جستجوی مقالات مرتبط با کلیدواژه « حوضه کسیلیان » در نشریات گروه « کشاورزی »

Soil erosion is one of the most important problems in natural resources management, especially on the e rangeland hillslopes. Further, soil erosion estimation using field measurement is expensive and time-consuming. Therefore, models can be efficient tool for performing an exact estimation in a short time and a low cost. The aim f this study is to present a methodology to estimate soil erosion on the rangeland hillslopes.

 In this study, the annual rates of soil erosion have been studied using erosion pins on the rangeland hillslope in the of Kasilian watershed in Mazandaran Province. Annual soil erosion rates were measured using 109 erosion pins (one year after the its establishment) due to changes in soil surface and soil specific gravity. An artificial neural network (ANN) was used in NeuroSolutions software. Soil erosion rates were as the model output and the affecting factors of soil erosion were the inputs. The model inputs rangeland cover percentage, land slope, slope length, slope shape (land curvature) and soil texture (sand, clay and silt percentage). The modeling process was performed using the MLP network. All of the data were separated into three classes included training (65% data), cross-validation (10%), and test stage (25% data). The model was performed and optimized. Further, geographic information system (GIS) was used for mapping soil erosion rates based on the simulated erosion values.

The results of the test stage proved the high performance of the ANN in estimating soil erosion (Rsqr = 0.9). Further, statistical analysis using SPSS software  and the optimum structure of the network and sensitivity analysis showed that the most important factors of soil erosion are vegetation cover, slope shape, land slope, slope length and soil characteristics, respectively. Finally, the optimized network inputs were combined in a GIS environment with a pixel size of ten meters, and annual soil erosion map was generated by coupling the capabilities of ANN and GIS on the studied rangelands.

The proposed methodology can be used as an efficient and alternative method for field measurements of soil erosion in the highlands with a high performance.

Flood is one of the deadliest natural disasters in the world, which causes enormous sufferings to human societies and changes in environment. In recent years, flooding has become a growing concern for both communities and governments across several parts of the world. This problem, has made the needs for the experienced managers and flood engineers to reduce the flood impact on the society and environment. Flood forecasting is one of the non-structural flood risk management method which gives valuable information for flood alert services and people and various organizations will have enough time to prepare flood control measures in emergency situations. In this study standardized precipitation index (SPI) and standardized precipitation-evapotranspiration index (SPEI) in Darzikala and Sangadeh weather stations located in Kasilian basin, Mazandaran province were calculated using R codes to determine the wet period. In order to explore the efficiency of SPI and SPEI in flood detection, the results were compared with river discharge time series in Valikbon gage station. To compare the results of SPI and SPEI indices, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were applied. The results showed that maximum and minimum POD for SPI and SPEI were 0.89 and 0.68 respectively. These values for CSI were 0.71 and 0.61 and for FAR parameter were 0.32 and 0.11 respectively. The trend analysis of mentioned indices showed that the SPEI index was relatively better than SPI due to the use of temperature data and hence the calculation of evapotranspiration, especially at the end of spring and summer in the Kasilian basin which has rainfall–snow regime Based on the POD, FAR, and CSI statistics, it is suggested that the application of SPEI index is more suitable for flood forecasting in rainfall-snow regime due to the use of temperature data for snow melt, especially in spring and summer. The SPI index is also an important indicator because of its ability in providing desirable results in flood forecasting with only one parameter and also due to this fact that the rainfall is the only accurate data with appropriate historical period in all watersheds, and at least two organs in the country are collecting information about it. Based on the POD, FAR, and CSI statistics, it is suggested that the application of SPEI index is more suitable for flood forecasting in rainfall-snow regime due to the use of temperature data for snow melt, especially in spring and summer. The SPI index is also an important indicator because of its ability in providing desirable results in flood forecasting with only one parameter and also due to this fact that the rainfall is the only accurate data with appropriate historical period in all watersheds, and at least two organs in the country are collecting information about it.

Rainfall-runoff simulation models can be used in many water resources applications such as flood control, drought management. Although modeling is both continuous and single-event, continuous modeling has been less important in our country. In continuous models, more hydrological parameters are involved in comparison with single-event models, although this leads to more complicated modeling, but instead of a more realistic conditions of the hydrological system of the watershed will be illustrated and, in continuous systems, the surface water status can be monitored over a long period of time. Single-event models simulate only one incident, hence the moisture content between rainfall events is not considered, in contrast to continuous models of longer periods for estimating the response of the hydrologic information of the basin considered throughout the length of the rainfall events and between them (Lastoria, 2008). The American Hydrological Engineers Center (HEC), along with continuous hydrologic modeling, added the Soil Moisture Accounting (SMA) soil moisture content algorithm based on the PRMS model to the HMS software (Bennett, 1998). In this research, the aim is to provide an automatic calibration model based on the anion colony for the HMS-SMA soil moisture model. In this continuous model, the multiplicity of the considered parameters of the model, in addition to causing the difficulty of calibration by the method of trial and error, which also allows the automatic calibration of the software package to fail. For this purpose, in this research, by selecting a continuous HMS-SMA rainfall-runoff model, an external optimization program (Anion Cluster Algorithm (ACOR)) was used to overcome the weakness.

HEC-HMS and Artificial Neural Network (ANN) were applied to simulate the rainfall-runoff process in the Kasilian basin. The study site is located at north of Iran with an area of 68 km2. The ANN has high capability in establishing connection between input and output data and the HEC-HMS model has shown its capability in optimizing simulated hydrographs. Initial loss is a quantitative parameter which is dependent on three main factors including soil، vegetation and Antecedent Moisture Conditions (AMC). In this study after optimizing the initial loss parameter using the HEC-HMS model، this parameter along with incremental rainfall were applied qua inputs in the ANN to simulate runoff or discharge values. Comparison of the obtained results under two scenarios of optimized and non optimized initial loss showed that optimized initial loss highly enhance the accuracy of simulated runoff and flood hydrograph by 100% in some events.

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.