Streamflow Reconstruction Using Tree Rings Chronology, Modeling and Classification of Hydrological Drought in the Karkheh Basin

Document Type : Research Paper


1 PhD Candidate Water Resources Eng, Department of Irrigation and Reclamation Eng., Faculty of Agricultural Engineering & Technology, University College of Agriculture &Natural Resources, University of Tehran, Iran

2 Assistant Professor, Department of Irrigation and Reclamation Eng., Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Iran

3 Associate Professor, Department of Geography, University of Tehran

4 PhD Candidate, Department of Geography, University of Tehran


Through an assessment of the annual growth of tree rings and making up of the chronology, the possibility of the study and reconstruction of streamflows in the habitats could be provided. The aim followed in this research was to reconstruct streamflow, applying dendrochronology, modeling, and classification of hydrological drought in Karkheh basin. Throughout the research the chronology indexes of two oak species, Quercus brantii, and Quercus infectoria, in the central Zagross region during the period 1840-2010 were used to reconstruct streamflow in Karkheh basin. Three gage stations were selected within the basin and their high flow periods determined. Correlations between streamflow, in high flow periods, as well as regional chronology index for gage stations were found out as positive and significant at 1% confidence level. With regard to these data  average streamflows at high flow periods recorded at these gage stations (from year 1840 to 2010) were reconstructed. The values of the observed and reconstructed streamflows. Within the timely common statistical periods are well consistent with each other. Hydrological conditions during the chronology period were studied and accordingly, hydrological drought analyzed within Karkheh Basin for the years 1840 to 2010. Severity and duration of the droughts as well as low vs high flow decades were determined. In addition, the results were compared with those obtained by other researchers as well. Following the reconstruction of streamflow a probabilistic neural network model was employed to classify the periods of very low, low, high, and very high flows. The results revealed that through the provided model it would be possibleto recognize the mentioned periods with a high accuracy within the region.


Main Subjects

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