Evaluation of Nonstationarity in Annual Maximum Flood Series and the Associations with Large-scale Climate Patterns and Human Activities

Li, Jianzhu; Liu, Xueyang; Chen, Fulong

doi:10.1007/s11269-014-0900-z

Cited by 29 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another covariate, the reservoir index (RI) is employed to consider the effects of reservoirs on flood variations. According to the previous studies (Batalla et al, 2004; Li et al, 2015; López & Francés, 2013; Lu et al, 2020; Wang et al, 2017; Xiong et al, 2019), there are different formulas to define the RI. For clarity, the RI for a gauge station is defined as

RI = \frac{\sum_{i = 1}^{M} C_{i}}{R_{m}},

where M is the total number of reservoirs upstream of the gauging station, C i is the maximum storage capacity of reservoir i upstream of the gauge station, and R m is the multiyear mean runoff at the gauge station.…”

Section: Case Studymentioning

confidence: 99%

Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017

Xiong

Guo

et al. 2020

Water Resources Research

View full text Add to dashboard Cite

Censored data (CD) of floods, that is, the combination of systematic data (SD) and historical data, can help improve the robustness of flood frequency analysis, due to its temporal information expansion. However, in nonstationary flood frequency analysis, the approach to utilize the CD has rarely been investigated. In this study, a covariate‐based nonstationary flood frequency analysis framework based on various likelihood functions using the generalized extreme value (GEV) distribution was built to utilize the CD, with uncertainty considered. This framework was applied to the study of the annual maximum flood frequency of the Yichang gauging station 44 km downstream of the Three Gorges Dam over the period from 1470 to 2017. A summer precipitation anomaly and a reservoir index were used as covariates to explain the variation of the distribution parameters. The results show that for either the SD or CD, the nonstationary models are preferred to the stationary ones by the deviance information criterion, and these nonstationary models may prove to be practical in engineering application, due to the acceptable uncertainty range in flood quantiles derived from covariates. Compared to the stationary or nonstationary models based on the SD, the corresponding model based on the CD results in a higher posterior mean and a smaller posterior standard deviation for the shape parameter of the GEV distribution. It is concluded that the use of historical information under the nonstationary frequency analysis framework may be remarkable in reducing design flood uncertainty, especially for the very small exceedance probability at the tail.

show abstract

RI = \frac{\sum_{i = 1}^{M} C_{i}}{R_{m}},

Section: Case Studymentioning

confidence: 99%

Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017

Xiong

Guo

et al. 2020

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…The watershed is characterized by steep slopes and bedrock outcrops. The dominant land covers are forest and grass, with less than 2% of urban areas (Li et al, ). The long‐term annual precipitation in Wangkuai reservoir catchment ranges from 220 to 1000 mm, with an average of 640 mm (Sakakibara et al, ).…”

Section: Study Area and Datamentioning

confidence: 99%

“…More than 60% of the total precipitation occurs between June and September due to the East monsoons (Song et al, 2011). The annual mean temperature is approximately 7 ∘ C. Complete hourly precipitation records of the flood season (June-September) during 1956-2005 at eight precipitation stations (see Figure 2) are provided by the Hydrology and Water Resources Survey Bureau of Hebei Province (Li et al, 2015). The eight stations are distributed uniformly over the catchment, hence are deemed representative of the precipitation characteristics in the study area.…”

Section: Wangkuai Reservoir Catchment Northern Chinamentioning

confidence: 99%

Three resampling approaches based on method of fragments for daily‐to‐subdaily precipitation disaggregation

Meshgi

Wang³

et al. 2018

Intl Journal of Climatology

View full text Add to dashboard Cite

Precipitation data of finer timescale and higher spatial density are crucial for continuous hydrological modelling and flood risk assessment. Disaggregation methods are often used to transform the coarser‐timescale precipitation data into finer resolutions. The nonparametric approach based on method of fragments (MOF) has received broad attention in precipitation disaggregation literature, given its superiority in reproducing the at‐site statistical attributes. However, a detailed literature review has shown that the MOF‐based resampling approaches are mainly focused in the single‐site precipitation disaggregation context, which may subject to limitations such as the unavailability of at‐site precipitation records and the incapability to preserve the inter‐site correlation structure. To address these issues, we propose three resampling approaches based on MOF. The first approach is a single‐site interval‐based resampling approach which only draws subdaily fragment vectors from at‐site record. The second one extends the first one to a regionalized version where subdaily fragment vectors are drawn from both the at‐site and neighbouring stations. The third one is a multi‐site approach developed to preserve the observed inter‐site correlation. The performances of the three methods are evaluated with applications to daily‐to‐hourly precipitation disaggregation at six rain gauges in Singapore and eight precipitation stations in Wangkuai reservoir catchment in northern China. An elaborate list of performance measures, including standard validation statistics, spatial correlation, inter‐day connectivity, annual extreme analysis, and intra‐day dry and wet spell characteristics are used to assess the performance. The proposed three methods are shown to be effective in reproducing the at‐site attributes, and no significant deterioration of performance is observed when moving from the single‐site method to the regionalized and multi‐site versions. As expected, the multi‐site approach is the only one method that is able to reconstruct the spatial correlation in the disaggregated precipitation field. The approaches can be applied for daily‐to‐subdaily precipitation disaggregation in different regions.

show abstract

“…Vogel et al 1993;Katz et al 2002;Kwon et al 2008;Gilroy and McCuen 2012;Katz 2012;Li et al 2015a). FFA is conventionally conducted under the assumptions of: (i) independence of the flood series, which means that the probability distribution parameters to be estimated come from independent and identically distributed observations, and (ii) stationarity of the flood series, which means that the environmental factors that generate or modify floods, such as climate and land cover, are the same in the past, present and future (Khaliq et al 2006;Gilroy and McCuen 2012;Li et al 2015b).…”

Section: Introductionmentioning

confidence: 99%

Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series

Xiong

et al. 2015

Water Resour Manage

View full text Add to dashboard Cite

Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming Water Resour Manage (2015) 29:3615-3633 constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series.

show abstract

Evaluation of Nonstationarity in Annual Maximum Flood Series and the Associations with Large-scale Climate Patterns and Human Activities

Cited by 29 publications

References 41 publications

Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017

Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River From 1470 to 2017

Three resampling approaches based on method of fragments for daily‐to‐subdaily precipitation disaggregation

Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series

Contact Info

Product

Resources

About