JWSS - Journal of Water and Soil Science

The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.

The existence of silty sand in the infrastructure under concrete constructions, hydraulic structures, and irrigation systems has always caused challenges. Improving this kind of soil is always a challenging approach to increase compressive strength and shear stress. There is a conception that adding some extra material such as concrete can increase the stability of this soil against contributed forces. The present study investigated the effects of curing time (3, 7, 14, 21, and 28 days) and different percentages of various additives (3%, 5%, and 7%) on the strength of the silty sand soils. A series of laboratory tests were carried out to measure the Uniaxial Compressive Strength (UCS) and California Bearing Ratio (CBR) by evaluating the effect of additives on the strength parameters of silty sand soil. In total, 299 experimental tests have been conducted in the soil mechanics laboratory of SRBIAU. Results indicated that adding additives such as concrete to silty sand soil improved significantly the compressive strength and shear strength. The comparisons among the experimental test illustrate that due to increasing the curing time, the aforementioned parameters were increased significantly; however, Confix and Bentonite aggregates did not have a marginal effect on the compressive strength and shear strength. Also, after the 21^st day of the curing time, the rate of increment of the UCS and CBR reached slightly and then attained a constant value. Also, after this duration, the curing time is an independent factor in the variation of the UCS and CBR tests. Furthermore, the addition of 5% Pozzolana cement and 7% Portland cement with 28 days of curing had the highest CBR number and UCS resistance of 176.26 and 17.58 kg/cm², respectively. Also, the sketch of the different failure patterns was shown during the curing time. Finally, by increasing the curing time, the behavior of specimens from semi-brittle to brittle made them harder.

The impact of different management options on the region and the existing conditions can be evaluated with minimal cost and time to select the most practical case using various tools including mathematical models. In this study, the SWAT hydrological model was performed from 2009 to 2019 using climatic, hydrological, and hydrometric data in the Malayer catchment, and the final model was validated by SWAT-CUP. To reduce the amount of uncertainty in the input parameters to the MODFLOW model, using the values of surface recharge from the implementation of the SWAT hydrological model, quantitative modeling of Malayer aquifer was performed more reliably in GMS software by using MODFLOW model. After modeling the study area in the 2009-2018 period and calibrating the model in the years from 2018 to 2019, the mean values of absolute error (MAE) were 0.35-0.65 m, and root means square error (RMSE) was 0.62-0.94 m, which seems acceptable considering computational and observational heads equal to 1650 m. Results of water level changes in observation wells located in the Malayer region indicate that the groundwater level in the aquifer has decreased by an average value of 9.7 m in the 10-year study period.

Class A pan evaporation method as one of the most common methods for reference evapotranspiration (ET₀) estimation has been widely used in the world due to its simplicity, relatively low cost, and ability to estimate daily ET. In this study, the performance of 8 empirical methods consisting of Allen and Pruitt (1991), Cuenca (1989), Snyder (1992), modified Snyder, Pereira, et al. (1995), Orang (1998), Raghuwanshi and Wallender (1998), and FAO/56 were analyzed to estimate class A pan coefficient and ET₀ at Fasa synoptic station located in Fars province. The calculated pan evaporation coefficients from the above equations were compared with measured pan evaporation coefficients which were obtained from the ratio of evapotranspiration calculated by the FAO-Penman-Monteith method to the rate of evaporation from the pan. The results showed that all empirical methods did not predict pan coefficient values well (R² < 0.3 and NRMSE > 0.25). The comparison results between ET₀ from empirical methods and ET₀ obtained from FAO-Penman–Monteith indicated that the FAO/56 method had the best performance (R² = 0.72 and NRMSE = 0.3). To increase the accuracy of empirical pan coefficient equations, these equations were modified with eight years (2007-2015) of meteorological data from the Fasa synoptic station and validated using two years of independent data (2015-2017). The results showed that the accuracy of all empirical models was improved and the Cuenca equation with NRMSE = 0.16 and R²= 0.63 was selected as the best equation for pan coefficient estimation and ET₀ (R² =0.85; NRMSE =0.18) in Fasa region. The sensitivity analysis revealed that the estimated pan coefficient is more sensitive to wind speed, followed by relative humidity, fetch distance, the slope of the saturation vapor pressure curve, sunshine hours, and air pressure. According to statistical results and sensitivity analysis, an equation was expanded for the Fasa region and other areas with the same climate.