JWSS - Journal of Water and Soil Science

Nowadays, greater recognition of drought and introducing its monitoring systems, particularly for the short-term periods, and adding predictability to these systems, could lead to presentation of more effective strategies for the management of water resources allocation. In this research, it is tried to present appropriate models to predict drought in city of Semnan, Iran, using time series, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (MLP and RBF). For these modeling processes, average monthly meteorological parameters of rainfall, temperature, minimum temperature, maximum temperature, relative humidity, minimum relative humidity, maximum relative humidity and SPI drought index were used during the period 1966 to 2013. The results showed that among the many developed models, the ANFIS model, with input data of average rainfall, maximum temperature, SPI and its last-month value, 10 rules and Gaussian membership function, showed appropriate performance at each stage of training and testing. The values of RMSE, MAE and R at training stage were 0.777, 0.593 and 0.4, respectively, and at testing stage were 0.837, 0.644 and 0.362, respectively. Then, the input parameters of this model were predicted for the next 12 months using ARIMA model, and SPI values were predicted for the next 12 months. The ANN and time series methods with low difference in error values were ranked next, respectively. The input parameters SPI and temperature had better performance and rainfall parameter had weaker performance.

Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The  percentages of the model in estimating the region's  annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.

One of the major issues in the contemporary world is climate change. The behavior and characteristics of parameters affecting climate change can cause them to be seen and hidden. As one of the effective ways to detect overt and covert behaviors for periodic climatic data series, Spectral analysis can be used. It is the analysis of each of the wavelengths series, making this behavior clear. Accordingly, the present study was an attempt to use the method of spectral analysis, data cycles in the minimum temperature, maximum temperature and precipitation in Ramsar station (located in the western regions of Mazandaran province) an nd Babolsar (located in the central parts of this province) in a period from1961 to 2014. For this purpose, temperature and precipitation data were obtained from these stations; MATLAB software environment and the environment for the software were logged for each of the variable in the stations. The results revealed that the minimum temperature at both stations had significant cycles, with the return period being 2 to 5 years; Remote Link could be fit into the cycle parameters such as NAO, AO and ENSO. Analysis of the period gram showed cycles 8 and 5/13-year-old and 5-year-old period in Ramsar and Babolsar. During the rainy cycles, the difference between the two stations and the difference in the geographical position affected systems, and rain accounted for the difference in speed dual-zone climate indicator for Remote Link.

The effects of climate change can be released from the surface to the soil depth, thereby affecting soil thermal regime. Thermal energy in the soil plays a very important role in causing climate changes. In this study, for the assessment and detection of  the climate changes, soil depths temperature, the measured data related to the  daily air temperature at a height of 2 meters (screen) during the years (1951-2014), and the soil depths daily temperature (5-10-20-30-50 to 100 cm), for 3, 9 and 15 hours, were obtained during a period (1992-2014) in Shahrud station. The climate change detection was employed to compare the treatment mean. As well, for detection of trends related to the annual, seasonal and monthly time series and their relation to the soil depths temperature, parametric methods (regression analysis and Pearson) and nonparametric (Mann-Kendall, Spearman) were applied. The results showed that the soil temperature was increased in all months except January, February and March. Also, in the seasonal time series, the soil depths temperature was increased in all seasons except winter. In fact, based on the results, the soil temperature in spring, summer and autumn was increased. Detection trends of the annual soil depths temperature showed that, except for the Pearson correlation coefficient method, soil temperature was increased at all soil depths.

Shortage of water resources and renewable per capita in last 30 years is put Iran on crisis threshold. Wastewater reuse is one of the battle solutions for water shortage and prevents wastewater depletion and environmental pollution. Thus, a pilot scale experiment was carried out to evaluate an integrated anaerobic/aerobic treatment for removal of BOD₅ and COD, also to reduction of hydraulic retention time by considering optimum removal efficiency. The pilot was an anaerobic/aerobic bioreactor type under continuous-feeding regime based on a central composite design. The pilot was studied in different retention time and aeration was carried out between 5-15 hours. According to different retention times for COD removal efficiency, 24 hours was selected as optimum hydraulic retention time, that it is comparable to those obtained for 48 hours and over in plant roughly and could remove COD and BOD in acceptable ranges, results showed that average removal efficiency for BOD₅ were 63.86 and 83.99 percent in aerobic and anaerobic phases, respectively. The average removal efficiency for COD was 76.5 and 74.35 percent for anaerobic and aerobic sections, respectively. The average removal efficiency for BOD₅ and COD in this integrated aerobic-anaerobic pilot 95.24 and 94.8 percent, respectively.

Expert aquatic designers face many problems; among these, in hydrology, defective occurrences in time-series can cause errors in the ultimate results of the study. This more often happens in the regions where the number of hydrometric and rain gauge stations is limited. In addition, assessing, developing and maintaining the use of water resources require accessible long-term and high-quality quality hydrological time-series. Thus, this necessitates correcting the statistical flaws and magnifies the importance of how to deal with the problems in the hydrological analyses. Statistical methods are, currently, used to infill data and statistical gaps. In this study, in order to introduce a multivariate method for estimating the missing data on rainfall and runoff, in a hydrologic homogeneous region in the Mazandaran province, self-organizing map methods were examined under two scenarios and some reliable estimates were obtained. In this regard, the correlation coefficients between the observational data and the model output were calculated for the precipitation data up to 0.92 and up to 0.95 for the runoff data. Therefore, to avoid the reduction of uncertainty caused by the inadequate data in water resource management, this method could be used.

Groundwater supplies a major portion of two basic human needs: drinking and agricultural water. Forecasting, monitoring, evaluating the performance and planning of this vital resource require modelling. The lag time of the groundwater level fluctuations against the rainfall is one of the essential data of the models. The purpose of the present study was to evaluate the piezometers behaviour by using the Pearson cross-correlation method between SPI and GRI indices in the Shiraz alluvial plain in order to determine the mentioned lag time. The results showed a similar behaviour for 86.2% of the piezometers. In 79.3% of the piezometers, groundwater level was declined one month after the rainfall event. The best correlation coefficient between the aforementioned indices was observed along the southwestern to the northeastern axis of the plain. The northern alluvial plain has a better correlation, as compared to the southern section because of the northern-southern slope of the plain. The central area of the plain had the highest correlation coefficient. The maximum correlation coefficients occurred at a time scale of 48 months. Also, since 2004, due to the decline in the atmospheric precipitation in the Shiraz plain, the SPI index has surpassed the drought level, although the trend has not been significant. However, the GRI does not follow this trend, showing a significant hydrological drought. The reason can be the disproportionate water extraction to recharge ratio in the alluvial aquifer of the plain.

Drought as a natural hazard is a gradual phenomenon, slowly affecting an area; it may last for many years and can have devastating effects on the natural environment and in human lives. Although drought forecasting plays an important role in the planning and management of water resource systems, the random nature of contributing factors contributing to the occurrence of and severity of droughts causes some difficulties in determination of the time when a drought begins or ends. The present research was planned to evaluate the capability of linear stochastic models, known as multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model, in the quantitative forecasting of drought in Isfahan province based on the Standardized Precipitation Index (SPI). To this end, the best SARIMA models were chosen for modelling the monthly rainfall data from 1990 to 2017 for every 10 synoptic stations in Isfahan province to forecast their monthly rainfall up to five years. The monthly time scale SPI values based on these predictions were used to assess the drought severity of different stations for the 2018- 2022 time period. The station results indicated a weak drought at the 2019- 2022 period for Isfahan, Kashan and Naeen, a severe drought in 2019 for Ardestan and Golpaygan, and a weak one in 2019 for the East of Isfahan, KabootarAbad and Shahreza stations. All other stations, except Golpayegan, Isfahan, Kashan and Naeen, faced a severe drought in 2018.

In regard to wide piers, the pile group rather than single pile is used frequently to bear the loading of the structure in a particular arrangement; piles group composed of only one column of piles in the flow direction has a great effect on supporting the bridge deck. In this study, local scour at a single column arrangement of the piles group made up of four rows of piles characterized by different piles spacing was studied for clear-water conditions with two flow discharges of 20 and 35 l/s (the effect of increasing the flow depth with the same flow intensity). The results indicated that an increase in the flow depth not only greatly enhanced the scour depth and the width of the scour hole. Besides, the investigation of the relative flow depth on scour extent showed the need for revision in deep water conditions, as reported in the literature. The results of the pile group experiments revealed the noticeable impacts of piles spacing on the local scour. The bigger pile spacing caused a feeble interaction of wake-horseshoe vortices, leading to a decrease of the scour depth; the separate view of the scour holes was generated at individual piles. Finally, the results were compared with commonly used comprehensive models. The findings of this study can be applied for the appropriate selection and positioning for the countermeasure of the scour at bridge piers.

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.

The reuse of treated wastewater in countries such as Iran that suffers from drought is considered an important challenge in water management programs. The application of modern wastewater treatment systems particularly attached growth systems, owing to the short time required for start-up, low land requirements, and the absence of problems associated with sludge handling may be a resolution. The objective of this study is to investigate the performance of the Moving Bed Biofilm Reactor (MBBR) in treating synthetic municipal wastewater and selecting an appropriate model. In this way, a bench-scale reactor possessing an effective volume of 15 liters, and synthetic wastewater with influent COD of 500 mg/l (similar to typical municipal wastewater) has been used and the experiments with media filling percentages of 30%, 50%, and 70% and hydraulic retention times (HRT) of 4, 8, and 12 hours have been carried out. The observed data show that the optimum bulk density and hydraulic retention time are 50% and 4 hours, respectively. Also, the kinetic study of reactor performance indicates that Grau second-order model has better conformation with Moving Bed Biofilm Reactor results. In addition, a regression model for predicting effluent COD based on the filling percentage and retention time is presented.

This study was conducted to investigate the temporal variations of runoff and rill erosion in various soil textures under different slope gradients. So, a laboratory experiment was set up in three soil textures (loam, clay loam, and sandy clay loam) and four slope gradients (5, 10, 15, and 20%) using the completely randomized design with three replications. Runoff production and rill erosion were measured at a flume with 4 m×0.32 m in dimensions using a simulated water flow with 0.5 lit min^-1 in discharge during 30 min. Results indicated that runoff and rill erosion and their interaction were significantly affected by soil texture and slope gradient (P < 0.001). Significant relations were found between rill erosion and runoff both in three soils and four slope gradients, and the strongest relations were in loam (R²= 0.86) and 15% slope gradient (R²= 0.94). Runoff and rill erosion varied considerably in the soil textures and slope gradients during the experiment. A 10-min pick time was found for runoff and rill erosion. In contrast to runoff, rill erosion appeared an irregular and gradual increasing pattern during the experiment which was associated with the frequency of transportable soil particles. Clay loam had more sensitive particles due to a higher percentage of fine particles and weaker structure, and most of them were washed in early times, and finally, rill erosion was reached to a constant pattern. This study revealed that temporal variation patterns of runoff and rill erosion are influenced by soil type (texture and structure) and slope gradient.

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.

Runoff is formed by spending some time after rain and significantly depends on rainfall intensity, soil moisture, and slope. One of the fundamental questions about runoff is the time that it starts to create. In this research, the runoff start time in sandy loam soil was evaluated experimentally under different conditions using a precipitation simulator machine. The rainfall intensity parameters of (60, 80, and 100 millimeters per hour) and the slope of (0 and 5 percent) were investigated. The rainfall was created in the three soil treatment types completely dry (S_dry), the dry soil that had been saturated 24 hours before the test (S_24hrlag), and the dry soil that had been saturated 48 hours before the test (S_48hrlag). Eighteen tests were conducted on this soil. At the end of each test, the soil moisture was measured. The experimental results were compared with the numerical model of Green-Ampt. According to the Kendall and Spearman correlation test results, as the rainfall be intense, the start time of the runoff is lower. Also, the runoff starts at a faster time in the slope of 5 percent for every three types of soil. Also, the results of starting time of the runoff in the soil with a delay of 48 hours in the rain compared to the soil with a delay of 24 hours in the rain are closer in all of the rainfall intensity and slopes compared to the case of dry soil. Therefore, in the experiments related to a delay of 24 hours, the time of the start of runoff decreases. While in tests with a delay of 48 hours, it was not much different from completely dry soil. Also, the Green-Ampt results are close to the experimental results (R²=0.9775), and the maximum difference between the two mentioned methods is 4.8 minutes. Therefore, it can be used with the Green-Ampt method to calculate the start time of runoff in sandy loam soil in different states of rainfall intensity and bed slope.

Digital elevation models (DEMs) are one of the most important data required in watershed modeling with hydrological models and their spatial resolution has a significant impact on the accuracy of simulating hydrological processes. In the present study, the effect of spatial resolution of five DEMs derived from the topographic map (TOPO) with a scale of 1:25000, ALOS PALSAR, ASTER, SRTM, and GTOPO with a spatial accuracy of 10, 12.5, 30, 90, and 1000 m, respectively, on the estimation of parameters of geomorphological and geomorphoclimatic unit hydrographs models has been evaluated in Amameh watershed. Thirty-four single flood events were used during the years 1970 to 2015. The results showed that in the GUH method, the application of the TOPO and ALOS PALSAR DEMs had the best results with root mean square error (RMSE) of 1.7 and 1.8 m³/s and Nash-Sutcliffe Efficiency (NSE) of 0.4 and 0.3, respectively. While the GTOPO DEM had the least efficiency with RMSE of 2.8 m³/s and NSE of -2. Similarly, the lowest and highest RMSE in the GCUH method belonged to TOPO and GTOPO DEMs with RMSE of 3.8 and 18 m³/s and NSE of 0.2 and -6, respectively. Generally, the GUH method had more favorable results than the GCUH method in all DEMs.