رامین پاپی

Tropospheric aerosol particles play an important role in the Earth's radiative energy balance directly by scattering and absorbing solar radiation and indirectly by modulating the microphysical and radiative properties of clouds. Aerosol optical depth (AOD) based on satellite remote sensing data is a quantitative estimate of the amount of aerosol in the atmosphere and can be used as an indicator of aerosol particle concentration. In general, the review of previous studies indicates the high importance of aerosol products based on satellite remote sensing data in modeling the spatial-temporal patterns of dust storms and in particular the identification of dust sources. The advantages of using satellite AOD to identifying dust events are possible in arid areas with relatively little cloud cover. The presence of clouds in the sky also severely limits AOD terrestrial and satellite measurements. Thus, AOD datasets sometimes have a gap due to factors such as cloudiness. Since the possibility of monitoring and measuring aerosols in cloudy conditions is limited, the use of proxy datasets to fill the gap will be an advantage. In this regard, several studies based on the analysis of satellite data have emphasized the association between climatic parameters and dust events (specifically AOD) in different regions. Therefore, considering the relationship between climatic parameters and AOD, these parameters can be used as a proxy data set to estimate AOD values for areas without data or with cloud cover. Also, using the predicted values of climatic parameters, AOD values can be predicted. Accordingly, in order to achieve reliable AOD prediction results, it is necessary to use a generalizable approach that can model the complex relationships between large data sets and satisfactorily solve the mentioned problems. For this purpose, one of the efficient data mining algorithms called M5P was considered to analyze and extract the relationships between climatic parameters and AOD to obtain predictive models. The M5P algorithm is a combination of tree and regression models with capabilities such as high prediction accuracy and ease of interpreting results.

In this study, in order to derive AOD predictive models based on climatic parameters, M5P data mining algorithm based on tree structure and multivariate linear regression analysis were used. Accordingly, a spatial database of remote sensing time series data related to 4 climatic parameters (as independent variables) including surface air temperature (SAT), precipitation (P), surface relative humidity (SRH) and wind speed (WS), and AOD (as dependent variable) was generated. WEKA software was used to implement the M5P model. After analyzing the relationships between independent and dependent variables through the tree model structure and linear multivariate regression, AOD predictive rules were extracted. Statistical indicators were used to validate the linear predictive models.

After pre-processing the time series data of climatic parameters and AOD as training data set, the input independent and dependent variables of the M5P were defined. Implementation steps of the M5P algorithm, including homogenization of independent input data sets by forming decision-making trees based on a series of "if-then" rules, multivariate linear regression analysis in homogeneous classes, and finally validation of the model results was performed in WEKA software. Thus, a total of four linear models (LM) or predictive rules for estimating AOD based on the values of climatic parameters were extracted. Finally, by placing the values of climatic parameters in the obtained linear models, the AOD value can be estimated based on the thresholds defined by the M5P algorithm. The obtained linear models are able to predict AOD values in different conditions (based on climatic parameters). Validation of the results of the M5P algorithm based on correlation analysis between input variables and evaluation of prediction errors through MAE and RMSE statistics shows the acceptable performance and accuracy of linear models in relation to AOD prediction. Given the dynamics of aerosol particles (especially dust) and their ability to transportability by the wind even at very far distances from their source of emission, it is likely that the amount of measured AOD for a pixel by a satellite sensor, does not exactly belong to the same area on earth. Therefore, in relation to the prediction error of the models, it should be noted that this may be due to the ability of the aerosol particles to be carried by the wind. Due to the strong correlation between AOD and climatic parameters, possible discrepancies may be due to the mentioned reason. Because a dust storm arising from a source may have no relation with the values of the climatic parameters at the destination.

In general, in this study, the capability of M5P data mining algorithm in order to AOD prediction was evaluated. Using the M5P algorithm based on inductive learning and using remote sensing time series data, through the formation of decision trees based on the set of "if-then" rules, four linear predictive models based on climatic parameters were extracted. Predictive models were extracted and validated using a data set for Ahvaz city. AOD, as an indicator of the state of the atmospheric aerosol, has great importance for dust storms studies.Access to AOD data is restricted in some parts of the world and in some seasons due to some limitations such as cloud cover. On the other hand, it is important to be aware of future spatial-temporal patterns of dust storms in order to adopt crisis management measures. Using the obtained predictor linear models in this study, it is possible to make an acceptable estimation of AOD in some areas, there are restrictions on access to AOD. Also, by entering the predicted values of climatic parameters, it is possible to estimate the future spatial-temporal patterns of AOD.Dust storms generally occur as a function of a wide range of environmental conditions, including atmospheric properties, as well as surface parameters such as vegetation, soil moisture, and soil texture. With this background, only considering the atmospheric conditions and their impacts on the spatial-temporal patterns of AOD may sometimes not produce the desired results. Therefore, it is recommended in future studies in this field, in addition to climatic parameters, which are mostly indicators of the atmospheric condition; ground surface parameters should also be used in modeling. By doing so expected to increase the accuracy of linear models for predicting AOD.

Land subsidence, as one of the natural and geological hazards, can be caused by human activities such as long-term discharge of groundwater and traditional irrigation farming. In the last two decades, climate change and successive droughts, unsustainable management of water resources and overexploitation of groundwater as well as population growth have caused land subsidence in Shahriar plain in the west of Tehran province. The present study aims to monitor and estimate land subsidence using Interferometric Synthetic Aperture Radar (InSAR) approach. Moreover, it tries to investigate the relationship between groundwater level and subsidence rate. Therefore, the time series of the ENVISAT-ASAR satellite images from 2003 to 2010 were used. The Small BAseline Subset (SBAS) algorithm was applied to analyze the time series of land surface displacement and to generate the mean displacement velocity map. The findings from time series analysis of InSAR data indicate a continuous subsidence occurrence in the agricultural lands of the region. The mean velocity of deformation along the satellite line of sight (LOS) in the time period of study, shows the displacement at an average rate of -10 cm / year and a maximum rate of -27 cm / year in the Shahriar plain in the west of Tehran province. Over this time period, groundwater level decreases about 0.5 to 1.5 m in the aquifer storage at 12 observational wells located in the study area. The overall correlation between changes in groundwater level and subsidence rate was estimated circa 89.45 percent, which indicates a strong relationship between subsidence and groundwater exploitation in the region.

Land subsidence, as a natural phenomenon, is defined as the gradual subsidence or abrupt sinking of the ground surface due to the subsurface material’s compression. One of the common causes of the land subsidence formation is the overexploitation of underground aquifers. The occurrence of land subsidence due to groundwater extraction from aquifers has been studied in several researches and documented in various regions of the world. This phenomenon is known as a global problem and leads to many environmental consequences such as damaging human structures like buildings, streets, bridges and power lines, creating holes on the earth surface, intensifying floods and flooding and reduction of aquifer capacity for water storage and ultimately it poses social and economic risks for human societies. In regions with bounded groundwater aquifers, groundwater discharge causes the reduction of pore pressure and subsequently sedimentary layers are compacted and condensed. This process leads to the downward movement of the ground surface and so-called land subsidence. Surface deformation is often measured using Interferometric Synthetic Aperture Radar (InSAR) technique. Generally, InSAR technique measures the phase difference of radar waves caused by the deformation created on the earth surface in time interval between two satellite pass.

In this study, radar interferometry techniques were used to monitor, estimate and analyze time series of land displacement in the west of Tehran province. Based on the historical archive and free accessibility, ENVISAT images were identified as the best dataset to estimate the land subsidence. Therefore, the level 2 time series data product (IMS) of ENVISAT-ASAR sensor related to frame number of 2889 from descending track of 149 were acquired. The time series selection of SAR data was determined according to the availability of well depth data. In the first step, the radar interferometry method was applied by analysing all possible differential interferograms with respect to temporal and spatial baselines to detect deformation signals (in particular, land subsidence). In the next step, by selecting a set of optimal generated interferograms regarding spatial and temporal baseline and using the SBAS algorithm, the land surface displacement time series were evaluated and the mean displacement velocity map for the region was produced.

The results of using InSAR approach indicate some subsidence event in the eastern part of Shahriar and northwest and west of Eslamshahr with mean velocity of -10 cm per year. Also, the estimated maximum subsidence rate in this region is -27 cm per year. The results of time series analysis, using the SBAS algorithm, showed that the subsidence signals before 2005 occurred at a faster rate compared to the 2005 to 2010. Groundwater level data of study area for the period of 2003 to 2010 generally represents drop in groundwater levels. Due to the slope and elevation (topographic status), the wells located in the northern part of the study area are much deeper than those in the southern part. Besides, the trend of water level decline in this area has not always been descending and sometimes the water level has increased limitedly at certain times. The results indicate that the water level decline has been more severe in the north and west than in the south and east part of region. The results of correlation analysis between changes in groundwater level and land subsidence indicate a high correlation between land subsidence signals and groundwater extraction with an average estimate of 89.45%. The lowest correlation was observed in well No. 2 with a correlation of 52.11%. The highest correlation of 99.96% was observed in well No. 7. The poor correlation between water depth in observational wells and land surface subsidence signals is related to the geological properties of the area and the type of soil which ultimately causes a time delay between groundwater exploitation and subsidence signals. The results also indicate that an average subsidence of 5 to 12 cm have been occurred per 1 meter drop in groundwater level. The results of previous studies related to InSAR processing in Shahriar plain were examined to confirm the accuracy of the obtained results. The results of the time series analysis of land displacement signals to extract the spatial-temporal pattern of the region's subsidence, despite minor differences, are very similar to the results of other studies in the same region. Minor discrepancies in the estimated subsidence rate (sometimes up to 3 cm per year) can be attributed to the type of applied algorithm and radar images, as well as the considered time period.

Having recognized land subsidence phenomenon in the current study, time series images were estimated using InSAR technique in the west of Tehran province. Then, the relationship between subsidence rate and groundwater level changes in the area was investigated. The high correlation indicated that the main cause of the subsidence in this area was the overexploitation of groundwater. Also, the conformity of the spatial patterns of subsidence signals to agricultural lands in the study area indicates the relationship between subsidence event and land use. In this study, for the first time with a deeper look, the relationship between land subsidence and water underground level changes in observational wells was evaluated separately. Due to the observation of nonlinear patterns in relation to the correlation analysis between subsidence and groundwater levels in some areas of the study area, it is suggested that the geological structures of each well be separately examined and compared with the amount of subsidence in future research.

The climate change over the past few decades, and consequently decrease in the precipitation, along with the population growth in different regions in Iran have led to an increase in demand for water for domestic agricultural, industrial, etc. consumption. This has led to uncontrolled exploitation of groundwater resources, causing severe decrease in the groundwater level. Artificial recharge technique is one of the methods to compensate for the groundwater deficit, especially in arid and semi-arid regions. Selection of suitable sites before artificial recharge can help improve the efficiency of the project and ensure its success. Having in mind the problems related to decrease in groundwater resources in Tehran due to the increasing population and the expansion of industry and agriculture. This study aims to identify and zoning of regions that are suitable for artificial recharge of groundwater in Tehran Province. The GIS can help determine such regions more precisely, faster, and with better results. For this purpose, the present study integrated GIS and Fuzzy AHP to weigh and combine factors that play a positive role in artificial recharge, such as the depth and changes in the groundwater level, precipitation, drainage density, elevation and land slope, distance from fault, distance from river, geological properties, and land use. After investigating the views of experts about the binary comparison of the criteria, and prioritizing them using AHP, it was found that the hydrological properties were the most effective criteria for the subject under study. Results indicated that 6.2% and 15.75% of the entire area of the region under study are very suitable and suitable for artificial recharge of groundwater, respectively. Very suitable regions are mostly located in the east of the province, with suitable geologic formations, short distance from river, and predominant rangeland and agricultural land use. They also, have a very low and decreasing groundwater level.

As a highly productive product, walnut plays an important role in the social and economic condition of farmers in various regions across Iran, and there has been a dramatic increase in the area under cultivation of this product in recent years due to its economic return. Walnut trees are highly sensitive to the climate conditions. Therefore, before Cultivation of this product, which requires a relatively high initial investment, it is necessary to take certain measures to identify and assess suitable lands for planting walnut trees. Therefore, this study aims to identify and assess suitable lands for planting walnut in the entire of Tehran province that would result in a desirable production efficiency. For this purpose, this study uses climatic parameters (including temperature, relative humidity, precipitation, and wind speed) from 12 synoptic stations within Tehran Province and 8 stations from adjacent provinces for an 11-year statistical period (from 2004 to 2014), topography, and land use for site selection and zoning of suitable regions for cultivation of Persian walnut. First, raster data layers were generated using all the parameters under study. Then, through a review of literature and expert views on physiological properties and growth requirements of walnut, the desirable and undesirable thresholds for its cultivation were determined. Next, analytic hierarchy process (AHP) and a researcher-made questionnaire were used to calculate the effectiveness and priority of each parameter. Furthermore, a fuzzy membership function was used to normalize the data layers. Finally, the final zoning map was prepared and extracted using fuzzy overlay tools in ArcGIS. According to this map, an area of 243882.9562 hectares, equal to 17.82% of the entire area of Tehran Province proved very suitable for cultivation of Persian walnut. Moreover, the findings indicate that the three parameters of temperature, relative humidity, and aspect have a special significance and effect on the growth of walnut trees.