Arid Regions Of Iran Research Articles

Analyses of water dependency of Haloxylon ammodendron in arid regions of Iran using stable isotope technique.

The complex relationships within desert ecosystems and their environmental conditions are reflected in patterns of plant water use. Thus, understanding the sources of water used by plants in these areas is crucial for effective resource management. In this study, we investigated the water use pattern of Haloxylon ammodendron in Semnan province, in the central plateau of Iran, using the stable isotope analysis. We employed a simple, homemade cryogenic vacuum distillation (CVD) system to directly extract water from soil samples and different plant components for subsequent analysis by mass spectrometer. The contribution of each possible water source to the plant xylem water was estimated using the IsoSource mixing model. The pattern of 18O values in the xylem water of H. ammodendron indicated its reliance on groundwater as a primary water resource during the wet season. Additionally, the correlation of sand particles with both δ2H and δ18O was found to be 0.32. Moreover, the 18O values of H. ammodendron xylem water were mainly similar to those of groundwater, suggesting the species' dominant use of groundwater. The findings of this study provide valuable insights for strategically planting H. ammodendron to mitigate impacts on groundwater resources and ensure long-term sustainability in arid and semi-arid regions.

Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran

The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics.

Effects of topography, climate, mineralogy and physicochemical properties on potassium forms in various soils of Fars province, southern Iran

The relationship between potassium (K) and soil physicochemical properties can be used to understand the K cycling. Topography, as a soil forming factor, has an important effect on the soil characteristics of every region. This study was conducted to investigate K and its relationship with climate, mineralogy, and physicochemical properties in toposequences of two regions (Sepidan and Darab) in southern Iran. XRD analysing patterns showed montmorillonite, illite, chlorite, and kaolinite as diagnostic minerals of these regions. Due to being located in arid regions of Iran and lack of good rainfall, the results did not show any significant difference between the selected areas. So, climate could not be considered as an effective factor in supplying K for plants. In both regions, topography affected soil development as a soil forming factor, so that anywhere the more developed soil was observed, the more extracted K forms were found. In different physiographic units of Sepidan region, topography, by affecting soil properties through microclimate, drainage and sunshine quality, caused differences in distribution of K forms. The correlation between different K forms and soil properties in both regions because of weathering and releasing K from K-bearing minerals like illite, showed a relationship with soils finer section.

A novel objective for improving the sustainability of water supply system regarding hydrological response.

In general, the sustainability of the water supply system is indicative of an adaptive operational approach, wherein the decision-maker adjusts the system's performance based on the availability of water resources in a given time frame. In light of this, a novel framework is proposed in this study to evaluate sustainability, including three key indicators: resilience, reliability, and vulnerability. To address stressors that may lead to system failure, a multisectoral water allocation optimization is undertaken. In order to account for the future implications of climate change on the hydrological cycle, a simulation step, is incorporated, utilizing the Soil and Water Assessment Tool (SWAT) under various emission scenarios (RCP4.5 and RCP8.5), prior to integrating the streamflow data into our proposed optimal framework. To calibrate and validate historical data (2014-2019) and simulate future streamflow patterns (2025-2085), the Sistan Basin, located in an arid region of Iran, is analyzed as a case study. In light of the anticipated adverse impacts on the water supply system, certain adaptation measures, such as demand shrinkage scenarios, are considered to further appraise the proposed framework. Based on the final output, it is evident that the agricultural and industrial sectors, being the primary water consumers, are more susceptible to negative impacts resulting from the reduction in system sustainability. This susceptibility is primarily attributed to their highest vulnerability and comparatively lower reliability.

Introducing a stratified vertical gravel tube subsurface drip system under different irrigation regimes for pistachio: Growth, yield and water productivity

AbstractHigh evapotranspiration and low precipitation are known as the main challenging factors for pistachio (Pistacia vera L.) orchards situated in arid and semi‐arid regions. Therefore, it is necessary to take some measures to mitigate surface evaporation. This study was carried out to assess water productivity (WP) as well as yield and annual shoot growth of pistachios using a new method of irrigation known as the stratified vertical gravel tube subsurface drip irrigation (SVGTSD) system in a 15‐year‐old pistachio orchard in an arid region in Iran. In this system, each tree contained four vertical gravel columns with different lengths. A randomized complete block design with a split‐plot arrangement with three replicates (five trees in each plot) for 3 years was used. Three irrigation regimes included 100% crop evapotranspiration (100% ETc), 85% ETc and 70% ETc (as the main plots), and seven vertical gravel column with a depth arrangement of 40–40–40–40 cm (using a gravel column from the lateral pipe level and without a gravel tube as the control) and gravel‐filled tubes with depths of 10–10–10–10 cm, 10–10–20–20 cm, 10–10–30–30 cm, 10–10–20–30 cm, 10–10–20–40 cm and 10–10–20–50 cm (all 30 cm below ground level) (as subplots) were used. The results showed that the different depths of gravel tube placement significantly affected the annual shoot growth and yield. The deeper the tube was placed, the greater the yield and annual shoot growth, resulting in 10–10–20–50 cm being the most productive. Regarding tree growth, yield and WP, the best performance was observed at full irrigation (100% ETc) with a 10–10–20–50‐cm vertical gravel tube arrangement. Furthermore, in this subsurface irrigation method, there was no concern regarding emitter clogging by roots, root accumulation around the emitters or root intrusion into the emitters. In addition, having a low additional cost (only 13.9%) in comparison with conventional subsurface drip irrigation together with a higher yield (3475 kg ha−1), WP (0.9 kg m−3) and shoot growth (35 cm), SVGTSD is more economical and feasible compared to other irrigation methods and can be extensively applied in pistachio orchards.

Potential application of spectral indices for olive water status assessment in (semi-)arid regions: A case study in Khuzestan Province, Iran.

Spectral indices can be used as fast and non-destructive indicators of plant water status or stress. It is the objective of the present study to evaluate the feasibility of using several spectral indices including water index (WI) and normalized spectral water indices 1-5 (NWI 1-5) to estimate water status in olive trees in arid regions in Iran. The experimental treatments involved two olive cultivars (Koroneiki and T2) and four irrigation regimes (irrigated with 100%, 85%, 70%, and 55% estimated crop evapotranspiration [ETc]). The results obtained showed that olive trees subjected to the different irrigation regimes of 85%, 70%, and 55% ETc experienced soil water content (SWC) deficits by 4.5%, 12%, and 20.5% that of the control, respectively. Significant differences were observed among the treatments with respect to measured relative water content (RWC), SWC, and the spectral indices of WI and NWI 1-5. The normalized spectral indices combining NIR and NIR wavelengths were found more effective in tracking changes in RWC and SWC than those that combine NIR and VIS or VIS and VIS wavelengths, respectively. Spectral indices were closely and significantly associated with RWC () and SWC (). Among all the spectral indices investigated, NWI-2 showed the least consistent associations with RWC (ranging from 4-15% lower than the other indices examined) and SWC (ranging from 1-23% lower than the others). Based on the pooled data on spectral indices, RWC, and SWC collected during the study period, WI, NWI-1, NWI-4, and NWI-5 showed stronger correlations with RWC and SWC than did NWI-3 and NWI-2. In conclusion, the spectral indices of WI and NWI 1-5 measured at the leaf level are found useful as fast and non-destructive estimators of plant water stress in arid regions.

Water Quality Index Estimations Using Machine Learning Algorithms: A Case Study of Yazd-Ardakan Plain, Iran

Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.

Hotspot and accumulated hotspot analysis for assessment of groundwater quality and pollution indices using GIS in the arid region of Iran.

Because groundwater quality representatives for drinking usage (i.e., Schuler method, Nitrate and Groundwater Quality Index) have been abruptly changing due to extreme events induced by global climate change and over-abstracting, applying an efficient tool for their assessments is vitally important. While hotspot analysis is introduced as an efficient tool concentrating on sharp changes in groundwater quality, it has not been closely examined. Accordingly, this study is an attempt to determine the groundwater quality proxies and assess them through hotspot and accumulated hotspot analyses. To this end, a GIS-based hotspot analysis (HA) applying Getis-Ord Gi* statistics was used. The accumulated hotspot analysis was launched to identify the Groundwater Quality Index (AHA-GQI). Moreover, Schuler method (AHA-SM) was utilized to determine the maximum levels (ML) for the hottest hotspot and the lowest levels (LL) for the coldest cold-spot, and compound levels (CL). The results revealed that a significant correlation (r = 0.8) between GQI and SM was observed. However, the correlation between GQI and nitrate was not significant and the correlation between SM and nitrate was so low (r = 0.298, sig > 0.05). The results also demonstrated that using hotspot analysis on only GQI, the correlation between GQI and SM increased from 0.8 to 0.856, while using hotspot analysis on both GQI and SM increased the correlation to 0.945. Likewise, when GQI was subjected to hotspot analysis and SM underwent accumulated hotspot analysis (i.e., AHA-SM (ML)), the correlation degree increased to the highest extent (i.e., 0.958), indicating the usefulness of including the hotspot analysis and accumulated hotspot analysis in the evaluation of groundwater quality.

The observational evidence of association between types of aerosol mode-cloud-precipitation interaction over Iran

The present study quantifies for the first time the statistical relationship between aerosol properties with Cloud microphysical Properties (CPs), and consequently effect on precipitation over Iran. The daily means of Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and CP including Cloud Fraction (CF), Cloud Top Temperature (CTT), Cloud Top Pressure (CTP), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER) were obtained from the MODerate resolution Imaging Spectroradiometer (MODIS). The precipitation was retrieved from Tropical Rainfall Measuring Mission (TRMM) during 2003–2019 for 156 1 × 1 geographical degree grid-points over Iran. The different types of aerosols including the dust and fine aerosols were separated using the AOD and AE values for each season. We identified grid points, which had statistically significant correlation (at the 5% level) between aerosol modes and at least one of CPs, the precipitation and one of CPs, and between the precipitation and aerosols modes, at the same time. With this approach, we identified 10 points showing a statistically significant relationship between AOD, CPs, and precipitation, stimulatingly. For instance, at a point in the southwest of Iran, the correlation coefficient between the AOD with precipitation (r = 0.54), AOD with CTP (r = −0.51), and precipitation with CTP (r = −0.61) were statistically significant simultaneously. Based on statistical calculations, the relationship between precipitation and AOD (for 10 points) is often positive, which indicates the positive effect of aerosols on precipitation. However, in two points, the relationship between precipitation and AOD is negative, and subsequently significant. In addition, a negative correlation coefficient is also observed between CTT and CTP with AOD, as well as a positive relationship between AOD and COT for both fine (black carbon) and coarse mode (desert dust) of aerosols over Iran. Due to the lack of ground station data in Iran, this study can serve as a starting point to better understanding the complex, and uncertain response to cloud, radiation and precipitation to aerosol over arid region of Iran.

Soil potassium relations in an arid soil treated with biochar and chemical fertilizers after cultivation with safflower (Carthamus tinctorius L.) under water stress

Safflower cultivation has been developed in arid regions of Iran in the recent decade. Potassium (K) is an important plant nutrient and its content has been decreased in arid soils due to the intensive agriculture and no K fertilizer application. We tried to monitor K pools distribution and K release in a calcareous soil cultivated with safflower (Carthamus tinctorius L.) under normal irrigation and water stress when treated with wheat biochar (WB), cotton biochar (CB), nitrogen + phosphorus (N + P), N + P + WB and N + P + CB. All fertilizer treatments except N + P increased the contents of soluble, exchangeable, non-exchangeable and HNO3-extractable K by 81-124, 64-98, 14-28 and 31-50%, respectively. The N + P treatment decreased soluble and non-exchangeable K by 10 and 6%, respectively and increased exchangeable K by 12%. Ten times extraction of control soil with 0.01 M CaCl2, 0.01 M oxalic acid and 1% wood vinegar released 270, 250 and 256 mg K kg−1, respectively and cumulative K release increased with all fertilizer treatments except N + P. The CaCl2 solution extracted more K from soil samples, as compared to the oxalic acid and wood vinegar, and this difference was considerable in the first stages of K extraction. Analysis of Al and Ca release proves that exchange reaction is the main mechanism of K extraction with all solutions. Water stress increased soluble K content by 17%. Cumulative K released from soil samples under normal irrigation and water stress were 404 and 417 mg kg−1, respectively. It is concluded that irrigated agriculture in arid regions may decrease soil available K and use of different biochars improves soil K availability and its K release potential.

Dust Storm Frequency in Connection with Climatic Change in the Arid Region of Iran

Dust Storm Frequency in Connection with Climatic Change in the Arid Region of Iran

Integration of Multiple Models with Hybrid Artificial Neural Network‐Genetic Algorithm for Soil Cation‐Exchange Capacity Prediction

The potential of the soil to hold plant nutrients is governed by the cation‐exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm‐neural network (MM‐GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporation into the MM‐GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM‐GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, silt, and CCE) as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. The proposed MM‐GANN model is a reliable intelligence‐based approach for the assessment of soil quality parameters intended for sustainability and management prospects.

Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0–30 cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R 2). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping.

Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory

Dust pollution is one of the major environmental crises in the arid regions of Iran and there is a need to predict dust pollution and identify its controlling factors to help reduce its adverse effects on the livelihood of residents of these areas. Although deep neural networks (DNN) are powerful tools in the modelling of environmental phenomena, they are recognized as being challenging to interpret due to their black-box nature. To address this issue and understand the importance of each environmental control on dust pollution, game theory (i.e., Shapley values) was used to better understand the performance and interpretability of DNN models. Here, monthly mean values of precipitation, air temperature, surface wind speed, potential evapotranspiration, normalized difference vegetation index, normalized difference salinity index, Palmer drought severity index, soil heat flux, and surface pressure were selected as explanatory variables. The dust storm index (DSI), an indicator of dust pollution, was the predicted response variable for the cold and warm months. The results showed that the accuracies of the DNN model in predicting cold months DSI (CMDSI) and warm months DSI (WMDSI) were higher compared to other traditional machine learning algorithms. DNN model increased the R2 by 13% and 15% for predicting CMDSI and WMDSI, respectively, compared to the Random Forest model, which was the second most effective approach. According to the Shapley values, the most important controls on the occurrence of dust storms during the cold months of the study period (2000–2018) were wind speed, soil heat flux, and precipitation. During the warm months, wind speed was the most important controlling factor and was followed by precipitation, soil heat flux, and potential evapotranspiration. Overall, the results demonstrate the effectiveness of the DNN model and game theory in identifying the factors affecting dust pollution, which may help mitigate its impacts on the residents of western Iran.

Comparison of different irrigation methods for onion by means of water and nitrogen response functions

ABSTRACT Considering the cost of two common agriculture inputs, water and nitrogen as well as their effects on the environment, optimum use of them is necessary. In this way, the production functions are applying to achieve maximum profits and environmental protection. Therefore, spilt-plot experiments were conducted on onion (Allium cepa L.) in a completely randomised block design for two years (2014/15-2015/16) in an arid region of Iran. The treatments included three irrigation methods (subsurface drip irrigation (SDI), drip irrigation (DI) and furrow irrigation (FI)), four levels of irrigation water (50(I1), 75(I2), 100(I3) and 120(I4) % of crop water requirement) and four levels of nitrogen fertiliser (25(N1), 50(N2), 75(N3), and 100(N4) % of crop nitrogen requirement). Regression analyses were done by linear, Cobb-Douglas, Quadratic and Transcendental functional forms of onion production. Results showed that the quadratic fits the data best, for the given local condition. The onion reached the highest increase in yield rise by increasing a unit of I (irrigation water) and N (nitrogen fertiliser) in SDI and the lowest in FI. Moreover, the pressurised irrigation methods are found to be advantageous to the substitution of N by I. The marginal rate of technical substitution demonstrates that in comparison to FI, more amounts of I and N are required to achieve equal yield in the pressurised irrigation methods.

Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.

Automatic surface water distribution systems: A reliable alternative for energy conservation in agricultural section

The poor performance of surface water distribution systems in reliable, sufficient, and timely agricultural water distribution has significantly increased groundwater extraction. This has posed severe environmental challenges such as the significant decline in groundwater levels and increases in energy consumption. This study investigated the potential for automation of the surface water distribution systems to decrease groundwater extraction and energy conservation. The proposed approach was implemented in Nekoo-Abad Irrigation District, located in the arid region in Iran. The undesirable surface system’s performance results in the annual extraction of 82 Mm3 water from nearly 4181 active tubewells and the annual consumption of 236 MkWh to pump the groundwater for agricultural purposes merely. The automation improved the system performance so that annual groundwater extraction decreased by 16%. Reducing groundwater extraction decreased energy consumption by nearly 26%, 18%, and 14% in the normal and water scarcity scenarios. Moreover, the spatial analysis of research results indicated that groundwater extraction declined by 61–88% in shallow wells (<30 m deep), by 48–75% in wells 30–60 m, and by 5–10% in >120 m. It is expected that the entire system's automation will help achieve more of the research objectives, something which requires additional socioeconomic studies.

Revitalization of bio infrastructure network, an approach to urban regeneration: A case study of the qanats of Tehran

Over the past decades, replacement of natural ecosystems with built-up areas has put cities under increasing pressures, such as degradation in water and air quality. The main natural resource diminished significantly is water. In arid regions of Iran, settlements have traditionally been supported by qanat, which is the oldest and most efficient method of extracting water. Qanats- These sustainable systems- play a vital role in meeting local water needs and have served Iran as the main water supply throughout history. Due to the technological and socioeconomic changes, many of these systems have been replaced and abandoned during the past decades. It is necessary to create suitable solutions to increase the efficiency of qanats in order to achieve sustainable development especially in populated cities. One of the aims of this study is to suggest ways to revive the use of qanat routs in District 7, Tehran. For this purpose, the qualitative method was carried out due to determine the capabilities of urban environment and specify the green patches and open spaces. First of all, field surveys and data collection were conducted in order to detect the exact locations of the qanats’ routes, Identify environmental potentials and opportunities and finally explicate the research participant’ attitudes through map and interview analysis. Another objective of the study is to investigate the power of these systems in planning public open spaces and investigating how revitalization of this ancestral infrastructure can be served to fertilize open spaces, restore the fragmented orchards and bring oxygen to the polluted megacity of Tehran. Finally, an ambience was designed in order to create lively public open spaces and improve the social life in this residential district.

Recent changes in physical properties of the land surface and their effects on dust events in different climatic regions of Iran

The physical characteristics of the Earth’s surface play an important role in controlling the velocity of the wind erosion threshold. Therefore, to reduce the risks of dust storms, it is necessary to determine the impact of changes in this characteristic on wind erosion events in different regions. In this regard, this study was carried out to examine the effects of soil moisture (SM), land surface temperature (LST), and vegetation cover (VC) on wind erosion phenomenon using a combination of satellite data and terrestrial observations from 2010 to 2018. In this work, the dust storm index (DSI) was selected to monitor the wind erosion across the country. The multivariate regression method was utilized to explore the effect of alterations in these parameters on changes in soil erosion at both local and regional scales in Iran. In the local scale, regression coefficients between LST-DSI, SM-DSI, and VC-DSI were estimated in the range of 0.002 to 0.77, −0.029 to −0.51, and − 0.001 to −0.82, respectively. On the regional scale, alterations in the LST and EVI could show about 40% of changes in the wind erosion activity over arid regions of Iran (R2 = 0.43; P value β2LST = 0.15). In semi-arid regions, 36% of changes in DSI was due to alterations in LST and SM (R2 = 0.36; P value 0.05). These results can be used by planners and managers to develop programs to combat wind erosion and reduce the risks posed by dust storms in arid and semi-arid regions of Iran.

GIS-based multi-criteria decision making and entropy approaches for groundwater potential zones delineation

In arid and semi-arid, due to the lack of surface water, groundwater plays key role in people survival. Therefore, assessing groundwater potential zones is vital to conserve and manage this resource properly. The main object of this study was to compare two methods of AHP and entropy for delineating groundwater potential zone in arid region of Iran. For this regard, withdraw in groundwater level in 16 piezometer wells along with eight GWP conditioning factors of slope degree, stream power index (SPI), landuse, geology; rainfall volume, elevation, soil and lineament density have been considered in the training process while the discharge in the other 16 wells was involved in verifying process of the two models. GWP maps were provided by overlaying all the thematic maps in terms of the obtained weights from AHP and entropy methods. Finally, the models performance was compared through statistical measures of Sensivity, Specificity and True Skill Statistics (TSS). The results show that IOE model (TSS = 0.16) have higher prediction performance than AHP approach (TSS = 0). Therefore, IOE was more promising tools for GWP modeling.