زاهده حیدری زادی

Drought is an unpleasant climatic phenomenon that directly affects different dimensions of human societies. In order to know and choose the right management decision, it is necessary to design and develop an integrated approach to more effectively control this phenomenon and provide early warnings.In this study, twelve various remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) were used to monitor drought during 2000–2018 growing season. Standardized Precipitation Index (SPI) with time scales of 1 to 12 months was used as reference data. The relations between thirteen indices and SPI with different time scales were modulated using machine learning approach. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model which was made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that 2 Counties are at very high risk of drought, 4 Counties are at high risk and 4 Counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for assessment of regional drought. Also based on this model, Counties with high vulnerability can be identified to provide timely management programs to help improve the situation.

Desertification is a global phenomenon which can occur in any place and cause significant damages. Therefore, classification and generation of desertification intensity and risk map is a reasonable basis for planning.  The present study aims to evaluate desertification risk in the Abu Ghoveyr Plain in Ilam province, using general risk equation. To this end, first, desertification hazard map was prepared using the Iranian 9-criteria model of IMDPA. For preparing this map, working units map (geomorphologic facies) was created using slope, geology, and land use maps. Using this method, working units were considered as the main unit of desertification then a map was generated for each index according to assigned weights, such that the qualitative map of the desired factors were obtained using the geometric mean of indicators. Then, through integration and determining the geometric mean of layers obtained from indices and finally classification of the obtained maps, map of the current status of desertification in the studied area was generated. Then, elements at hazard in each working units were identified and were transformed to a map. Considering the hazard class of elements, degree of vulnerability of elements was determined. Finally, desertification risk was prepared in 4 classes by combining the risk intensity map, frequency, and the degree of vulnerability of elements. The results suggested that 49.73% of the area was in the high and very high damage classes.

Protecting an ecosystem and conserving natural resources requires knowledge of the conditions and land-use changes. The purpose of this study was to monitor land-use changes in the past and evaluate the performance of GEOMOD and LCM models in simulating land-use changes in order to select a more appropriate model for predicting land-use changes in the future. Landsat satellite images were used during the periods of 1990, 2003, and 2016 and land-use changes were monitored by using these images. The simulation of land use status by the LCM model for 2016 was done using the maps of the years 1990 and 2003. Using MLP and Markov chain, the land use map was simulated for 2016. To run the GEOMOD model, the image of the single-user map of 2003 and 2016 were used and the map of "appropriateness of changes" was made by the use of variables affecting land-use change and they were introduced into the model. The results of the accuracy of the simulation map of 2016 showed that LCM and GEOMOD had Kappa coefficients of 81% and 71%, respectively. Therefore, the LCM model was chosen as the most appropriate model and the map of the year 2029 was predicted and prepared.

  Principal land use management requires accurate and timely information in the form of maps. Considering the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, it is essential to study the changes in land covers over the time using satellite imagery. Because the conservation of natural resources requires continuous monitoring of an area, land-use change models are now used to identify and predict land-change trends and land degradation. One of the most widely used models in predicting land use change is the automated Markov chain model. The purpose of this study is to monitor land use changes in Abu Ghovair Plain in the past years and predict their status in the next 13 years. Material and In this study, in order to detect the changes in the study, TM, ETM+, and OLI images of Landsat satellite were used in the years 1990, 2003 and 2016, respectively. After applying geometric and atmospheric corrections to images, the land use map was created for each year. Then, to predict the changes in 2029 using the Markov chain in the Idrisi Selva software, the mapping of the years 1990 and 2003 were selected as the input to the model. Then, 13 years of forecasting changes were considered until 2016 to get the matrix of the likelihood of user changes. Then, data from the Markov chain method and the map of 2016 were used as input data for the CA-Markov cell method. The results of monitoring satellite images from 1969 to 2016 indicated a gradual increase in sandy areas by 62 km2 and its movement towards poor rangelands and shrubs. The agricultural lands were increased so that at the end of the period their size has increased by 67.68 km2. Residential land has also been expanded over the years, and the size of the shrubland has been reduced. After tracking the changes, the 2016 map was simulated by the model. Evaluating the accordance between the simulated map and the actual map with the Kappa index confirmed the accuracy of the model. Then, the 2029 map was prepared to predict the changes over the coming years. The discovery of changes in 2029 indicated that if the current trend continues, the area of the sand zones will increase to the extent of covering 15% of the area. In this period, the most changes will occur in the middle part of the southeast to the south of the area. The size of the shrubland will decrease by 13 km. The changes in agricultural lands continue to grow and will encompass 10% of the whole region in 2029. Comparison between the simulated map of 2016 generated by the model and actual map with Kappa index showed that Auto-Markov model is a suitable model for predicting land use change and can be used to accurately assess the future status of land use and vegetation.

With increasing the population growth, the extraction of these resources has increased and these natural resoursees have faced with serious threat. Therefore, the aim of this study is investigating the groundwater status and effective factors on the quality of Mehran plain resources. For this purpose, data of 23 piezometric wells during the years 1337-1373 in Mehran plain aquifer were used and groundwater hydrograph for the plain was plotted. In order to investigating the relationship between land use and groundwater, land use map changes was prepared during three periods of 1994, 2004 and 2014 using satellite images of TM and  ETM+ of Landsat satellite in Mehran region. Supervised Classification Method and Maximum Likelihood Method were used to images classification. The results showed that during this period, the size of agricultural land, residential areas, very poor pastures, and impoverished lands have been increased and in contrast, the size of poor pastures were reduced. The results showed that groundwater level decreased about 16.9 meters during this period as well as a direct relationship between ground water level decreasing and the expansion of residential areas and agricultural lands. The increase of agricultural land use and consequently the increasing of exploitation wells have led to overexploitation and falling of ground water level in this plain. In addition, another factor in reducing the groundwater level in the aquifer of the region is the trend of rainfall changes in the region.

The importance of planning and managing water resources, as well as an increasing population growth and the limitation of surface water resources in the country, has made the accurate prediction of rivers' flow by using modern tools and methods of modeling, as an inevitable necessity. In addition, proper river flow prediction in river management, flood warning systems, and especially planning for optimal operation is required. In order to predict the flow of a river, several methods have been developed over the past years. In general, these methods can be classified into two categories of conceptual models and models based on statistics or data. The basis of the most of the predictive methods is the simulation type of the current status of the system which is called "modeling". Considering that in most cases the conceptual models require accurate data and knowledge of processes affecting the phenomenon, and this has so far been accompanied by many problems, researchers have turned to the statistical models. Over the past four decades, time-series models have been widely used in predictive river flow as a statistical model. In each science, the collected statistics corresponding to the variable which is going to be predicted and which is available in the past periods are called time series. Indeed, a time series is a set of statistical data collected at equal and regular intervals. Statistical methods that use such statistical data are called analytical methods of time series. The basis of many decisions in hydrological processes and decisions on exploitation of water resources is according to the prediction and analysis of time series. Assessment of the temporal changes of base flow in watersheds, particularly in low flow seasons is very important. Time series models are represented in three main forms: self-correlated models (AR), moving average (MA) models, and self-correlated and moving average (ARMA) models. The condition of using these models is the static nature of the used data. If the data is not static, the data series must be static with the existing methods. The existence of "I" in ARIMA indicates the non-static nature of the original data and the change in the data for modeling. If the data series has a cyclic and rotational state, the type of model is seasonal or SARIMA. Time series models have two components of (p, d, q) and s (P, D, Q). S (P, D, Q) is a seasonal component. P and q are respectively autoregressive parameters and non-seasonal moving average. P and Q are autoregressive parameters and seasonal moving average. The other parameters, D and d, are differential parameters for making the time series static. The statistical and probabilistic models have been presented and developed. This study aimed to analyze and compare the performance of series 30 and 56 years and monthly average discharge related to the Kakareza River in the Selsele city and the Kashkan Afrineh River in the Poldokhtar city in Lorestan province. To this end, the first climate in this region was determined. Next, the autocorrelation function and partial autocorrelation real data draws in XLSTAT software was done. Subsequently, the data was normalized using the Box-Cox and logarithmic. Then, the data trend that indicated non-stationary was determined. After that by using the different operator in MINITAB software, the data trend was removed and the suitable model with the lowest Akaike was selected. Then both periods 12 and 24 months for the two regions were simulated. Results showed that the selected models in 12 and 24 months periods had respectively a correlation coefficient of .92 and .86 for the kakareza river and .94, .88 for the Kashkan Afrineh river. Discussion and conclusion: The most significant difference between the observed and the simulated values is in two months of Esfand and Farvardin. In addition, due to high precipitation, there was a significant increase in the amount of discharge in Farvardin. According to the climatic conditions in the study areas, the model showed a better performance in semi-arid areas.

Desertification is one of the global phenomena affecting natural resources which occurs not only in arid and semi-arid regions but also in some of the areas in semi-wet regions. Evaluating desertification in a region with vulnerable climate faces unpredictable and damaged conditions plays a significant role in evaluation of the environmental capabilities, prevention of desertification, and restoration and rehabilitation of degraded areas. Destroying the water resources, both their quality and quantity, can lead to decrease in biomass and result in desertification. The decreasing levels of groundwater resources and deteriorating water quality are considered among desertification processes.
Nowadays, groundwater exploitation in arid and semi-arid regions for agricultural lands has increased and subsequently over-exploitation of these sources has degraded groundwater quality. Therefore, investigating the groundwater quality and its effects on the increasing rate of desertification intensity is a necessary task. In this research, the effects of groundwater quality on the desertification in Mehran plain have been investigated by employing water quality parameters including electrical conductivity, total dissolved solids, sodium adsorption ratio, and chloride using the Iranian model of IMDPA.
In the IMDPA model, a weight ranging from 1 to 4 was assigned to each layer based on its influence on desertification according to literature reviews and researches performed by other researchers and geographical condition of the Mehran plain. Value 1 and value 4 indicate the best and the worse conditions of desertification, respectively. Numerical values of produced indices were transformed to thematic maps in ArcGIS Desktop 9.3. In order to determine the current quality of water in Mehran Plain, the data obtained from 9 quality wells located in the area have been used and analyzed over three time periods (2001-2004, 2005-2008, and 2009-2012). Then, to investigate the desertification intensity of Mehran Plains based on the IMDPA model, the criterion of water quality was used. After determining the numeral value of each parameter, the standard numerical value is determined based on the geometrical average of the selected criteria.
The information layers related to each index and criterion for all the three periods were prepared using interpolation and weighting methods in GIS. After preparing the related layers for the criterion of water quality in selected wells, the measures were combined in Arc GIS 9.3 software application to obtain the final map for the desertification status of the region for all the three selected time periods. Finally, they were classified in desertification classes of low, medium, intense, and severe intensification.
The results show that the indices of chloride and sodium absorption ratio due to their locations in the low desertification class and quantitative value of less than 1.5 are not very effective on the intensity of desertification in the region. Based on the results, the amount of chloride and the sodium absorption ratio over the entire study region during the recent years have not fluctuated significantly, so that many of the areas are classified as low desertification and the effects of this index are not readily visible in this region. The total dissolved solids show a greater change compared to chlorine and sodium absorption ratios in the last period in so that based on this index, during the two first periods, the region is classified as low desertification while in the last period, due to higher concentration of these substances, the region is classified as mild desertification. The desertification intensity map of Mehran Plain based on the index of electrical conductivity shows that the intensity of desertification from the beginning of the period has been increased towards the end of the period in a way that 8.95 percent of the region in the first period is classified as intense desertification and in the last period, the extent of this area reaches 14.47 percent of the entire region. Moreover, the desertification map based on the indices of groundwater quality during these periods shows that almost 72 percent of the region is classified as low and negligible desertification.
Decreasing of ground water levels caused by digging multiple wells has led to an increased electrical conductivity of the water from these wells as well as their salinity. Intense decline of water level due to uncontrolled withdrawal and other factors such as climate change, drought, and irrigation methods have increased the water salinity, which in turn increases the salinity of the soil in the study area.
The results obtained from evaluating the average weight of the quantitative values of the selected indices for the water quality showed that in the studied region, the average weight of the criterion of water quality is in negligible and low desertification class. Among the indices assessed for water quality, the obtained value (the score) for the electrical conductivity is higher than those for other measures, so, it has more effects on increasing the desertification intensity in the region regarding the quality of water criterion (i.e. it has caused a rise in the final amount of desertification since the value of each criterion is obtained from the geometric average of the numerical values and the value of the total desertification score is obtained based on the geometric average of the numerical value of the criteria). Hence, the electrical conductivity index is the most important index in the desertification of the region.
Considering the fact that one of the reasons behind higher electrical conductivity is the low level of water, uncontrolled withdrawal should be avoided in this region as well as similar regions all over the country by making the wells smart using devices such as electronic cards for water withdrawal. In other words, after withdrawing a predefined quantity, there must be a power outage so that nobody can withdraw any more water. At the moment, unfortunately there are several unauthorized wells withdrawing water in all regions in the country which lead to decrease in levels of groundwater and caused aquifers go dry. Hence, as a recommend these unauthorized wells should be closed since preventing uncontrolled withdrawal is one of the effective ways to solve the water crisis.

Harvesting and recognition of water quantity and quality is an essential step to optimization of consumption. Water quality is affected by environmental conditions and human activities.The main objective of this study is to evaluate the quantitative and qualitative changes in the Abu-Ghoveyr aquifer in Ilam Province in a 14- year period from 2001 to 2014. To this end, the statistics for 33 quantitative and 10 qualitative wells were used, after analyzing the data using ArcGIS software, iso_potential and iso-decline maps were developed. Moreover, the unit groundwater hydrograph was obtained for the region. In order to evaluate the quality of the groundwater, Electrical conductivity (EC) and chlorine (Cl) were Sampled and analyzed. The results obtained from iso-decline maps show that the different sections of the plains have had a decline between 1 to 10 meters during the selected period. During this period, the water level had an average decline of 2.65 meters (annually 20 cm). In order to determine the effects of rainFall on the levels of groundwater, the precipitation of this period was also considered. The results revealed that the highest level of water in the year 2004 was 77 m and the greatest amount of precipitation was 350 mm during that year.The results indicate the presence of a significant relationship between rainFall and the water levels. Investigating the parameters of EC and Cl level indicate the increase of these parameters in this period. Moreover, the central regions of the plains have a lower quality compared to other sections which is due to the concentration of wells and higher decline of water level in this area. Hence, it can be concluded that the most important factors in reducing the underground water levels and lowering the quality of Abu Ghoveyr Plain include drought, over exploitations, and the excessive number of wells.

Desertification is a global phenomenon that is the outcome of natural factors and improper human exploits. Therefore, classification and generation of desertification intensity map, as an efficient tool, plays a significant role in evaluation of the environmental capabilities, prevention of desertification, and restoration and rehabilitation of degraded areas. The present study aims to evaluate the desertification hazard and determine the effective criteria affecting desertification in Abu-Ghuyer Plain in Ilam Province, using the IMDPA model. To this end, different indices of climate and water criteria were used and a map was generated for each index according to assigned weights, such that the qualitative map of the desired criteria were obtained using the geometric mean of indicators. Then, through integration and determining the geometric mean of layers obtained from criteria and finally classification of the obtained maps, map of the current status of desertification in the studied area was prepared. The obtained results indicated. Also, among the evaluated indices, amount of annual rainfall in climate criterion and the electrical conductivity in water criterion had the highest impact, with weighted averages of 2.24 and 3.04, respectively. Also, the quantitative value of severity of desertification was calculated to be 1.78 for the entire area, which was in the medium class.

Assessment of land use changes in the previous years and prediction of these changes in the following years is an essential tool for different planning and management of natural resources. Therefore, the aim of present study is modeling of land use changes in Mehran Plain using Markov- Cellular automata Model in 2028. In order to detect land use changes in the studied area, TM and ETM satellite images of Landsat were used during three time periods of 1989, 2002, and 2015, and a separate map was prepared for each year. Then according to model, 2015 land use map was provided. Assessment of the match between simulated and actual map of 2015 with kappa index showed that this model is an appropriate model for simulating of land use change. After that, land use map for the year of 2028 was simulated. The results showed that in 1989 to 2015, the area of agricultural lands and the area of very poor range lands have been increased up to 6778 and 5961 hectares, respectively. In contrast, the area of poor range lands has been reduced up to1534 hectares. The results of prediction showed that during the next 13 years, the area of agricultural lands and bare lands will increase about 1177 and 389 hectares, respectively. Poor range lands will decrease about 1950 hectares.