مجله مهندسی و مدیریت آبخیز
سال ششم شماره 2 (تابستان 1393)

Evapotranspiration is one of the most important parameters for designing irrigation systems and estimating crop water use. Joining the models of reference evapotranspiration with the data derived from satellite images provides the opportunity for studying spatial variations of this parameter in extensive levels. In this study, four empirical models were evaluated including Hargreaves-Samani, Blaney–Criddle, Turc, and Linacre, which are dependent on temperature data for estimating reference evapotranspiration. Daily LST data obtained from MOD11A1 and MYD11A1 of MODIS were used rather than temperature data in these models. The minimum and maximum daily temperature, relative humidity, daylight hours, and wind speed were collected from weather stations located in five units of sugarcane farms (Shoaybie- SH, AmirKabir-AM, Farabi-FA, Khazaie-KH, and Ghazali-GH) in order to estimate reference evapotranspiration by Fao-Penman-Monteith equation as the target output. Calibration was done by the data of Shoaybie, Amir Kabir and, Farabi stations while the data of Khazaie and Ghazali stations were used for verification. This research showed that estimation of reference evapotranspiration can accurately be performed by LST data of Modis (MOD11A1 and MYD11A1) without direct measurements. Besides, the results revealed that use of LST data of MOD11A1 can lead to more accurate result for verification than MYD11A1. This outcome was resulted by comparing the precision measures of reference evapotranspiration based on the type of used thermal product as an input. Furthermore, comparing the precision measure of the models imply that operating the Turc model based on LST data of MOD11A1 can have more accurate results than other models (RSME and R2 are 1.3 mm.d-1 and 0.86, respectively).

The establishment and development of fast-growing trees with effective root systems and avoidance of deforestation are effective ways for slope stability and to reduce landslides. Soil reinforcement and reduction of erosion by tree root systems are influenced by physiological and ecological characteristics of roots. In this research, root reinforcement of Diospyros lotus, one of dominant species in the forests of northern Iran, was investigated by reinforced soil sampling and laboratory testing. Based on the results, shear strength of reinforced soil is related to two factors of root density and diameter. Evaluating test results by RDR and RDDI indexes showed that soil cohesion and shear strength of reinforced soil by Diospyros lotus roots increased by 207 and 350 percent respectively compared to unreinforced soil and this process has an effective role in Soil reinforcement and massive erosion control, especially in shallow landslides.

In this research, two small sub-basins were selected in Darjazin Watershed of Semnan to evaluate the effect of ruck check dams on peak and volume of flood in small watersheds. Two watersheds were chosen as study area in a part of southern Alborz Mountain with arid and semi-arid climate. These watersheds are located in upland of Mahdishahr city and many check dams are constructed for flood control in past few years. All check dams were examined during field operation and their sedimentation were assessed. Results showed that all check dam reservoirs were filled by sediment. In this research, the peak and volume of floods were assessed in three scenarios of 1) watersheds with no check dams 2) watersheds with empty of sediment check dam reservoirs and 3) watersheds with full of sediment check dam reservoirs. HEC-HMS model was used for rainfall-runoff simulation.. Mean comparison test by paired method for all three scenarios showed that flood peak and volume have significant difference at α=1% level. But, flood peak and volume in scenario 2 are computed less than that of scenario 1 and in scenario 3 are computed more than that of scenario 2. On the other hand, flood peak and volume in the scenario 3 slightly decreased by 4.8 and 5.7 percent respectively compared to scenario 1 where is not significant based on the objectives of project.

Reflectance spectroscopy can be used to study agricultural and environmental aspect of soil that are sensitive to soil organic and inorganic compounds. Despite the extensive studies in the field of visible-near infrared reflectance spectroscopy, there are rare researches in gypseous and calcareous soils. The objective of this study was to obtain a model that can predict chemical properties of gypseous soils via reflectance spectroscopy methods. Soil samples were collected from 102 locations in five different provinces in 0-30 cm of depth. Some chemical properties of soils, such amount of gypsum, equivalent calcite, cation exchange capacity, pH, EC, exchangeable calcium, magnesium, sodium and potassium and amount of silt, clay and sand were measured by standard methods in the laboratory. Air-dried soil samples were scanned at one nm resolution from 350 to 2500 nm, and calibrations between properties and reflectance spectra were developed using cross-validation under Partial Least Squares Regression (PLSR) and Boosted Regression Trees (BRT). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30 percent of the full data, which were each used for calibration and validation. Strongest correlations were obtained with gypsum, equivalent calcium carbonate, cation exchange capacity, exchangeable Ca and Mg, organic matter, sand and clay contents. Overall, BRT provided better predictions when under cross-validation. However, PLSR and BRT results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties and showed its potential for substituting laboratory analyses or providing inexpensive co-variable data in environmental studies.

The use of groundwater is one of the ways to overcome the seasonal shortages of water.Underground dam that can be noted as one of the techniques to help the improvement of water resources management and increase the efficiency of these resources, is one of the water storage practices to use the groundwater.. It is a simple method for the collection and storage of water in arid and semi-arid areas. In this study, selection of suitable areas of underground dam in Gharasou watershed is investigated. Many factors are influenced in determining the suitable areas of underground dam. In this research, physiography, geology, hydrology, hydrogeology and land use of the watershed were investigated and analyzed with the use of information, maps, reports, required data and geographic information system. Then, derived maps from different stages were combined and finally suitable areas and streams for construction of underground dam were mapped. Results showed that about 30 percent of Gharasou watershed is suitable for construction of underground dam.

Increasing Green House Gases (GHG) may change the climate in different areas. Investigation of parameters are difficult due to induced changes in climate parameters, such as precipitation and temperature. For predicting global climate change, different climate scenarios are defined, using AOGCM models. AOGCMs are able to simulate global atmospheric circulation patterns. However, the spatial resolutions of such models are coarse; for example HadCM3 has spatial resolutions of 3.75 and 2.5 in longitude and latitude, respectively. Therefore, to study climate change in a given area, the outputs of the used AOGCMs must be downscaled properly. For this reason, statistical and dynamical methods have been developed. Statistical methods establish a relationship between AOGCM outputs and climate parameters such as precipitation and temperature. For example, many statistical methods use multiple regressions to predict future climate parameters. However, the accuracy of downscaling procedure varies depending on the geographical position of the studied station in relative to the nearby AOGCM grids. In this research, the accuracy of SDSM was tested in different synoptic stations of northwest Iran. This area has a complex topography and climate due to intrusion of different rain bearing weather systems to the region. First of all, daily climate data (precipitation, maximum and minimum temperature) were collected and their time series created. HadCM3 data for the girds over the studied area was obtained and SDSM model was applied for each climate parameters of all synoptic stations in the region. Then, the difference between the SDSM outputs and observed parameters were evaluated for all the stations and the performance of the downscaled outputs were evaluated by comparing the mean and variance of the model outputs and those of the NCEP/NCAR for the present climate. The morpho-climatic parameters were derived for each station and their relations with the magnitude of the model error were evaluated. Results showed that the error in precipitation has significant relation with the distance to the grid center, whereas the error in maximum temperature is related to the difference between the elevation of a given station and the mean elevation of the HadCM3 grids. For example, in Urmia station, the error is the highest of 104 mm while in Saqez the error is the lowest of 9.4 mm. Also, the maximum temperature accuracy in stations with elevation near to mean elevation of the grid is higher. Pars Abad station with 32 m elevation and with high elevation difference with the grid mean elevation, showed 1.14 ºC of error and Tabriz station with less elevation difference to grid mean elevation, showed 0.0.08 ºC of error.

Various models have been used for snow melt computation. From which degree-day model is one of the most common one that estimates snow melt based on one of temperature’s parameters. In this model, in addition to temperature, degree-day factor is used by calibration in each region and derivation of model factor based on gained data with correlation. This investigation was executed in Chari region in Chaharmahal va Bakhtiari province as "Snow gauge representative area” in Central Zagross. Seven sites were determined for snow operations and three indexes were installed with rectangular triangle arrangement with 20 m length in the sides in each site. Scale-density methods were used to determine the amount of snowmelt. The average daily temperature in each site was calculated based on information of weather stations around the region and thermal gradient relations. From November 2005 to May 2011, snow depth and density were recorded weekly and its water equivalent was measured simultaneously. The amount of snow melt was calculated due to the depth of equivalent snow water of two consequent records (with no rain periods). Data from four different temperature index including 1) sum of the average temperature of the period, 2) sum of the positive mean temperature of the period, 3) mean of the average temperature of the period, and 4) mean of the average positive temperature of the period, were analyzed and degree–day factor (K) was calculated. The results showed that the highest correlation coefficient (r2=0.63) was related to correlation between the sum of positive temperatures of the period (2nd index) and snow melt. The K value was 4.2 mm per degree–day for the study area which shows the rate of four mm per day of snow melt for each degree of positive temperature. This result is similar to the previous studies and it is proposed for application in central Zagross zone.

There is a very close relationship between two distributions of climate and vegetation maps. But, the relation between climate and vegetation is not clear. Therefore, the main objective of this research is to determine the effect of main climatic factors on distribution of main vegetal species and its zonation in Iran. For this purpose, 51 synoptic stations with 30-year data (1976-2005) or more were selected and their meteorological data were extracted. Using principal components techniques, 269 ​​climatic variables were considered and the numbers of variables reduced to 14 factors with eigenvalues ​​more than one that represent 96.4% of the total variance. The first three factors (the thermal component, moisture and precipitation, respectively) had the most influence on the climate, so, they were considered for climate zonation criteria. These three components were rasterized within ArcGIS using Kriging interpolation method. Resulted raster map was used to create the climatic zonation map by unsupervised clustering technique. Finally, ecological climate zonationmap was derived using two climate parameters (temperature and humidity) and elevation map of country and by using unsupervised clustering technique. This map shows that the boundaries of ecological climate regions are mainly based on climatic (temperature and humidity) and landform factors (elevation). Another result of the research was that most ecological climatic zones were related to the low elevation areas of the country (below 1200 m) and the rate of high ecological climatic areas (above 2400 m) were very low.