IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING
Year:2008 | Volume:2 | Issue:4|Papers Count:8

In many hydrologic and agricultural researches, the amount of water requirement to raise a shallow water table to the soil surface, i.e., soil water storage capacity (SWSC), is important. In this research, the amount of SWSC was measured and estimated. Then, relationship between SWSC and groundwater depth were obtained as a polynomial function for Kuye-Asatid soil (silty loam) using theoretical and experimental methods. Also the effect of encapsulated air on SWSC is investigated in both methods. Four water table depths of 30, 60, 90 and 120 cm were simulated in laboratory, and soil water contents at different depths were measured and the amount of SWSC was calculated under different water table depths. Regression analysis was used to obtain the relationships between SWSC and water table depth for the experimental data. The amounts of soil water storage capacity in both experimental and theoretical methods are compared using linear regression. The values of the coefficient of determination R2 for cases encapsulated and without encapsulated air were 0.96 and 0.99, respectively. Further, the values of slope and intercept of linear relationship indicated that the data obtained with encapsulated air are close to the results of theoretical approach. The results show the amount of encapsulated air in soil water storage capacity is about 21% of soil porosity. Therefore, it should be considered in SWSC calculation. The proposed equation accommodates the reduction effect of the capillary fringe on SWSC. By statistical comparison the proposed function for case of encapsulated air is similar to theoretical equation, but in case of without encapsulated air condition, they are not similar.

Many empirical and indirect methods such as SCS have been developed for runoff prediction. The application of such model out of the area where they have been developed causes error and their calibration is therefore necessary. For this purpose, the storm-wise simultaneous rainfall and runoff data were collected for Amameh, Kasilian, Darjazin and Khanmirza watersheds, and associated runoff values were predicted and ultimately compared with observed ones. The results of application of un-calibrated model showed fair agreement between predicted and measurement runoff data. To improve the results, the maximum storage index coefficient as well as curve number were then calibrated for the areas and the comparison was made again. The final results of application of calibrated model verified inefficiency of model in runoff prediction for Kasilian and Darjazin watersheds whereas its performance was approved in case of Amameh and Khanmirza watersheds with regression coefficient of 0.92 and 0.91, respectively.

Erosivity defines power of erosive factors in detachment of soil particles. The study was conducted to determine the proper erosivity index based on USLE, MUSLE, RUSLE and USLE-M models. Field experiments were carried out in the dry-farming lands in a zone with 30 km in dimension, in Hashtroud located in north west of Iran from March 2005 to March 2007. In the study area, 36 agriculture lands were considered and three standard plots were installed in each land. Runoff and soil loss were measured under 41 natural rainfall events causing runoff during the two-year period. Erosivity index in the models was calculated based on rainfall and runoff data. Results indicated that runoff has the highest correlation with the erosivity index of the USLE-M with a determination coefficient of 99%. Based on the results, the erosivity index of the USLE model is the best appropriate erosivity index (R2=0.88, p<0.001) for predicting soil loss in the study area. To make an easy prediction of the soil loss in the studied semi-arid region, the kinetic energy index of the reinfall can be reliably used (R2=0.80, p<0.001).

In research or applied study projects related to watersheds, stream network data layers are one of the most important data used to calculate the parameters such as watershed physical parameters, runoff, sediment, erosion and etc. These digital spatial data layers prepared in different formats and scales often have deficiencies in their topology structures such as the disconnection of stream networks and inaccurate coincidence of watershed flow direction pattern with digital direction of the streams. These deficiencies create many problems in calculating watershed parameters whereas built-in facilities in current GIS softwares also have basic restrictions in resolving them. In the present research, in the GIS environment, a new model has been designed and developed to solve the GIS softwares problems in creating artificial stream networks (from point of view of the numbers and position of streams and also threshold definition). In addition to this, the developed model is capable of converting disconnected stream networks to connected ones. The number and position of new created streams are equal to their original hydrographic networks and water flow direction has been corrected based on watershed flow direction pattern. The extracted statistics results of the research show high accuracy of the model outputs.

Geostatistical analysis was applied for snow depth and density estimating. This research was conducted in Orazan sub-basin located in Taleghan watershed which comprises 2706 km2 in area. Field sampling was performed based on random-systematic method with random start in different heights during February 2008 and distances between samples was equally 100 m. Depth and density of snow was measured in every point. The experimental variogram was plotted by using 98 field sampling points. The experimental variogram of snow depth and density indicated that spherical model is the best one for snow depth and density. The nugget, sill and effective range of the spherical snow depth model are 0.0010, 0.05, and 186 m, respectively and those variables of the spherical snow density model are 0.0103, 0.0485, and 7968 m, respectively. The experimental variogram of snow depth revealed more than 80% sill, implying strong spatial auto-correlation between samples but the experimental variogram of snow density revealed more than 80% nugget effect, implying weak spatial auto-correlation between samples. Estimating of snow depth and density was performed by using ordinary kriging and spherical model. Cross validation results indicated that kriging could make a precise estimation of snow depth and density due to low variability of snow depth and density and the strong spatial structure of snow depth.

Precipitation is the main component input of watershed in flood, water balance and water resources computation. Any rainfall-runoff simulation of basin response by models needs the accurate amount of rainfall over the basin. Therefore the simulation of spatial distribution of rainfall is an important factor. Measuring rainfall in scattered point stations and generalizing it to arial extent often causes considerable errors. Such source of error is one the most important in both rainfall-runoff simulation and continues hydrologic process and water balance modeling. A number of methods of interpolation and extrapolation of rainfall data has been developed which have some limitations and need to be evaluated accurately for finding the most appropriate one based on specific local conditions. For the purpose of the research a wide range area in the south west of Iran with about 258552 km2 was selected and different methods of interpolation were evaluated in regional homogeneity analysis. The results show that delineation of regional homogenity partly improves the spatial distribution of rainfall. Another conclusion is that using estimated data for data compellation has not improved the overall results.

Rainfall is one of the meteorological parameters which plays an important role in developmental projects. Three, twenty seven, and three recording rain-gauge stations were selected for North, Razavi, and South Khorasan provinces, respectively. These stations had more than 8 years of record length, end to 2002. All of the recorded storms were digitized. Minimum and maximum storms were 182 and 525 for Sangan-Khaf and Mashhad stations, respectively. However, all storms were not recorded completely. Yet no acceptable method could be utilized for re-construction of such storms. Gumble distribution, among the common distributions, better fitted the rainfall intensities of all stations. Considering 10 year-hourly rainfall (p60.10) as a key point of rainfall intensity-duration-frequency (IDF), constant ratios were derived between rainfalls over the vast geographical territory of 3 provinces under study. Nonetheless, no significant relationship could be derived for (p60.10) from other easily-computed parameters. Therefore, isohyetal map for (p60.10) was plotted. From this map, one can interpolate for a corresponding value for any desired ungaged-point. It was shown that the bias for estimation of rainfall corresponding to any time duration and frequency for all stations, except one, was negligible.

Drought is the one of most important natural hazard which damages human social and natural environment every year. For reducing the damage of drought future condition of drought should be forecasted. In this study the application of time series and artificial neural network (PML) models in SPI value forecasting were compared. First of all, SPI 3,6,9 and 12 were calculated then, by using artificial neural network (PML) and time series models SPI values were forecasted. The results showed that time series model had better application than neural network and SPI 9, 12 were better forecasted than SPI 3,6.