Monthly discharge is one of the most important factors considered in designs and hydrological works. Some watersheds are not equipped with needed hydrometric equipment. In such a case average monthly discharge could be estimated from regional monthly water balance models of representative watersheds. In this study, Thornthwaite & Mather (TM) model were used in the Pishkouh watershed in arid climate of Yazd, Iran. The water balance was used for computing seasonal and geographical patterns of water availability to facilitate better management of available water resources. The water balance study using the TM model with the help of remote sensing and Geografhic Information Systems (GIS) is very helpful in finding out the periods of moisture deficit and moisture surplus for an entire basin. This study indicates that there is an annual deficit of 442.7 mm in the study basin and an annual surplus of 26.4 mm. The Pishkouh watershed has a period of moisture surplus from June to August and the remaining months are a period of deficit. Generally these mean estimated runoff values fall between the 90% confidence intervals for the measured runoff and quality of the model was satisfactory (Qual=2.86). These results indicate that the TM model can be satisfactorily applied to estimate mean monthly stream flow and potential runoff map in the arid regions of central Iran, too. پرونده مقاله

Classifying remote sensing imageries to obtain reliable and accurate LandUse/Cover (LUC) information still remains a challenge that depends on many factors suchas complexity of landscape especially in arid region. The aim of this paper is to extractreliable LUC information from Land sat imageries of the Pishkouh watershed of centralarid region, Iran. The classical Maximum Likelihood Classifier (MLC) was first applied toclassify Land sat image of 15 July 2007. The major LUC identified were shrubland(rangeland), agricultural land, orchard, river, settlement. Applying Post-ClassificationCorrection (PCC) using ancillary data and knowledge-based logic rules the overallclassification accuracy was improved from about 72% to 91% for LUC map. The improvedoverall Kappa statistics due to PCC were 0.88. The PCC maps, assessed by accuracymatrix, were found to have much higher accuracy in comparison to their counterpart MLCmaps. The overall improvement in classification accuracy of the LUC maps is significantin terms of their potential use for land change modeling of the region. پرونده مقاله