Return period and risk analysis of nonstationary low-flow series under climate change

Abstract

The concept of return period and the associated risk of occurrence of extreme events are critical considerations in the management of water resources. A precondition for conducting hydrological frequency analysis to estimate return period and risk is the assumption of stationarity of the hydrological variable of interest, but this is problematic because climate change and human activities can act to make hydrological phenomena nonstationary. Two different interpretations of return period, i.e. the expected waiting time (EWT) and expected number of exceedances (ENE), have been proposed to consider nonstationarity in return period and risk analysis by introducing the time-varying moments method into nonstationary frequency analysis, under the assumption that the statistical parameters are functions only of time. This paper explored the use of meteorological variables to improve the characterization of nonstationary return period and risk under the ENE interpretation by employing meteorological covariates in the nonstationary frequency analysis. The advantage is that the downscaled future meteorological variables from the General Circulation Model (GCM) outputs can be used to calculate the statistical parameters and exceedance probabilities for future years. The EWT interpretation using time as covariate was also applied for comparison. Both interpretations of return period were applied to the low-flow (annual minimum monthly streamflow) series of the Wei River, China. Both interpretations gave an estimate of nonstationary return period and risk that was significantly different from the stationary case. The nonstationary return period and risk under ENE interpretation using temperature and precipitation as covariates were more reasonable and advisable than those of the EWT case using time as covariate. We concluded that nonstationary analysis can improve decision making in the management of water resources of the Wei River basin during dry seasons exacerbated by climate change.

Impact of Climate Change on Flood Frequency and Intensity in the Kabul River Basin

“…IPCC's Fifth Assessment Report provides four Representative Concentration Pathways (RCPs) for climate-change projections and modelling. However, in our current study, climatic data for two representative concentration pathways (RCPs) were selected and used: RCP4.5 represents a medium concentration stabilization scenario in which greenhouse gas emissions peak around 2040 and then decline and RCP8.5 depicts a very high baseline scenario where emissions continue to rise throughout this century [17]. The CMIP5 daily climatic data for the four GCMs both RCP scenarios were downloaded from the Earth System Grid Federation Portal (http://cmip-pcmdi.llnl.gov/cmip5/; https://esgf-data.dkrz.de/search/cmip5/) to be used in our analysis.…”

Multiple causes of nonstationarity in the Weihe annual low-flow series

“…Therefore, the investigation of the magnitude and frequency of low flows is of primary importance for engineering design and water resources management (Smakhtin, 2001). In recent years, low flows, as an important part of river flow regime, have been attracting an increasing attention of hydrologists and ecologists in the context of the significant impacts of climate change and human activities (HAs; Bradford and Heinonen, 2008;Du et al, 2015;Kam and Sheffield, 2015;Kormos et al, 2016;Liu et al, 2015;Sadri et al, 2016). In general, under the im-B.…”

“…For example, Jiang et al (2015b) used reservoir index as an explanatory variable based on the time-varying copula method for bivariate frequency analysis of nonstationary low-flow series in Hanjiang River, China. Du et al (2015) took precipitation and air temperature as the explanatory variables to explain the inter-annual variability in low flows of the Weihe River, China (also known as the Wei He River). Liu et al (2015) took the sea surface temperature in the Nino3 region, the Pacific Decadal Oscillation, the sunspot number (3 years ahead), the winter areal temperature and precipitation as the candidate explanatory variables to explain the inter-annual variability in low flows of Yichang station, China.…”

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

“…It is widely known that, in rain-dominant watersheds, river floods are commonly triggered by extreme precipitation events [8,9]. Therefore, in practice, reducing the flood risk also primarily focused on low flow and drought conditions [39,40]. Studies investigating the bivariate risk of flood and extreme precipitation events for the LP are still few.…”