مجله ترویج و توسعه آبخیزداری
پیاپی 11 (زمستان 1394)

Avalanche is one of the events snowy areas that if science does not deal with it possible dangers and problems caused irreparable. The main objective of this study was to evaluate the effect of 9 effective factor in avalanche slope, aspect, elevation, temperature, rainfall, land use, type Formation, type of soil and road and determine the importance of each of these parameters in falling avalanches and avalanche hazard zonation in each area of work using analytical Hierarchy Process Model (AHP) is. In order to study impact of the factors in the avalanche of software ARCGIS10.1 for mapping parameters of the avalanche and software Expert choice for the weighting factors were used in the avalanche. The results showed that most of the factors that affect the avalanche slope, aspect, elevation. And about 20.8% of the area is in danger of avalanche.

A lot of soil loss and sediment data have been measured at the end of erosion plots during recent decades under natural as well as simulated rainfall. As an important task, the rate of soil loss could be estimated using this kind of data. To achieve reliable average erosion, several years of record are nessasary, which requires a lot of cost and time. In this article, the role of extreme events on plot soil loss and basin sediment load was explained by giving a few exmples from Iran and the world. Based on the reviewed refrences, two different approaches were found in order to reach reliable average erosion rate in the absence of adequate data. In the first approach, which is based on the greatest erosion/ sediment events, it had been realized that the summation of a few events are responsible for about 50 percent of soil loss and sediment yield produced. The second approach is based on the probabilitiies and could be categorized into two strategies including a) application of sampling designs in field sampling and b) measuring only the yield of events with predifined high return periods.

It is predicted that the water scarcity will rise in the coming years due to a population growth, increased pollution sources, climate change and occurrence of droughts can ultimately affect the amount of available water resources. A global cooperation and foresight and joint planning of water resources is necessary to resolve the water crisis. In recent years, development and designing new multidimensional and comprehensive indices seems to be the most important issue for assessment of the current condition and predicting the future trend of surface and ground water resources changes. Towards this attempt, the Water Poverty Index (WPI) is recommended as a multi-dimensional indicator to assess the availability of surface water resources. Some applications of the WPI is highlighted at different countries and also its calculation steps is demonstrated through different criteria. According to the results, the WPI is derived based on the weighted combination of five components in different studies. Main WPI components are: resources (availability, seasonal and inter-annual variability and water quality), access (accessibility of water for effective use), capacity (managing water resources based on socio-economic variables), use (level of water use by different sectors), and environment (environmental impact of water management on ecological integrity). According to multidimensional nature of Water Poverty Index along with considering all affecting factors on water scarcity and availability behind social and economic characteristics can be a useful tool in prioritizing critical areas can be considered as an effective step in optimal planning of water resources. Water Poverty Index is a combination of influencing factors on water stress and scarcity, which provide a basis to prioritize and specifying management options for different watersheds. It should be noted that determination and analysis of water scarcity in different areas is depend on the condition of water resources, data availability and the employed sub-criteria in the calculation of water poverty index.

Direct measurement of soil loss especially in hillslope and watershed scales is expensive and difficult thus soil erosion models are widely used and the new models with more accuracy and advantages are developed day by day. G2 is a new USLE-based soil loss model with the ability to predict soil loss in monthly, seasonal and annual timescales and provide the spatial soil loss map in watershed scale. The input data in G2 model includes meteorological and pedological data and ASTER, SPOT and MERIS satellite images. The results of G2 in Southeast Europe (Greece and Bulgaria) with Mediterranean and semi-humid climate showed that this model has the acceptable accuracy to predict monthly soil loss. The main characteristics of G2 model are simplicity, being realistic and data-driven. It’s recommended to use G2 model to prepare the spatiotemporal soil loss map in some representative watersheds of Iran, because of the availability of data and satellite images.

The forest roads supply accesses to various points in forest in order to timber transportation in forest plans, Silviculture operations, Training and other using of forest. Road construction made a surface without vegetation cover that lead to creation of erosion and sediment in forest area. In order to estimating of erosion and sediment in forest roads, a lot of models has been designed and introduced. This paper investigated of abilities and limitations of estimation model of forest roads erosion soil that has been designed. First the models have been divided in two physical and empirical, then Characteristics data and usable area for each models has been explained. The physical models were explained include WWEP, KINEROS2 and empirical model include ROADMOD¡ STJ-ERO, SEDMODL ¡SEDMODL2, WARSEM, FORECALT and CULSED. Eventually ability and limitation of each model was presented and discussed. Result of this paper showed that number of parameters required, temporal scale, operational area, estimation spatial and type of estimation (erosion and sediment) are the important Characteristics to judging about models. The overall conclusion of this research showed the most fundamental limitations this models include assembling total practicable data unusable for various regions.

Leafy vegetables such as spinach or root crops like radish, sugar beet, potato, etc. pick out a significant amount lumps of soil (up to 20% of the product weight) when move out during the harvest practices. This kind of soil that removed from the farm can be called as soil erosion. This adhered soil from the field after harvesting of taproot and tuberous root plants enter to the processing cycle from transportation to marketing, thus more cost pay for the transport of throughput. On the other hand, this soil will be then converted into the municipal solid waste. In addition, more water should be spent for washing and in resulted there is more sewage due to removing of products soil in the consumption time. A good way to prevent these problems is to initial clean and package of agricultural products on the farm. Despite of many developments in the world, using post-harvest cleaning machines in the Iranian farm is in the early stages in which needs to more attention and promotion. Thus, the present article firstly describes the most important new sources of soil loss through product harvesting, and finally attempts to show that on-farm cleaning and packaging of the agricultural products can be prevented from the soil loss due to crop harvesting (SLCH) because of their implementation in the field. It can also be reduced the municipal wastes, decrease the transportation costs, protect the environment, and prevent from the wash water consumption and waste production in the next steps, including in homes.

A Management of water resources is essential in arid and semi-arid regions. Pre-knowledge about the amount of possible precipitation is important in planning water recourses, management of agriculture and droughts. Previous studies show that large-scale climate phenomena influence on the of climate and amount precipitation in different part of the world. In this study, first, among the 45 climate signals, 8 Index were selected as the most effective indicators; the total encompasses 81% of the variance in the principal component analysis (PCA) method. Subsequently, the correlation of large-scale climate signals in monthly Standard precipitation index (SPI) (one, 3, 6 and 12) of Maharlu-Bakhtegan basin simultaneously and the delay has been analyzed by using of cross correlation. Finally, multivariate regression equation was developed to predict. The results of cross correlation method showed that more of indices are significant with time lag with standardized precipitation index. Taylor diagram and error parameters showed that performance of regression equations for the scale of one month is better than the other scales.