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Title: A probabilistic drought forecasting framework: A combined dynamical and statistical approach

Abstract

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.

Authors:
ORCiD logo; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1347861
Report Number(s):
PNNL-SA-123469
Journal ID: ISSN 0022-1694
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Hydrology; Journal Volume: 548
Country of Publication:
United States
Language:
English
Subject:
data assimilation; particle markov chain monte carlo; initial condition uncertainity; probablistic drought forecasting

Citation Formats

Yan, Hongxiang, Moradkhani, Hamid, and Zarekarizi, Mahkameh. A probabilistic drought forecasting framework: A combined dynamical and statistical approach. United States: N. p., 2017. Web. doi:10.1016/j.jhydrol.2017.03.004.
Yan, Hongxiang, Moradkhani, Hamid, & Zarekarizi, Mahkameh. A probabilistic drought forecasting framework: A combined dynamical and statistical approach. United States. doi:10.1016/j.jhydrol.2017.03.004.
Yan, Hongxiang, Moradkhani, Hamid, and Zarekarizi, Mahkameh. Mon . "A probabilistic drought forecasting framework: A combined dynamical and statistical approach". United States. doi:10.1016/j.jhydrol.2017.03.004.
@article{osti_1347861,
title = {A probabilistic drought forecasting framework: A combined dynamical and statistical approach},
author = {Yan, Hongxiang and Moradkhani, Hamid and Zarekarizi, Mahkameh},
abstractNote = {In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.},
doi = {10.1016/j.jhydrol.2017.03.004},
journal = {Journal of Hydrology},
number = ,
volume = 548,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}