JWSS - Journal of Water and Soil Science

Water resources management depends on the precise assessment of water storage and access in each region, as well as environmental interactions of these resources. The man objective of this study was to delineate the potential zones of groundwater storage using FAHP. Mapping and assessment of it required maps of geomorphology, drainage, density, lineament density, slope and vegetation, which were initially prepared as the input layers in FAHP; the appropriate weights were attributed to them based on FAHP. Potential zones of ground water were classified into five classes of poor, average, good, very good and excellent. The number and density of available wells and springs in the study area dealt with the potential of the region for groundwater storage. So, ROC was used to assess the validation of results, considering spring points as signs of water resources. According to the results, classes of very good, good, average, weak, and very weak were ranked as the first to the last in terms of privilege order with an area of 37.7, 55, 40, 107, and 98.4 square kilometers, respectively.

Wetting pattern in a trickle irrigation system is one of the most important characteristics that should be taken into consideration for designing the irrigation systems. Improving the dimensions of the wetting pattern will increase the water use efficiency and irrigation systems. The objective of this study was to investigate the effect of rice husk and its biochar application on the wetting pattern in a silty clay soil under surface trickle irrigation. A box with the length of 200, the width of 50 and the height of 100 cm was used. To easily fill and empty the model, it was filled up to a height of 50 cm. The rice husk and its biochar were added to the soil at the rates of 0, 1 and 2 mass percentages based on a factorial arrangement of the treatments in a completely randomized design with three replications. Biochar was prepared in a special furnace at 500°C without oxygen. The experiments were done with a flow rate of 4 liters per hour with the irrigation time of 3 hours. The results of the analysis of variance showed that the organic treatments increased the soil water content in the range of field capacity to a permanent wilting point; the highest increase was observed for the biochar 2% treated soil. Also, the addition of rice husk and biochar in the silty clay soil reduced the horizontal advance and increased the vertical advance wetting pattern.

In the present study, the effects of different levels of irrigation, organic mulch and planting method on the mungbean yield in Badjgah were investigated. The experimental plan in the first year was full randomized block, while in the second year, it was full randomized split-split plot block design, in three repetitions. The results showed that in the FI treatments, the yield was increased up to 2% for the first year and 5% for the second year by changing the planting method from on over-ridge planting method to the in-furrow planting one. Also, the results of the first year showed that there was no significant difference between the yield in the fully-irrigated treatments without mulch and the treatment with mulch and 0.75 FI. The amount of the irrigation water could be decreased up to 25% by adding organic mulch in both planting methods, as compared to the fully-irrigated treatments without mulch. The maximum water productivity equal to 0.4 kg/m³ was observed in 0.5 FI, in-furrow planting method with mulch treatment. It can be, therefore, concluded that the water productivity may be maximized with the application of both deficit irrigation and mulching strategies.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models. 

The purpose of this study was to model biomass with soil parameters in Hir-Neur rangelands of Ardabil Province. Initially, considering the vegetation types and different classes of environmental factors, at the maximum vegetative growth stage, using one square meter plot, biomass was estimated by clipping and weighing method. For each transect, a soil sample was taken and transferred to the soil laboratory and the various parameters were measured by conventional methods. The relationship between soil factors and the rangeland biomass was analyzed and simulated using linear multiple regression. Among the measured soil factors, the Silt, EC, Ca, K_soluble, OC, POC, pH, Mg, TNV, clay, P, and volumetric moisture had the highest effect and percentage of biomass forecast (p<0.01). The accuracy of the simulated maps was analyzed using RMSE criteria and for grasses, forbs, shrubs, and total biomass were equal to 0.81, 0.65, 0.34, and 0.46, respectively. The results of this study, not only point out the importance of soil factors on the biomass but also as a baseline data for managing rangelands, supply-demand, and carbon balance can be used in the current section.

Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.

Integrated assessment of the watershed is critical in arid and semi-arid areas due to the severe water stress in these regions. Data and information are an essential part of decision making and water governance to obtain integrated water resources management at the watershed scale. Water accounting is a helpful tool to organize information and present them as the standard indicators to achieve this goal. Therefore, the objective of this study is to implement the Water Accounting Plus framework (WA+) in the Ferizi watershed located in the Khorasan-e Razavi Province. In this study, water accounting indicators of the Ferizi watershed for a period of 28 years (1990-2017) and wet (1990-1997) and dry (1998-2009) periods were calculated using the SWAT model. The calculated indicators showed that the amount of manageable water and usefulness of consumption (transpiration) is low in the watershed and a large part of the share of irrigation in the watershed is provided by groundwater resources. Generally, the results of this study showed that the use of the SWAT model, WA+ framework, and analysis of water accounting indicators play a significant role in assessing the agricultural and hydrological conditions of the watershed. The proposed approach in this study can help managers make enlightened decisions to keep the sustainability of the watershed.

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.

In precision agriculture, a productivity rating system is a significant tool to quantitatively assess soil quality. An experiment was conducted in Bilavar, Kermanshah to evaluate the spatial variability of physical indicators of soil quality of a rapeseed (Brassica napus) field. Spatial variability analysis of soil physical properties measured on a rectangular grid (100 m×100 m) was carried out using a geostatistical analyst extension of Arc-GIS software. Five physical soil quality indicators including bulk density (BD), non-capillary porosity (NCP), field saturated hydraulic conductivity (Ks), available water retention capacity (AWC), and organic carbon (OC) were determined. The physical rating index (PRI) at each sampling point was determined by multiplying the rating values for all five parameters. Results revealed that major ranges of semivariogram for Ks and AWC varied between 137-145 m and for BD, OC, and NCP they were relatively long (161-205 m). Clay and NCP showed moderate spatial dependence (0.68 and 0.28, respectively) whereas the rest of the parameters showed weak spatial dependence. Also, the correlation between PRI and the biological yield of rapeseed was fairly good (R²=0.68). Investigation of zoning maps of soil physical properties showed an increase in BD and a decrease in AWC and NCP parameters depending on changes in soil texture and organic matter content in some parts of the field. In general, the PRI index is an important tool in the quantitative assessment of soil physical conditions, and based on it and zoning maps can improve the physical quality of soil in agricultural fields.

change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.