حیدر زارعی

Predicting future landuse changes is an important step in the proper planning and management of watersheds. Therefore, in this study, the land use modeling of the Doroodzan Dam watershed was discussed. First, the land use maps of 1996, 2005, 2016 and 2021 were extracted using Envi software, and the maximum likelihood method. The transition potential maps were modeled for each of the sub-models by using multi-layer perceptron artificial neural network and 6 variables and Markov chain was used to calculate the allocation change to each land use. Then, the land use map for 2021 was predicted using Land Change Modeler (LCM) and 2005-2016 calibration period. To verify modeling accuracy, Hits 1.92%, Misses 8.8%, False Alarms 2.94% were calculated. The ratio of Hits to the total pixels has changed 14% indicates that model results are acceptable. Then, the land use map for the year 2050 was predicted. The results showed that given the predicted LC for year 2050 in comparison with the year 2021, bare land, agricultural land, residential areas and orchards will increase by 54290, 7621, 4494, and 2391 ha, respectively. Whereas grassland and forest cover will decrease 68441, and 689 ha, respectively.

Hur al-Azim wetland in the southwest of Iran has become one of the major sources of air pollution in Khuzestan province in recent years due to widespread fires. Sustainable harvesting of this huge source of biomass and controlled combustion, in addition to reducing current pollution, can play an effective role in sustainable renewable energy production. Therefore, in this study, it is tried to estimate the amount of sustainable harvestable biomass from this wetland. In order to identify and estimate biomass amount, NDVI index was used in satellite images. Also, by visiting 30 points with different NDVI index, and harvesting biomass, yield per hectare was estimated. Also, the thermal characteristics of the reed plant were investigated for use in a biomass burning power plant. Based on the results of the estimation, approximately 3,500,000 tons of reeds can be harvested annually from the total of 33,712 hectares of reeds in the Iranian part of Hur al-Azim wetland, and according to the average biomass moisture (65%), the amount of dry biomass that can be harvested annually will be about 300 thousand tons, which is a very significant number. Also, according to the results, the calorific value of the reed plant (about 20 MJ/kg), the amount of ash, the composition of the ash, as well as the composition of the elements of the reed plant organs are comparable with reference energy plants, which makes it possible to use as a fuel in biomass power plants.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level (Sanchez et al., 2008). The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods (Alizade and Kamali, 2007). Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations (Chihda et al., 2010). Examples of such algorithms include SEBAL (Bastiaanssen et al., 1998 Bastiaanssen, 2000;), METRIC (Allen et al., 2007), SEBS (Su, 2002). Among the above mentioned algorithms, energy billing algorithms have been used (Bagheriharooni et al., 2013; Teixeira et al., 2009). Among the factors of superiority of the SEBAL algorithm, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models (Bastiaanssen et al., 2002).

Different methods are used for baseflow separation, filtering method one of these methods. In this study, the filtering methods with different algorithms, inclusive One-Parameter, Two-Parameter, Three-Parameter, Lyne & Hollick, Chapman, Furey & Gupta, Eckhardt and Ewma used for daily baseflow separation of Kahman karst spring in Aleshter county. The statistical period used in this study was a period of 27 years. The Isotope content method used as the main method for baseflow separation. Samples analyzed at the Mesbah Energy Company laboratory. Each algorithm has different parameters. First the parameters of each algorithm optimized based on the isotopic content method in the water year of 2017-2018. Then the optimized parameters used for the period 27 years.  At the end, using the evaluation of different critical compared to the different algorithms. The results showed that the Eckhart algorithm performs better than other algorithms. This algorithm estimated baseflow and surface water indexes, respectively 81 and 19 percent.

Achieving basin runoff estimation requires a more realistic simulation of the precipitation process. Simulation closer to the reality of the process has always been the goal of the researchers. How the first starting point of runoff at the basin level is, leads to different patterns, therefore, is effective in the accuracy of the process simulation. The lagged pattern of flow formation in the catchment area, due to the assumption of the formation of the flow from the center of the surface, is closer to the current flow reality than the conventional pattern in simulation, such as HEC-HMS. The present study was designed to evaluate the latent pattern in the catchment area of the Roudzard River, comparing it with the conventional model in the HEC-HMS simulator. The results showed that the lagged pattern, in contrast to the conventional model, increased the number of parameters for the three rainfall events, the values of the Nash and PWRMS were 0.13, -37.0, -0.08 and 2.49, 8.35, 6.41 respectively Which had a lower accuracy in comparison with the values of them such as 0.57, 0.68, 0.43 and 1.03, 6.96, 3.87 in the conventional pattern. However, sensitivity analysis of the parameters indicated the type of pattern is different in the relative sensitivity of the parameters. As in the delay pattern, the K parameter is more sensitive than the conventional pattern compared to the CN parameter. Comparison of two selected patterns in estimating flood volume showed that the delay pattern has a better performance than conventional pattern.

The construction of dams in downstream basins can have significant impacts on the environment and water resources. This study investigates the effects of the Mulla Sadra Dam in the upstream area of the Dorodzan Dam in southern Iran, where drought conditions and over-water withdrawal are prevalent. Data from various meteorological and hydrometry stations in and around the basin were analyzed for a 45-year period, and the discharge affected by Mulla Sadra Dam was calculated using the Chamriz hydrometry station as a reference. The natural discharge of Chamriz station was estimated using the Trend removal method, and water withdrawal upstream of the station was calculated. The discharge of 11 sub-basins was estimated using the gross runoff method or experimental methods such as Justin and Icar. Results show that the average annual natural discharge of Chamriz station was 33.62 m3/s, and the average annual water withdrawal upstream of the station was 9.47 m3/s. The average annual natural discharge of the upstream basin of Mulla Sadra Dam and Dorodzan Dam was 18.64 and 43.40 m3/s, respectively.

In this study, the groundwater level of Aspas Plain, located in the northwest of the catchment area of Tashk and Bakhtegan Lakes in Fars province, was simulated using the data of 39 piezometers in the aquifer plain in the period of 2001-2017. In this regard, K-Star and ANN models with 1, 2 and 3 lags were used in two phases of training and testing. ANN model in this study was also investigated with the number of hidden layers and different neurons in addition to the mentioned lags. According to the drop and fluctuations of the groundwater level in the mentioned statistical period, the studied aquifer was divided into 4 regions a, b, c and d. The results of the simulation of the groundwater level in area “a” showed that the K-Star model with 2 lags performed better than the ANN model in different lags. But in the other three regions, the ANN model with a lag-1 has provided the best performance. ANN model with 2 hidden layers and 4 neurons in region b, with 2 hidden layers and 6 neurons in region c, and with 4 hidden layers and 5 neurons in region d were introduced as the best model for simulating groundwater level values. On average, the best models introduced in regions b, c and d were able to improve the error rate by 57, 42 and 81%, respectively, compared to the K-Star model. The poor performance of the K-Star model in two regions b and d is also clearly visible. In general, the results showed that lag-1 of the ANN model had the best performance in simulating the values of the groundwater level in the three regions b, c and d with the number of hidden layers and different neurons.

Excessive extraction of groundwater has caused most of Iran's groundwater aquifers to face a drop in water level in recent years. This has subject caused the use of most of the aquifers to be prohibited, most of the Qanats have dried up and most of the permanent springs have had a significant reduction in their water supply. Therefore, the investigation of the groundwater level should be given more attention. Various methods and tools have been used to investigate this issue. Artificial intelligence models have been used in most of these studies. Among these intelligence models, Support Vector Regression (SVR) model has performed well. In order to improve the performance of these models, in recent years, the use of pre-processing tools and the formation of hybrid models have been considered. One of these tools is complementary ensemble empirical mode decomposition (CEEMD). In this research, the combination of this tool with the SVR model was used to check the groundwater level in the Aspas aquifer. Then their results were compared with the results of the Gravity recovery and climate experiment (Grace) satellite.

The Aspas subbasin with code 4321 is located northwest of the Tashk-Bakhtegan and Maharlu basin in Fars Province. To check the groundwater level in this sub-basin, the SVR model with 4 kernels include: polynomial kernels, RBF kernel, sigmoid kernel, and linear kernel (Lin) was used. Then discusses the formation of a hybrid model obtained from the combination of CEEMD with the SVR intelligence model. When an initial signal is decomposed using the CEEMD method, and the resulting sub-signals are used as inputs to the SVR intelligence model, the hybrid model of CEEMD-SVR is obtained. Satellite data was used to compare the performance of artificial intelligence models. For this purpose, Grace satellite products with 6 different algorithms were used. The parameters the coefficient of determination (R2), root mean square error (RMSE), and the Akaike information criterion (AIC), was used to examine the efficiency of the methods.

The results showed that intelligent models had better performance than Grace satellite products. Therefore, it is more appropriate to use intelligent models, especially the CEEMD-SVR model, to predict the values of the groundwater level. One of the advantages of using satellite data is that it is available up-to-date. If the satellite data values can be approximated to the observed values (in a similar statistical period) based on a suitable method, the groundwater level data can be estimated in an up-to-date manner.

In this study, the SVR model was used to evaluate the groundwater level changes in the Aspas alluvial aquifer located in the Tashk-Bakhtegan-Maharlu basin. Using observation wells in the area the aquifer groundwater hydrograph was plotted. Changes in groundwater level in the aquifer were estimated using the values of precipitation, temperature, and evaporation parameters obtained from drawing different maps, and groundwater level in the aquifer. The preprocessing tool of CEEMD was used. The results showed that the use of the CEEMD has improved by 3.08% the performance of the SVR model. The GRACE satellite products are used. The comparison of the results of processing algorithms showed that the GFZ processing algorithm had the best performance with a coefficient of determination of 0.71 and an RMSE value of 39.15. In the next step, the performance of the CEEMD-SVR model was compared with the GFZ algorithm. The results showed that the CEEMD-SVR model performed better (R2=0.77, RMSE=25.90) and has the ability to be used for modeling and predicting the groundwater level in aquifers, especially the Aspas aquifer.

Climate change caused by global warming has altered temporal-spatial distribution as well as rate and form of precipitation, the magnitude of floods, annual precipitation ​​in rivers, seasonal variation of probable maximum precipitation and flood, water quality, evaporation rate, concentrations of nutrients in aquifers, etc. the Atmosphere-Ocean Coupled General Circulation Model (AOGCM) is currently the most reliable tool to study the effects of climate change on different systems. This model simulates climate parameters. Estimation of probable maximum precipitation (PMP) is an important and practical research method that not only identifies behavior of extreme rainfall in climatology, but also helps hydrologists to design various large water control structures, especially dams. Climate change affects PMP in the coming periods. Consequently, PMP estimates will be modified by hydrologists.

Evaluation as one of the basic steps of integrated management of water resources is one of the most important stages of its planning and implementation. In this regard, the water accounting system as a tool for organizing and combining data collected from different sources in order to compile an information system and provide the possibility of integrated assessment of water resource systems through the link between physical data and Economic is introduced. The area in question is the study area of the middle Karun basin. The economic-environmental accounting framework for water, after compiling water accounts related to the region, corresponding to the agricultural year 2012-2013, The status of water resources was analyzed by extracting indicators. Then, in order to simulate and observe the effects of different scenarios of the cultivation pattern, the studied water system, including all harvests and inputs to Karun, was modeled with the help of WEAP simulator in the period of 1335-93. With the help of water accounting tables and the calculation of indicators such as value-added productivity, economic efficiency of water in the region, etc., the results showed that the agricultural sector, with the amount of 7461 million cubic meters, has allocated the largest amount of water consumption, so that among the products Agriculture of sugarcane and corn plants have the highest and lowest amount of water consumption with 6545 and 10 million cubic meters, respectively. Drinking and service sector with 232 million cubic meters and industry and mining with 85 million cubic meters in terms of water consumption, respectively They are in second and third place. Based on the results obtained from the model of changing the cultivation pattern (50% reduction of sugarcane plant and replacing it with sugar beet plant) and 10% and 20% reduction of environmental nodes will improve the water resources system

Using water resources require the determination of components of the hydrological cycle. Understanding of natural systems and physical laws that manage each component of the hydrological cycle is important for the water resources management. One of the crucial components of the hydrological cycle is evapotranspiration. Most methods that have been presented in this study use point measurements to estimate evapotranspiration. Due to dynamic and changing nature of regional evapotranspiration is not generalizable to the basin, the applied methods are only suitable for the local areas. Technology of satellite remote sensing and remote sensing-based methods can be used for the temporal and spatial estimation of actual evapotranspiration in the small and large basins. Remote sensing data derived from satellite imagery calculates the amount of actual evapotranspiration using surface energy balances model. In this study, actual evapotranspiration at the Bakhtegan-Maharloo basin were estimated and evaluated using Landsat 8 satellite images sensor OLI/TIRS and SEBS energy balance models. The values of evapotranspiration derived from energy balance model and FAO-Penman-Monteith method were compared.The results showed that the evapotranspiration values obtained from the energy balance model has a root mean square error (RMSE) and mean absolute difference (MAD) equal to 0.62 and 0.49 mm/d, respectively, indicating its acceptable performance to estimate the evapotranspiration

Development of agricultural lands, the occurrence of droughts, and climate change have made the Maroon-Jarahi basin to become one of the most stressful basins both quantitatively and qualitatively. Due to the existence of numerous irrigation and drainage networks in this basin, it is very important to investigate the effect of returned drainages on the river. Due to having suitable water potential, the study basin has always been suitable for the development of irrigation and drainage networks, also Shadegan Wetland at the end of the basin has increased the importance of quantitative and qualitative water resources management. Therefore, in this study, the effect of drainage from irrigation and drainage networks and traditional agriculture on the quantity and quality of rivers, and more importantly, the endpoint of the Jarahi River and the entrance to Shadegan wetland was investigated. Quantitative and qualitative simulation of the basin for a period of 60-year was performed by the WEAP model. Considering that none of the studies conducted in the Maroon-Jarahi basin has comprehensively and accurately examined all agricultural drains and critical points of the basin from a quantitative and qualitative perspective, so the study was conducted in this regard.

In this study, the WEAP model was used as a comprehensive tool for quantitative and qualitative Modeling and their effect on downstream. First, the WEAP model was calibrated and validated in a period of 5-year (2012-2016) using the observed data. In order to evaluate the results of calibration and validation of the quantitative model, the statistical indices of root mean square error (RMSE), squared correlation coefficient (R2), and NASH model efficiency coefficient were used. The model was implemented for a 60-year simulation period (2017 to 2077) under the control management scenarios of network drainage in terms of quantity and quality to determine the effect of each drainage on the river system of the basin.

The results of the model showed that the amount of salinity in the reference scenario is 3.6 (dS m-1) which in comparison with other scenarios only in Matbag drainage transmission scenario with a decrease of 34% (1.2 (dS m-1)) salinity compared to the reference scenario. This rate of salinity improvement is very important at the end of the Jarahi River and the entrance of the Shadegan Wetland and will have an excessive effect on the environment. Also, according to the reliability values of the environmental node of the Jarahi River, the difference between the reliability in the two references and the Matbag drainage transmission scenarios is 0.4%, which shows the minor effect of the Matbag drainage on the flow of the Jarahi River and also the reliability of other scenarios is equal to the reference scenario.

According to the results of quantitative and qualitative modeling in the Maroon-Jarahi basin, the Matbag drainage transmission scenario (left drainage network of Ramshir) has the maximum effect in terms of quality compared to other scenarios. If this drain is transferred, the salinity at the end of the basin during the simulation period will decrease by 1.2 (dS m-1) (34%).

The groundwater level of Tuyserkan plain has dropped by more than 11 meters in the last 22 years. This study surveyed the trend of groundwater quality parameters in the study area using data from 15 observation wells from 2005 to 2016, the trend of groundwater quantity parameters using data from 17 observation wells, and also the trend of rainfall changes using 11 rain gauge stations data from 1993 to 2014 by non-parametric Mannkandal test. For each time series, the trend line slope was calculated using the Sen's slope method. The quality parameters included 13 parameters that were measured twice a year (i.e., during the wet and dry periods). Based on the results, the quality parameters of the plain decreased, and the trend of significant decreasing changes in the water table dropped by an average of 53 cm per year. Moreover, the results of precipitation analysis showed that the average annual precipitation decreased by 3.75 mm.

Baseflow separation is one of the important topics in hydrology and water resources management. Different methods have been used to baseflow separation in the river and karstic springs. In this study, the wavelet transform was used as a new tool to baseflow separation in Gamasiyab spring in Nahavand County and Kahman spring in Aleshter County. In this study the data of the 49 years period for the Gamasiyab spring and data of the 27 years period for the Kahman spring were used. For evaluation of this tool, the Hydrochemical method was used by sampling quality data in water year of 2017-2018 for the two springs. The results showed that, in Gamasiyab spring, wavelet function Db4 at level 6 has the best performance. This best performance has the adjusted R2 of 0.96, and RMSE of 0.651 (m3/s). The best performance in Kahman spring related to wavelet function Db2 at level 5, that has adjusted R2 of 0.99, and RMSE of 0.187 (m3/s). Generally, the Wavelet Transform has high accuracy in baseflow separation.

Nowadays, monitoring of river quality information is one of the most important issues in water resources engineering because of the direct relationship of water quality with environmental health and quality of life. Today, traditional methods of river monitoring are receiving less attention due to the fact that they are costly and time-consuming for the researcher. Instead, the recent, low-cost methods are favorable to many researchers in this filed. Different methods have always been considered for river monitoring, but the application of spectral indicators and remote sensing technologies to control and monitor the water quality of rivers and reservoirs is very cost-effective and could be a good alternative to traditional methods. Since it is time-saving and less costly, it would be a good indicator for the whole region and a good alternative to manual methods (Bonansea et al., 2015). Although satellite imagery has been widely used in estimating water quality indices (Onderka and Pekárová, 2008), the complexity of hydrological systems and the presence of noise in images can increase the calculation error. Wavelet transform and intelligent models are among the most efficient methods that can significantly increase computation accuracy by filtering and noise reduction. Good research has been done on the use of wavelet transform in image processing (Graps, 1995) and fuzzy inference system to estimate water quality parameters (Solgi et al., 2017). In this study, using wavelet transform, Landsat 8 images were processed, then the processed images were considered as inputs of ANFIS model.

Despite being helpful to explore and analyze large multidimensional datasets, visualization Techniques have been rarely considered in hydrology. One of the techniques is Pixel-Based (Raster-Based) graphs. Pixel-based graph is a graphing technique that maximizes displayed information using a pixel or raster-based approach.

This study  two types of raster-based graphs, including Raster-Hydrograph and Raster Hyetograph were evaluated, for Gamasiyab Karstic Spring located in Nahavand. The graphs were drawn by applying discharge and rainfall daily information of gamasiyab spring in 1969-2018. The MATLAB was employed to draw the graphs. To calculate the spring discharge, recorded data from Sang Sorakh and Variane Canal station were used. The data gathered for Sang Sorakh and Variane were recorded from 1969 and 2005, respectively. Thus, the spring discharge was the summation of both stations. The maximum, minimum and average discharge was, respectively, 37.97, 0.3 and 4 m3/s. It is important to note that the basin area is about 60 Km2.  

By applying the graphs, six different phenomena were investigated:Snowmelt: According to the raster hydrograph of the Gamasiyab spring, snowmelt occurs in the first 200 to 300 days of year (e.g. early April to late July). According to this graph, during the recent years, snowmelt period shortened. As of 2004, that the number of snowmelt days showed a considerable reduction as compared to the previous years. This issue has become more intense for the years after 2013 indicating a change in the spring discharge regime.Drought: According to the raster hydrograph of the Gamasiyab spring, droughts were observed in 1998 and 1999.Storm Flow: According to the raster hydrograph of the Gamasiyab spring, a storm flow was observed in the middle of April,1986. Storm flows were also observed in late February of 1986 and 2005, and the late March of 2016.Dry Year: Dry Year is a year that the discharge is less than the average. 2008 and 2009 were the examples of dry years. In addition, 2014 was one-year low water.Dry Month: Determine dry months are used for baseflow separation. In dry months, discharge is due to baseflow, and rainfall and snowmelt play a very small role in the discharge.Monthly changes: Monthly changes happen when rapid changes in discharge are observed from one month to another. For example, the discharge regime suddenly changes from a dry to wet condition. According to the raster hydrograph of the Gamasiyab spring, the monthly changes in April and May, 2014 were observed. It was observed that the rainfall was almost equal to 0 in June to September. In the other words, rainfall period is from early November to early June. Maximum rainfall is in April and May.Better results can be achieved by using both Raster Hydrograph and Raster Hyetograph. Discharge of Gamasiyab spring is affected by snowmelt and groundwater flow since late May to late September, and rainfall has no effect on spring discharge in this period. According to these graphs, it can be also concluded that springtime rainfall was impacted with one-month lag time. According to raster hydrograph, the minimum discharge occurs in October, however, the area receives rainfall during October based on raster Hyetograph. Therefore, the discharge increase in the November can be attributed to the precipitation falling during October.

Main benefits of this graphs are: 1. a way to view large datasets.  2. Quickly review and interpret. 3. Develop new types of products. 4. Cost and time efficiency. This method is able to show systematic error, missing data, outliers, comparison different places, potential new products. Results show that the snowmelt period in Gamasiyab spring decreased from 1969 to 2018. This period shortened from 100 to 30 days per year. The year of 2008 was the driest year during the statistical period of the spring, and a drought was also observed in 1998. According to raster hydrograph, the driest month was found to be October. Determining this month is very useful for base flow separation. One can conclude that these graphs including large amount of information, accelerate the processes of scanning and interpretation.

Evaluation of the interactions between surface water and groundwater in order to efficiently manage water resources is essential. Having Dezful-Andimeshk plain as the largest and the most important agricultural plain in Khuzestan province and also experiencing increasing demand in surface water and groundwater resources in the agricultural use, affirm that the assessment of the relationship between aquifers and rivers for the proper management of water resources is of great importance. In this study Cluster analysis has been performed to determine the interactions between groundwater resources and surface waters in the designated plain. In this paper, the qualitative parameters of 37 groundwater wells and 3 hydrometric stations related to Shavour, Dez and Karkheh rivers were investigated in normal, dry and wet periods. The parameters are: EC (Electrical conductivity), main cations (Ca2 +, Mg2 +, Na+, K+) and major anions (Cl-, HCO3-, SO42-). Then, through using cluster analysis, the similarity between stations was determined, the dendrogram indicated three distinct groups, the first group demonstrated the similarity between the Shavour river, Dez river and the right-hand seafront aquifer, the second group of the qualitative repository wells the southern plain and the third group reflected the similarity of the Karkhe River with the wells around the river. Results have shown that discharging the Dez River directly affects the quality of the aquifer in the plain, and it seems that the Karkheh River has the least similarity to the surface water and groundwater resources in the plain.

Iran is facing severe water shortage.Modernization of irrigation systems and improving the efficiency of water use is typically presented as an opportunity for large water saving in the agricultural sector. For this reason, the Iranian government strongly supports the modernization of irrigation systems and has implemented many water-saving irrigation measures with the objective of improving water productivity and reducing agricultural water use. In this regard, some progress has been made. But total agricultural water use did not decline as expected. Thus, agricultural water use in Iran may experience a rebound effect. The hypothesis of a rebound effect as a consequence of water saving investments is taken from the Jevons paradox in energy economics. The objective of this study was to evaluate the magnitude of the water rebound effect (WRE) at farm level in Bakhtegan basin using corrected land allocation model and direct comparison approach. The results indicated that the magnitude of the agricultural WRE in Bakhtegan basin can fluctuate between 10.1% and 109.38%.

Introduction In the pursuit of detecting the trend and the shift in trend in hydro-meteorological variables, various statistical methods have been developed and used over the years. Of the two methods commonly used (parametric and non-parametric), the non-parametric method has been favored over parametric methods. Long term trend analysis can reveal the beginning of the trend year, trend changes over time, and abrupt trend detection in a time-series. It is expected that the findings of this study will bring about more insights on understanding the regional hydrologic behavior over the last several decades in Iran.   Methodology This paper analyzes the behavior of annual and seasonal temperatures, precipitation, and relative humidity. Datasets of 37 stations in Iran were analyzed from 1961 to 2010. The  pre-whitening technique was used to eliminate the effect of the autocorrelation of data series. The Mann–Kendall (MK) test and the Sen's slope estimator were applied to quantify the significance of the trend and the magnitude of the trend at a 95% confidence level, respectively. The surface interpolation technique was used to prepare a spatial temperature, precipitation, and relative humidity data map over Iran from the point data measuring stations within the ArcGIS framework. For spatial distribution of trends in maps, contours are generated using an inverse-distance-weighted (IDW) algorithm.

Monitoring and evaluation of hydrological drought has become a necessity due to its frequent occurrence in various basins of Iran, and in order to prevent and manage the resulting crisis. In most studies, drought indices have been used to investigate it. Drought indices like Streamflow Drought Index, using statistical calculations on river flow data, ultimately leads to a number that does not have the ability to completely investigate and analyze drought in the studied area. Low flow or minimum river flow is one of the most important drought indices that using different criteria can get a comprehensive understanding of the hydrological drought situation in the region. In most studies, deficit characteristics or low flow frequency analysis have been used. Using different low flow indices is necessary to recognize different aspects of drought and its management. Therefore, the aim of this research was to study hydrological drought of Armand river using all of the low flow indices including flow duration curve, deficit characteristics, low flow frequency analysis and base flow index, which can be used to determine characteristics such as dry and wet periods, amount of flow during drought, drought threshold, duration of drought, volume and intensity deficit and frequency of low flows. Also, in this study, the environmental flow of river was calculated and analyzed.

The Armand River basin with 9961 Km2 area located in Chaharmahal and Bakhtiari province was selected as the study area and the daily flow statistics of the Armand hydrometric station were used from 1957 to 2013. First, by plotting the flow duration curve, three low flow indices Q70, Q90 and Q95 also, the river status including wet, normal and drought periods was determined. Then, the minimum environmental flow was calculated based on the two indices Q75 and Q90, and the deficit characteristics using the threshold level Q70. In order to estimate the return period of low flow series (AM7), a suitable two-parameter gamma distribution function was considered appropriate. Finally, the base flow index was calculated using recursive digital filter method in different time scales.

Based on the results, the Armand River in 28, 22 and 14 years (from 57 years of statistical period) has a flow of less than Q70, Q90 and Q95, respectively (45, 32 and 27 m3/s). Due to the gentle slope of the flow duration curve of basin, also the high value of the BFI index (0.95), it was concluded that groundwater had a high participation in the river flow. The dry period in this river begins when the flow reaches less than 45 m3/s (the threshold level Q70). The results showed that the deficit volume and intensity increased over the past 50 years. Based on the frequency analysis of low flow, in the return periods of 2, 5, 10, 20, 50 and 100 years, the low flow was 34.26, 26.8, 23.1, 21.1, 19.1 and 17 m3/s. Comparison of these values with the minimum environmental flow showed that the river flow becomes less than its minimum environmental flow on average every two years.

Despite the sustained flow in the Armand River (due to the snow-rainy regime of the region, the ability of the basin to save water and drain it during the dry season), the dry periods with different intensities occurred over the past 57 years. Also, the severity of drought has increased due to the increase in the volume deficit of the river in recent years, and if this continues in the future, the severity and frequency of the drought occurrence will increase and the overall flow of the river will decrease.

Predicting rivers flow, as one of the main resources of water for human beings, has always been one of the important issues discussed in hydrology and water resources. So, different models have been used for modeling and predicting rivers flow.
 In this study, two genetic models, Gene Expression Programming (GEP) and genetic programming (GP) were evaluated. Flow, temperature, precipitation, and evaporation data were used to model the daily flow in Tale Zang station. The results showed that GEP model, with R2=0.86 and RMSE=0.0030 m3/s, had the better performance than GP model, with R2=0.85 and RMSE=0.0037 m3/s. Moreover, the speed of GEP was more than GP and had ability to present results in a short time. By increasing the number of data, the speed of GP decreased and sometimes it couldn’t present results, while GEP had the ability to work with more data and model the river flow.

One of the most important factors, in a good management in any field, is having a proper perspective of the upcoming events. There is no exception in water resources management and the environment and awareness of the condition of water resources, in an area, plays a decisive role for planning water and agriculture. In this study, the Adaptive Neural Fuzzy Inference System (ANFIS) was used for the monthly forecast of Dalaki Basin groundwater levels in the province Bushehr in a 12-year period (2002- 2013). In order to improve the results of the model, the wavelet transform was used and the original signal was decomposed to sub-signals. Then, sub-signals were entered, as input, into ANFIS model to obtain the hybrid model, Wavelet-Adaptive Neural Fuzzy Inference System (WANFIS). To forecast the groundwater level of five observed wells has been used, using groundwater levels, precipitation, evaporation, andtemperature. Results showed that hybrid model, WANFIS, has better performance than ANFIS model. Also, it was showed that hybrid model has better performance in estimate extreme points. So, this method, using wavelet theory, increased the performance by 14%. At the end, groundwater levels were estimated by the best model in a year. The results of thepredicted groundwater levels showed that theincrease of having access to groundwater in the Dalaki area. This problem is noted to authorities of the area regarding the effects on water resources and the environment of the area.

Rainfall-runoff simulation models can be used in many water resources applications such as flood control, drought management. Although modeling is both continuous and single-event, continuous modeling has been less important in our country. In continuous models, more hydrological parameters are involved in comparison with single-event models, although this leads to more complicated modeling, but instead of a more realistic conditions of the hydrological system of the watershed will be illustrated and, in continuous systems, the surface water status can be monitored over a long period of time. Single-event models simulate only one incident, hence the moisture content between rainfall events is not considered, in contrast to continuous models of longer periods for estimating the response of the hydrologic information of the basin considered throughout the length of the rainfall events and between them (Lastoria, 2008). The American Hydrological Engineers Center (HEC), along with continuous hydrologic modeling, added the Soil Moisture Accounting (SMA) soil moisture content algorithm based on the PRMS model to the HMS software (Bennett, 1998). In this research, the aim is to provide an automatic calibration model based on the anion colony for the HMS-SMA soil moisture model. In this continuous model, the multiplicity of the considered parameters of the model, in addition to causing the difficulty of calibration by the method of trial and error, which also allows the automatic calibration of the software package to fail. For this purpose, in this research, by selecting a continuous HMS-SMA rainfall-runoff model, an external optimization program (Anion Cluster Algorithm (ACOR)) was used to overcome the weakness.

Without a doubt, the first step to manage water resources is accurately predicting of river flow. In This study, to predict time series of daily and monthly of kakareza and sarab seyed ali stations, artificial neural network and Adaptive neuro-fuzzy inference system models is used. the daily and Monthly rainfall and runoff time series of Sarab seyedali and kakareza basins were decomposed into sub-signals in various resolution levels using wavelet analysis and then these sub-signals entered to the ANN and ANFIS models to reconstruct the main forecasted time series. The model results show the high merit of Db-4 wavelet in comparison with the Haar wavelet. Too, results showed that WANFIS model produced better forecasts than either the ANN, WANN, ANFIS models.