Abstract. Many studies have shown that downstream flood regimes have been
significantly altered by upstream reservoir operation. Reservoir effects on
the downstream flow regime are normally performed by comparing the pre-dam
and post-dam frequencies of certain streamflow indicators, such as floods
and droughts. In this study, a rainfall–reservoir composite index (RRCI) is
developed to precisely quantify reservoir impacts on downstream flood
frequency under a framework of a covariate-based nonstationary flood
frequency analysis using the Bayesian inference method. The RRCI is derived
from a combination of both a reservoir index (RI) for measuring the effects
of reservoir storage capacity and a rainfall index. More precisely, the
OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related
variables selected out of five variables that describe the multiday
antecedent rainfall input (MARI) is used to measure the effects of
antecedent rainfall on reservoir operation. Then, the RI-dependent or
RRCI-dependent distribution parameters and five distributions, the gamma,
Weibull, lognormal, Gumbel, and generalized extreme value, are used to
analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang,
and Huangzhuang gauging stations of the Han River, China. A phenomenon
is observed in which although most of the floods that peak downstream of
reservoirs have been reduced in magnitude by upstream reservoirs, some
relatively large flood events have still occurred, such as at the
Huangzhuang station in 1983. The results of nonstationary flood frequency
analysis show that, in comparison to the RI, the RRCI that combines both the
RI and the OR-JEP resulted in a much better explanation for such phenomena of
flood occurrences downstream of reservoirs. A Bayesian inference of the
100-year return level of the AMDF shows that the optimal RRCI-dependent
distribution, compared to the RI-dependent one, results in relatively
smaller estimated values. However, exceptions exist due to some low
OR-JEP values. In addition, it provides a smaller uncertainty range. This
study highlights the necessity of including antecedent rainfall effects, in
addition to the effects of reservoir storage capacity, on reservoir
operation to assess the reservoir effects on downstream flood frequency.
This analysis can provide a more comprehensive approach for downstream flood
risk management under the impacts of reservoirs.
Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference
