Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran

MONITORING OF SNOW COVER VARIATION USING MODIS SNOW PRODUCT

Snow is one of the integral components of hydrological and climatic systems. Needless to say, snow cover areas (SCA) are considered as indispensable input of hydrological and general circulation models. Studying the spatial and temporal variability of SCA is of the paramount importance for tremendous variety of research such as climate change, water supply and properly managing water resources. In this study by means of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product, the variation of snow cover extent (SCE) in Karoun basin located in western part of Iran is evaluated for twelve years' duration; since 2000 to 2012. The results show that the paramount occurrence of SCE is observed during February months of 2003, 2010 and 2011 as well as during December months of 2006 and 2009.The utmost occurrence of SCE is considered during January months of the other remaining years. Annual average shows that SCE varies from 11% in 2011 to 22% in 2006. According to Mann-Kendal trend test, throughout twelve years; 2000 to 2012, a majority of the pixels in the study area have no considerable trend although there is a decreasing trend on a small portion of the pixels located in the eastern part the study domain

Application of Atmosphere-Land Surface Interaction System (ALSIS) scheme in simulating the streamflow with emphasis on the spatial heterogeneity of land surface in sub-grid scale (Case study: Karkheh river basin)

This study evaluates the application of Atmosphere-Land Surface Interaction System (ALSIS) scheme in simulating the streamflow in Karkheh river basin. The Climate Forecast System Reanalysis (CFSR) data for the period 1982-2011 are used as atmospheric forcing data and sub-grid scale heterogeneity of the land-surface is represented by soil-vegetation mosaics. The cascade of linear reservoirs model is used for modelling the base flow and a routing model, linked to the land surface scheme, is used for modelling river discharge. The comparison of simulated and observed streamflow in six hydrometric stations over Karkheh basin reveals the model ability in simulating the monthly streamflow. Moreover, the model has a good ability in simulating the monthly regime of water balance components, spatial distribution of long-term average of components and their relationships. <br /

A New Feature Selection Methodology for Environmental Modelling Support: The Case of Thessaloniki Air Quality

Part 2: Air and ClimateInternational audienceEnvironmental systems status is described via a (usually big) set of parameters. Therefore, relevant models employ a large feature space, thus making feature selection a necessity towards better modelling results. Many methods have been used in order to reduce the number of features, while safeguarding environmental model performance and resulting to low computational time. In this study, a new feature selection methodology is presented, making use of the Self Organizing Maps (SOM) method. SOM visualization values are used as a similarity measure between the parameter that is to be forecasted, and parameters of the feature space. The method leads to the smallest set of parameters that surpass a similarity threshold. Results obtained, for the case of Thessaloniki air quality forecasting, are comparable to what feature selection methods offer