Watershed Management Research
Year:2020 | Volume:33 | Issue:2 (127)|Papers Count:7

Water is a key element of land use planning and sustainable development, and recognizing and analyzing of water resources capacity is a prerequisite for land use planning. By analyzing the daily stream flow data collected at the hydrometric stations, 43 stations with the common period between 1976 and 2011 in the Province of Kerman were selected. The most important components of the surface water resources including base flow, flow duration curve (FDC) indices, flood discharges, runoff threshold, flood intensity, potential snowfall elevation, and the rainfall components were analysed. The average base flow index in the catchments of the province was 0. 61. About 78% of the catchments had a base flow index of more than 0. 50. The mean coefficient of variation of the FDC indices was 0. 11. Changes in the snow line potential in every catchment of the region followed the same trend. The runoff threshold range was 0 to 25 millimeters and a class of 0 to 5 with 44. 9% of the surface coverage was the most frequent, covering the north and northeast of the province. More than 50% of the province land is in the low flood intensity. About 50% of the catchments had more than 50 rainy days and 45% of the catchments were in the range of 200-300 millimetres of average annual precipitation. Results of the sustainable water access indices including base flow index, coefficient of variation of FDC indices and snow line potential showed proper capability of watersheds of the Province of Kerman to optimize the water-related projects and control the overexploitation of the groundwater resources in the arid regions.

Landslide is one of the most destructive types of erosion of slopes, causing substantial financial losses. Identification of causative factors in the landslide occurrence and providing a zoning map of the susceptible areas is one of the basic tools for minimizing the possible damages. In this research, the landslide susceptibility map in the Neyshabur Watershed was prepared using three algorithm including, Support Vector Machine, Maximum Entropy, and Random Forest Algorithm. A map of landslide distribution was prepared along with 12 thematic layers including slope, aspect, plan curvature, profile curvature, elevation, land use, geology, distance from the road, distance from the rivers, distance from the fault, topographic wetness index, and drainage density in the GIS environment. The landslide susceptibility map of the studied area was prepared using three methods of random forest algorithm, maximum entropy, and support vector machine algorithm, and using Receiver Operating Characteristics and 30% of unused landslide points in the modeling process. The results of an assessment of the models indicated that the accuracy of the estimated maps prepared by the Support Vector Machine, Maximum Entropy, and Random Forest Algorithm were 86, 75, and 82 percent, respectively. Therefore, it can be stated that the presented maps can play an important role in identifying the slide-prone areas as well as in the implementation of development plans, especially road construction in the studied area. Given the potential of tourism in the catchment area, it is necessary to pay attention to the landslide potential in the catchment.

The present study was conducted to predict land use change and its impact on runoff of the Bidgol Watershed in the Fars Province in 2032. The SWAT hydrological model was first calibrated using the hydrometeorological collected data during the 2004 to 2013 period based on the SUFI-2 algorithm and validated using the 2014 to 2018 data. The values of the statistical parameters of the Nash – Sutcliffe model efficiency coefficient (NS), coefficient of determination (R ), deflection amplification factor (P-factor) and the response modification factor (R-factor) at the calibration stage were 0. 74, 0. 77, 0. 8, 0. 83, respectively while for the validation stage, the obtained values were 0. 68, 0. 66, 0. 72 and 1. 3, respectively. Then Landsat satellite images of 2004 and 2018 were used and flowing the necessary steps, the images were classified into six main land use classes. Using the Markov Chain method and Cellular Automata based on the land use maps of 2004 and 2018, the land use forecasting map of 2032 was prepared. The land use maps were imported into the SWAT calibrated model and the impact of land use change from 2018 to 2032 on the basin runoff was predicted. The results showed that the highest change of land use will be related to the conversion of rangelands into agricultural land, and the highest percentage of change will be related to the residential land use. In response to these changes, the annual average runoff value of 2032 represents a 19% decrease as compared to 2 that of 2018.

Fires cause physical, chemical and biological changes in the soil such as porosity, specific gravity, infiltration rate, runoff, erosion, pH and nutrient cycling. The Eijkelkamp rainfall simulator and 0. 09 m2 plots were used to investigate the temporal variations of runoff, erosion, suspended load concentration and the amount of total nitrogen, total phosphorus, and extractable soil potassium, and finally, how the burned areas in the Zarivar Lake Watershed returned to the previous conditions from 2014 to 2017 were analyzed. Results at the 95% confidence level showed a two-fold increase in the runoff in the short term, and a decreasing trend in the long term. The suspended load removed from the plots increased threefold in the short term. The fire also significantly increased total nitrogen, total phosphorus, and extractable potassium in the first year by 70, 286 and 48%, respectively. But in the long term, only the extractable potassium returned to the normal condition. Therefore, it was found that, after four years of fire in the oak forests of the Zaribar Lake Watershed, except for the suspended load concentration and extractable potassium, the other variables did not return to the normal conditions.

Climate change is among the most important challenges affecting the natural ecosystems and various aspects of the human life. The global warming imposes serious impacts on the hydrology and water cycle in the nature, and quantitative evaluation of such impacts provides further preparedness for confronting their anticipated consequences. The so-called statistical downscaling model (SDSM) was used to forecast the trends of precipitation and temperature during the 2006 – 2100 period based on the CanESM2 large ensembles. The impact of climate change on hydrologic conditions on the Kan Watershed was evaluated using the SWAT and ANN models. The results indicated that an in-crease in precipitation and temperature are probable in the forecasted future period (2006 – 2100). In general, it can be stipulated that the temperature will rise by 0. 8 – 5. 6℃ and the precipitation will increase by 4 – 55%. Given its structure, the ANN exhibited a superior performance over the SWAT. The results of the runoff studies indicated that for the forecasted future period (2006 – 2100), the ANN model predicts 2% and 4% decrease under the RCP2. 6 and RCP8. 5 scenarios, respectively, and a 25% increase under the RCP4. 5 scenario. However, the SWAT model forecasted 42%, 43%, and 49% increase under the RCP2. 6, RCP4. 5, and RCP8. 5 scenarios, respectively. A 49% increase in the runoff to 200 m3/s will not only add to the suspended sediment load of the Kan River, but also will bury the Emamzadeh Davood, Rendan, Kiga, Sangan, Suleghan, and Keshar villages under sediment, but also will cause extensive financial and life damages.

Dependence of agriculture on groundwater in the Sarvestan Plain has necessitated the implementation of appropriate policies for sustainable utilization of this resource. An assessment of the quantity and quality of groundwater in the plain, and evaluating the effect of overdraft on them, is of paramount importance. The purpose of this research was to investigate the long-term impact of groundwater overdraft in the Sarvestan Plain on its quantity and quality. Water level and salinity data collected from 24 observation wells from 1991 to 2016, and from 24 operational wells from 1998 to 2015 were analyzed. Results indicated that water level in the long-term has dropped 47 cm/yr, which is slightly faster than the water level trend in the last five years (39 cm/yr), indicating a lower rate of water level drop in the recent years. Forecasting water level in 2030, based on the last five years trend, indicated that the drop in water level will be very high in areas such as Kohenjan, Dehnow and north of the city of Sarvestan, reaching a depth of more than 80 meters. The salinity has increased by an average rate of 100 micro Siemens per centimeter per year. Furthermore, the average water-level and salinity trends in the plain indicated that water salinity has increased with an increase in depth to groundwater; however, this has not been the case everywhere in the plain.

Infiltration is the most important process in soil hydrology. As the early Miocene Gachsaran Formation (GF), and the late Miocene to Pliocene Aghajari Formation (AJF) cover a substantial area in western and southwestern Iran, it is very desirable to investigate the effective factors that determine their infiltrability, particularly in different land uses. Therefore, two sub water-sheds of the Margha (AJF, 1609 ha) and the Gach Mountain (GF, 1202) in the vicinity of the City of Izeh, were selected. Infiltration rate (IR) was measured in seven plots on the AJF and in six plots on the GF, both with three replication (rangeland, farm field, residential areas). A comforts’ (? ) rainfall simulator, which delivered the intensities of 0. 75, 1. 00 and 1. 25 mm per minute was used on both formation. The 0-20 cm of soil was sampled at each plot on which the IR had been determined. Percentage of the very fine sand, sand, clay, silt, pH, EC, soil moisture, %CaCo3 and %OM were determined using common laboratory procedures. A multivariate regression was performed to identify the characteristics which affect the determined IR. It was observed that silt, very fine sand, EC, pH, %OM and %CaCo3 demonstrated the most important roles in the runoff production. However, the CaCo3 content of each formation had the highest positive role in the IR.