A country’s water resources, especially groundwater, is a national treasure. For optimal use of available water resources comprehensive planning is necessary to make informed and accurate understanding of this important component of water resources will not be possible. The purpose of this study was to evaluate the accuracy of interpolation techniques for spatial zonation of quantity parameters (water table and depth) and changes in these parameters for a ten years period base on the data collected from peizometric wells of Alashtar plain in Lorestan province, several geostatistical approaches such as simple Kriging, ordinary Kriging, simple CoKriging and ordinary CoKriging as well as other deterministic interpolation methods includes Inverse Distance Weighing (IDW) with different powers were investigated. After quality control and normalization of the data, the variogram was drawn. According to the Residual Mean Square Error (RMSE), below, the most appropriate variogram was chosen. Then validation approach and Mean Absolute Error (MAE) criteria were considered to determine the most appropriate interpolation technique. The results showed that among the all methods, Kriging method is most suitable for zonation of groundwater depth, simple CoKriging is most suitable for zonation of groundwater table (using H variable).

Main part of Iran is in arid and semi-arid region and water is a limiting factor of human activities in this area. One of the ways to overcome seasonal shortages of water is using groundwater. Underground dam is a storage methods and use of groundwater that it can be used to improve water resources management and increase utilities of these resources. In this study, It has been tried to identify the most effective locations for the construction of underground dams in the watershed of Alashtar by combing GIS and multi criteria decision systems. For this purpose, were prepared data of 8 affecting parameters were prepared slope, geology, land use, geomorphology units, faults, streams, wells and residential areas in studied area by GIS. Weight of each criterion and weight classes of each layer was calculated by ANP method and paired comparison in Super Decision software. In next stage, restricted areas were removed. Then each of the defined criteria was classified for area using the GIS analytical functions. At the end, the final map was prepared in five classes from very good to poor by combination of zoning maps based on the weight of the ANP method. Results indicated that 15% of the study area is very suitable, 11% suitable, %18 elatively suitable, %37 unsuitable and %19 relatively unsuitable.

Alashtar watershed is one of the sub- basins of keshkan river covers an area of 78542.43 hectares that leads to north of grain mountain, northeast to the Borujerd, West to the Delfan and south to the Khorramabad. In order to review and potential preliminary identification of this watershed pelvis were evaluated in the field of topology, a consistent and desirable urban development, ecological power. For this purpose, after identification of ecological resources (climate, Hydrography, geology, soil, vegetation, etc.) was management in all layer information in GIS. Then by using of ecological special models of Iran was done and by the help of GIS tools and modeling step by step method (knock-out) the final analysis and ecological evaluation power in order to urban development, industry and rural areas. The results of this study showed that the power development urban in this area having regard to the slope and appropriate cover plants density development urban under the influence of soil types in the region that is mostly soil component of hydromorf and marshy fields. 29.7% of the land on the east and north is appropriate in this city for urban development.

Extended AbstractIntroductionDrought monitoring entails the simulation of indices which are categorized into single and combined types. Historically, simulations have predominantly relied on single indices, including Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), resulting in limited research on drought simulation using combined indices (i.e. MSPI and SPTI), particularly in conjunction with combined models. Over the years, several single models have been developed for simulating individual drought indices. For instance, the Autoregressive Integrated Moving Average (ARIMA) model has been applied to simulate drought indices like Standardized Precipitation Index (SPI) and Standard Index of Annual Precipitation (SIAP). Additionally, models such as Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) have been used for simulating indices like SPI, DI, SIAP, and SHDI. Recent studies suggest that combined models outperform single models. Wavelet ARIMA ANN (W-2A) and Wavelet ANFIS combined models to simulate the single drought index SPEI. Other researchers have developed combined models such as ARIMA-LSTM, Wavelet-ARIMA-LSTM, Wavelet-ARIMA-ANN and LSTM-CM to simulate single drought indices SPI, DI, SIAP. Despite the progress in developing drought simulation models, including single models and particularly combined models, their application has primarily focused on individual indices. Historically, simulations have predominantly relied on single indices, resulting in limited research on drought simulation using combined indices, particularly in conjunction with combined models. This study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) to test new methods of hybrid models for their ability to drought simulations based on the new combined index SRGI, employing the combined models W-AL and W-2A.Materials and MethodsDrought simulated in the Alashtar sub-basin between 48, 15 east longitude and 33, 54 north latitudes, covering an area of 811 square kilometers from 1991 to 2020, utilizing individual indices such as SPI, SRI, SGI, and the combined index SRGI. The study area encompasses the Karkheh River basin. Both single models (ARIMA, LSTM, ANN) and combined models (W-AL and W-2A) were employed for this purpose. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were used to evaluate the performance of the models. Also, relative frequency and error distribution charts were used to evaluate and compare the results of the models.Individual indices were calculated based on fitting the best cumulative probability function to monthly precipitation, monthly discharge, and monthly water table data, respectively for indices SPI, SRI, and SGI, and then inversely transforming to a N (0,1). The SRGI index is a combination of two drought indices, SGI and SRI (Feng et al., 2020). For this purpose, the copula function is used to obtain the best joint probability distribution function governing precipitation and water table data. The selection of the best copula function was done through the Kolmogorov-Smirnov K-S test at a significant level of 5%. In the current research, four copula functions of Frank, Clayton, Gamble and Joe were used.The process of building the combined models includes the analysis of the time series of the studied drought index, using DWT and decompose into two series named approximate and partial. Then, the approximate and detail series modeled by ARIMA and ANN respectively, in W-2A model and ARIMA and LSTM, respectively, in W-AL model. Results and DiscussionThe results demonstrate that the combined models W-AL and W-2A exhibit higher accuracy across all indices, both individual and combined, compared to single models ARIMA, LSTM, and ANN. The RMSE ranges for the combined models were 0.44 to 0.71, while for single models, they ranged from 0.47 to 1.54. Specifically, model W- AL displayed superior accuracy across all individual indices, with RMSEs of 0.44, 0.62, and 0.59, in contrast to model W-2A, which yielded RMSEs of 0.49, 0.71, and 0.63. However, W-AL's performance lagged behind W-2A for the combined SRGI index, with respective RMSEs of 0.64 and 0.61. Thus, the simpler model yielded more acceptable results in simulating the composite index.ConclusionAmong all the combined and individual models, the combined models perform better in simulating drought, based on all indices, compared to the individual models. Therefore, it can be said that combined models are more suitable for simulating and monitoring drought compared to individual models. However, the performance of the two combined models, W-2A and W-AL, in simulating the combined SRGI index is different. The performance of the simpler W-2A model is better than the more complex W-AL model, with RMSE values of 0.61 and 0.64, respectively. Therefore, in combined indices, despite the complexity of their computational process, there is not necessarily a need to use a more complex combined model. Overall, the use of combined models is recommended for monitoring various types of indices, especially drought based on combined indices such as SRGI. The major objectives of this study are: (1) to use hybrid models Wavelet-ARIMA-LSTM (W-AL) and Wavelet-ARIMA-ANN (W-2A) methods to predict monthly drought. (2) To analyze drought characteristics in Alashtar basin based on the new combined drought index, SRGI. It is expected that the research results will help to provide decision support which in turn will help in planning adaptative measures to reduce drought impacts and provide decision support for disaster prevention.

Groundwater resources are the most important source of fresh water in the world. In Iran which is located in the arid and semi-arid region, mismatch of consumption needs with rainfall time along with climate change has increased the demand for groundwater. Excessive extraction of groundwater in the Alashtar Watershed reduced the water level in the aquifer of the plain and water resources of this area have faced a negative balance. Due to the inherent limitations of the country's water resources and increasing the split between supply and demand, the use of economic instruments and the establishment of the water market for optimal allocation of groundwater is a necessity. Accordingly, this study in order to determine the market price of groundwater with the aim of accurately identifying the local markets for groundwater and also measure the views of local water market operators about some of the strategies for advance the water market designed and implemented. The statistical population in this research was all rural exploiters are the main reservoirs of groundwater in the basin who contribute to the buy and sale of these resources. Regarding market prices in the study area, the average price of groundwater was 1, 749 Rials per cubic meter, and the total value of groundwater was estimated as 555, 307. 5 Milion Rials. The results showed that in 103 villages there is a market for the buy and sale of groundwater. Those villages that have a local groundwater market, 76% of the villages have been established by creating popular organizations and the water regulatory council and 43% of the villages by create a bulletin board agreed to facilitate the buy and sale of groundwater resources.

Utilization of water resources, using a combination of simulation and optimization, are a useful and powerful set for specifying decision-making and management strategies. In this regard, MODFLOW mathematical model, as an efficient and cost-effective tool to evaluate different management options was used. After providing a conceptual model of the aquifer, requirement data were defined in different versions of the V. MODFLOW (V. 5. 33) software. Then, the model was calibrated by the manual method that has higher accuracy than software techniques and validation operations were also carried out. In the simulation process, with the goal of water resource management, the results of modeling and calibration model were used to optimize the groundwater table level. In this study, a linear programming model was used for optimizing the management. Lingo software was used to implement this model and achieve the optimal management. The information needed to solve the problem, was based on the output of a simulation model and optimization options. Due to linearity of objective function, a multi-objective linear programming model was selected to solve the problem. General multi-objective optimization problem with n decision variables, m constraints and pobjectives is presented in optimizing relationships. The objectives of the optimization problem were maximizing the profits of agricultural products and minimizing the use of aquifer water. Results indicated that the amount of yield and water use were different before and after optimization. . The amount of annual water consumption before optimization was 92. 22 million m3 while after optimization was 75. 51 million m3. In other words, the rate of water consumption was reduced by 18 prevent. Results also showed that annual water consumption in agriculture was 31. 79 million m3 that shows 65. 5 percent reduction compared to the present situation. The amount of benefits before optimization was 47, 798 million Rials, while after optimization it was 63, 689 million Rials. In other words, the amount of total profit increased by 33. 25 percent. Based on the scenario of a national document (pure water requirement), total profits increased by 15 percent.

Geomorphologic parameters are the decisive and influencing factors on the quality of water resources, particularly surface waters, such as rivers and lake. Chemical quality of the water can be affected by formation litho logy of the drainage basin, river sediments transported, material and ecological factors, such as human activities, agricultural, industrial and urban activities that combining these items as Ecogeomorphology checked. In this study Impact of influence factor on the Chemical quality of the Aleshtar River in the 2 eststion: Dare tang and Sarabseydali and as well as the impact of season changes in the water quality of the Alashtar River, have been studied. In this study vitro analytical method was used that the water samples taken during the twelve months of the Daretang and Sarab Seydali (91-92 water years) in Aleshtar drainage basin and the geology map (1/100000) and topography map (1/25000). The results showed that variety of geological formations, such as limestone and erodibilit formation in this drainage basin and role of the runoff on the creating and transport of sediment in the Aleshtar River affected the river water quality, But it noting bad the quality of water. One-way analysis of variance showed that the effect of the seasons change on pollution, only affected on parameters of sodium, magnesium, nitrate and nitrite on the 5 percent level is significant and the other parameters shows that the Season change, have no effect on the amount of pollution in the Alashtar river.

The current investigation concerns the flora of a Chal-e Kabod mountain, 20 km north of Alashtar, Lurestan province. A total of 178 plant species belonging to 40 families and 134 genera were identified during 2015. The largest families in terms of species richness, were Asteraceae (20 species), Lamiaceae (17 species), Apiaceae, Brassicaceae and Poaceae (each with 14 species), respectively. Hemicryptophytes (51.7%), geophytes (18.5%) and therophytes (15.7%) constitute the dominant life forms of the study area. From a chorological point of view, Irano-Turanian elements were dominant chorotypes (81.5%) and the other species were belonged to two, three or more phytogeographical regions. The area comprises 29 Iranian endemic species which according to IUCN conservation categories were classified as vulnerable (VU) and lower risk (LR) in Iranian red data list. Among the plants collected from the area, 99 species (55.6%) in the zone of 2300-2700 m and 79 species (44.4%) in the zone of 2700-3100 m had the most appearances.

Water resources scarcity and increasing demand have led to unbalanced supply-demand in the watersheds and water systems. Therefore, planning for efficient allocation has been a matter of importance. To resolve water allocation problems, various models and tools have been developed. Selection of these models is also another issue that needs to be done by an holistic approach. WEAP and MIKE basin are two of the well known models in this regard and comparison of their abilities construct objective of the present paper. To do this, capabilities and features of the two models were firstly outlined. Then in two assumptive examples, models were compared based on priority-base allocation and reservoir operation. Furthermore, models ran for a part of Talvar River as one of the main tributaries of the Ghezelozan River. This system consists of ten water demand nodes (domestic and agriculture) and three reservoirs. The results showed that in the priority-base allocation the performances were different, but they were identical in the operation rules and reservoir simulation. The results of reliability index showed that MIKE basin simulation matched better with reality due to their ability to extract more water. While, WEAP showed better results for optimization and even distribution of deficits among nodes.

Seed priming is one of the seed improvement methods that could increase seed performance (germination and emergence) under stress conditions, such as salinity, temperature and drought stress. In order to study the effect of seed priming on seed germination index and some physiological properties of sweet corn in different levels of salinity, an experiment conducted based on completely randomized design with factorial arrangement with three replications. Sweet corn grain was primed with 2% potassium chloride, 3% potassium nitrate, polyethylene glycol (8000) 10%, potassium dehydrogenate phosphate 1% and one treatment as a control at 20oC for 24 hours and then primed grain were treated with four levels of NaCl concentration (0, 50, 100,150, mM). Results showed that seed priming and salinity had significant effect on germination rate, germination percent and some antioxidants such as catalase and peroxidase. In general, results of this experiment revealed that seed priming improved the seed vigor and increased resistance to salinity stress at germination stage.