Hydro-Meteorological Drought Assessment Based on Aggregate Drought Index (ADI) and its prediction with Markov Chain in Sarbaz River Basin (Southeast of Iran)

摘要

Drought is a climatic anomaly that associates with a significant decrease (lack) of precipitation and water resources availability, which spreads on vast temporal and spatial scales, and significantly affects various aspects of life and environment. One of the most common methods of drought assessing and monitoring is calculating drought indices (DIs). Drought areal and temporal extent and its severity are determined by these indices. In this study, an aggregate drought index (Hydro-Meteorological) has been developed for the assessment of hydrological and meteorological droughts in Sarbaz river basin located in southeastern of Iran. The Aggregate Drought Index (ADI) comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. In this case, monthly values of Stream flow Drought Index (SDI) and Standardized Precipitation Index (SPI) indicators were used for four similar reference periods with principle component analysis and aggregate hydro-meteorological index was defined based on its first component. The study time span was set between 1981-82 to 2010-11, which begins of October in Iran. Results based on the aggregate drought index (ADI) revealed that a long period of hydro-meteorological drought occurred from 1999-2000 to 2005/06 in southeast of Iran, in which, 2003/04 water year has been extremely a drought year. The ADI methodology provides a clear, objective approach for describing the intensity of drought. This index is appropriately able to represent the behavior of Hydro-Meteorological droughts and recommended as an integrated index for assessing and monitoring of regional droughts. Finally, different states of hydro-meteorological drought have been extracted based on conventional regional thresholds, and have been modeled by Markov chain. This made the estimation of drought state transition frequency possible, and made the prediction of next drought state time more real. State transition frequency matrices, are the main instruments for predicting drought states in real time. Results of validation tests and conforming the predicted results with real data indicate that predicting hydrological drought state transitions in the study area using Markov chain method is valid.