Monday, May 13, 2013

New Research Idea : Selection of Conceptual Hydrologic Models for Estimating Runoff from Arid Watersheds


Abstract : 

The selection of ideal conceptual hydrologic model for prediction of surface runoff from any type of watershed is useful for the estimation of future hydrologic conditions of the basin.The predicted runoff is also important for design of hydraulic structure,preparation of watershed management plans ,disaster management etc. related activities.The future runoff will give an idea of future uncertainties that may trouble the watershed and its inhabitants.The hydrologic models which follows the basic physical laws of water balance to estimate the desired output cannot exactly estimate the runoff.There will be some differences with the actual situation and the predicted one.The deviations between the two is allowable but to a certain extent.There are many factors for this deviation.Some models are suitable for some specific types of watersheds.The availability of required dataset is also another important issue.The equalizing constants that are used to linearize interrelationships are also a factor for the error produced by the hydrologic model.That is why  selection of a suitable hydrologic model which can estimate at a desired accuracy under the restrictions imposed by the study watershed  and dataset availability is important for successful execution of projects involving runoff predictions.The present study tries to select a suitable hydrologic model based on the related factors and decision making algorithms for predicting surface runoff from an arid watershed.The decision making algorithms are popular for their impartial and scientific decision making based on historical,social and expert inputs.The advancements of such algorithms can be utilized to select an ideal hydrologic model which can minimize the error between the predicted and actual output.

Tools Utilized : AHP and Fuzzy,various hydrologic models

Keywords : hydrologic models,decision making algorithms,arid watershed.