge in the study area was used which has had a successful performance on balancing the water table level, reducing destructive floods and increasing vegetation. Finally, the accuracy of the gridding technique and AHP method was 87.5 percent. In conclusion, the zonation of artificial recharge potential which was obtained by gridding technique and AHP method was reliable and it is recommended to be used in the site selection of flood spreading systems to be able to carry out artificial recharge projects.

Ground water is one of the major resources of fresh water needed for humans, especially in arid areas. It will be seriously threatened in future due to climate change and global warming. The purpose of this study is to examine the effects of climate change on groundwater resources in the Kerman-Baghin plain. Accordingly, climate prediction at 2055 horizon (2065-2046) was used by the atmospheric general circulation model HadCM3 with A2 emissions scenario. Also groundwater table during the years (1986 -2009) and the quality situation (salinity) over the years (2003-2009) were studied. The results of LARS-WG model showed that the average annual rainfall at the 2055 horizon will be reduced about 3.6 mm. Also the annual maximum and minimum temperatures will increase during the 2055 horizon, 2.8°C and 2.8 °C, respectively. Assessing of unit hydrograph and chemograph in Kerman- Baghin plain highlighted a drop in the water table (0.89 meter depletion per year) and increase of aquifer salinity. According to the results of atmospheric general circulation model (HadCM3), this trend in the water table and increase in aquifer salinity expected to be continued.

Drought is an atmospheric phenomenon that can effect on various parts of the environment. Groundwater resources is the drought-affected sectors which less are considerate than other sectors. Aspas Plain in Fars province in recent years have a significant reduction in groundwater levels. So, the role of management in this plain is important. In this research, we use a cross-correlation function (CCF) for investigating long-and short-term drought on groundwater resources. For this purpose, the standardized precipitation index (SPI) Monthly (one, three, six, nine, 12, 18, 24 and 48 months) in the period (1985-2010) was calculated, also groundwater resources index (GRI) of the plain during the statistical period (2002-2010) were obtained from 61 wells in the plain. Cross-correlation function showed that short-term drought with delay and long-term drought simultaneously have been effected on ground water resource. Most relationship is between groundwater and SPI 24, which linear regression results showed that almost 80 percent of the average variance water table of Aspas Plain affected by changes in rainfall (SPI 24) and nearly 20 percent of it is influenced by other items.

To optimal and sustainable management of water resources in watershed scale, estimation of water balance and important to know of the different parameters of the water balance is inevitable. Goal of this study, is estimating the volume of actual evapotranspiration, infiltration, surface and subsurface flow based on hydrologic balance in Marghab and Abgelal watersheds. Hydrological balance in the hydrological stations these watersheds was calculated using the Salas approach. Results indicated that in Abgelal watershed, from 597 mm average of rainfall, about 438 mm is evaporated and about 95 mm as runoff out of the watershed. Thus, 64 mm of the total rainfall infiltrated. In the Marghab watershed, maximum of rainfall as runoff out of the watershed, so from amount of annually rainfall that is equal with 641 mm, amount of 192 mm as evapotranspiration and amount of 72 mm as infiltration and 377 mm as runoff out of the watershed. Model evaluation for the two stations was conducted using the error parameters such as Nash – Sutcliffe Error (NSE), Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE), results indicate that performance of model is better in Marghab station than Baghmalek station.

Recognizing of groundwater quality، which have the most important role in preparing of the drinking and agriculture water particularly in arid regions، is essential. In this study، the hydrochemical status of Semnan aquifer was drawn and analyzed using qualitative diagrams (Piper، radical، Collins، Schuler and Wilcox diagrams) during August 2008. Then، groundwater quality zonation (water type، drinking and agriculture purposes) were prepared using Arc GIS software. The results showed that the groundwater dominant type is chlorure sodium in Semnan plain. Also، groundwater quality for drinking in northern and northeast and a small area in west of plain is medium to acceptable، and in other regions especially in south and southwest of plain is low. According to Wilcox diagram، the most groundwater resources of the study area located in C4S1 to C4S3 classes. These resources are very saline and almost alkaline water and are not suitable for agriculture. Some groundwater resources in northern and northwestern of plain located in C3S1 and C3S2 classes. These resources are saline water، but can be used in agriculture.

Modeling in arid regions to better manage water resources is very important. Groundwater is an important water resource in arid regions. The purpose of this study was to assess the performance of adaptive neuro fuzzy inference (ANFIS) and time series models to predict the water table. In this study, groundwater levels of Shiraz plain for one month ahead were forecasted by using time series models and ANFIS model. In the ANFIS model has been used Gamma and M-test for determine of the optimal input combination and training and testing data length. Performance of different models was compared with the parameters of the error and Taylor diagrams. ANFIS model results showed that this model with membership function of Π-shaped has better performance than the rest of membership functions. Performance comparison of the models indicated very suitable performance of the ARIMA (2, 1, 2) model than ANFIS models with different membership functions.

Ground water has been important water source within all centuries. Due to this important, necessity is aware of how spatial and temporal changes in these variables that in many areas is important. Exploitation of the Aquifer Aspas and also continuous drought in recent years has caused a dramatic reduction in groundwater levels. In the past, the level of groundwater at a depth of 7-5 meters from ground level, but already because of excessive use numerous wells has reached to a depth of 60-50 meters. As a result, management factors are important in this the plains. The aim of this study, has been investigated the relationship between changes in the water table aquifer and salinity during the period of (2002-2010). For this purpose, were used the meteorological data (the average annual precipitation for the precipitation process), Groundwater monthly quality data (including electrical conductivity to investigation salinity), quantitative data Groundwater (includes monthly Groundwater level of observation wells of plains to show status of groundwater fluctuations and the unit hydrograph the plains). unit hydrographs shows that during the period of (2002-2010), groundwater level declined and the average of drop has been 0.7 meters per year, On the other hand can be seen an inverse relationship between the water table and salinity. Comparing of changes in electrical conductivity and groundwater level in the aquifer showed that Salinity is higher in areas where the water table is lower.

In arid and semi-arid ecosystems shortage of surface water has led to a greater use of groundwater resources. Water table in groundwater due to climate and human intervention is always fluctuating. Assessing change groundwater resource in planning and sustainable management of water resources in each region has of great importance. In this study were investigated changes of water table in the plain aquifer Aspas. For this purpose monthly data of 30 well of groundwater observation to demonstrate groundwater fluctuations in statistics course (2002_2009) was used and using nonparametric tests Mann - Kendall and Spearman significant trend for monthly and annual time series were evaluated at a significance level of 95%. For each series, the slope of trend line was computed using Sen's Estimator method. The results showed that in all stations (except stations Pahlevani side drainage and riverside Hajiabad, Babai, kenas - sefli and kenas sefla) groundwater level has a negative trend. Mann - Kendall showed that nearly 83 percent of stations have negative significant trends. Investigate slope of trend line in the Aspas plain shows that water table declined 73.5 centimeter in each year.