Rouzbeh Nazari

The Determination of Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics

The reference evapotranspiration (ET0) is an essential variable in the agrohydrological systems and its estimation on a regional scale is limited to its spatial variability. This study compares two approaches for preparation of spatial distribution maps of ET0 in Mazandaran province of Iran. In the first approach, ET0 was calculated using climatic data and Hargreaves-Samani equation in weather stations locations and then were interpolated. In the second approach, the components of the Hargreaves-Samani equation were interpolated and then ET0 maps were prepared by applying the Hargreaves-Samani equation and suitable commands in GIS. The 10-year climatic data for 51 weather stations (46 stations for preparing ET0 maps and 5 stations as validation station) were gathered over Mazandaran province. Semivariograms were calculated and the best semivariogram model was selected on the basis of the least value of Residual Sums of Squares (RSS). The spatial correlation of the data was compared on the basis of Nugget to Sill ratio. The data were interpolated using Ordinary Kriging method and the interpolation error was computed by cross validation technique based on Root Mean Square Standardized Error (RMSSE). The predicted ET0 values were compared to the computed ET0 in validation stations and sensitivity analysis was conducted. Results show the second approach had better spatial correlation and lower interpolation error and the difference between these two approaches were not significant. Therefore, the accuracy of the ET0 maps is more related to the method of computing ET0 than the type of climatic data is being interpolated.

Wind Speed Regionalization Under Climate Change Conditions

Green energy and renewable energy are one of the most essential fundamentals for future developments of countries all over the world. Running out of fossil fuels in near future, makes the use of renewable energies almost inevitable. Evaluation of the capability of using these types of energies is an essential issue at present. Extreme dependency of mankind to the limited energy resources is both dangers and unsustainable and finding a way out is one of the most important challenges that we face. Achieving some unlimited energy sources has been human’s dream. Daily increase of energy demands and limitation of fossil energy resources from one side, and increase of environmental pollutants caused by using these resources in the other side, has made application of renewable energies more essential and widespread. The wind, as one of the climatological factors has a wide effects on agriculture, transport, pollution, energy, manufacturing and industrial plans. Wind power is one of the first energy sources discovered by humans. It is applied for different purposes such as power source for ship movement, irrigation and milling in some countries such as Iran and China. However, construction of wind power plants would make this type of energy more applicable. Nowa‐ days, coal, oil and gas are the main resources to provide energy. In recent years, an increasing trend in price of the mentioned materials has become vivid due to the globalization and political events and mostly because of water crises. These increasing prices make us to find an appropriate solution to decrease the expenses and increasing the stability. Furthermore, renewable energies are considered as available, exchangeable and inexhaustible resources. New energies are available until these resources exist. Wind energy is used in two ways, first in direct way in which the wind is applied for drying and ventilation, and secondly, the wind is utilized indirectly for milling the grains, to pump water to the fields and to generate

Water Reuse and Sustainability

Water reuse simply is the use of reclaimed water for a direct beneficial purpose in various sectors from home to industry and agriculture. For a number of semi-arid regions and islands, water reuse provides a major portion of the irrigation water. In addition, the reuse of treated wastewater for irrigation and industrial purposes can be used as strategy to release freshwater for domestic use, and to improve the quality of river waters used for abstraction of drinking water. Specific water reuse applications meet the water quality objectives. Water quality standards and guidelines which are related to irrigation and industrial water reuse are described in this chapter. Other reuse consumptions such as urban, recreational and environmental are also discussed.

An application of fuzzy factor analysis for sustainable bridge maintenance and retrofit projects

Abstract Sustainability in bridge maintenance and retrofit projects is an essential factor to achieve sustainable infrastructure development. Many studies have discussed issues relating to sustainable design and construction of transportation infrastructure; however, no major study has been conducted to identify the most significant factors involved in sustainable bridge rehabilitation. The aim of this study is to address the lack of Critical Green Factors (CGFs) for bridge rehabilitation projects. An extensive review was carried out to explore and identify the factors that are appropriate for rehabilitation projects. The research data were collected from a questionnaire survey and discussions with bridge engineering experts. Because of the imprecision and vagueness in subjective responses, the fuzziness in data analysis was taken into account by applying fuzzy set theory. The identified factors were then classified applying factor analysis to reduce the green factors into smaller meaningful groups. Three cut-off values, λ = 0.85, 0.90, and 0.95, were tested which resulted in the identification of 120, 111 and 77 factors, respectively. Accordingly, 77 critical factors were selected and classified together as a cluster of similar items into 14 groups. The findings of the study are expressed as groups of weighted CGFs that can be applied in green bridges rating systems.

Development of a Mid-Infrared Sea and Lake Ice Index (MISI) Using the GOES Imager

An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 µm band, a requirement for NDSI; however, the newly proposed Mid-Infrared Sea and Lake Ice Index (MISI) incorporates the reflective component of the 3.9 µm or mid-infrared (MIR) band, which the GOES-13 imager does operate. Incorporating MISI into a sea or lake ice mapping algorithm allows for mapping of thin or broken ice with no snow cover (nilas, frazil ice) and thicker ice with snow cover to a degree of confidence that is comparable to other ice mapping products. The proposed index has been applied over the Great Lakes region and qualitatively compared to the Interactive Multi-sensor Snow and Ice Mapping System (IMS), the National Ice Center ice concentration maps and MODIS snow cover products. The application of MISI may open additional possibilities in climate research using historical GOES imagery. Furthermore, MISI may be used in addition to the current NDSI in ice identification to build more robust ice-mapping algorithms for the next generation GOES satellites.

Application of dynamic threshold in sea and lake ice mapping and monitoring

Ice detection and monitoring algorithms using visible and infrared images are generally founded on thresholds-based approaches. Classification of features over ice covered sea requires a series of reliable thresholds. The change in surface conditions throughout the season affects these thresholds and makes their adjustment necessary. This study proposes an operational method based on a set of dynamic thresholds for ice and water identification using data from a geostationary satellite. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the Meteosat Second Generation (MSG) satellite data has been used. The proposed approach has been tested and validated over the Caspian Sea. Visible, near infrared and thermal infrared channels are being used to automatically create a cloud mask for a single image. The dynamic threshold is being developed to clarify the misclassification of ice and water pixels. The constant and dynamic thresholds have been used in comparison and applied to classification model. Dynamic threshold is used with reflectance channels R01 (0.6µ) and R02 (0.8 µ) and the near infrared channel R03 (1.6µ).