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Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. [ABSTRACT FROM AUTHOR]

A physically-based image processing approach, based on a single-source surface energy balance framework, is developed here to model the land surface temperature (LST) over complex/rugged geologic terrains at medium to high spatial resolution (<102 m). This approach combines atmospheric parameters with a bulk-layer soil model and remote-sensing-based parameterization schemes to simulate surface temperature over bare surfaces. The model's inputs comprise a digital elevation model, surface temperature data, and a set of land surface parameters including albedo, emissivity, roughness length, thermal conductivity, soil porosity, and soil moisture content, which are adjusted for elevation, solar time, and moisture contents when necessary. High-quality weather data were acquired from a nearby weather station. By solving the energy balance, heat, and water flow equations per pixel and subsequently integrating the surface and subsurface energy fluxes over time, a model-simulated temperature map/dataset is generated. The resulting map can then be contrasted with concurrent remote sensing LST (typically nighttime) data aiming to remove the diurnal effects and constrain the contribution of the subsurface heating component. The model's performance and sensitivity were assessed across two distinct test sites in China and Iran, using point-scale observational data and regional-scale ASTER imagery, respectively. The model, known as the Surface Kinetic Temperature Simulator (SkinTES), has direct applications in resource exploration and geological studies in arid to semi-arid regions of the world. [ABSTRACT FROM AUTHOR]

The rapid decline of Lake Urmia in northwest Iran, caused by both human and natural factors, motivated our utilization of the Soil and Water Assessment Tool (SWAT) model. The objective was to identify the most effective reservoir outflow simulation scheme and assess the influence of land use changes in the Zarrineh River basin on Lake Urmia. The target release approach for controlled reservoirs and the measured outflow schemes were evaluated. Despite improved calibration results in the latter method, the former demonstrated superior performance during the validation phase. Three land use scenarios – a model with the 2001 land use map, a model with the 2008 land use map, and a model with a gradually updating land use map – were considered. The gradually updating land use scenario exhibited a significant enhancement in validation results; it showed a 250% increase in water yield, while evapotranspiration declined by 3%. [ABSTRACT FROM AUTHOR]

Introduction: The fugal importance in terrestrial ecosystems is significant. However, our knowledge based on fungal distribution and its diversity in natural communities, especially in semi-arid ecosystems, seems to low. To have proper information on fungal community structure and the impact of environmental factors on the community composition, we assessed fungal diversity accompanied by soil chemical properties. Materials and Methods: Soil sampling was done in two seasons, spring and autumn, from two different sites, which were both cold and warm in a semiarid ecosystem located in Kerman province, Iran. Soil chemical properties were measured according to standard methods. The fungal community was estimated by the next-generation sequencing of the ITS region (ITS1f-ITS2). Results: By Illumina Miseq data analysis, 1542 Operational Taxonomic Units in soil samples were identified. Variation in the fungal community between the two sampling sites may be because of remarkable differences in environmental conditions, such as moisture and soil organic carbon. The dominant phylum was Ascomycota, followed by Basidiomycota. Discussion and Conclusion: Various fungi were identified in this ecosystem. Further studies are necessary to reveal the possible roles of the dominant fungal species inhabiting semiarid soils. [ABSTRACT FROM AUTHOR]

Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa. [ABSTRACT FROM AUTHOR]

Groundwater resources are the most reliable freshwater supply in arid regions where many aquifers face dramatic depletion due to natural and anthropogenic causes. The annual average rate of decline of groundwater level is about 1.65 m. This research focuses on an aquifer that suffers from severe groundwater stress, and it aims to identify the main causes of the stress and to understand the effects of climate change and human activity. Monthly data on groundwater level, precipitation, temperature, river discharge, evapotranspiration, soil moisture, and vegetation cover were collected from 2000 to 2020. The results indicate that declining groundwater levels mainly resulted from the expansion of vegetation cover rather than changes in hydro-climatic variables. Finally, this work highlights how significant financial investment in improving irrigation efficiency in the absence of socio-economic plans, education, awareness, and monitoring programmes unproductively resulted in the expansion of agricultural activities rather than preserving groundwater storage. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation. [ABSTRACT FROM AUTHOR]

Background: Due to the opportunism character of Acanthamoeba, the presence of this parasite in the thermal water of recreational baths and hospital environments can be a risk to the health of staff, patients and others. The aim of this study was to investigate the distribution of potentially pathogenic Acanthamoeba genotypes isolated from the hospital environment and the thermal water of recreational baths in Markazi Province, central Iran. Methods: Overall, 180 samples including thermal water from recreational baths in Mahallat City and dust, soil and water from different hospitals of Arak, Farahan and Komijan cities, central Iran were collected. The presence of Acanthamoeba was investigated using microscopic examination and molecular methods. The PCR and sequencing was performed based on a specific 18S fragment of ribosomal DNA. Results: Based on the microscopic survey, totally 134 positive samples were detected including 35% in thermal water samples and 44.7% in hospital samples. In molecular analysis, 53.5% of the samples were identified as Acanthamoeba and 46.7% as Protacanthamoeba bohemica. The genotypes were detected as T4 (33.3%), T2 (10%), T11 (6.7%), and T5 (3.3%). Conclusion: The T4 was the most common genotype found in hospitals sampling sites while the T2 genotype and P. bohemica were detected in thermal water sampling sites. [ABSTRACT FROM AUTHOR]

Introduction: The fugal importance in terrestrial ecosystems is significant. However, our knowledge based on fungal distribution and its diversity in natural communities, especially in semi-arid ecosystems, seems to low. To have proper information on fungal community structure and the impact of environmental factors on the community composition, we assessed fungal diversity accompanied by soil chemical properties. Materials and Methods: Soil sampling was done in two seasons, spring and autumn, from two different sites, which were both cold and warm in a semiarid ecosystem located in Kerman province, Iran. Soil chemical properties were measured according to standard methods. The fungal community was estimated by the next-generation sequencing of the ITS region (ITS1f-ITS2). Results: By Illumina Miseq data analysis, 1542 Operational Taxonomic Units in soil samples were identified. Variation in the fungal community between the two sampling sites may be because of remarkable differences in environmental conditions, such as moisture and soil organic carbon. The dominant phylum was Ascomycota, followed by Basidiomycota. Discussion and Conclusion: Various fungi were identified in this ecosystem. Further studies are necessary to reveal the possible roles of the dominant fungal species inhabiting semiarid soils. [ABSTRACT FROM AUTHOR]

Soil moisture content is one of the critical parameters in water resource studies and watershed management. Large-scale field measurement is a tough, time-consuming, and costly task. newly, models based on remote sensing indicators have found special importance with high accuracy to investigate the soil and water resources. This study aims to use Landsat 8 satellite imagery to study variation in topsoil moisture content of dust hotspots of southeastern Ahvaz of Iran, over the five months (from February to June 2019). After monthly field sampling, satellite images were applied to determine the NDMI index and the topsoil moisture content using Bands 5 and 6 (main Bands) of Landsat 8 satellites. Then, by fusion, with Band 8 (panchromatic Band), the soil moisture content map was obtained for each month. The Pearson correlation positive was obtained between the NDMI of the main band and the NDMI of fusion with the soil moisture content of field for April month, with a correlation coefficient 0.543, and a significance level of 0.05 (P-value < 0.05). Also, for each month, the humidity index modeling was obtained for both data (the main (original) band and the fusion band). The proposed model was evaluated using statistical metrics namely R2, RMSE and MAE to April month, and the results were 0.57, 1.25 and 5.45, respectively. After validating the models, the best ones were selected for estimating the soil moisture content. Finally, the obtained results showed that Landsat 8 data presented satisfying outcomes for estimating map soil moisture content. [ABSTRACT FROM AUTHOR]

Acanthamoeba, a free-living and opportunistic protozoan parasite, is a causative agent of severe human infections of the cornea and brain. The present study evaluated the distribution and genotyping of Acanthamoeba spp. in water and soil of recreational places in various areas in Guilan province in northern Iran. Eighty water and 20 soil samples were collected from the study area. Water samples were vacuum filtered through a 0.45 μm pore-size membrane filter. Soil samples were washed with sterile distilled water, and washings were similarly filtered, as mentioned for water samples. The filtered material was cultured on non-nutrient agar plates seeded with heat-killed Escherichia coli. Molecular analysis was performed by PCR and sequencing using specific primers for Acanthamoeba. Finally, 26 isolates were successfully sequenced. According to culture and PCR methods, 54% of water and 100% of soil samples were contaminated with Acanthamoeba. Based on the sequencing data, genotypes T4 (47%), T5 (35.29%), T3 (11.76%), and T11 (5.88%) were identified in water samples. Genotypes T4 (66.6%), T5 (22.2%) and T15 (11.1%) were identified in water samples. Most isolates might present a potential health hazard for humans in this region. To the best of our knowledge, this is the first comprehensive survey of water and soil of recreational areas in northern Iran and the first report on identifying genotype T15 from soil sources. [ABSTRACT FROM AUTHOR]

The present study aims to investigate Iran's agrometeorological drought history and its properties using Multivariate Standardized Drought Index (MSDI). For this purpose, precipitation and soil moisture recorded at 99 synoptic stations in Iran during 1985 to 2018 were selected. Based on MSDI time series, drought properties including duration and severity were calculated and trend analysis was carried on using Trend Free Prewhitening Mann Kendall test. Also, the standard normal homogeneity test was applied to investigate chronological drought change-point detection. From agrometeorological perspective, results showed that longer and more severe droughts are more dominant in southern, southeastern, northeastern, and some western regions of the country. Trend analysis revealed that most study stations have rising frequency and significant drying trend especially in winter at central plateau and western regions. Also, we recognized two main periods 1991–2000 and 2006–2010 that many stations experienced change-point in their MSDI time series, mainly because of climatic and anthropogenic causes. Drought properties assessment shows 6 stations with serious upward trend in drought duration and severity in northeast and western regions. Sens's Slope statistics in these stations was calculated around 1.5 degree per event for drought severity and 1 month per event for drought duration. It is also observed that the 75th quantile of drought severity for most parts of the country is between 13 and 18 and the 75th quantile for drought duration varies between 8 and 24 months in different regions. The probability analysis revealed that most stations have a ratio around 2–5 times increase in drought occurrence probability after change point, leading to longer and more severe droughts in these stations. The paper indicates a serious situation related to agrometeorological drought occurrence, persistence, and severity in Iran because of many climatic and anthropogenic factors over the last 30 years. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area. [ABSTRACT FROM AUTHOR]

The consequences of climate change, primarily due to natural and human factors, have caused many problems in the last decade that have affected different sections of society. One of the most important consequences is the occurrence of natural atmospheric—climatic disasters (drought). One of these regions is Iran in the Middle East, which has been suffering from this drought in the last decade. This investigation aims to evaluate and monitor drought and its impacts on environment changes in Iran. To conduct this research, the TERRA satellite data and the MODIS sensor were used over a 21‐year period, and the Normalised Differential Vegetation Index and Palmer Drought Severity Index Indicators were used to monitor drought and to estimate the soil moisture by the Soil Moisture Calculation satellite. Finally, to estimate the amount of precipitation and evapotranspiration, cumulative precipitation and real evapotranspiration, actual evaporation and transpiration were used. Findings showed that in the investigation region, the severity and frequency of drought have increased in recent years. Soil moisture has also decreased, indicating the inverse relationship between drought intensity and soil surface moisture, meaning that as the intensity of drought increases, the amount of soil surface moisture decreases. However, in the investigation region during the study course, the accumulation of rain showed a direct relationship with evaporation and transpiration. The highest monthly drought intensity of Palmer was 5.65% in May 2000. Also, the highest amount of wet year in November 2018 is 3.6%. The highest amount of rainfall was 61.25 mm in October 2018. Based on the results obtained in this research, drought has occurred in the investigation region in recent years, the intensity of which in the south, eastern and centre half of the country, especially the provinces of south Khorasan, Semnan, Hormozgan, Sistan and Baluchestan, Khuzestan and Ilam more it has been more than other provinces. To manage and control drought in the investigation region, in the various sectors affected by drought (agriculture, environment, surface water resources, forests, pastures and watershed management and natural ecosystems) strategies and plans in the short term, medium‐term and long‐term in line with natural and human factors and climate change to be adopted. [ABSTRACT FROM AUTHOR]

The impact of different tillage systems on the structural stability of the soil as quantified by high energy moisture characteristics has not been studied to date in the semi-arid region of northeastern Iran. Therefore, in this research, the effects of conventional, reduced, and no-tillage treatments under wheat-corn crop rotation on the organic carbon content, soil organic carbon stocks and microbial biomass carbon and also the aggregate stability of a clay loam soil (Aridisol, in USDA soil classification) were evaluated in northeastern Iran. Measurements were made at two depths (0-15 and 15-30 cm) three years after the establishment of experiment. Aggregate stability was determined using the high energy moisture characteristics, mean weight diameter and the percentage of water-stable aggregates. The high energy moisture characteristics stability indices (i.e. structural index, volume of drainable pores, and slope at the inflection point of high energy moisture characteristics) were higher for the slow (on average 0.053 hPa-1, 0.4 g g-1, and 0.022 hPa-1 for structural index, volume of drainable pores and slope at the inflection point, respectively) than the fast wetting rate (on average 0.014 hPa-1, 0.2 g g-1, and 0.012 hPa-1 for structural index, volume of drainable pores and slope at the inflection point, respectively). The modal suction was higher for the fast (18.61 hPa in average) than for the slow wetting rate (7.63 hPa in average). Organic carbon, soil organic carbon stocks and microbial biomass carbon were higher for no-tillage as compared with the reduced and conventional treatments, due to the use of a cover crop (clover) and because crop residues were retained in this system. The treatments contained greater organic carbon, soil organic carbon stocks and microbial biomass carbon (no-tillage at both depths and reduced at a depth of 0-15 cm) showed mainly high stability ratios and indices, regardless of the wetting rate. The values of modal suction were lower in the treatments which contained greater organic carbon, soil organic carbon stocks, and microbial biomass carbon, which results in a stable soil structure. The no-tillage system, which produced the maximum organic carbon and soil organic carbon stock among the tillage systems, showed the highest amounts of mean weight diameter (0.15 mm), and water-stable aggregates (92%). Our findings revealed that the high energy moisture characteristics method was highly sensitive to macroaggregate stability in water. [ABSTRACT FROM AUTHOR]

Evidence has shown that drought-induced crown dieback has significant interaction with soil-plant water and nutrients relationships, but there isn't sufficient knowledge of the water and nutrients connections in declining Persian oak (Quercus brantii) trees. To investigate the relationships between crown dieback and leaf nutrients concentrations, leaf water content, soil nutrients concentrations and soil moisture, one of the dieback affected stands was selected. This stand was located in Shalam forested area in northern Ilam province, west of Iran. Persian oak trees were evaluated using a crown dieback classification and divided into four classes with four replicates based on the severity of crown dieback. Sampling from leaves of trees and soil was implemented randomly in four sides of tree crown in August. Trees with moderate to severe declining showed reduced leaf water content (WC) and reduced relative leaf water content (RWC). Leaf N and P concentration of declining trees was significantly (p-value <0.05) more than that of healthy trees. The leaf K concentration of declining trees was significantly (p-value <0.10) more than that of the healthy. The leaf N:P amounts of oak trees was statistically different among crown dieback classes. But no significant differences was observed between the healthy and declining trees in terms of soil N, P, K, Ca and Mg concentrations and soil moisture. It was concluded that drought has effects on the nutrients status in the leaves of Persian oak trees and their changing in the declining trees is for resistance to drought condition, improving water use efficiency and possibly performance of vital activities. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the interaction effect of conservation tillage and irrigation methods on the soil properties, crop yield and water use productivity in wheat-maize cropping system. A split plot experimental design with three replications was used for this study in Fars province, Iran. Surface irrigation (SI), drip tape irrigation (DTI), and sprinkler irrigation (SpI) were considered as main plots, and no-tillage (NT), reduced tillage (RT), and conventional tillage (CT) were considered as sub plots. Results showed that SI had the maximum wheat yield on average and the maximum maize yield was obtained from DTI on average; while tillage methods had no significant effect on crops yield. Results also indicated that the maximum water use productivity in wheat and maize production were obtained from DTI on average and the minimum use productivity were related to the SI on average. DTI and SpI saved water compared to the SI by 43% and 22% on average in wheat and 57% and 36% on average in maize, respectively. NT increased water content of soil compared to the CT by 51%. SI had the lowest soil infiltration rate, and NT reduced soil infiltration rate compared to the CT by 26% on average. Conservation tillage (CoT) increased organic carbon of the soil depth of 0.00 to 0.10 m by 12% compared to the CT. Therefore, DTI combined with CoT is recommended for wheat-maize cropping system in a semi-arid climate condition. [ABSTRACT FROM AUTHOR]

The study of soil genesis and classification for land management, restoration and protection of soils in the mountainous basin of Darrezar copper mining company were done. With attention to the effects of the vertical zones on soil climate variability, the soil thermal and moisture regimes and soil classification in the area was established. The landforms based on topography and lithology parameters altitude, slope, aspect and depositions) were classified. Soil classifications were done using direct soil samples studies and geographic information system (GIS), showing temperature and moisture regimes. and Using GIS with digital elevation model (DEM) till mining and comparison to current soil map were simulated 6 soils subgroups in degraded lands. More than 70% of lost soils were in 3 subgroups, Lithic Torriorthents, Lithic Haploxerepts and Typic Haplocalcids. The vegetation types in these three soils were in two association types: Amygdaluse sp.–Pistacia sp.–Artemisia sp. and Amygdaluse sp.–Artemisia sp. The soil loss was estimated to about US$ 8 million. Also in Darrezar copper mine in average has destroyed about 27 ha land surface with minimum cost about US$ 2 million per year from 2008 until 2020. [ABSTRACT FROM AUTHOR]

Appropriate planting date is directly related to the geographical situation and climatic conditions of the region. Due to the declining trend of rainfall and lack of soil moisture in most parts of Iran, applying the maximum possible delay in planting is essential due to less autumn irrigation and getting closer to autumn rainfall. Therefore, to investigate the effect of delay in planting on quality traits, yield, and yield components of rapeseed, an experiment was conducted in 2012 as split plots in a randomized complete block design with three replications in Hamedan province, Iran. The main factor was planting date in 4 levels, including 10 September, 20 September, 30 September, and 10 October. The second factor was four genotypes of Okapi cultivar and hybrids of Tassilo, Octans-NK, and Elite. The results showed that the number of pods per plant, biological yield, seed yield, harvest index, and oil yield at 1% probability level and the number of seeds per pod and Thousand seed Weight (TSW) at 5% probability level were affected by the interaction of planting date and genotype. Also, the simple effect of genotype on the percentage of seed oil was significant at the level of 1% probability. The highest seed yield in the first planting date with 5083.3 kg/ha belonged to the Octans-NK genotype and the lowest yield in the last planting date (1244.4 kg/ha) belonged to the Okapi genotype. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered. [ABSTRACT FROM AUTHOR]

Iran, and especially its northern provinces, lose a large amount of agricultural soil annually due to erosion, and this is one of the reasons why productivity in Iranian agriculture is low. Hence, there is a great need for measures to protect water and soil resources and prevent their pollution, which are called “water and soil conservation measures” (WSCM). The main purpose of this study was to investigate the factors that were effective in determining the adoption of WSCM by farmers in Lasht-e-Nesha district (northern Iran). Binary logistic regression was used to analyze the data. The results showed that the variables of the total amount of rice production, cooperation with other farmers and participation in development-extension and training courses, which all are quantitative, had a positive and significant relationship with the adoption of WSCM. Other quantitative variables used in this study (e.g. number of household members, farmer's experience in rice cultivation, farm size, number of paddy plots, dealing with other agricultural activities) showed a negative and significant relationship with the adoption of WSCM. Also, all the attitudinal variables used in this study, including behavioral intention, facilitating conditions and initial trust, had a strong, positive and significant effect on the adoption of WSCM by Lasht-e-Nesha farmers. The strong suggestion of the authors of this article is that by holding various meetings in rural areas and the presence of experts in all branches of agriculture, especially water and soil, farmers should be more familiar with the benefits and efficiency of WSCM. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the impacts of organic fertilizers on soil water holding capacity in four different suctions (0, 0.05, 0.33 and 15 bar) and their impacts on water retention curve in three different soil textures in five governing climates of Iran, which were cultivated under wheat and maize for two consecutive years. Furthermore, the role of organic materials in aggregation process was surveyed. The influence of organic materials on soil water holding capacity was evaluated in five treatments, including 10000, 20000 kg/ha of animal manures; 10000, 20000 kg/ha of compost, and control treatment in factorial and completely randomized statistical design. The results revealed that the addition of materials resulted into an increase in the mean weight diameter of soils aggregates with more concentration on 250-500 and 500-1000µ diameter. Additionally, the improvement of soil aggregation was more in sandy loam soil and less in clay loam soil. Interestingly, the role of organic materials in increasing soil water holding capacity in different plants and consecutive years was not significant. By adding organic materials in field capacity and permanent wilting point, soil volumetric moisture increased far better than other studied soil moisture points. Sandy loam texture and semi-arid climates indicated the greatest variability to the additional organic materials. In conclusion, compost fertilizer in arid and semi-arid climates with sandy loam texture had the most influence on soil water holding capacity, particularly in FC and PWP moisture points. [ABSTRACT FROM AUTHOR]

This study presents a robust approach to assess climate change impact variability on future extreme events (e.g., rainfall depth and river discharge) over Dehbar catchment in Iran. Climate change impact is assessed using five general circulation models (GCMs) including EC-EARTH, GFDL-CM3, HadGEM2-ES, MIROC5, and MPI-ESM-MR with several emission scenarios (e.g., RCP26, RCP45, and RCP85). Daily discharge data is simulated based on the distributed rainfall-runoff model called Soil and Water Assessment Tool (SWAT), while calibration and validation phases are performed using SWAT-CUP. Future annual extreme events (i.e., rainfall depth and river discharge) are computed by means of frequency analysis. Results show that future annual maximum values are increased significantly, where the most increase occurs in the future annual river discharge and rainfall depth according to the EC-EARTH-RCP85 as 142% and 81% with MPI-ESM-MR-RCP85 model. The highest future extreme river discharge and rainfall depth values through different return periods (50–1000 year) are obtained from EC-EARTH-RCP85 as 6.8~8.08 cms and 57.41~105.76 mm based on EC-EARTH-RCP45 model. Uncertainty analysis results indicate that climate models/scenarios have significant effect on the future extreme events variability, while the same for extreme river discharge is the least sensitive to different return periods. [ABSTRACT FROM AUTHOR]

Soil moisture is an influential parameter in land surface hydrology and precise soil moisture data that can help researcher to realize the climate changes and land-atmosphere interactions. A initial struggle for the utilize of soil moisture data from satellite sensors is their reliability. It is important to appraise the dependability of those data before they can be regularly used at a global or local scale. In this study, the satellite soil moisture data was evaluated from the Soil Moisture Active/Passive (SMAP) over Simineh-Zarrineh Catchment in Bokan region, NW of Iran. A total of 287 soil samples as ground-based observations in the time period of 03 April to 03 December 2017 were taken for SMAP data validation. Results showed that the satellite data and in situ observation has a good correlation, with a mean correlation (r) value of 0.63 in total. This correlation level showed that, commonly, the SMAP soil moisture products over Simineh-Zarrineh Catchment (Bokan) have great quality, and it would be valuable for versatile utilization, including monitoring of land surface, weather prediction, modeling of hydrological process, soil loess monitoring, and climate studies. The results reveal that the remotely sensed data demonstrate the good correlation with in situ observation across the dry land with mean correlation (r) values of 0.67 throughout the time period. Particularly, SMAP soil moisture reveal a constant structure for obtain the spatial distribution of surface soil moisture. Additional researches are necessary for well realizing the SMAP data. [ABSTRACT FROM AUTHOR]

In this study, rainfed wheat and barley yields response to severity and persistence of cold temperature extremes in the January-February-March (JFM) and high temperature, low rainfall, and soil water shortage extremes in the October-November-December (OND) and March-April-May (MAM) was evaluated. For this purpose, the associations between the yields and the extreme indices were tested using Pearson's correlation coefficient for 10 sites located in the western Iran over 1982–2013. The FAO-56 soil water balance model was employed to develop the soil water deficit extreme indices. The impacts of extremes on the yields were also investigated under El Niño–Southern Oscillation (ENSO) phases. The yields were anti-correlated significantly (p < 0.05) with the soil water shortage and rainfall deficit extreme indices, but not with the temperature-related extreme indices, for the majority of sites. On average, a more negative association also existed between the yields and the soil moisture-related indices relative to the rainfall and temperature extreme indices. The yields seem, therefore, to be more adversely impacted by agricultural droughts. Compared with the OND extremes, the high temperature, precipitation shortage, and soil water deficit extremes in MAM had more negative effects on the yields. This can be attributable to strong sensitivity of reproductive processes commonly occurring during MAM to such stresses. In most cases, the yields were more adversely influenced by the short-term extremes. The results also revealed that more frequent occurrence of high temperature and drought-related extremes during La Niña can well explain the substantial yield loss in cold ENSO years. [ABSTRACT FROM AUTHOR]

As the remaining widespread Tertiary forests, the Hyrcanian forest of northern Iran, together with the south-eastern forests of Azerbaijan, are the last habitat ecosystems for chestnut-leaved oak species (Quercus castaneifolia C.A. Mey.) in the world. The present study aimed to investigate the influence of oak forest stands with different development stages (i.e. initial, optimal and decay) on soil functional indicators, i.e. fertility and biological activities, in Loveh forest, northern Iran. During the summer season, a total of 18 forest floor and topsoil samples (30 × 30 × 10 cm) from each development stage were transferred to the laboratory. In order to obtain a temporal pattern of soil climate, biota and microbial characteristics, samples were also collected during the autumn season. Concerning the independent individual factors, including development stage, season, and development stage × season, two-way ANOVA and general linear models as well as a PCA were applied. The first factor explained the observed variations in the total earthworm density (36.3 %) and biomass (33.5 %), acarina (47.3 %), nematodes (52.3 %), protozoa (54.6 %), bacteria (60.9 %), fungi (53.4 %), basal respiration (35.9 %), substrate-induced respiration (37.2 %), microbial biomass C (25.7 %), microbial biomass N (35.4 %), and the ratio of microbial biomass C to microbial biomass N (18.8 %). In contrast, the season factor explained changes in soil water content (31.2 %), temperature (22.6 %) and collembola density (59.7 %). Based on heat plots (as a part of the principal component analysis output) of studied properties, the decay stage of the oak stand created hot spots of soil functional indicators in the study area. Although all forest stand stages of the oak have an important ecological role, and place value in the evolutionary process, the old-growth (i.e. decay) stage is crucial for the soil functions exhibiting the highest fertility and biological relevance. As a management strategy, forest decision-makers may be advised to introduce diverse tree species into the stands, especially in areas with patchy canopy, and thus enhance forest soil microbial activity, nutrient cycles and, finally, variation of soil properties within the area (pedodiversity). [Display omitted] • At all oak development stages, the highest activity of soil biota was observed in the fall season; • Especially in the decay stage, the properties of forest floor layers, tree roots and mineral soil are highly dynamic; • Soil organic matter accumulation is mainly governed by high nutrient contents, as well as by soil biota and enzyme activities; • The succession of forest floor functions is crucial in sustainable forest management. [ABSTRACT FROM AUTHOR]

Today, geotechnical and geophysical techniques are used for landslide evaluation. Geotechnical methods provide accurate data, but are time consuming and costly. Geophysical techniques, however, are fast and inexpensive, yet their accuracy is lower than that of the geotechnical methods. Therefore, simultaneous use of geotechnical and geophysical methods provides a suitable solution for landslide evaluation. Availability of geotechnical and geophysical data makes it possible to investigate correlation between different parameters. Correlating geotechnical and geophysical parameters ends up lowering field investigation costs and enhancing subsurface survey speed in a landslide zone. In the present study, in order to evaluate Nargeschal landslide in Iran, ambient noise measurement, ERT survey, and geotechnical investigations were used. Once finished with data processing, the data obtained from geotechnical and geophysical investigations were correlated. These included SPT-N - electrical resistivity, soil moisture content - electrical resistivity, and SPT-N - shear wave velocity correlations. The correlations were examined using two methods, namely Spearman's coefficient test and least square regression analysis. The results obtained from the two methods were in good agreement with one another. The correlations obtained in this study were of moderate to very strong strength and fell in the range of the results of previous studies. Investigation of the results indicated significant influences of ground water on electrical resistivity and soil stiffness on shear wave velocity. Results of this study can be used for soil classification and determination of mechanical and seismic characteristics of soil across various areas. [ABSTRACT FROM AUTHOR]

About two-thirds of the Iran's area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities. [ABSTRACT FROM AUTHOR]

Mapping landscape variation in tree species richness (SR) is essential to the long term management and conservation of forest ecosystems. The current study examines the prospect of mapping field assessments of SR in a high-elevation, deciduous forest in northern Iran as a function of 16 biophysical variables representative of the area’s unique physiography, including topography and coastal placement, biophysical environment, and forests. Basic to this study is the development of moderate-resolution biophysical surfaces and associated plot-estimates for 202 permanent sampling plots. The biophysical variables include: (i) three topographic variables generated directly from the area’s digital terrain model; (ii) four ecophysiologically-relevant variables derived from process models or from first principles; and (iii) seven variables of Landsat-8-acquired surface reflectance and two, of surface radiance. With symbolic regression, it was shown that only four of the 16 variables were needed to explain 85% of observed plot-level variation in SR (i.e., wind velocity, surface reflectance of blue light, and topographic wetness indices representative of soil water content), yielding mean-absolute and root-mean-squared error of 0.50 and 0.78, respectively. Overall, localised calculations of wind velocity and surface reflectance of blue light explained about 63% of observed variation in SR, with wind velocity accounting for 51% of that variation. The remaining 22% was explained by linear combinations of soil-water-related topographic indices and associated thresholds. In general, SR and diversity tended to be greatest for plots dominated by Carpinus betulus (involving ≥ 33% of all trees in a plot), than by Fagus orientalis (median difference of one species). This study provides a significant step towards describing landscape variation in SR as a function of modelled and satellite-based information and symbolic regression. Methods in this study are sufficiently general to be applicable to the characterisation of SR in other forested regions of the world, providing plot-scale data are available for model generation. [ABSTRACT FROM AUTHOR]

Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic complex processes release salts into the environment. These change the balances of natural systems in the watersheds. Models can assist study of salinity processes throughout watersheds. This study proposes a flexible water and soil salinity module for the Soil and Water Assessment Tool (SWAT) that can profitably incorporate expert scientific judgments into a deterministic salinity framework to adapt predicted salinity processes to actual salinity processes watershed-scale. The proposed flexible salinity model (SWAT-SF) was developed using new equations for salinity assessment and then successfully applied to the Dehram River Basin located south of Iran that endangers natural and anthropogenic salinity problems. The SWAT-SF model uses a general framework that can be utilized with/without the direct guidance of experts for the determination of salinity processes, the extent of the salinity, identification and investigation of the saline site, assessment and management of the salinity, and evaluation of salinity economics throughout the world. • The proposed model advances salinity modelling of complex watershed systems. • Expert scientific judgments can directly interface with parameterizing the salinity process. • Improvements can be made for efficient assessment for specific applications and time steps. [ABSTRACT FROM AUTHOR]

the wild pistachio (Pistacia Atlantica Mutika) exists in 2.4 million ha of Iranian forest. In this way the pistachio of Syrjan was chosen as a case study. The objective of this study was to determine physical and mechanical properties of wild pistachio nut of Syrjan rejoin as a function of moisture content. The physical properties including thousand unit mass, length, width, height, arithmetic mean diameter, geometric mean diameter, surface area, true density, bulk density, porosity, repose angle (emptying and filling) and coefficient of friction were determined at four different levels of moisture content. The nut was compressed along the length and thickness at 0.045, 0.069, 0.089 and 0.188 (db) moisture contents to determine required force, deformation, and energy per volume (toughness) at rupture point. The nut average length, width, thickness were 7.31, 6.17, and 4.90 mm respectively at moisture content of 5.5 (%, wb). The greatest true density was 1081 kg cm-3. With increase moisture from 4.84 to 13.65 (%, wb) the bulk density of nut decreased from 658.21 to 636.68 kg m-3, porosity decreased from 37.52 to 32.86%, the emptying angle of repose increased from 32.04 to 34.6 degree, and the filling angle of repose increased from 29.95 to 31.59 degree. The greatest friction coefficient of the nut was obtained on galvanized iron surface and the minimum value on aluminum surface at all moisture content levels. At all levels of moisture content the rupture force, deformation at rupture point, and toughness of the nut were the minimum values along the nut thickness (Z-axis). The minimum values of the rupture force, deformation, and toughness were 175.16 N, 1.03 mm, and 0.49 mJ mm-3 respectively. [ABSTRACT FROM AUTHOR]

Thymus daenensis is a medicinal plant endemic to semi-arid regions of Iran. A field experiment using a randomized complete block design with four replications was conducted to evaluate the effect of 20, 50 and 80% soil water depletion on morpho-physiological traits, essential oil content and composition and water use efficiency of T. daenensis during 2010-2011. Water stress reduced growth, herbage production, chlorophyll and carotenoid content, while increased proline, K+, essential oil content and irrigation water use efficiency based on essential oil yield (IWUEeso). Thymol was the highest essential oil composition (63.3-73.5%) followed by carvacrol (3.6-16.0%), ρ-cymene (3.8-7.4%), γ-terpinene (3.3-4.7%), β-caryophyllene (2.8-4.0%) and borneol (1.4-3.4%), respectively. Thymol, ρ-cymene and γ-terpinene were increased, while the other compositions decreased under water stress. It is concluded that irrigation of T. daenensis based on 50% water depletion should be an appropriate choice for first growing season and 80% water depletion for the second growing season in semi-arid climatic conditions. [ABSTRACT FROM AUTHOR]

Abstract: In past decades, Iranian desert areas have undergone widespread land use transformation. Sand dune stabilizing projects and anti-desertification programs were initiated in 1965 and now cover about one million hectares of degraded land in Iran. This project aimed to stabilize sand dunes, to induce afforestation, to establish windbreaks and to apply mulch. Currently, more than three million hectares of arid land has been reclaimed across the country. In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat and TM/ETM images acquired in 1991 and 2002, respectively to map the anti-desertification process in southwestern Iran. A supervised classification was carried out for each of the two images. The results of this classification were refined using auxiliary data, visual interpretation and expert knowledge of the area. A post-classification change detection technique was used to produce change image through a cross module. Changes were assessed according to different classes. Observations made during the course of this study demonstrate that an obvious, positive change has taken place as a result of the petroleum mulch project. Results show that sand dune areas decreased from 66.4% to 56%, and in this case afforestation areas increased from 2.1% to 7.3%. However afforestation increased soil organic matter and recuperation of soil moisture storage conditions and chemical reactions such as dissolution of minerals and established more suitable conditions for crop growth. [Copyright &y& Elsevier]

In the present work, the joint response of key hydrologic variables, including total precipitation depths and the corresponding simulated peak discharges, are investigated for different antecedent soil moisture conditions using the copula method. The procedure started with the calibration and validation of the soil moisture accounting (SMA) loss rate algorithm incorporated in the Hydrologic Engineering Center - hydrologic modeling system (HEC-HMS) model for the study watershed. A 1000 year long time series of hourly rainfall was then generated by the Neyman-Scott rectangular pulses (NSRP) rainfall generator, which was then transformed into the runoff rate by the HEC-HMS model. This long-term continuous hydrological simulation resulted in characterizing the response of the watershed for various input conditions such as initial soil moisture content (AMC), total rainfall depth, and rainfall duration. For each initial soil moisture class, the copula method was employed to determine the joint probability distribution of rainfall depth and peak discharge. For instance, for dry AMC condition and 1 h rainfall duration, the Joe family fitted best to the data, compared with six other one-parameter families of copulas. Results showed that the bivariate analysis of rainfall-runoff using the copula method can well characterize the watershed hydrological behavior. The derived offline curves could provide a probabilistic real-time peak discharge forecast. [ABSTRACT FROM AUTHOR]

The direct measurement of the soil water retention curve (SWRC) in a laboratory is time-consuming, difficult, and costly. Thus, many attempts have been made to predict the water retention curve indirectly from the physical and chemical properties of soil. The particle size distribution curve is one of the indirect methods used to predict the water retention curve. The Arya and Paris (AP) model predicts the soil water retention curve from soil particle size distribution (PSD) data. The AP model estimates pore radius from the radius of spherical particles by using a scaling parameter (α). The objective of this study was to investigate the effect of predicting the scaling parameter with different methods to improve estimation of the SWRC. The evaluation of methods was done on 35 soil samples with different textures from the eastern region of Guilan Province in Iran. The results showed that estimated curves with different α values gave different results that depended highly on the scaling parameter. Therefore, α determination has a key role in estimating the soil water retention curve. The results also showed that a linear α and a constant α with a maximum coefficient of determination and minimum error are the best scaling parameters to estimate the SWRC. [ABSTRACT FROM AUTHOR]

In many part of Iran, the reproductive growth stages of sunflower (Heliamthus annuus L.) are exposed to water deficit stress. Therefore, the investigation of irrigation management in the firm conditions is a necessary element for increasing irrigation efficiency and decreasing water losses. The objective of present study was to investigate the effect of different rates of super absorbent polymer and levels of water stress on water use efficiency (WUE), yield and sonic morphological traits of sunflower (cultivar 'Master'). Factorial experiment was carried out in completely randomized design with 3 replications. Factors were water stress in three levels (irrigation in 075; 0.50 and 0.2596 of field capacity) and super absorbent polymer in five levels (0: 0.75: 0.150; 2.25: 3 g/kg of soil). Super absorbent polymer was added in eight leaves stage of sunflower to pots in deepness of roots development. Water stress treatment was also applied in this growth stage of sunflower. For stress application, pots were weighted every day and irrigated when soil water received to 0.75: 0.50 and 025 of field capacity, respectively. The results of ANOVA indicated that the effect of different rates of super absorbent polymer and different rates of consumed water in all traits were significant (P<0.001). ANOVA also revealed that the interactive effects of two mentioned factors were significant (P<0.001) except for seed yield trait. Polynomial model based on the ANOVA results was fitted for each trait. The results indicated that water stress significantly convert in decreasing the number of leaves per plant, chlorophyll content, 100 weight of seeds, seed yield and WUE in sunflower, whereas the application of super absorbent polymer moderated the negative effect of deficit irrigation, especially in high rates of polymer (2.25 and 3 g/kg of soil). The above mentioned rates of polymer have the best effect to all characteristics of sunflower in all levels of water stress treatment. The findings strongly suggest that the irrigation period of sunflower cultivation can be increased by application olpolymer. [ABSTRACT FROM AUTHOR]

There are different approaches to define the soil available water (SAW) for plants. The objectives of this study are to evaluate the SAW values of 12 arable soils from Hamadan province (western Iran) calculated by plant available water (PAW), least limiting water range (LLWR) and integral water capacity (IWC) approaches and to explore their relations with Dexter’s index of soil physical quality (i.e., S-value). Soil water retention and mechanical resistance were determined on the intact samples which were taken from the 5–10 cm layer. For calculation of LLWR and IWC, the van Genuchten-Mualem model was fitted to the observed soil water retention data. Two matric suctions ( h) of 100 and 330 cm were used for the field capacity (FC). There were significant differences ( P < 0.01) between the SAW values calculated by PAW, PAW, LLWR, LLWR and IWC. The highest (i.e., 0.210 cm cm) and the lowest (i.e., 0.129 cm cm) means of SAW were calculated for the IWC and LLWR, respectively. The upper limit of LLWR for all of the soils was h of 330 cm, and that of LLWR (except for one soil that was air-filled porosity of 0.1 cm cm) was h of 100 cm. The lower limit of LLWR and LLWR for five soils was h of 15,000 cm and for seven soils was mechanical resistance of 2 MPa. The IWC values were smaller than those of LLWR for two soils, equal to those of LLWR for three soils and greater than those of LLWR for the rest. There is, therefore, a tendency to predict more SAW using the IWC approach than with the LLWR approach. This is due to the chosen critical soil limits and gradual changes of soil limitations vs. water content in the IWC calculation procedure. Significant relationships of SAW with bulk density or relative bulk density were found but not with the clay and organic matter contents. Linear relations between IWC and LLWR or LLWR were found as: IWC = −0.0514 + 1.4438LLWR, R = 0.83; and IWC = −0.0405 + 2.0465LLWR, R = 0.84, respectively (both significant at P < 0.01). Significant relationships were obtained between the SAW values and S indicating the suitability of the index S to explain the availability of soil water for plants even when complicated approaches like IWC are considered. Overall, the results demonstrate the importance of the choice of the approach to be used and its critical limits in the estimation of the soil available water to plants. [ABSTRACT FROM AUTHOR]

The impact of rainfall and light interception by litter on maintenance of surface soil water content is determined in an arid rangeland in Khabr National Park in south-east of Iran. Litter weight sampling is done by 90 square plots, each 1 m2, that are randomly placed within site. After determining the intensity of a typical storm of the region (20 mm/h), the rainfall duration required to saturate the litter from dry-weight to constant-weight is determined from the litter wetting curve. To plot the wetting curve, an outdoor rainfall simulator is used to wet the litter. Then drying curve of litter moisture content is determined from obtained field data with four replications. For measuring soil water content, three treatments are tested i.e. bare soil, soil with wetted litter and soil with dry litter. Average of the measured interception loss of four samples was 0.64% of this specific simulated rainfall (5.2 mm). This study clearly showed that rangeland litter decrease evaporation of the soil water content and light interception by litter have more important role in decreasing evaporation from the soil water content than the rainfall interception by litter. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications. [ABSTRACT FROM AUTHOR]

Native to Eurasia, Eastern Europe, and Western Asia, Oriental beech (Fagus orientalis Lipsky) stands extend from the north-western part of Turkey, the eastern part of the Caucasus Mountains in Georgia and Russia to the Alborz Mountains in Iran. The effect of different development stages of beech stands on the forest floor and soil characteristics are so far almost unknown. For this purpose, the Langa forest that is located in northern Iran was investigated to detect the effect of forest development on major traits of soil biology and related soil characteristics. For this aim, 45 forest floors and soil samples (15 samples per each development stage i.e. initial, optimal, and decay stage) were analysed. In addition, the water content, temperature, and biological dynamics were monitored over the different development stages in summer and fall. Among the considered independent factors, the development stage of beech had the largest explanatory power for the variations of the soil water content (34.7%), total earthworm density and biomass (29.6% and 32.3%, respectively), Acarina (30.3%), nematode (64.3%), protozoa (57.7%), bacteria, (62.8%), fungi (59.5%), basal respiration (53.0%), substrate-induced respiration (53.8%), microbial biomass carbon (30.4%), microbial biomass nitrogen (36.0%), microbial biomass phosphorous (59.1%) and net nitrification rate (19.1%). Soil temperature (42.5%), collembola density (18.2%), net ammonification rate (29.9%), and net N mineralization rate (2.1%) were predominantly determined by the seasons. With the ageing of the forest stands, soil fertility, biota abundance, and soil function were enhanced. To figure out the most vital soil features affecting C and N sequestrations, a stepwise regression model was applied. Carbon sequestration correlated positively to N, the C/N ratio, and negatively to porosity, and the nematode abundance. In addition, nitrogen sequestration correlated positively to C, the sand content, and fine root biomass and negatively to the C/N ratio, soil porosity, and aggregate stability. Although all forest stages of the Oriental beech have an important ecological role and their place in the evolutionary process, the old-growth (i.e. decay) stage is especially important for the soil functions exhibiting the highest fertility and biological relevance. The understanding of ecological changes within different development stages can be helpful in forest management and the evaluation of silvicultural methods. • The development stages of Oriental beech forests affect forest floor nutrients. • The decay stage exhibits a more abundant soil biota and more nutrients. • The population of the soil fauna was more abundant in fall than in summer. • Old-growth Oriental beech forests give rise to increased soil fertility and biological activity. [ABSTRACT FROM AUTHOR]

This study assessed the aerosol climatology over Iran, based on the monthly data of aerosol optical depth (AOD) derived from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, sea level pressure, wind speed, temperature, relative humidity, precipitation, and soil moisture from the ERA5 reanalysis dataset were applied to investigate the climate-related effects on temporal AOD changes. Our analysis identified positive and negative AOD trends during 2000–2010 and 2010–2018, respectively, which are likely linked to aeolian dust changes. The dust-driven AOD trends were supported by changes in the Ångström exponent (AE) and fine mode fraction (FMF) of aerosols over Iran. During the early period (2000–2010), results of AOD-meteorology correlation analyses suggest reduced soil moisture, leading to increased dust emissions, whereas our results suggest that during the later period (2010–2018) an increase of soil moisture led to decreased AOD levels. Soil moisture appears to be a key factor in dust mobilization in the region, notably in southwestern Iran, being influenced by adjacent mineral dust sources. These phenomena were affected by large-scale sea level pressure transformations and the associated meteorology in the preceding winter seasons. Using a multiple linear regression model, AOD variability was linked to various meteorological factors in different regions. Our results suggest that climatic variations strongly affect the dust cycle, with a strong dependence on wintertime conditions in the region. [ABSTRACT FROM AUTHOR]

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas. [ABSTRACT FROM AUTHOR]

• We studied the externalities of nomadic livestock on some ecosystem services. • The externalities were compared with nomad's direct benefits from rangelands. • Results confirm the financial justification of this exploitation system for nomads. • This system is not economically justified considering its negative externalities. There is a great deal of pressure on common rangelands through excessive usage of forage resources. It brings private benefits for local communities while imposing ecological and economic damages to ecosystem services as community products. This is evident especially in arid environments where ecosystems are often in poor conditions. Khafr and Sivar rangelands were selected in central regions of Iran to calculate the external costs caused by ecosystem service loss due to nomadic utilisation system. Water and soil conservation services were considered in this study as two major ecosystem services provided by rangeland ecosystems. The impacts of nomadic livestock grazing on soil conservation service was determined by estimating soil erosion in pre and post grazing conditions using MPSIAC model and the impacts on water conservation function was evaluated by calculating the runoff volume before and after grazing through the empirical CN method. The economic value of these ecosystem services was estimated using replacement cost approach. Direct benefits of nomads from livestock grazing was studied using a questionnaire-based approach and structured questionnaires. Economic justification of this utilisation system was revised finally considering the economic damages to ecosystem services as external costs. Results showed that ecosystem service losses as hidden social costs are significant under the current utilisation system. The annual damage to water and soil conservation services was 794,839 US$, as part of the negative externalities of the nomads. Meanwhile, the annual net present value of direct benefits of nomadic livestock grazing were only 601,562 US$, and combining annual external costs with the private costs reduced the net present value of the system to −193,276 US$ per year. As a conclusion, it should be noted that contrary to the importance of the nomadic system from cultural and social aspects, the current system is not economically justified in arid, poor and common rangelands. This highlights the need for appropriate policy to maintain both ecological and socio-cultural values. [ABSTRACT FROM AUTHOR]

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to investigate the performance of two radar backscattering models; the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization. [ABSTRACT FROM AUTHOR]

Understanding the climatic and direct human drivers of abrupt drying of lakes in many parts of the world is a high research priority, particularly for water resources management and restoration. Lake Urmia, a shallow endemic lake in north-west Iran and one of the major saltwater bodies on earth, has undergone a dramatic decline in its water level (WL) in the past decades, resulting in about 80% of loss in the lake's water content. Different explanations have been discussed regarding the primary cause of this WL decline, including climate change, and regional human-driven land- and water-use changes. This paper reports a comprehensive study of the possible primary drivers of the lake's desiccation. Using classical exploratory statistical methods, the analysis accounts for the relationships between climate change (precipitation P, temperature T), land-surface and hydrological evolution (soil moisture SM, and WL), and vegetation cover (VC) change in the region. The latter includes human-driven changes of irrigation and expansion of agricultural lands. Observations over the period 1981-2015 indicate that the observed variability in P, T, and SM cannot explain the sharp decline in the lake WL since 2000. Instead, land-surface analysis shows VC was nearly doubled after 2000 in the Lake Urmia watershed, indicating that the WL decline is mainly associated with human-driven agricultural increase of VC. The expansion of agricultural activities in the watershed has led to increasing transpiration and hence, total evapotranspiration, decreasing the runoff during the increase in VC (inherently including irrigation, storage, and water diversion). Our results are consistent with similar cases around the world identifying human activities and interventions as the main driver of hydrologic changes, while climate change will likely increasingly contribute to such changes and their impacts in the future. [ABSTRACT FROM AUTHOR]