JWSS - Journal of Water and Soil Science

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As unit hydrograph is an important item in flood estimation of the rivers and since flood hydrograph and simultaneous rainfall hyetograph is needed to derive a unit hydrograph, hydrologists recommend synthetic unit hydrographs for areas lacking these hydrometeorological data. A research was conducted in the Zayandehrud-dam watershed (Pelasjan sub-basin) to test the efficiency of synthetic unit hydrographs (Snyder, SCS, and Triangular methods) in hydrological evaluations. For the purposes of this study, natural and synthetic unit hydrographs were determined and compared, using all morphologic, hydrometric and rainfall data. The results showed that Triangular and SCS methods fit natural unit hydrographs better than Snyder method does, but peak instantaneous flow is estimated to be higher than the observed flow. So, the constant 2.083 in peak flow equation is recommended to be changed to 1.74 in this watershed. The Snyder method predicts good peak flows, compared with the other two methods. Generally, it is concluded that Triangular, SCS, and Snyder methods are ranked 1 to 3 for determination of synthetic unit hydrographs in this watershed.

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Tillage system effect, Conventional (CT) vs. No-Till (NT), on soil physical properties and Br transport was studied at two locations in North Carolina. The soil types were a Typic Paleudults at Coastal Plain (site 1) and a Typic Kanhapludults at Piedmont (site 2). Bulk density (BD), total porosity (TP), macroporosity (MP), and saturated hydraulic conductivity (K_s) .were measured in plant row (R), and trafficked (T) and untrafficked (N) interrows. A rainfall simulator was used to apply two early season rainfalls to 1 m² plots where KBr suspension was surface applied for Br leaching study. The first simulated rainfall event (30 min) consisted of a low (1.27 cm h^-1) or a high (5.08 cm h^-1) rate applied, 24 h after Br application. One week later, the high rainfall rate was repeated on all plots.

 Soil samples were taken two days after applying first and second simulated rainfall (a week between them) and the end of season from different depths for measuring Br concentrations. Soil physical properties were affected by both tillage system and position.

Bulk density was higher in NT versus CT and in T position versus Rand N positions. Total porosity was lower in NT versus CT but MP was significantly higher in NT. Saturated hydraulic conductivity was about 90% lower at T position versus N and R positions. Coefficient of variation was quite large, making it difficult to obtain statistical differences between tillage systems. The surface l0-cm of soil contained the highest Br concentration for depth treatments with treatment differences occurring primarily in 0 to 25 cm depth. In first and second sampling dates, more Br leached under NT versus CT system. However, there was no significant difference between the two tillage systems in Br leaching at the end of the growing Season. In site 1, Br leached more due to the coarse texture and high Ks of the soil.

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Point data of weather stations are not important in and by themselves. Therefore, it is necessary to change these point data into regional information. Undesirable distribution of weather stations and their data deficiency hinder the direct determination of the regional information, unless sufficient data in the study area could be provided. Providing extra data using the geostatistical methods is practical, scientific, simple and quick, but adopting a suitable method is the basic question. The objective of the present study is to find a suitable method to estimate monthly rainfall in the central region of Iran. In this regard, the methods of kriging (ordinary kriging, log-kriging, co-kriging), weighted moving average (WMA, with the power of 1 to 5), thin plate smoothing splines (TPSS, with the power of 2 and 3 and with covariable) were used. Cross validation technique was used to compare these methods. Based on the variography analysis, the range of influence of monthly rainfall in the central region is about 450 km. The results show that TPSS, with the power of 2 and with elevation as a covariable, was the most accurate method to estimate monthly rainfall. In addition, it is preferable to use the selected interpolation method in the sub-basins with homogeneous climates instead of considering the whole region.

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The study of geographical extent of precipitation pattern is important because of its impact on agriculture, water resources, tourism, industry, dams, and irrigation. The principal component analysis (PCA), as an elegant mathematical tool, was applied for the regionalization of winter precipitation in central south Iran (Fars, Boushehr, and Kohgiloye and Boyerahmad Provinces). Averaging monthly rainfall data of Dey, Bahman and Esfand (20 December to 20 March) produced the time series of winter rainfall. In each individual station, correlation matrix of the normalized data was then performed for the computation of the standard PCA. Eigenvalues, eigenvectors, PC time series and the loading of the principal components were then computed. The Screet test technique was applied as a trial for addressing the problem of determining the number of PC modes that should be retained. Two of the first PCs, which account for 68.1% of total variance in the rainfall data, were kept and used for the regionalization of rainfall data. The rotation solution was then selected as a suitable tool for delineating the rainfall region associated with the retained PCs. The results indicated that for the first PC, loading became high over most part of the study area. Therefore, the time series of PC1 that accounts for about 60.4% of the variance in raw data, could be used as the regional time series of winter rainfall over most parts of the provinces studied. The second PC revealed a high loading over a small area in northern part of the regions studied (Bavanat in Fars Province). Rainfall in this station showed poor correlation with the precipitation over the neighboring station in Fars Province. It seems that the rainfall in Bavanat is mostly influenced by the Mediterranean air masses entering the area through the northern and western districts. For the other parts of the regions studied, Sudan current which encroaches the country through southwestern borders (Persian Gulf regions) make up an essential portion of winter rainfall.

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An intense storm occurred on June 6, 1992 in Mashhad city and its suburb, which caused a flood in the residential areas of Najafi, Chahar-cheshmeh and Nodareh. As a consequence of the abnormally huge flood, 25 people lost their lives and many structures were destroyed. Three recording rain gages located in Mashhad city (airport, College of Agriculture, and Water Authority) and one in the suburbs (Toroq dam) recorded the temporal distribution of the storm. Twenty five other nearby rain gages in the region also recorded the event. The rainfall hyetographs and rainfall amounts were analyzed. The results of this survey showed that: a) maximum rainfall intensity over a 15-minute duration in the College of Agriculture station, one of the centers of the storm, was 112 mm/hr, b) temporal distribution of rainfall for all recording stations were nearly the same, c) these patterns were in close agreement with all historic storms, and d) a return period of 380 years was implied via comparing the maximum rainfall intensity with relevant IDF curves. As a consequence, although an intense storm had happened, it was simply an outlier.

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Soil erodibility in arid regions, particularly in less developed soils, greatly depends on parent material. The objectives of this study included comparison of the potential of runoff and sediment production in soils with different parent materials and identification of the highly sensitive parent materials in Golabad watershed, 60 km northeast of Isfahan, with about 160 mm of annual precipitation and various geological formations, as one of the highly erodible watersheds in Iran. Soils formed on twelve different parent materials were selected. Rainfall simulator was run for 80 minutes on three replicates of each soil. To have an idea about the rate of runoff and sediment generation with time, runoff loaded with sediment was collected every 10 minutes using plastic containers. After measuring the volume of each runoff sample, it was dried and the amount of sediment was measured. The mechanical parameters of the applied rain were: intensity about 40 mm/hr, rain drop average diameter: 6.56 mm plot size: 1 m² and kinetic energy of 13.7-17.2 J/m².mm. Based on the rainfall simulation experiments, soils formed on green andesite and slightly dissected alluvium derived from both sedimentary and igneous rocks created the highest amount of runoff. They also created runoff much more rapidly as compared to other soils. In contrast, soils developed on granodiorite and moderately undulating alluvium produced the least volume of runoff. Furthermore, maximum quantity of sediment was produced from the soils occurring on green andesite and shale. The least sediment yield was observed in soils developed on granodirite and moderately undulating alluvium. Soils formed on shale created the highest sediment concentration and no significant differences were observed among other soils. Based on the results obtained, soils were ranked according to sensitivity to erosion. It is concluded that soil parent materials have a high influence on the production of runoff and sediment yield in Golabad watershed.

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Rainfall intensity with different frequencies is needed for many hydrologic models. Rainfall intensity–duration–frequency relationships (IDF) have been investigated for different regions using previously recorded data. Iran Meteorological Organization has prepared IDF curves for 66 stations in Iran since which are the most updated data. Comprehensive IDF relationships were developed for these data. The results showed remarkable changes when compared with previous studies of Iran. This may be due to a change in parameters of probability distribution function as a result of increased record length of stations. Some relationships were also developed for ten year-hourly ( P₆₀¹⁰) rainfall estimation from some parameters such as average annual rainfall and average maximum daily rainfall. Such a relationship was made for all stations and also for different classifications of regions (based on average annual rainfall and apparent climatically divisions). The validity of all relations was analyzed for eight independent stations with suitable spatial distribution. It was shown that all relationships are nearly valid as far as a suitable region is choosen. The previous relationship for Iran is not useful at present.

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Orumiyeh Lake basin is one of the important regions in Iran from water resources and environment standpoints. In this basin, substantial part of the annual precipitation occurrs in spring, winter, and fall seasons. Due to semi-arid climate of the basin, rainfall forecasting is an important issue for proper water resources planning and management, particularly in drought years. On the other hand, investigations around the world show that there is a good conection between climatic signals and the amount of precipitation. In this paper, the relationship between climatic signals and seasonal rainfall was investigated in Orumiyeh Lake basin. For this purpose, monthly SPI (Standard Precipitation Index) was calculated and used along with six climatic signals including SOI (Southern Oscillation Index), PDO (Pacific Decadal Oscillation), PNA (Pacific North America), NAO (North Atlantic Oscillation), NINO3.4 and, NOI (North Oscillation Index). A new method employing the negative and positive phases of signals was proposed and tested to distinguish the relationship between the climatic signals and the individual stations rainfall in the basin. Furthermore, it was found that using joint signals substantially improves the precision of the forcast rainfalls. The results showed that fall and winter rainfalls had the highest correlatetions with SOI and NAO, respectively. Therefore, it would be possible to forecast the basin rainfall using climatic signals of the previous seasons.

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The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R²) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.

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Improper use of natural resources, especially soil, causes its degradation and severe soil erosion. Water erosion is an important factor causing soil degradation. Land use change of pasture would result in severe soil erosion mainly due to the reduction of vegetation cover and also surface soil disturbance. The objectives of this study were to estimate the amount of sediment, runoff and nutrient loss in four different land uses including a pasture with good vegetation cover (> 20%), a pasture with poor vegetation cover (< 10%), a currently being used dryland farm and a degraded dryland farm which is not used. Soil samples were taken from the depth of 0–10 cm in a completely randomized design with four replications. A rainfall simulator was run for two hours to estimate the amount of sediment, runoff and nutrient loss. Organic matter, total N, available P and distribution of particles size in soil and sediment were measured. The results showed that a very high degradation has occurred in the area mostly due to water erosion created as a result of overgrazing in pasture, susceptibility of geological formations and more importantly, the change of land use pasture to inefficient dryland farming. Maximum and minimum runoff was observed in the abandoned dry landfarm and pasture with good vegetation cover, respectively. Maximum sediment content was observed in dryland farm. Sediment content in dryland farm, abandoned dry landfarm and pasture with poor vegetation cover were 54.5, 21 and 10.4 times more than that in the pasture with good vegetation cover, respectively. Enrichment ratio (ER) of soil particles in sediment was highest for fine silt (2-5µm), followed by clay. A minimum of ER was obtained for sand fraction. Percentages of organic matter, total N and available P in sediment were higher in the first hour as compared to the second one. This is mainly due to the fact that fine particles are removed at the beginnings of the rainfall event. Total removal of these chemical factors was highest in dryland, intermediate in pasture with poor vegetation cover and abandoned dryland and lowest in pasture with good vegetation cover. In general, cultivation and disturbance of the pasture in the area land have caused a great decrease in soil quality and made the surface very sensitive to erosion.

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Climate change has important effects on earth environment and human life. Therefor, investigation and study of climate change is very essential. This study investigated rainfall, temperature, relative humidity and wind variability by analyzing data for annual and monthly climatic factors collected at 13 synoptic stations (industrial cities of Iran) by using Mann-Kendall test. The results of monthly rainfall trends showed that most of synoptic stations have significant positive and negative trends in winter and spring months. About 23% and 1.7% of stations have significant negative and positive trends, respectively, in annual trend of this factor. The results of monthly number of rainy days showed the major number of significant trends occurs in spring. In autumn (September, October and November) like as summer most of the stations have no significant trends. Analyzing the annual number of rainy days trends also showed that 4 stations have significant positive trends and 2 stations negative trends. Trend of greatest daily precipitation is low throughout the year, so there is not any significant trend in winter. Annual investigations confirm the seasonal investigations. The major number of significant trends in monthly mean maximum temperature occurs in summer but there are not any significant trends in winter and March. The trend of mean minimum temperature is approximately high in all of the seasons and the major number of significant trends occurs in summer and autumn and then in spring and ultimately in winter. In annual investigation, most of the stations showed positive trends and only Oroomieh station has negative trends. Trend of mean temperature is high except for winter. Most of the stations showed positive trend, indicating increasing trends in this factor. Annual studies vertify the positive trends and about 63% of stations have significant positive trends.

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Rainfall erosivity force as on important factor in soil erosion and sediment yield has been introduced in different indexes. The objective of this study was to determine suitable rainfall erosivity indices for two climates of semi-arid in Maravetape and very humid in Sangdeh, both in Khazar watershed, by correlation between rainfall erosivity indices and sediment outflow from erosion plots. For this purpose, the rainfall intensities in different time steps and the amount of rainfalls of 12 events in Maravetape and 11 events in Sangdeh have been used. Twonty five rainfall erosivity indexes were calculated based on rainfall intensity. The amount of soil loss measured after each rainfall event in 1.8×22.1 m2 erosion plots. The results of the study revealed that in very humid climate of Sangdeh and in semi-arid climate of Maravetape had high correlation of 0.803 and 0.727 (at the level of 99 percent) with sediment yield and they were applied indices in these climates of Khazar watershed. In general, the groups of 10 and 30 minutes are better than other erosivity indices in the study areas.

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Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.

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Soil erosion is one of the most important factors affecting soil quantity and quality and is environmental problems in developing countries like Iran. It can have deteriorating effects on ecosystems. This research was carried out in farm lands of the Chehel-Chai watershed, Golestan province to investigate the effect of soil properties on runoff and soil loss. Runoff and soil loss were measured in a completely randomized design in 36 plots with 10×10 m sizes in farm lands. For this reason, this study was conducted using rainfall simulator with 2 mm/min intensity and 15 min duration in 4 replicates. Soil samples were also taken in each plot. Sampling was conducted in October 2009. Results of the Pearson correlation showed that among soil properties, the contents of the lime, silt and fine sand had positive correlations with runoff at 1% confidence level. Also, soil surface resistance at 1% confidence level, the contents of the organic matter and nitrogen at 5% confidence level had negative correlations with soil loss. Finally, the results of multiple linear models showed that the content of lime is effective in estimating runoff and soil surface resistance, and organic matter is effective in estimating soil loss.

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In this study, the influence of spatial heterogeneity of rainfall on flood hydrograph prediction in three mountainous catchments in south west of Iran was studied. Two interpolation techniques including Thiessen polygons method and Inverse Distance Weighting method were applied to compare the rainfall patterns of surrounding rain-gages in hydrograph simulation with rainfall patterns of nearest rain-gage from the catchment outlet. It was found that the best simulated hydrograph is obtained from rainfall pattern of the nearest rain gage. Moreover, the results did not show any relationship between spatial variation of rainfall and outlet hydrograph. Formation of different local rainfall patterns due to non-stationary rainfall field provoked by irregular topography and their effect on interpolation procedure caused important biases in interpolated rainfall hyetographs obtained by Thiessen and IDW methods. It seems that the observed biases in the response of the catchments are the result of inaccurate representation of spatially averaged rainfall rather than its spatial variability. Hence, in mountainous catchments with irregular topography, the lack of sufficient records caused by poor rain gage arrangement can be highlighted as the dominant source of uncertainty in modeling the spatial variations of rainfall.

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The main scope of this research is evaluation of Soil Conservation Service Procedure in derivation of initial abstraction of precipitation in watershed scale. For this purpose Dalaki watershed which is located in south east of Iran was selected then by using hec-hms and GIS models and a number of observed rainfall runoff events some parameters like CN of watershed ,K and X of Muskingam method and initial abstraction of precipitation were calibrated through two different search algorithm of univariate and Nelder & Mead methods. The early results of this research indicated the superiority of Univariate search algorithm over the Nelder&Mead method both in calibration and also validation processes. Then using calibrated CN and Initial abstraction parameters which were derived through Univariate search algorithm, the factor between initial abstraction and potential retention of surface runoff (S) in each of sub basins were estimated. 0.13, 0.43 and 0.19 were derived as the above mentioned factor respectively for Minimum, Maximum and mean of the above mentioned factor in this step of the research which showed an acceptable compatibility to the offered factor of 0.2 by SCS. Then in rainfall runoff modeling process of this watershed SCS offers a reliable method of initial abstraction estimation.

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In this research, sediment production and delivery amount by Darabkola forest roads was estimated using the SEDMODL model. To evaluate the model results, the sedimentation rate in the above roads was directly measured using rainfall simulator. Also, the paired t-test, BIAS, RE and RMSE were used to assess the results. The analysis showed that the rate of sediment production from study roads' surface using the SEDMODL model and direct measurement under the rainfall simulation were 420.97 and 341.19 tons per year, respectively, and rate of sediment delivered to the stream with sediment delivery ratios of 42% and 51%, respectively, was about 177.58 and 174.02 tons per year. Also, results of the statistical methods of BIAS, RE and RMSE for the aforesaid model were 0.04, 17.59 and 0.71, respectively, and at 95% confidence level, no significant difference was obtained between the observed and estimated data. Therefore, the aforesaid model has the appropriate accuracy and efficiency to estimate the sedimentation rate of the Darabkola forest roads. It was also found that from among the input parameters of model, longitudinal slope of road, precipitation and sediment delivery factors were the most influential factors in the sediment production and transport, respectively.

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Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.

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Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO₃; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.

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Knowledge of variation in soil properties from each event to another is very important for the determination of critical periods during which soil is susceptible to erosion processes. This study was carried out to investigate soil loss in sequential rainfall events in Zanjan Province. Toward this, ten soil textures samples were taken and were transported to small plots (60 cm×80cm) with 20-cm depth) on a 8% slope land at three replications. The plots were exposed to ten simulated rainfalls with an intensity of 55 mm h^-1 for 30-min and 5-day intervals. A total of 300 simulated rainfall trials were carried out at the plots.  Results indicated that soil moisture, runoff production and soil loss were significantly affected by rainfall events (P< 0.001). Increasing soil moisture and consequently decreasing soil infiltration capacity were the most crucial element in increasing runoff production and soil loss in the sequential rainfall events, in a way that about 84% of soil loss variation in the rainfall events could be explained based on antecedent soil moisture. After the fifths rainfall event, no significant differences  was found in soil infiltration capacity as well as runoff production because of soil moisture reaching to the water-holding capacity. Nevertheless, an increasing trend was observed in soil loss after fifth event which could associate with presence of more erodible soil particles on the surface and consequently increasing the concentration of surface flows.