Hydrologic Risk Analysis for Nonstationary Streamflow Records under Uncertainty

The frequency and magnitude of hydrologic extreme events is critical to water resources management. Traditional hydrologic frequency analysis approaches rely on the inappropriate assumption that hydrology is stationary. To tackle the non-stationarity in the streamflow records, we proposed a hydrologic risk analysis framework for the Xiangxi River, one of the largest tributaries of the Three Gorge Region, China. The year 1989 was identified as the change point of the 50-year flow records through a CUSUM approach combined with a Bootstrap  test. Annual peak flow frequency analyses were then carried out for the 50-year time series and the records after the identified change point, respectively. The results revealed that, by taking into consideration nonstationarity, the return period of high peak flood at the Xingshan Station would actually increase. Bayesian inference combined with a MCMC sampling algorithm was also conducted to address uncertainties in parameter estimation  and translate them to flow quantile estimates. It was found that the uncertainty in parameter estimation greatly affected the hydrologic design. To better support the associated risk assessment, two risk concepts, the exceedance risk and the occurrence risk, were proposed and analyzed. The results provided important insights into hydrologic nonstaionarity and uncertainty, and the proposed framework can provide scientific bases for engineering design and risk management in many other rivers in China and around the world.

[1]  P. Gelder,et al.  Linking Three Gorges Dam and downstream hydrological regimes along the Yangtze River, China , 2015 .

[2]  Mitja Brilly,et al.  Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River , 2015 .

[3]  Andrés Díez-Herrero,et al.  Flood frequency analysis of historical flood data under stationary and non-stationary modelling , 2015 .

[4]  Chong-Yu Xu,et al.  Observed trends of annual maximum water level and streamflow during past 130 years in the Yangtze River basin, China , 2006 .

[5]  W. A. Taylor.  Change-Point Analysis : A Powerful New Tool For Detecting Changes , 2000 .

[6]  J. Salas,et al.  Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events , 2014 .

[7]  Ning Zhang,et al.  An Inexact Two-Stage Water Quality Management Model for Supporting Sustainable Development in a Rural System , 2014 .

[8]  Q. Zhuang,et al.  Equifinality in parameterization of process‐based biogeochemistry models: A significant uncertainty source to the estimation of regional carbon dynamics , 2008 .

[9]  Sebastian Mosbach,et al.  Bayesian parameter estimation for a jet-milling model using Metropolis–Hastings and Wang–Landau sampling , 2013 .

[10]  D. Y. Miao,et al.  Planning Water Resources Systems under Uncertainty Using an Interval-Fuzzy De Novo Programming Method , 2015 .

[11]  Scott A. Sisson,et al.  A fully probabilistic approach to extreme rainfall modeling , 2003 .

[12]  Guohe Huang,et al.  Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed, China , 2013, Stochastic Environmental Research and Risk Assessment.

[13]  Vijay P. Singh,et al.  Flood frequency analysis with consideration of hydrological alterations: Changing properties, causes and implications , 2014 .

[14]  Jery R. Stedinger,et al.  Bayesian MCMC flood frequency analysis with historical information , 2005 .

[15]  S. Yue,et al.  A review of bivariate gamma distributions for hydrological application , 2001 .

[16]  Guohe Huang,et al.  Development of a Stepwise-Clustered Hydrological Inference Model , 2015 .

[17]  Zhongjing Wang,et al.  Hydrograph-Based Hydrologic Alteration Assessment and Its Application to the Yellow River , 2014 .

[18]  Michael D. Dettinger,et al.  On Critiques of “Stationarity is Dead: Whither Water Management?” , 2015 .

[19]  Pan Liu,et al.  Deriving Optimal Refill Rules for Multi-Purpose Reservoir Operation , 2011 .

[20]  G. A. Assumaning,et al.  State and Parameter Estimation in Three-Dimensional Subsurface Contaminant Transport Modeling using Kalman Filter Coupled with Monte Carlo Sampling , 2014 .

[21]  Pan Liu,et al.  Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir , 2015 .

[22]  Gene Jiing-Yun You,et al.  An assessment of long-term overtopping risk and optimal termination time of dam under climate change. , 2013, Journal of environmental management.

[23]  Christian Bernhofer,et al.  Long term variability of the annual hydrological regime and sensitivity to temperature phase shifts in Saxony/Germany , 2011 .

[24]  Chong-Yu Xu,et al.  Statistical behaviours of precipitation regimes in China and their links with atmospheric circulation 1960–2005 , 2011 .

[25]  Tao Tang,et al.  [Influence of cascaded exploitation of small hydropower on phytoplankton in Xiangxi River]. , 2007, Ying yong sheng tai xue bao = The journal of applied ecology.

[26]  Abduwasit Ghulam,et al.  Assessing the impacts of future urban development patterns and climate changes on total suspended sediment loading in surface waters using geoinformatics , 2014 .

[27]  Guohe Huang,et al.  Inexact Optimization Model for Supporting Waste-Load Allocation in the Xiangxi River Basin of the Three Gorges Reservoir Region, China , 2015 .

[28]  Yonghong Hao,et al.  A Bayesian analysis of nonstationary generalized extreme value distribution of annual spring discharge minima , 2014, Environmental Earth Sciences.

[29]  Dayong Qin,et al.  Study on the general model of hydrological frequency analysis , 2001 .

[30]  Guohe Huang,et al.  Heterogeneous Precipitation and Streamflow Trends in the Xiangxi River Watershed, 1961-2010 , 2014 .

[31]  Mahmoud M. Smadi,et al.  A Sudden Change In Rainfall Characteristics In Amman, Jordan During The Mid 1950s , 2006 .

[32]  A. Gül,et al.  Nonstationarity in Flood Time Series , 2014 .

[33]  Yonghong Hao,et al.  A nonstationary extreme value distribution for analysing the cessation of karst spring discharge , 2014 .

[34]  Maliheh Madanian,et al.  Change Detection through Four Techniques Using Multi-Temporal Landsat Thematic Mapper Data: A Case Study on Falavarjan Area, Isfahan, Iran , 2014 .

[35]  B. J. C Perera,et al.  Extreme Rainfall Nonstationarity Investigation and Intensity–Frequency–Duration Relationship , 2014 .

[36]  Reagan Waskom,et al.  Introduction to the Featured Collection on “Nonstationarity, Hydrologic Frequency Analysis, and Water Management” 1 , 2011 .

[37]  Hone-Jay Chu,et al.  Change-point detection of long-duration extreme precipitation and the effect on hydrologic design: a case study of south Taiwan , 2012, Stochastic Environmental Research and Risk Assessment.

[38]  Chong-Yu Xu,et al.  Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series , 2015, Water Resources Management.

[39]  Z. F. Yang,et al.  Evaluation of Sustainable Environmental Flows Based on the Valuation of Ecosystem Services: a Case Study for the Baiyangdian Wetland, China , 2014 .

[40]  Keith Beven,et al.  Equifinality, sensitivity and predictive uncertainty in the estimation of critical loads , 1999 .

[41]  Sophie Duchesne,et al.  Bayesian estimation of intensity–duration–frequency curves and of the return period associated to a given rainfall event , 2010 .

[42]  Wei Xiang,et al.  Land use change and causes in the Xiangxi catchment, Three Gorges Area derived from multispectral data , 2010 .

[43]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[44]  Wolf M. Mooij,et al.  A Comparison of Three Approaches to Predict Phytoplankton Biomass in Gonghu Bay of Lake Taihu , 2014 .

[45]  T. Ouarda,et al.  Bayesian Nonstationary Frequency Analysis of Hydrological Variables 1 , 2011 .

[46]  D Kofi Asante-Duah,et al.  Hydrological Dimensioning and Operation of Reservoirs: Practical Design Concepts and Principles , 2002 .

[47]  Jichun Wu,et al.  Probabilistic Forecast and Uncertainty Assessment of Hydrologic Design Values Using Bayesian Theories , 2010 .

[48]  Hui Tao,et al.  Trends of streamflow in the Tarim River Basin during the past 50 years: Human impact or climate change? , 2011 .

[49]  Zhongmin Liang,et al.  Bayesian flood frequency analysis in the light of model and parameter uncertainties , 2012, Stochastic Environmental Research and Risk Assessment.

[50]  Deliang Chen,et al.  Decreasing reference evapotranspiration in a warming climate—A case of Changjiang (Yangtze) River catchment during 1970–2000 , 2006 .

[51]  Qinghua Cai,et al.  Changes in benthic algal communities following construction of a run-of-river dam , 2009, Journal of the North American Benthological Society.

[52]  Faisal Hossain,et al.  Hydrological Risk Assessment of Old Dams: Case Study on Wilson Dam of Tennessee River Basin , 2012 .

[53]  P. C. Nayak,et al.  Improving peak flow estimates in artificial neural network river flow models , 2003 .

[54]  Gordon H. Huang,et al.  Inexact two-stage stochastic credibility constrained programming for water quality management , 2013 .

[55]  Xiangming Xiao,et al.  AN ASSESSMENT OF ECOSYSTEM SERVICES: WATER FLOW REGULATION AND HYDROELECTRIC POWER PRODUCTION , 2000 .

[56]  Jose D. Salas,et al.  Quantifying the Uncertainty of Design Floods under Nonstationary Conditions , 2014 .