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Probabilistic prediction of hydrologic drought using a conditional probability approach based on the meta-Gaussian model



Public Access Version Available on: January 01, 2030, 12:00 AM
Please check back on the date listed above.
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  • Journal Title:
    Journal of Hydrology
  • Personal Author:
  • NOAA Program & Office:
    NWS (National Weather Service)
  • Description:
    Prediction of drought plays an important role in drought preparedness and mitigation, especially because of large impacts of drought and increasing demand for water resources. An important aspect for improving drought prediction skills is the identification of drought predictability sources. In general, a drought originates from precipitation deficit and thus the antecedent meteorological drought may provide predictive information for other types of drought. In this study, a hydrological drought (represented by Standardized Runoff Index (SRI)) prediction method is proposed based on the meta-Gaussian model taking into account the persistence and its prior meteorological drought condition (represented by Standardized Precipitation Index (SPI)). Considering the inherent nature of standardized drought indices, the meta-Gaussian model arises as a suitable model for constructing the joint distribution of multiple drought indices. Accordingly, the conditional distribution of hydrological drought can be derived analytically, which enables the probabilistic prediction of hydrological drought in the target period and uncertainty quantifications. Based on monthly precipitation and surface runoff of climate divisions of Texas, U.S., 1-month and 2-month lead predictions of hydrological drought are illustrated and compared to the prediction from Ensemble Streamflow Prediction (ESP). Results, based on 10 climate divisions in Texas, show that the proposed meta-Gaussian model provides useful drought prediction information with performance depending on regions and seasons. (C) 2016 Elsevier B.V. All rights reserved.
  • Source:
    Journal of Hydrology, 542, 772-780.
  • DOI:
  • Document Type:
    Journal Article
  • Compliance:
    Library
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