SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Evaluation of drought severity with a bayesian network analysis of multiple drought indices 
Authors:Kim, S., P. Parhi, H. Jun and J. Lee 
Year:2018 
Journal:Journal of Water Resources Planning and Management 
Volume:144(1) 
Pages: 
Article ID:05017016 
DOI:10.1061/(ASCE)WR.1943-5452.0000804 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:drought assessment 
Secondary Application Category:hydrologic and/or pollutant indices 
Watershed Description:6,648 km^2 Chungju Dam drainage area, located in northern South Korea. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Drought indices assimilate meteorological and/or hydrological information to come up with a comprehensible index. Over the last few decades, hundreds of drought indices have been developed in order to improve monitoring and impact assessment. For a particular drought event, these multiple indices sometimes indicate different levels of drought severity, creating confusion among stakeholders and posing challenges for decision making. To overcome the problem, this study suggests a novel methodology using a Bayesian network. There are several advantages of this proposed method: (1) it pools information from multiple drought indices and comes up with a better estimate for drought severity; (2) instead of a deterministic drought-severity outcome from the individual indices, it offers probabilistic estimates for drought severity; and (3) it reduces the uncertainty of the individual drought indices. The robustness of the methodology is further checked with a case study of an actual drought event in South Korea. 
Language:English 
Keywords:Drought; Bayesian network; Standardized precipitation index.