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Soil moisture plays a key role in hydrology, water soil balance modeling, and drought assessment. In this study, the effects of snow properties in winter on the soil moisture in spring were evaluated in a catchment in the northwest of Iran from 2000 to 2020. For this purpose, the snow depth, water equivalent to snow, and snow-covered area were calculated based on satellite images in four regions with different altitudes. Furthermore, the soil moisture was obtained at two depths of 0–10 cm and 10–40 cm using satellite images. The results showed that the Pearson's correlation coefficient was between 0.4 and 0.5 in all cases, which indicates an average correlation between the snow and soil moisture in the catchment under study. Moreover, the correlation of the snow depth and snow water equivalent with the soil moisture at the depth of 10–40 cm was more than the one at the depth of 0–10 cm. Also, the snow-covered area demonstrated the highest correlation with the soil moisture at a depth of 0–10 cm. Finally, a linear relationship was obtained between the snow properties and soil moisture at two depths using the available data. Such relationships can be used to predict soil moisture for various applications. [ABSTRACT FROM AUTHOR]

Saffron (Crocus sativus: Iridaceae) is a fall-blooming perennial plant and its dried stigma is the priciest spice and a key non-oil export for Iran's economy. The bulb mite, Rhizoglyphus robini, is a polyandrous and multivoltine species and its damage to saffron corms directly and indirectly causes lower yields of saffron crops. Environmental conditions and abiotic factors, such as temperature, humidity, density, and diet affect the morphological traits of living organisms and subsequently affect biological abilities. In this study, changes in temperature, soil moisture, density (nymphs + adults), time, and corm weight on the morphological traits of the saffron bulb mite, including body length and width, and leg sizes of adult females were investigated in a saffron field in the Dargaz County of Iran during 2022. The results of variance analysis of the morphometrical parameters of the mite species, including body length, body width, and four pairs of legs in different months were significant. Based on simple and multiple linear regression models as well as non-linear regression, the effect of temperature and density (nymphs + adults) was reversed and the effect of soil moisture and corm weight was direct on morphometrical parameters of this species. Based on our results, soil moisture has a strong relation with female body size traits (body length, width, and leg lengths). This indicates that irrigation cycle management might be an important factor in bulb mite management in saffron agroecosystems. [ABSTRACT FROM AUTHOR]

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]

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates. [ABSTRACT FROM AUTHOR]

Our objectives were to examine the species composition and diversity of soil seed bank (SSB) and standing vegetation (SV), explore association between compositional diversity of SV, SSB and selected environmental factors and assess the implications of SSB on regeneration of flooded riparian vegetation in Hamedan province, Iran. We estimated the ground cover of SV and SSB composition in 90 plots (1 m × 1 m) distributed across 15 sites in the river riparian zone. We evaluated the SSB by seedling emergence method. Canonical correspondence analysis (CCA) was used in a direct gradient analysis of the SV/SSB with the environmental factors. In total, 136 species were identified from germinated seed bank in the greenhouse and there were 131 plant species recorded in the aboveground vegetation. 31 species were observed in the SSB while they were absent in the SV, while there were also 26 species that were only present in the SV. Dominant species in the SSB were floatable seed species, i.e. Cyperus difformis and Dactylis glomerata. In addition, the results indicated a more pronounced effect of environmental factors on SV than on SSB distributions in which a higher number of environmental factors associated significantly with SV than with SSB (6 vs. 4). However, elevation, soil moisture content and total organic matter had significant effects on community distribution of both SSB and SV. The species diversity and composition evenness were significantly higher in the SSB than SV. Although, 105 species were common to SSB and SV, the mean Czekanowski similarity between SV and SSB was very low (15.5%). However, we argue that the recovery of vegetation in degraded sites can still rely on SSB. We concluded that the seed movement among plant communities through hydrochory led to a spatial homogenization of SSB, resulting in a decrease in SSB-SV similarity and an increase in SSB species diversity and compositional evenness. Differences in plant diversity and richness between the SSB and the SV are supposed to be a complementation of diversity between below- and above-ground and therefore, greater community resilience is predicted under stochastic disturbance events such as flooding in the riparian area. [ABSTRACT FROM AUTHOR]

Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability. [ABSTRACT FROM AUTHOR]

This study investigated the capability of remote sensing soil moisture (SM) datasets to estimate in-situ SM over the Lake Urmia Basin in Iran. A novel meta-estimating approach, called Voting Regression (VR), was used to combine the Gradient Boosting (GB) and Support Vector Regression (SVR) algorithms for developing a new hybrid predictive model named GB-SVR. Six SM products from the Global Land Data Assimilation System (GLDAS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) were used to predict SM at 40 in-situ SM sampling locations. The performance of the proposed novel forecasting technique was evaluated using Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed the superiority of GB-SVR compared to GB and SVR, with an average improvement of 17%, 10%, and 13% in CC, RMSE, and MAE, respectively, in predicting in-situ SM. The model performance in different climates, soil textures, and land covers showed its better prediction accuracy in croplands (R 2 = 0.86) , loam soil (R 2 = 0.74) and cold climate R 2 = 0.71 , while the least in clay soil and barren lands. Besides, the in-situ SM prediction using remote sensing SM data performed better than that obtained using in-situ air and soil temperature. The proposed methodology can be used for accurate SM prediction in regions lacking in-situ SM data. [ABSTRACT FROM AUTHOR]

The numerical modeling of the land surface can make up for the insufficient station data in terms of number, dispersion, and temporal continuity. In this research, to evaluate the Noah-MP land surface model, the water balance components were estimated in the Neyshaboor watershed in the monthly time step during 2000–2009. Model input data were obtained from the global land data assimilation system version 1 (GLDAS-1), and the SWAT (soil and water assessment tool, a semi-distributed for small watershed to river basin-scale model) model output was used for the evaluation of the Noah-MP model. In this study, the ability of the Noah-MP model in simulating vegetation dynamically was studied. The precipitation was corrected before running the model for a more reliable evaluation. The time between 2000 and 2001 was considered a spin-up period and 2002–2009 for calibration and validation. The model has the best simulation in the mountainous areas; the runoff simulated by the Noah-MP model is in good agreement with the modeled runoff by SWAT in these areas. (R2 = 0.78, NSE = 0.62, RMSE = 1.98 m3/s). The R2 for simulated soil moisture for soil layers (0–10, 10–40 cm) was 0.62 and 0.57, and RMSE was 0.059 (m3/m3) and 0.052 (m3/m3), respectively, in Motamedieh field. The annual amount of evapotranspiration estimated by the two models is comparable to the average annual evapotranspiration in the watershed (about 300 mm). Based on the results from the research, the model has well simulated: the runoff in the mountainous areas, the moisture in the upper layer of the soil, and the average annual evapotranspiration in the study area. [ABSTRACT FROM AUTHOR]

The analysis of hydroclimate extremes is gaining more attention due to the devastating effects of intense floods, droughts, etc. This study aims to analyze the stationary (S) and non-stationary (NS) behavior of the annual maximum temperatures (AMT) for two different climatic zones of Iran including the arid and excessively humid provinces of Kerman and West Azerbaijan, respectively. The research datasets included maximum temperature (from CRU TS) and soil moisture (from ERA5) on a monthly time scale (spanning 1901–2019 and 1979–2019). Trend, homogeneity, and stationarity tests were applied to define the basic characterization of the AMTs. The frequency analyses of the AMTs were carried out using generalized extreme value (GEV) under two assumptions of S-GEV and NS-GEV. Moreover, the fitted distribution parameters were estimated using a maximum likelihood estimator. In addition to time-varying NS-GEV investigations, the soil moisture during summer (SM-June, July, and August) was also employed as the covariate to quantify the relationship between drought and AMTs in these climatic zones. The research findings revealed that the Akaike information criterion in S-GEV and NS-GEV estimations decreased from 309 to 223 and 329 to 254 for arid and excessively humid climatic zones, respectively. Therefore, the NS-GEV frequency analyses has increasing effects on return levels of the AMTs than the S-GEV. In the following, the spatial NS-GEV investigations in all 12 and 15 stations of both provinces, showed that NS-GEV with SM as a covariate has better performance in excessively humid climatic zones. [ABSTRACT FROM AUTHOR]

Forests serve as habitats for a diverse group of animals, with one of the significant animal groups being land snails. One of the influential factors in the distribution of snails are the physicochemical properties of soil. The aim of this study is to explore the relationship within the geographic expanse of the Hyrcanian forests located in northern Iran. This study examined six soil parameters, including pH, Ca, Mg, Ca++, Mg++, and soil saturation moisture content in the Hyrcanian forests of northern Iran. Identification of snails was conducted using DNA barcoding, and Canonical correlation analysis (CCA), was employed for analyzing the relationship between land snails' community and soil physicochemical factors. The analysis, determining the level of correlation between parameters, presents "eigenvalues" to illustrate how these variables are associated with the distribution of species in different locations. The samples belong to 10 OTUs. The PCA results suggest that three factors, namely Ca, Mg, and soil saturation moisture content, influence the snail fauna in the study area. CCA with eigenvalues of 0.374 and 0.202 for the first and second axes, respectively, also indicate a high correlation between these three parameters and most identified OTUs. These studies provide fundamental information to develop a biological indicator based on land snails in the Hyrcanian Forest. such indicators can be practical for developing management strategies for the conservation of snail fauna. [ABSTRACT FROM AUTHOR]

The unplanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to simulate the nitrate concentrations for different crops at the river basin scale by applying the Nash–Sutcliffe model efficiency coefficient (NSE) as a measurement index. Then, a novel model was developed to optimize the cultivation pattern by applying different metaheuristic algorithms. Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was applied as a decision-making system to select the best optimization algorithm. The results demonstrated that using a SWAT in the optimization model structure is a robust method for the design of an optimal cultivation pattern. The Nash–Sutcliffe model efficiency coefficient (NSE) was 0.74, demonstrating the robust predictive skills of the water quality model. The decision-making system indicated that particle swarm optimization and shuffled complex evolution were the best evolutionary algorithms to optimize the cultivation pattern using the proposed method. The proposed method opens a new window regarding the optimization of cultivation patterns in agriculture and provides an environmental-based optimization to design cultivation patterns on the catchment scale. [ABSTRACT FROM AUTHOR]

Context: Cannabis is a herbaceous annual plant that belongs to the Cannabaceae family, which is used in the production of fibre, paper, oil and pharmaceutical products. Aims: The aim of this study was to identify drought-tolerant ecotypes and medicinal and industrial populations. Methods: Due to the medicinal and industrial importance of cannabis, 12 cannabis ecotypes were collected from different regions of Iran. Then, their agronomic and phytochemical characteristics were evaluated under different soil moisture conditions. Key results: The soil moisture levels had significant effects (P < 0.01) on the studied traits except for the 1000 seed weight. Based on duration of the growth period, the Tabas and Dasht-e-Moghan ecotypes were identified as early and late maturing ecotypes, respectively. Also, the highest stem dry weight and stem height/diameter ratio and the lowest seed yield were related to the Dasht-e-Moghan ecotype, which is valuable for fibre production. Based on seed yield (relative reduction) and some tolerance indexes, the Tabas and Tabrize ecotypes were shown to be the most tolerant and sensitive ecotypes, respectively. As soil moisture decreased, tetrahydrocannabinol levels increased and cannabidiol levels decreased. The highest amount of tetrahydrocannabinol was related to the Qom ecotype at 50% soil moisture and the highest amount of cannabidiol was related to the Rasht ecotype at 100% soil moisture. Conclusions: Generally, these ecotypes had different responses to soil moisture. Some ecotypes were valuable in terms of the production of pharmaceutical metabolites and some in terms of fibre production. Implications: Tolerant and sensitive ecotypes might be considered in production and also breeding programs. Cannabis is a herbaceous annual plant that belongs to the Cannabaceae family, and is used in the production of fibre, paper and oil. This plant has been used for the treatment of rheumatism, epilepsy, asthma, skin burns, gastrointestinal activity, osteoporosis, schizophrenia, cardiovascular disorders, cancer and metabolic syndrome-related disorders. Generally, our findings showed that these ecotypes had different responses to soil moisture, and some ecotypes were valuable in terms of the production of pharmaceutical metabolites and some in terms of fibre production. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions. [ABSTRACT FROM AUTHOR]

Skidding operations affect soil physical properties, which may impact soil sustainability and forest productivity. While the relationship among harvest machine traffic, slope gradients, and increased soil damage has been well-investigated, it is still unclear how soil damage due to repeated passes of different skidder types is interacting with soil moisture and slope conditions. We examined dry bulk density (BD), total porosity (TP), and rutting depth (RD) of skid trail soil in an Iranian temperate forest. The study took into consideration a combination of five different traffic intensities (TI) (1, 4, 8, 12, and 15 passes), two levels of slope gradients (SGs) (≤20% and > 20%), two rubber-tired skidder types (STs) (Timberjack 450C and TAF E655), and two soil moisture contents (SMC) (18% and 31%). Results showed that changes in BD and TP were mainly related to TI regardless of the ST and the skid trail slope. Regardless of TI, SG, and SMC, the TAF E655 skidder caused a higher dry BD increase and soil porosity reduction than the Timberjack 450C. Furthermore, the higher the SMC, the deeper the ruts at all combinations of TI, SG, and ST. Our findings highlighted that ST, SMC, TI, and SG strongly affect rutting and physical properties of soil. [ABSTRACT FROM AUTHOR]

The propagation of climatological drought to water cycle components, including soil moisture, surface, and groundwater resources, was investigated in upper Dez basin, southwestern Iran. Standardized precipitation index (SPI), standardized stream discharge index (SQI), standardized surface runoff index (SRI), standardized base flow index (SBFI), standardized groundwater level index (SGI), and standardized soil moisture index (SRI) were calculated for different time scales: 1-, 3-, 6-, 12-, 18- and 24-month. The correlation coefficient was used to analyze the relationship between indices. The results indicated that (1) comparing meteorological drought to hydrological drought showed the SQI correlated better with SPI than SBFI and SRI. The SRI and SPI correlated better when both indices had the same time scales, which emphasized that SRI can show the propagation of meteorological drought earlier than SQI and SBFI with a correlation coefficient (cc) equal to 0.74. (2) Correlation between the SPI and SGI resulted in the maximum correlation coefficient for SPI-24 and SGI-03 (maximum correlation coefficient (MCC) =0.63). This result revealed that seasonal depletion in groundwater table correlated stronger with more prolonged meteorological drought. (3) SGI showed stronger correlation than SBFI for considering groundwater drought. (4) Correlation between SPI and SSI showed a more substantial relation as the time scale was lengthened, and at deeper soil depths, CC tended to be constant. (5) Each drought index explains a part of drought and applying an individual index cannot describe the drought propagation well. The development of a method that can combine drought indices as an optimal one is recommended for the future. [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]

Agricultural drought refers to soil moisture deficit. It can directly impact crops and adversely affect various economic sectors. Hence, its accurate and precise prediction can help develop plans for agricultural drought and reduce crop damage. This research used a long short-term memory (LSTM) model for estimating drought over Iran during spring and summer. The predictions were made based on the large-scale indicator named El Nino-Southern Oscillation (ENSO), normalized difference vegetation index (NDVI), and meteorological parameters (i.e., precipitation and temperature). For this purpose, the agreement between NDVI, meteorological parameters, and ENSO was studied at time lags of 1–4 months to understand the agricultural drought predictors. The calculated RMSE and NSE of the LSTM model revealed that the model showed a better performance in predicting NDVI values in semi-arid and arid areas such that the RMSE and NSE of the LSTM model are less than 0.3 and higher than 0.8, respectively. Furthermore, the prediction performance in humid and semi-humid areas was better in spring than in summer. The average RMSE across Iran was 0.4 in the spring, whereas the RMSE increased to 0.78 in the summer. In addition, precipitation deficit exhibited its effects on agricultural drought at a time lag of 1 month, whereas ENSO caused agricultural drought at longer time lags. Moreover, In April, agricultural droughts in the north, northwest, and southwest of Iran occurred under the ENSO effects. In northern regions (Caspian Sea coasts), the correlation between agricultural drought and ENSO is close to 0.1. The correlation equals 0.8 in the west and northwest of Iran. ENSO had the greatest impact on NDVI in these regions in April, with a 1 month delay. These findings can be exploited for issuing agricultural drought warnings in Iran. [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) and soil respiration (SR) are essential in carbon cycling and storage. Consequently, proper soil management requires knowledge of factors influencing DOC and SR in soil. The main objective is to identify the most critical indicators that control DOC and SR in the major climatic regions of Iran. The carbon cycling of sixteen pedons from six study sites with different soil moisture and temperature regimes was measured. Soil samples were collected from various genetic horizons to assess their physicochemical properties. Stepwise multiple linear regression (MLR) models were conducted on all horizons and B horizon properties (representing the subsoil) to identify the strongest correlation with DOC and SR. The MLR model revealed that organic carbon (OC) was negatively affected by pH, electrical conductivity (EC), amorphous and crystalline iron oxide contents in all horizons (R2 = 0.69), EC, and cation exchange capacity (CEC) in B horizons (R2 = 0.62). On the other hand, SR was negatively affected by pH, EC, soil water content, and clay contents in all horizons (R2 = 0.67), and calcium carbonate equivalent (CCE), pH, and sodium adsorption ratio in B horizons (R2 = 0.67). The most important indicators associated with carbon cycling included EC and pH in the different soil types of Iran. This study contributes to improving crop yield and mitigating soil degradation, mainly by controlling and managing salinity and pH threats for soil fertility to increase carbon storage in such climatic regions. [ABSTRACT FROM AUTHOR]

In recent decades, the earth's surface data have been collected more efficiently using remote sensing, which needs drought indexes update. In this study, soil moisture (SM) data were collected from the surface layer of a high humidity climate in northern Iran using Soil Moisture Active Passive (SMAP) and field measurement. After analyzing the data, we found that the average RMSE between the field and SMAP measurement was 0.054 m 3 m 3 . Considering the same agricultural land use and the strong correlation of 0.92 between them, the validated SMAP data were used to propose an agricultural drought index. After data validation, the extreme learning machine (ELM) model was put to the test using sigmoid, triangular, sine, and hard-limit activation functions. Of all the activation functions tested, the model with the sigmoid activation function yielded the lowest amount of error and was therefore chosen. Five years of continuous daily SM as a target, five-year daily normalized difference vegetation index, land surface temperature, and precipitation were inputs to predict one-year daily SM time series in the humid climate. From 2021 to 2022, daily surface SM was predicted with the average RMSE = 0.03 m 3 m 3 compared to the SMAP data. Finally, a new regional agricultural drought index based on 4 years of SMAP and 1-year prediction of SMAP from 2022 to 2023 was proposed. Further investigation is needed to conclude that the application of the presented index is reliable in other climates. [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]

Purpose: Although dryland streams are one of the most important vegetation shelters against drought, the effects of ephemeral stream characteristics in arid regions, especially their geometrical features, on vegetation distribution are still largely unknown. Therefore, the main aim of the current study is to clarify how different geometrical characteristics of streams in floodplains and piedmonts influence riparian vegetation distribution in arid ecosystems. Methods: In this study, along the 10-m length and uniform cross-sections of streams, streams were separated into three main sections: (a) the stream valley and (b) the cross-sectional channel for the measurement of geometric parameters, (c) the riparian vegetation zone for the measurement of vegetation cover (%) and soil sampling in four geomorphic units. Moreover, based on the depth and width of the valley, streams were classified into small, medium, and large sizes. Furthermore, in different stream sizes, vegetation canopy was measured and the soil in riparian zones for moisture determination (%) and sediment in channels for grain size analysis were sampled. Results: Our study revealed significant differences among geometrical parameters, vegetation canopy, sediment grain size, soil moisture content, and other soil characteristics of the different stream sizes in the study units (p < 0.05). The results showed the highest positive correlation between vegetation canopy and channel width and valley depth (r = 0.69, p < 0.01 and r = 0.61, p < 0.01, respectively) at all study sites. In addition, our study found that vegetation canopy was mainly positively correlated with stream width, sand, gravel, soil moisture, and the large stream size in the fine-grained geomorphic unit, particularly in piedmont B. In contrast, in the coarse-grained piedmonts, vegetation was more dependent on the medium stream size and soil moisture. Conclusion: The current study found that stream geometric parameters control soil quality for vegetation growth and affect vegetation distribution in this arid area. The findings may be useful for riparian vegetation management programs in drylands. [ABSTRACT FROM AUTHOR]

Furrow irrigation is the most common method of irrigating pistachio orchards in Iran. Water use efficiency using this method of irrigation is close to 35%. Ten years ago, a subsurface drip irrigation (SDI) project was initiated with highly saline and alkaline water for the first time in Iran at the Feyzabad pistachio research station, at 34° 54' 15'' N, 58° 45' 37'' E, located in the northeast of Iran, at an altitude of 850 meters from the sea. The water salinity and sodium adsorption ratio were 11.5 dS m-1 and 15.5 at the beginning of the project, and they reached 16.25 dS m-1 and 17.7 in 2021. The amount of SDI water consumption in the first year of the project (2012) was 1640 m³ ha-1 yr-1, and as the trees aged, water consumption reached 3000 m³ ha-1 in 2022. The amount of water utilized for furrow irrigation was about 6912 m³ ha-1 in 2022. Salt entering the soil through irrigation water differed by about 41 t ha-1 in 2022 between the two methods. After ten years of project implementation, the root development layer was more than 100 cm and less than 50 cm, respectively, for SDI and furrow. The trend of soil salinity has changed from 2012 to 2022: it went from 34.7 dS m-1 in 2012 to 110 dS m-1 in 2018, and then decreased to 65 dS m-1 in 2022 in the surface layer of the SDI method. In contrast, salinity increased almost twofold within the root development layer (60-140 cm) in the SDI. In the last two years (2021 and 2022), the height, crown width, trunk circumference, and yield of the pistachio trees were not significantly different between the two irrigation methods. However, there was a significant difference in the irrigation treatments during the two last years (2021 and 2022) in the annual growth, the average number of flower buds in the branch, and the average number of flower buds abscised. The branch vegetative growth in 2022 had decreased by almost 50% compared to 2021 in both methods. The results showed that the lowest annual growth was achieved in the SDI in 2022. Overall, the efficiency of water consumption in the SDI and furrow was equal to 0.09 and 0.06 kg m³, respectively. SDI net income was 1284 $ higher after expenses were deducted from 2012 to 2022. Therefore, due to severe climate change and excessive salinity increase in soil and water resources, a well-managed SDI can provide a more sustainable production method for salt-affected soils for pistachio orchards by reducing water consumption and salt entry into the soil. [ABSTRACT FROM AUTHOR]

This study was carried out to investigate the effect of irrigation with treated municipal wastewater and agricultural drainage water on Ahvaz (southwest Iran) soil chemical properties in 24 plots with 1 × 1 (m) area in farm No. 1 of Shahid Chamran University of Ahvaz in 2018–2019. The experiment was carried out as a factorial experiment in a completely randomized block design with three replications. The experiment factors included eight irrigation treatments (A: Irrigation with Karun River water as control), (F: Irrigation with treated municipal wastewater). (Z: Irrigation with agricultural drainage water), (Z1F1: 30% Agricultural drainage water +70% Treated municipal wastewater), (Z2F2: 70% Agricultural drainage water +30% Treated municipal wastewater), (Z3F3: 50% Agricultural drainage water +50% Treated municipal wastewater), (Z4A4:50% Agricultural drainage water +50% Karun River water), (F5A5: 50% Treated municipal wastewater +50% Karun River water), at five time levels (including pre-treatment, first month, second month, fourth month, sixth month were performed at three depths (0–20), (20–40), (40–60) cm. The traits examined in this experiment were pH (Acidity), EC (Electrical Conductivity), Ca2+ (Calcium), Mg2+(Magnesium), Na+(Sodium), K+(Potassium), HCO3-(Bicarbonate), CO3−2(Carbonate), SAR (Sodium Adsorption Ratio). Experimental factors had different effect on studied traits. Results showed that with increasing soil depth, all treatments except carbonate and acidity decreased. Calcium, magnesium, sodium and bicarbonate showed significant differences over time (P < 0.01). Non-significant difference in potassium was observed during this time (P > 0.01). There was significant increase in salinity over time (P < 0.01). In T1 period Karun River water showed the highest salinity amount irrigation treatment. Also, F5A5 treatment had the lowest concentration of Ca2+, Mg2+, K+ and the highest concentration of SAR. The Z4F4 treatment showed the lowest concentration of SAR and the highest concentration of Mg2+.The Z treatment had the lowest concentration for Mg2+. The A treatment showed the lowest concentration for HCO3-. The F treatment had the highest concentration for Ca2+; the Z3F3 treatment had the highest concentration for K+, the Z1F1 and Z2F2 treatments showed the highest concentration in terms of SAR. [ABSTRACT FROM AUTHOR]

Since climate change is a major challenge that affects the environment, especially the hydrological flow, this research has used the Soil and Water Assessment Tool (SWAT) to study the climate change effects on the runoff in Alvand mountain basin in Iran. The SWAT model calibration was done based on the SUFI-2 algorithm and the validation results for a 20-year prediction period (2020–2040) showed that the model had high accuracy in simulating the runoffs. CMIP5 models were used to predict the basin's temperature and rainfall (the best scenario was Miroc5) and the results showed that the basin's future climate conditions increase in temperature and decrease in rainfall were appropriate. According to Miroc5 (RCP8.5), the annual runoff in the mentioned period would decrease by 8.36% compared to the past. In a seasonal scale, the largest runoff reduction would be 75% in summer (under the Miroc5 (RCP4.5)), and in a monthly scale, it would be 79% in April increasing dramatically to reach 335% in September (under Miroc5 (RCP8.5)). The trend of the future runoff was investigated using the Mann–Kendall model and the Gresson estimation method. Results showed that there would be no significant trend in the mentioned period and the trend of the future drought intensity would be increasing under all studied scenarios. [ABSTRACT FROM AUTHOR]

Over the last three decades, there has been a general tendency for changing research methods on soil resource management from conventional and mainly qualitative methods to quantitative ones based on spatial correlation models, which are called Digital Soil Mapping (DSM). The present study was carried out in Shabankareh Plain (15,000 ha) with different physiographic units, in Bushehr Province, southern Iran. The target sites (172 points) were selected for soil sampling at depths of 0-30 cm. Soil texture classes DSM was produced by two methods. The first method was Conventional DSM, in which data of soil particles was obtained from laboratory analysis for each sampling point along with their geographical location. Also, the study area boundary was added to ArcGIS software in UTM format and was analyzed by operating Kriging or IDW estimators. The map produced by this method was a low quality digital map containing extra and scattered texture classes with unrealistically sharp boundaries. The second method used CoKriging of L8 multispectral imagery data (OLI bands) and soil samples analysis was operated. Results showed that using B1 band (0.433-0.453 µm) of Landsat 8 satellite imageries of the study area in April 2020 produced high quality digital maps. In this method, soil textures were the same as the ones in the study area. Salt accumulation and water content of surface soil were possible reasons indicating why satellite imageries in the other periods of year were not suitable for DSM. The highest and the lowest ranges of influence among soil texture parameters were 684 meters and 388 meter for clay and sand particles, respectively. [ABSTRACT FROM AUTHOR]

This study examines the effects of land use and slope aspect on soil erodibility (K-factor) and the fractal dimension (D) of soil particle size distribution (PSD) in calcareous soils at the watershed scale in western Iran. The study analyzed 113 soil samples collected from four land uses (slope-farmland, farmland, pasture, and woodland) at a depth of 0–20 cm, measuring common soil properties such as soil texture, soil organic matter (SOM), calcium carbonate (CaCO3), pH, and cation exchange capacity (CEC). The PSD of soil samples was measured using the international system of soil size fractions, and the D for PSD was calculated. The K-factor was calculated using the RUSLE model. The results showed that the K-factor was highest in slope farmlands with SOM at 1.6% and lowest in woodlands at 0.02 Mg h MJ−1 mm−1 with SOM at 3.5%. The study also found that there were significant correlations between D and clay content (r = 0.52), sand content (r = −0.29), and CEC (r = 0.36). Woodland soils had the highest SOM content, with a mean D value of 2.895, significantly higher than the mean D value of slope farmland soils, which had the lowest SOM at 1.6%. The study concludes that woodland soils retain finer particles, particularly clay, resulting in lower soil loss and land degradation compared to other land uses. Finally, the study suggests that shady slope aspects (south aspect) contain more organic matter due to less solar radiation and higher soil water content, resulting in lower soil erodibility (0.02 Mg h MJ−1 mm−1) and higher D values compared to other slope aspects. [ABSTRACT FROM AUTHOR]

This study aimed to explain the farmers' pro-environmental behaviors toward plant, soil and water conservation by adapting the value–belief–norm theory. This cross-sectional survey investigated 342 farmers in Mazandaran province, Iran. Data collection was done using a structured questionnaire and analyzed through structural equation modeling. The findings of the study confirmed the causal paths of variables in the value–belief–norm theory. As expected, in a causal chain, values influenced beliefs, and beliefs influenced norms, and finally norms influenced the plant, soil and water conservation behaviors among farmers. The results indicated that personal norms explained 60%, 10% and 5% of the variance of plant, soil and water conservation behavior variables, respectively. Also, ascription of responsibility explained 82% of the personal norms variance and awareness of consequences explained 35% of the variance of ascription of responsibility. In the following, ecological worldview (New Ecological Paradigm) explained 33% of the awareness of consequences' variance, and altruistic and egocentric values explained 72% of the New Ecological Paradigm' variance. In addition to providing support for the application of the value–belief–norm theory within the context of Iranian farmers, this study increased the understanding of the factors influencing the environmentally friendly behavior of farmers, helping agricultural policy makers and decision makers to formulate their policies for sustainable agricultural development with a better understanding of farmers' decisions and behaviors. [ABSTRACT FROM AUTHOR]

Xerophilic subshrubs exhibit multiple functional types and frequently show hydrological niche segregation. In the poorly studied Irano-Turanian gypsum deserts, knowledge of the ecohydrological strategies of different plant species is essential to understand community complexity in these vulnerable ecosystems. We studied the ecohydrological strategies of five co-existing subshrub members of Caryophyllales, ascertaining if their rooting architecture, gypsum affinity or photosynthetic pathway determined their water uptake, and if gypsum crystallisation water could be a relevant water source for plants in different seasons. We conducted soil and xylem sampling for isotope analyses in spring and summer and extracted water by cryogenic vacuum distillation. Oxygen and hydrogen isotope compositions were determined and compared with visual representation and Bayesian Mixing Models to determine species ecohydrological strategies. Species – season interactions were related to differences in xylem sap isotopic composition. Three basic strategies relying on contrasting the use of free topsoil moisture and deep soil water could be detected and were in part explained by rooting architecture. Plant gypsum affinity and photosynthetic pathways did not have a significant effect on the water sources used by the plants. Ecohydrological niche segregation was explained partly by rooting architecture and species-specific traits. Gypsum crystallisation water was not used in summer by the studied species. [ABSTRACT FROM AUTHOR]

Univariate drought indicators are insufficient for characterizing the complicated effects and conditions of droughts. Accordingly, this study aimed to introduce and assess a composite drought index called the Integrated Drought Index (IDI), composed of the most important water balance variables including, temperature, precipitation, streamflow, and soil moisture to simultaneously monitor hydrological, agricultural, and meteorological drought. To this end, four widely used linear and non-linear combination approaches—namely the kernel mean component analysis (KMCA), copula function (CF), entropy weighting (EW), and the principal component analysis (PCA)—were used here, whose products are called IDI-KMCA, IDI-CF, IDI-EW, and IDI-PCA, respectively. The research data were extracted from ERA5 (ECMWF Reanalysis v5) datasets on a monthly scale for the 1979–2020 period. According to the findings, all proposed composite indices exhibited a mostly similar variation pattern as the individual indices and performed well in monitoring drought conditions—except for IDI-CF, which slightly deviated from the pattern during the 1989–1990 period. High values of the index of agreement (with the average values ranging between 0.5 and 0.9) and correlation coefficient (with the average values ranging between 0.7 and 0.9) also suggested a good agreement among the proposed composite indices. Since climate and hydrologic conditions in the region were not complex, they evaluated the same drought conditions through linear and non-linear approaches. In addition, Frank functions were selected to derive the joint distribution functions of drought characteristics for bi-variate and tri-variate functions. Finally, considering the spatial distribution of the drought return period, the probability of mild droughts remained the same under bi-variate and tri-variate conditions, whereas the occurrence probability of extreme drought changed (increasing and decreasing in the case of "and" and "or"). [ABSTRACT FROM AUTHOR]

Landfill leachate contains heavy metals that contaminate water and soil near the landfill site. Phytoremediation is a process of decontaminating soil or water by using native plants to adsorb and accumulate or break down pollutants. This study aimed to evaluate absorption and accumulation of heavy metals from simulated landfill soil by nettle. Nettle seeds were collected from the Tonekabon landfill site and planted in 243 pots. After reaching the 6-leaf stage, the plants were exposed to three concentrations of four heavy metals (Pb, Cd, As, and Ni) during the growing season. An atomic absorption spectrometer was used to measure the concentrations of the heavy metals in nettle plants and the morphological features of nettle fibers. The results showed that increases in the concentrations of the heavy metals in the soils led to their higher concentrations in all organs of the nettle plants (Pb> Ni> Cd> Ar), and larger quantities of the heavy metals were accumulated in the aerial parts of the plants. In parallel with this result, the dry weights of stem, leaf and root, and leaf area index also decreased. Values of TF> 1 and BAC <1 in plant organs showed the ability of nettle to accumulate more heavy metals when they were present at higher concentrations in the soil. Bioremediation using native plants for removal of heavy metals from soil and their accumulation in these plants is a low-cost and environmentally friendly technology. Nettle is a plant that grows wild in the landfills in northern Iran and, due to its ability to accumulative heavy metals in its organs, is a suitable plant species for their removal from soil. [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]

In the south of Fars Province in Iran, there are several closed basins where the salinity of water and soil resources is one of the main problems. Therefore, to manage the water and soil resources of these basins, identifying the source of salinity and its expansion in playa is necessary. Thus, the Izad-Khast pit in the south of Fars Province of Iran was selected as one of these basins for research. To achieve the goal, 16 soil samples were randomly taken from the basin, and their EC was determined. Landsat 8 image was selected according to sampling day, and ten salinity indices were extracted from it. Then, the best index was determined by the relationship between salinity indices and ground EC using linear regression. Using the determined index and linear equation derived from linear regression and Landsat 7 and 8 images, salinity maps were obtained in three periods 2010, 2015, and 2020. Then, using maps of the three mentioned intervals and the CA-Markov method, soil salinity prediction maps were extracted for 2025 and 2030. Based on the research results, salinity index S2 = (Blue − R)/(Blue + R) provided the best results. Salinity maps derived from this index show that the highest level is related to the area with no salinity or low salinity, and high degrees of salinity are concentrated in some parts of the hills and in some areas of the plain, respectively, which determines the origin of salts. The results also showed that the kappa coefficient of CA-Markov is 0.7282, which shows the high ability of the model to predict soil salinity, in which the distance factor from gypsum and salt minerals is the most critical predictor factor. According to the forecasts, from 2020 to 2025, about 1 km2 and then from 2025 to 2030, about 1.6 km2 will be added to the saline lands. [ABSTRACT FROM AUTHOR]

The purpose of the study is to predict drought changes in Dariun, Fars Province, and their impact on water and soil quality. To prepare drought, water, and soil quality zoning maps, Landsat satellite images and the kriging method were used. The fuzzy maps and weights for each parameter were then determined using fuzzy and analytic hierarchy process (AHP) methods. Additionally, cellular automata (CA)-Markov chains were used in order to predict the impact of drought changes on water and soil quality. Using the fuzzy-AHP method, water quality and soil fertility in 2020 were lower compared to previous years, mainly because of land use changes that increased pollution. Based on results of the Markov and CA-Markov chains, approximately 31% of the region will have very poor levels of soil fertility and water quality in 2050. Further, based on remote sensing indicators, it is determined that about 25% of the region will be at high risk of drought in 2050. Thus, if adequate management of the region is not done, the possibility of living in these areas may diminish in the coming years due to drought and deteriorated water and soil quality. [ABSTRACT FROM AUTHOR]

Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent. [ABSTRACT FROM AUTHOR]

Easy access to valid climatic data has always played a fundamental role in progressing hydrological studies. That is why numerous satellite-based precipitation products (SPPs) have been generated in the contemporary era. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is considered one of the most popular climatic databases which started to produce daily rainfall data with 0.25° × 0.25° temporal and spatial resolutions in 1983. The aim of this research is to evaluate how well PERSIANN-CDR has performed in a rainfall-runoff modeling application over the period of 1994 to 2015. In this regard, using Soil & Water Assessment Tool (SWAT), two rainfall-runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSIANN-CDR database is. Comparing the developed SWAT model calibration results using the satellite database precipitation (NS = 0.78, P-Factor = 0.52, and R-Factor = 0.41) to calibration results of the developed model based on GRGs (NS = 0.81, P-Factor = 0.54, and R-Factor = 0.42) showed that although PERSIANN-CDR precipitation magnitudes were typically less than GRGs records, accuracy indicators of simulated runoffs to Ghale-Shahrokh were almost the same. [ABSTRACT FROM AUTHOR]

Soil moisture content plays a pivotal role in biomass development of vegetation coverage at various growth stages. Moisture content of the soil is considered as a crucial parameter for agricultural studies which directly leads to higher fertility rate. Remote sensing techniques, specifically Synthetic Aperture Radar (SAR) sensors, provides suitable opportunity for continuous soil moisture monitoring at various spatial and temporal resolutions. In this study, field campaigns conducted to measure soil surface parameters, including soil moisture and roughness, synchronized with Sentinel-1 pass over an agricultural region near Mohammadshahr, Iran. Fieldwork for soil moisture sampling have done during plants' (canola and winter wheat) growth stages. The Gradient Boosted Regression Tree (GBRT), eXtreme Gradient Boosted (XGB), and Random Forest (RF) machine learning algorithms were employed to model the relationship between the ground measured soil moisture and polarimetric SAR derived features from Sentinel-1 imageries. The results showed promising results obtained for soil moisture estimation using the dual-polarized SAR dataset over crop-covered agricultural fields with R2 = 0.95 and RMSE = 0.023 m3 m−3 using the GBRT regression model. [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]

Drought is one of the most devastating natural hazards in the world, affecting millions of individuals in in different ways, so it's better monitoring and comprehensive assessment is important. Univariate or multivariate drought indices are able to monitor one type of drought and can not reflect comprehensive drought information from meteorological to agricultural aspects. For this purpose, by combining the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), the Soil Water Index (SWI) and the precipitation condition index (PCI) a comprehensive drought index called Combined Drought Index (CDI) was proposed. In this study, meteorological and agricultural droughts from 2001 to 1397 in Karkheh Basin in southwestern Iran were monitored. The Principal Component Analysis (PCA) method, which is a mainstay of modern data analysis tools for constructing a composite index, was applied to a data matrix that contains the time series of the computed PCI, VCI, TCI, and SWI indices for a given location, and the first leading component of the PCA was introduced as CDI index. The results indicated that the highest correlation (r = 0.53, r = 0.56) between the CDI, SPI-1 and SDI was observed respectively, which indicates the ability of this index for drought comprehensive monitoring. It is also suggested that in order to improve the performance of the CDI, in addition to the considered parameters in this study, other factors affecting the performance of each of the remote sensing indicators such as vegetation type, plant root, soil texture type, evaporation and Transpiration also be considered in future studies. [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]

Calcareous lands play an important role in the gene reservoir persistence through soil seed bank (SSB). This research was carried out to assess soil seed bank dynamics (SSBDs) in a calcareous and a non-calcareous land in central Iran through time. Plot-based sampling was done at two times in 6 and 18 months after seed dispersal. Environmental and soil characteristics and diversity and germination indices (DIs, GIs) were measured for SSB. Multivariate ordination (CCA) and liner regression was performed to detect relationship between SSBDs and soil factors. Nitrogen (N), organic matter (OM), phosphorous (P), and soil moisture (SM) affected SSBDs in short time and lime content had a positive effect in persistent SSB in long time, especially in grasses (Sig. < 0.05). In calcareous soil, the lime content showed a positive relation with GIs, where has led to an inducible seed dormancy and preserved vigor index (VI) {R2 = 0.67 and 0.45 in grasses and forbs, respectively} after 18 months. [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]

Sustainable crop and livestock planning encounter serious challenges when tasked with reducing the associated nutrient pollution entering the watershed environment. To overcome these challenges, approaches for specifying optimal crop pattern and livestock distribution to limit the pollution in the catchment are advised. In this research, a simulation–optimization approach is used in which the Soil and Water Assessment Tool (SWAT) is employed for simulating the complex soil–water–plant quantity and quality relations, and the Harmony Search (HS) algorithm linked with SWAT is used to discover the optimal crop pattern and distribution of livestock in the Ilam Dam basin, Iran. In the developed HS-SWAT model, the cultivation area and the number of livestock in SWAT's hydrologic response units (HRU) are the decision variables for maximizing the net benefit obtained from the crop's and livestock's productions, while the nitrate and phosphate calculated in the outflow of the basin are restrained to meet the allowable rates. Results show that the scattered livestock in the basin have a great impact on the generated pollution where about 90% of the nitrate entering the downstream reservoir is the consequence of animal waste. In the optimum state, by reduction of the cultivation area and the number of livestock across the watershed, the concentration of N and P in the surface runoff is reduced significantly to meet the allowable level. According to the results, the HS-SWAT model performance indicates its capability for solving watershed crop pattern and livestock planning problems. [ABSTRACT FROM AUTHOR]

Flood mitigation measures are aimed at reducing adverse flooding impacts. Flood source control schemes mainly concentrate on biological soil and water conservation measures (SWCMs) including the revival and preservation of soil and land use. Such activities prevent splash erosion, reduce the velocity of surface runoff, increase surface roughness that in turn improve infiltration and control soil erosion. Nevertheless, based on technical and budget constraints, uniform implementation of biological SCWMs is neither practical nor optimal. Previous studies on spatial optimization of BMPs (best management practices) have mostly focused on urban structural and/or biological BMPs. Besides, the incorporation of land use/cover and slope maps in source control site selection has not been thoroughly studied. In this research, cover–slope zones are proposed as spatial units for spatial optimization of biological SCWMs. The objective of this study was to determine the optimal spatial distribution of SWCMs using coupled simulation–optimization models. A fully distributed event-based deterministic hydrologic rainfall–runoff model was developed that involved SCS-CN (soil conservation service curve number), ModClark and Muskingum as infiltration, rainfall–runoff and stream routing model, respectively. Genetic algorithm was adopted to search the optimized areas based on two objective functions: minimization of the outlet peak discharge and the cost. Nardin sub-catchment in northern Iran was selected as the case study. For different degrees of flood peak reduction under 100-year design rainfall, the optimal patterns identified by the optimization algorithm were a combination of less effective/costly options (exclusion) and the most effective option (transplant). Accordingly, biological SCWMs were assigned from the farthest zone to the outlet, stretching into downstream zones. [ABSTRACT FROM AUTHOR]

Pistacia atlantica Desf. (Beneh) is an important woody species that has been facing significant challenges to its natural regeneration and reforestation in Iran. This study investigates the interaction of soil moisture and shade on growth, chemical contents, and morphological and physiological characteristics of Beneh saplings. One-year-old Beneh saplings were treated with varying amounts of soil moisture (20, 50, and 100% of field capacity) and shade (0, 30, and 50% of full sunlight) in a split-plot experiment of a randomized complete block design in semiarid conditions of the Alborz Research Station of the Research Institute of Forests and Rangelands (RIFR) in Iran. The results indicate that soil moisture significantly affects the water content of the leaf, total chlorophyll, proline content, activity of catalase enzyme, leaf dry biomass, leaflet area, and dry stem biomass in the leaf. Shade significantly affected total chlorophyll, catalase enzyme activity, specific leaflet area, relative water content of the leaf, proline content, dry root biomass, and leaflet area. The interaction of shade and soil moisture significantly affected seedling height, catalase enzyme activity, specific leaflet area, and nitrogen and potassium content of the leaf. Shade moderates the stress of drought on Beneh saplings, but shading of Beneh saplings is not recommended in conditions where there is no concern about soil moisture. These conclusions can be used to improve the production of Beneh saplings in nurseries. [ABSTRACT FROM AUTHOR]

Environmental pollution is regarded as a major environmental crime in most countries; Iran is no exception. This study examines water and soil polluting behavior among villagers in Jimabad, Mashhad County—a rural area in the Razavi Khorasan province in the northeastern region of Iran. A survey questionnaire was used to collect data from a random sample of 315 respondents in the population of the villagers of Jimabad. This article reports on the levels of water and soil polluting behavior among the respondents and the results are discussed in terms of techniques of neutralization, religiosity and cultural context. [ABSTRACT FROM AUTHOR]