| 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 (Issue): | 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. |