Volume 16 Issue 3
Sep.  2023
Turn off MathJax
Article Contents
Yi-ming Hu, Zhong-min Liang, Yi-xin Huang, Jun Wang, Bin-quan Li. 2023: Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment. Water Science and Engineering, 16(3): 226-233. doi: 10.1016/j.wse.2023.05.001
Citation: Yi-ming Hu, Zhong-min Liang, Yi-xin Huang, Jun Wang, Bin-quan Li. 2023: Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment. Water Science and Engineering, 16(3): 226-233. doi: 10.1016/j.wse.2023.05.001

Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

doi: 10.1016/j.wse.2023.05.001

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFC1508001), the National Natural Science Foundation of China (Grant No. 51709073), and the Fundamental Research Funds for the Central Universities of China (Grant No. B220202031).

  • Received Date: 2021-12-29
  • Accepted Date: 2023-05-22
  • Rev Recd Date: 2023-04-21
  • Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.


  • loading
  • Alila, Y., Mtiraoui, A., 2002. Implications of heterogeneous flood-frequency distributions on traditional stream-discharge prediction techniques.Hydrol. Process. 16(5), 1065-1084. https://doi.org/10.1002/hyp.346.
    Ammar, M.E., Gharib, A., Islam, Z., Davies, E.G., Seneka, M., Faramarzi, M., 2020. Future floods using hydroclimatic simulations and peaks over threshold:An alternative to nonstationary analysis inferred from trend tests. Adv. Water Resour. 136, 103463. https://doi.org/10.1016/j.advwatres.2019.103463.
    Arribas, A., Robertson, K.B., Mylne, K.R., 2005. Test of a poor man's ensemble prediction system for short-range probability forecasting. Mon.Weather Rev. 133(7), 1825-1839. https://doi.org/10.1175/MWR2911.1.
    Camici, S., Brocca, L., Melone, F., Moramarco, T., 2014. Impact of climate change on flood frequency using different climate models and downscaling approaches. J. Hydrol. Eng. 19(8), 04014002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000959.
    Cooley, D., 2013. Return periods and return levels under climate change. In:AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., Sorooshian, S.(Eds.), Extremes in a Changing Climate. Springer, Dordrecht, pp. 97-114.
    François, B., Schlef, K.E., Wi, S., Brown, C.M., 2019. Design considerations for riverine floods in a changing climate e A review. J. Hydrol. 574, 557-573. https://doi.org/10.1016/j.jhydrol.2019.04.068.
    Hu, Y.M., Liang, Z.M., Jiang, X.L., Bu, H., 2015a. Non-stationary hydrological frequency analysis based on the reconstruction of extreme hydrological series. Proc. Int. Assoc. Hydrol. Sci. 371, 163-166. https://doi.org/10.5194/piahs-371-163-2015.
    Hu, Y.M., Liang, Z.M., Liu, Y.W., Zeng, X.F., Wang, D., 2015b. Uncertainty assessment of estimation of hydrological design values. Stoch. Environ.Res. Risk Assess. 29(2), 501-511. https://doi.org/10.1007/s00477-014-0979-z.
    Hu, Y.M., Liang, Z.M., Chen, X., Liu, Y.W., Wang, H.M., Yang, J., Wang, J., Li, B.Q., 2017. Estimation of design flood using EWT and ENE metrics and uncertainty analysis under non-stationary conditions. Stoch. Environ.Res. Risk Assess. 31(10), 2617-2626. https://doi.org/10.1007/s00477-017-1404-1.
    Hu, Y.M., Liang, Z.M., Singh, V.P., Zhang, X.B., Wang, J., Li, B.Q., 2018.Concept of equivalent reliability for estimating the design flood under nonstationary conditions. Water Resour. Manag. 32(3), 997-1011. https://doi.org/10.1007/s11269-017-1851-y.
    Liang, Z.M., Yang, J., Hu, Y.M., Wang, J., Li, B.Q., Zhao, J.F., 2018. A sample reconstruction method based on a modified reservoir index for flood frequency analysis of non-stationary hydrological series. Stoch. Environ. Res.Risk Assess. 32(6), 1561-1571. https://doi.org/10.1007/s00477-017-1465-1.
    Petrow, T., Merz, B., 2009. Trends in flood magnitude, frequency and seasonality in Germany in the period 1951-2002. J. Hydrol. 371, 129-141.https://doi.org/10.1016/j.jhydrol.2009.03.024.
    Read, L.K., Vogel, R.M., 2015. Reliability, return periods, and risk under nonstationarity. Water Resour. Res. 51(8), 6381-6398. https://doi.org/10.1002/2015WR017089.
    Rootzen, H., Katz, R.W., 2013. Design life level:Quantifying risk in a changing climate. Water Resour. Res. 49(9), 5964-5972. https://doi.org/10.1002/wrcr.20425.
    Salas, J.D., Obeysekera, J., 2013. Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J. Hydrol. Eng. 19(3), 554-568. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820.
    Serinaldi, F., Kilsby, C.G., 2015. Stationarity is undead:Uncertainty dominates the distribution of extremes. Adv. Water Resour. 77, 17-36. https://doi.org/10.1016/j.advwatres.2014.12.013.
    Singh, V.P., Wang, S.X., Zhang, L., 2005. Frequency analysis of nonidentically distributed hydrologic flood data. J. Hydrol. 307, 175-195. https://doi.org/10.1016/j.jhydrol.2004.10.029.
    Xie, P., Chen, G.C., Xia, J., 2005. Hydrological frequency calculation principle of inconsistent annual runoff series under changing environments. J.Wuhan Univ. Hydraul. Elec. Eng. 38(6), 6-9 (in Chinese).
    Xiong, L.H., Du, T., Xu, C.Y., Guo, S.L., Jiang, C., Gippel, C.J., 2015. Nonstationary annual maximum flood frequency analysis using the norming constants method to consider non-stationarity in the annual daily flow series. Water Resour. Manag. 29(10), 3615-3633. https://doi.org/10.1007/s11269-015-1019-6.
    Xu, C., 2021. Issues influencing accuracy of hydrological modelling in a changing environment. Water Sci. Eng. 14(2), 167-170. https://doi.org/10.1016/j.wse.2021.06.005.
    Yan, L., Xiong, L.H., Liu, D.D., Hu, T.S., Xu, C.Y., 2017. Frequency analysis of nonstationary annual maximum flood series using the time-varying twocomponent mixture distributions. Hydrol. Process. 31(1), 69-89. https://doi.org/10.1002/hyp.10965.
    Zhang, J.Y., Wang, G.Q., Jin, J.L., He, R.M., Liu, C.S., 2020. Evolution and variation characteristics of the recorded runoff for the major rivers in China during 1956-2018. Adv. Water Sci. 31(2), 153-161. https://doi.org/10.14042/j.cnki.32.1309.2020.02.001 (in Chinese).
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (72) PDF downloads(0) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint