Next Article in Journal
Adaptation Tipping Points of a Wetland under a Drying Climate
Previous Article in Journal
Multi-Domain 2.5D Method for Multiple Water Level Hydrodynamics
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Water 2018, 10(2), 233; doi:10.3390/w10020233

Forecasting Quarterly Inflow to Reservoirs Combining a Copula-Based Bayesian Network Method with Drought Forecasting

Department of Civil Engineering, Pukyong National University, Busan 48513, Korea
Author to whom correspondence should be addressed.
Received: 4 January 2018 / Revised: 11 February 2018 / Accepted: 21 February 2018 / Published: 24 February 2018
View Full-Text   |   Download PDF [6994 KB, uploaded 25 February 2018]   |  


Especially for periods of drought, the higher the accuracy of reservoir inflow forecasting is, the more reliable the water supply from a dam is. This article focuses on the probabilistic forecasting of quarterly inflow to reservoirs, which determines estimates from the probabilistic quarterly inflow according to drought forecast results. The probabilistic quarterly inflow was forecasted by a copula-based Bayesian network employing a Gaussian copula function. Drought forecasting was performed by calculation of the standardized inflow index value. The calendar year is divided into four quarters, and the total inflow volume of water to a reservoir for three months is referred to as the quarterly inflow. Quarterly inflow forecasting curves, conforming to drought stages, produce estimates of probabilistic quarterly inflow according to the drought forecast results. The forecasted estimates of quarterly inflow were calculated by using the inflow records of Soyanggang and Andong dams in the Republic of Korea. After the probability distribution of the quarterly inflow was determined, a lognormal distribution was found to be the best fit to the quarterly inflow volumes in the case of the Andong dam, except for those of the third quarter. Under the threshold probability of drought occurrences ranging from 50% to 55%, the forecasted quarterly inflows reasonably matched the corresponding drought records. Provided the drought forecasting is accurate, combining drought forecasting with quarterly inflow forecasting can produce reasonable estimates of drought inflow based on the probabilistic forecasting of quarterly inflow to a reservoir. View Full-Text
Keywords: drought; copula; Bayesian network; inflow; reservoir drought; copula; Bayesian network; inflow; reservoir

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kim, K.; Lee, S.; Jin, Y. Forecasting Quarterly Inflow to Reservoirs Combining a Copula-Based Bayesian Network Method with Drought Forecasting. Water 2018, 10, 233.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top