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In recent years, Iran has experienced many landslides due to high tectonic activity, and a variety of geological and climatic conditions. This paper proposes a novel hybrid model based on step-wise weight assessment ratio analysis (SWARA) method and adaptive neuro-fuzzy inference system (ANFIS) to evaluate landslide susceptible areas using geographical information system (GIS). At first, based on an inventory map, landslide locations were randomly divided into two parts, 70% of which were used for generating the landslide hazard map and 30% of which were used for the validation of the model. Then, twelve landslide predisposing factors, such as lithology, slope angle, slope aspect, plan curvature, profile curvature, altitude, distance to streams, distance to faults, distance to roads, land use, seismicity, and rainfall were considered for the analysis. All the factors were then weighted by the SWARA method. Considering the nature of predisposing factors, they were split into two groups, factors with discrete data and factors with continuous data. For factors with discrete data, the SWARA method was used for final weight of each class, and for factors with continuous data, results related to the center of each class were obtained from the SWARA method. Subsequently, AFNIS was used to obtain weight of each value. All the values obtained from the model were then used to generate the landslide hazard map of the study area. Finally, the landslide hazard map was validated by receiver operating characteristics (ROC) using both success rate curve and prediction rate curve. 70% of observed landslides were used for the former while the remaining was used for the latter. The validation results showed that the area under the success rate curve and prediction rate curve (AUC) are 0.84 and 0.80 respectively. Additionally, the prediction performance of the SWARA method for landslide hazard mapping was investigated and the results were compared with those obtained from the proposed model. The comparison revealed that the developed model has better prediction ability for landslide hazard assessment. The results also indicated that the proposed model used in this study produced satisfactory and reliable landslide hazard map, which can be used for preliminary land use and infrastructure planning in Iran. [ABSTRACT FROM AUTHOR]

[Display omitted] • Stabilizing sand dunes with tar mulch can result in environmental pollution. • An alternative is to fingerprint key sources of aeolian sediment. • Geochemical data were combined with weathering indices to trace sand dune sources. • Alluvial fan material was the key source of the target sand dunes. • The weathering indices offer low-cost options for aeolian fingerprinting. Confirmation of cost-effective and reliable tracers for aeolian sediment (sand dune) source fingerprinting warrants further research. Accordingly, the main objective of the work reported in this paper was to investigate the efficiency of weathering indices in aeolian sediment fingerprinting using a case study of a fragile arid region in Qom Province, Iran. Eight geochemical elements (Al, Ca, Fe, K, Na, Mg, Si, Ti) and 17 associated weathering indices were measured in 34 aeolian source samples and 10 sand dune target sediment samples in three absolute particulate size fractions. For each fraction, three final composite fingerprints (i.e., geochemical elements only, weathering indices only and a combination of the two) for discerning and ascribing the aeolian sediment sources were selected. The Modified MixSIR Bayesian un-mixing model was used to apportion aeolian source contributions using the final composite fingerprints. Regardless of the composite fingerprint used, all results across the different size fractions suggested that the south-eastern alluvial fan is the dominant (average contribution 50.6%, SD 19.0%) source of the sand dune samples, with the western alluvial fan being the second most important (average contribution 38.4%, SD 20.4%) source. Comparisons of the posterior distributions for the predicted source proportions generated using the nine composite fingerprints (three kinds of composite fingerprints*three particle size fractions) showed that the composite fingerprints combining the geochemical elements and weathering indices generated the most powerful source material discrimination. Our results demonstrate the use of weathering indices alongside more conventional elemental geochemistry tracers for investigations into sand dune sediment provenance. [ABSTRACT FROM AUTHOR]

Abstract: The spatial scale dependency of runoff generation and sediment yield and accuracy to extending the results of experimental plots to surrounding watersheds is rarely taken into account. The present paper aimed to evaluate the accuracy of soil erosion plots with different lengths in estimation of runoff and soil loss from a small watershed in Iran. Towards this attempt, two similar sets of experimental plots with lengths of 2, 5, 10, 15, 20 and 25 and fixed width of 2m were installed on two north and south facing slopes (i.e. 6 plots on each slope and totally 12 plots) in Sanganeh watershed, Razavi Khorasan Province with an area of ca. 1ha. Runoff and sediment was collected at the outlet of each plot as well as main outlet of the watershed associated with 12 storm events occurred during November 2006 to June 2007. The results verified that the accuracy of plot estimates on sediment and runoff improved while the plot length increased. The optimal lengths for estimation of sediment and runoff parameters were found 15 and preferably 20m for plots located on southern slope. No significant difference was also proved among estimates obtained from plots established on north facing slope and those measured at the main outlet of the watershed. [Copyright &y& Elsevier]

Abstract: Landscapes are considered to be complex systems that are hierarchically structured and spatially scale-dependent. Geopedology allows a systematic approach in geomorphic analysis for soil mapping that extrapolates the results obtained at sample areas up to similar units. This paper examines the influence of sampling intervals on the accuracy of geopedological results in the Borujen region, Central Iran. After a primary interpretation of the study area on air photos (1:20000), a geomorphic unit that encompassed the maximum surface of the sample area (and also the study area) was selected and was surveyed at three different scales: 500, 250 and 125 m intervals. The credibility of generalization of the results of the geopedological approach for the studied unit was tested by comparison with three profiles in a similar unit outside the sample area, named the validation area. Although the type of mapping unit in the sample area and the validation area was the same (complex) at the three different scales, the dominant soil in the validation area was different in comparison with the sample area at 125 and 250 m intervals. The results indicate that the geopedological approach to soil mapping is better carried out in reconnaissance or exploratory surveys. Comparison of soil types between the unit in the sample area and the validation area at three scales indicated that by increasing the taxonomic accuracy, the hidden aspects of the soils may be more identifiable. Therefore, although the geopedological approach tries to distinguish more homogeneous soil mapping units, it still is not able to fully define and represent the variability and apparent chaotic nature of the soils. We recommend further investigations on different techniques of stratifying the landscape in order to better analyze and understand the soil forming processes and soil variability and to improve sampling and mapping approaches. [Copyright &y& Elsevier]

Abstract: This paper presents direct evidence in the form of a triangular cross-section channel (1 m in width and 0.24 m in depth), for Late Neolithic artificial water management on the Tehran Plain, which may represent the earliest example of artificial water management in Iran. The antiquity of this channel is supported by dating directly above and below by C14, associated ceramic sherds and correlation with Late Neolithic levels. The nature and function of this channel is evaluated through comparisons with natural channels (ancient and modern) together with evidence from palynology and sedimentology. It is here interpreted as a silted-up artificial canal with infill-deposits that indicate periods of shallow relatively quiet flow, periods of drying-out and occasional episodes of greater flow. This study strongly suggests that 6th millennium farmers at Tepe Pardis in Iran were irrigating their crops, and complements the evidence from Choga Mami in Iraq concerning early irrigation systems. [Copyright &y& Elsevier]

• Agriculture and land management need up to date and high-resolution soil maps. • Conventional soil mapping began in 1953 in Iran, but not reliable for large areas. • Digital soil mapping approaches are in early stages in Iran. • Most Iranian soils have not been mapped at an appropriate scale. • Iranian soil scientists have paid great attention to digital soil mapping recently. Demand for accurate soil information is increasing for various applications. This paper investigates the history of soil survey in Iran, particularly more recent developments in the use of digital soil mapping (DSM) approaches rather than conventional soil mapping (CSM) methods. A 2000–2019 literature search of articles on DSM of areas of Iran in international journals found 40 studies. These showed an increase in frequency over time, and most were completed in the arid and semi-arid regions of central Iran. Artificial Neural Networks (ANN), Random Forests (RF), and Multinomial Logistic Regression (MnLR) were the most commonly applied models for predicting soil classes and properties and ANN performed best in most comparative studies. Given the scale of inquiry of most studies (local or regional), quantitative environmental variables such as terrain attributes and remote sensing data were frequently used whereas qualitative variables such as geomorphology, geology, land use, and legacy soil maps were rarely used. The literature review of CSM showed that this method is incapable of defining the spatial distribution of soils and also provides a lower accuracy than DSM method. This review has identified research gaps that need filling. In Iran, coherent national scale DSM with consistent methodology is needed to update legacy soil maps, and to apply DSM in forestlands, hillslopes, deserts, and mountainous regions which have largely been ignored in recent DSM studies. This review should also be useful for producing more local and regional digital soil maps more rapidly as it helps show which covariates and mathematical methods have been best suited to this scale of DSM in Iran. [ABSTRACT FROM AUTHOR]

• Dust activity in the Middle East during 1980–2010. • The role of the parameters affecting the increase of dust activity. • Correlation analysis between the dust activity and affecting parameters. • Excessive water withdrawal from the Tigris and Euphrates rivers and the increase of the dust activity. • The key role of water resources management in suppressing dust activity. Dust events in the Middle East have resulted in serious environmental problems in many countries in the area. The dust activities in the Middle East were intensified in recent years due to many factors including climate change, drought, and poor water management strategies. This paper investigates the dust activity during 1980–2010 in the Lower Mesopotamia and southwestern Iran and demonstrates that the dust activity in the study area has increased after 1999. This study investigates the role of surface temperature, precipitation, water resources, vegetation, and surface wind velocity on the development of dust events during the study period. This investigation is performed using the correlation analysis of the normalized value of the 5 and 10 year moving average of the mentioned parameters to evaluate the role of each parameter on the increase of dust activity in the last decade of the study period. The correlation analysis demonstrates that the dust activity has the highest correlation and relationship to the water resources of the Tigris and Euphrates rivers and indicates the key role of water resources management on the dust activity in the study area. It is shown, The large scarcity of the Tigris and Euphrates rivers water resources has occurred when there was not a sharp reduction in the average precipitation in the study area. Hence, the excessive water withdrawal through the dam construction projects could be considered as the main reason of large reduction of the Tigris and Euphrates rivers water resources and high dust activity in the Middle East during the last decades. [ABSTRACT FROM AUTHOR]

The initiation and development of gullies as worldwide features in landscape have resulted in land degradation, soil erosion, desertification, flooding and groundwater level decrease, which in turn, cause severe destruction to infrastructure. Gully erosion susceptibility mapping is the first and most important step in managing these effects and achieving sustainable development. This paper attempts to generate a reliable map using four state-of-the-art models to investigate the Bayazeh Watershed in Iran. These models consists of boosted regression trees (BRT), binary logistic regression (BLR), numerical risk factor (NRF) and frequency ratio (FR), which are based on a geographic information system (GIS). The gully erosion inventory map accounts for 362 gully locations, which were randomly divided into two groups (70% for training and 30% for validation). Sixteen topographical, geological, hydrological and environmental gully-related conditioning factors were selected for modelling. The threshold-independent area under receiver operating characteristic (AUROC) and seed cell area index (SCAI) approaches were used for validation. According to the results of BLR and BRT, the conditioning parameters namely, NDVI and lithology, played a key role in gully occurrence. Validation results showed that the BRT model with AUROC = 0.834 (83.4%) had higher prediction accuracy than other models, followed by FR 0.823 (82.3%), NRF 0.746 (74.6%) and BLR 0.659 (65.9%). SCAI results indicated that the BRT, FR and BLR models had acceptable classification accuracy. The findings, in terms of model and predictor choice, can be used by decision-makers for hazard management and implementation of protective measures in gully erosion-prone areas. Unlabelled Image • Four models were applied and compared in gully erosion susceptibility mapping. • NDVI and Lithology played a key role in gully occurrence in the study area. • BRT model had higher prediction accuracy than other models. • The proposed methodology is adaptable and can be used in other regions. [ABSTRACT FROM AUTHOR]

Karstic springs are considered as an important source of drinking water in highland areas. Because of the high vulnerability of karst areas, any activities such as agricultural, livestock and even industrial ones in these areas can affect springs water quality. Therefore, it is essential to define qualitative limitations on human functions in these areas for the sake of springs protection from contamination. In this paper, a new method is introduced which combines the vulnerability index of a spring's outlet and the protection area of its basin. In the proposed method, the VESPA index is firstly applied to analyse the vulnerability of karst springs to contamination. Then, the protection areas of the springs are determined using the integration of the MDHT method and the result of VESPA. The method is examined to Parikedal and Sarab-e Taveh springs, located in the central Zagros, Iran. Based on the results, Parikedal vulnerability is classed as very high and two immediate protection zones are defined around Prikdal's outlet and its sub-basins, located on its inner protection zone. Sarab-e Taveh, in comparison with Parikedal, has less karstic development. Therefore, its vulnerability is classified as high. In addition, by considering the MDHT method, the main part of Sarab-e Taveh's water basin is located in outer protection zone and thus, some minor activities are allowed on it. The results of this study show that combining the analytical model and vulnerability could estimate springs' protection areas more accurately. Unlabelled Image • Springs vulnerability was determined by VESPA index. • Combining the MDHT model and vulnerability for estimation springs' protection • Generate spring protection area for conservation of groundwater resources [ABSTRACT FROM AUTHOR]

The main objectives of this paper were 1) to estimate soil organic carbon (SOC) using remote sensing covariates, soil properties, and topographic factors , and 2) to evaluate the interaction and the relative influence of the selected factors on the spatial variation of SOC. Thirteen factors were considered for digital mapping of SOC in the west Urmia Lake in Iran. To quantify multicollinearity among the predictor variables, Variance Inflation Factor (VIF) was calculated. Among them, nine independent factors were remained including silt, sand, slope, enhanced vegetation index (EVI), brightness, wetness, land cover, and latitude and longitude. A machine learning algorithm called Gradient Boosting Machine (GBM) was calibrated for understanding the spatial dynamic and prediction of SOC. Model performance showed that GBM explained 43.5% (R2) of the SOC variation, and root mean square error (RMSE) was 0.23%. Results showed that EVI and sand were the most influential factors of the SOC variation while slope and land cover were the least important ones. Furthermore, significant interaction among EVI-wetness-SOC and EVI-sand-SOC was detected. On the other hand, 45.2% of SOC variation was estimated by remote sensing covariates. These results suggested that GBM was a promising approach for an in-depth understanding of the SOC variation over space. • Remote sensing covariates and field data were integrated for SOC mapping. • A Gradient Boosting Machine (GBM) was calibrated for SOC mapping. • The results showed that EVI and sand were the most influential factors. • 45.2% of SOC variation was estimated by remote sensing covariates. [ABSTRACT FROM AUTHOR]

This paper tackles the issue of spatially predicting soil classes by combining at best soil information coming directly from legacy soil profiles with soil information indirectly obtained from spatial covariates. Based on Multinomial Logistic Regression (MLR) and Bayesian Maximum Entropy (BME) models, we first show that prediction models easily lead to very different soil maps while having at the same time quite comparable global performances. By relying afterwards on a Bayesian data fusion (BDF) approach, we emphasize the benefit of combining the output of these two prediction models in order to get a single final map that combines the major spatial features of the MLR and BME maps while at the same time improving the quality of the predictions. The advocated methodology is illustrated with the mapping of World Reference Base (WRB) soil classes over a 10,480 km2 area located in Iran. A set of 390 soil profiles allowed us to assign the WRB soil classes at these locations. In parallel, a set of potentially related covariates were computed from a 90 m resolution digital elevation model. Using MLR and BME models, predictions were obtained separately at the nodes of a 90 m resolution grid. Even if the performances of the MLR and BME models compare well, it is shown that a BDF procedure that combines both results yields improved performances, with spatial features that are a balanced combination of those found separately on the MLR and BME maps. These results emphasize the benefit of data fusion in order to improve the quality of the final map. Though the study was conducted here using MLR and BME models for predicting WRB soil classes, we believe this methodology and the corresponding findings are relevant when it comes to handle the results of spatial prediction models that are making use of distinct information sources in other soil science mapping contexts. • Spatial prediction of WRB (World Reference Base) soil classes based on Multinomial Logistic Regression (MLR) and Bayesian Maximum Entropy (BME) models lead to soil maps exhibiting very different soil patterns while having comparable global performances • The BME model does not require the use of covariates and its prediction performance is directly related to the sampling density, while this is the opposite for the MLR model • A MLR model can lead to a predicted soil map where some soil classes are missing due to its inability to predict them as maximum probability classes, while this is not the case for the BME model • When several predicted maps are obtained from different models and sets of information, a Bayesian Data Fusion (BDF) is useful for obtaining a single final map that accounts for the respective performance of each model [ABSTRACT FROM AUTHOR]