Skip to main content
SpringerLink
Log in

Evaluating flash flood simulation capability with respect to rainfall temporal variability in a small mountainous catchment

  • Research Articles
  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Rainfall temporal patterns significantly affect variability of flash flood behaviors, and further act on hydrological model performances in operational flash flood forecasting and warning. In this study, multivariate statistical analysis and hydrological simulations (XAJ and CNFF models) were combined to identify typical rainfall temporal patterns and evaluate model simulation capability for water balances, hydrographs, and flash flood behaviors under various rainfall patterns. Results showed that all the rainfall events were clustered into three types (Type 1, Type 2, and Type 3) in Anhe catchment in southeastern China. Type 1 was characterized by small total amount, high intensity, short duration, early peak moment, and concentrated hourly distribution. Type 3 was characterized by great total amount, low intensity, long duration, late peak moment, and uniform hourly distribution. Characteristics of Type 2 laid between those of Type 1 and Type 3. XAJ and CNFF better simulated water balances and hydrographs for Type 3, as well as all flash flood behavior indices and flood dynamics indices. Flood peak indices were competitively simulated for all the types by XAJ and except Type 1 by CNFF. The study is of significance for understanding relationships between rainfall and flash flood behaviors and accurately evaluating flash flood simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Archer D R, Fowler H J, 2018. Characterising flash flood response to intense rainfall and impacts using historical information and gauged data in Britain. Journal of Flood Risk Management, 11(Suppl. 1): S121–S133.

    Google Scholar 

  • Ashley S T, Ashley W S, 2008. Flood fatalities in the United States. Journal of Applied Meteorology and Climatology, 47(3): 805–818.

    Article  Google Scholar 

  • Barredo J I, 2007. Major flood disasters in Europe: 1950–2005. Natural Hazards, 42(1): 125–148.

    Article  Google Scholar 

  • Batisani N, 2011. Spatio-temporal ephemeral streamflow as influenced by climate variability in Botswana. Journal of Geographical Sciences, 21(3): 417–428.

    Article  Google Scholar 

  • Berghuijs W R, Woods R A, Hutton C J et al., 2016. Dominant flood generating mechanisms across the United States. Geophysical Research Letters, 43(9): 4382–4390.

    Article  Google Scholar 

  • Bonaccorso B, Brigandì G, Aronica G T, 2020. Regional sub-hourly extreme rainfall estimates in Sicily under a scale invariance framework. Water Resources Management, 34(14): 4363–4380.

    Article  Google Scholar 

  • Borga M, Anagnostou E N, Blöschl G et al., 2011. Flash flood forecasting, warning and risk management: The HYDRATE project. Environmental Science & Policy, 14(7): 834–844.

    Article  Google Scholar 

  • Brunner M I, Viviroli D, Furrer R et al., 2018. Identification of flood reactivity regions via the functional clustering of hydrographs. Water Resources Research, 54(3): 1852–1867.

    Article  Google Scholar 

  • Chen J X, Zhang J H, Peng J B et al., 2023. Alp-valley and elevation effects on the reference evapotranspiration and the dominant climate controls in Red River Basin, China: Insights from geographical differentiation. Journal of Hydrology, 620: 129397.

    Article  Google Scholar 

  • dos Santos J C N, de Andrade E M, Medeiros P H A et al., 2017. Effect of rainfall characteristics on runoff and water erosion for different land uses in a tropical semiarid region. Water Resources Management, 31(1): 173–185.

    Article  Google Scholar 

  • Fan Z H, 2012. Research on precipitation trend analysis and design storm of Tianjin city [D]. Beijing: Tianjin University. (in Chinese)

    Google Scholar 

  • Fischer S, Schumann A, Bühler P, 2019. Timescale-based flood typing to estimate temporal changes in flood frequencies. Hydrological Sciences Journal, 64(15): 1867–1892.

    Article  Google Scholar 

  • Gao W, Liu Y, Du Z P et al., 2023. Hedging effect alleviates the impact of land use on mainstream hydrological regimes: Evidence from Jinsha River, China. Journal of Geographical Sciences, 33(10): 2011–2030.

    Article  Google Scholar 

  • Garambois P A, Larnier K, Roux H et al., 2014. Analysis of flash flood-triggering rainfall for a process-oriented hydrological model. Atmospheric Research, 137: 14–24.

    Article  Google Scholar 

  • Gong J F, Yao C, Li Z J et al., 2021. Improving the flood forecasting capability of the Xinanjiang model for small-and medium-sized ungauged catchments in South China. Natural Hazards, 106(3): 2077–2109.

    Article  Google Scholar 

  • Habibi A, Delavar M R, Sadeghian M S et al., 2023. A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment. International Journal of Applied Earth Observation and Geoinformation, 122: 103401.

    Article  Google Scholar 

  • Huff F A, 1967. Time distribution of rainfall in heavy storms. Water Resources Research, 3(4): 1007–1019.

    Article  Google Scholar 

  • Jiang X L, Zhang L P, Liang Z M et al., 2023. Study of early flood warning based on postprocessed predicted precipitation and Xinanjiang model. Weather and Climate Extremes, 42: 100611.

    Article  Google Scholar 

  • Jie M X, Chen H, Xu C Y et al., 2018. Transferability of conceptual hydrological models across temporal resolutions: approach and application. Water Resources Management, 32: 1367–1381.

    Article  Google Scholar 

  • Khajehei S, Ahmadalipour A, Shao W Y et al., 2020. A place-based assessment of flash flood hazard and vulnerability in the contiguous United States. Scientific Reports, 10(1): 448.

    Article  Google Scholar 

  • Leal M, Reis E, Santos P P, 2022. Exploring spatial relationships between stream channel features, water depths and flow velocities during flash floods using HEC-GeoRAS and geographic information systems. Journal of Geographical Sciences, 32(4): 757–782.

    Article  Google Scholar 

  • Liu Y H, Li Z J, Liu Z Y et al., 2022. Impact of rainfall spatiotemporal variability and model structures on flood simulation in semi-arid regions. Stochastic Environmental Research and Risk Assessment, 36(3): 785–809.

    Article  Google Scholar 

  • Liu R Y, Liu N, 2001. A GIS-based method for flooded area calculation and damage evaluation. Journal of Geographical Sciences, 11(2): 187–192.

    Article  Google Scholar 

  • Lompi M, Caporali E, Mediero L et al., 2022. Improving flash flood risk assessment using a simple approach for extreme rainfall scaling and storms transposition. Journal of Flood Risk Management, 15(3): e12796.

    Article  Google Scholar 

  • Meresa H, Murphy C, Fealy R et al., 2021. Uncertainties and their interaction in flood hazard assessment with climate change. Hydrology and Earth System Sciences, 25(9): 5237–5257.

    Article  Google Scholar 

  • McMillan H K, Booker D J, Cattoën C, 2016. Validation of a national hydrological model. Journal of Hydrology, 541: 800–815.

    Article  Google Scholar 

  • Orth R, Staudinger M, Seneviratne S I et al., 2015. Does model performance improve with complexity? A case study with three hydrological models. Journal of Hydrology, 523: 147–159.

    Article  Google Scholar 

  • Park J, Onof C, Kim D, 2019. A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales. Hydrology and Earth System Sciences, 23(2): 989–1014.

    Article  Google Scholar 

  • Ritter J, Berenguer M, Corral C et al., 2020. ReAFFIRM: Real-time assessment of flash flood impacts: A regional high-resolution method. Environment International, 136: 105375.

    Article  Google Scholar 

  • Rozalis S, Morin E, Yair Y et al., 2010. Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. Journal of Hydrology, 394(1/2): 245–255.

    Article  Google Scholar 

  • Ruiz-Villanueva V, Borga M, Zoccatelli D et al., 2012. Extreme flood response to short-duration convective rainfall in South-West Germany. Hydrology and Earth System Sciences, 16(5): 1543–1559.

    Article  Google Scholar 

  • Soil Conservation Service, 1972. National Engineering Handbook (Section 4): Hydrology. Washington, DC: US Department of Agriculture.

    Google Scholar 

  • Song X M, Zhan C S, Kong F Z et al., 2011. Advances in the study of uncertainty quantification of large-scale hydrological modeling system. Journal of Geographical Sciences, 21(5): 801–819.

    Article  Google Scholar 

  • Theodorsson-Norheim E, 1986. Kruskal-Wallis test: BASIC computer program to perform nonparametric one-way analysis of variance and multiple comparisons on ranks of several independent samples. Computer Methods and Programs in Biomedicine, 23(1): 57–62.

    Article  Google Scholar 

  • Thorndike R L, 1953. Who belongs in the family? Psychometrika, 18: 267–276.

    Article  Google Scholar 

  • Uhlenbrook S, Leibundgut C, 2002. Process-oriented catchment modelling and multiple-response validation. Hydrological Processes, 16(2): 423–440.

    Article  Google Scholar 

  • Wagener T, Boyle D P, Lees M J et al., 2001. A framework for development and application of hydrological models. Hydrology and Earth System Sciences, 5(1): 13–26.

    Article  Google Scholar 

  • Yin J, Gao Y, Chen R S et al., 2023. Flash floods: why are more of them devastating the world’s driest regions? Nature, 615(7951): 212–215.

    Article  Google Scholar 

  • Zhai X Y, Guo L, Liu R H et al., 2018. Rainfall threshold determination for flash flood warning in mountainous catchments with consideration of antecedent soil moisture and rainfall pattern. Natural Hazards, 94(2): 605–625.

    Article  Google Scholar 

  • Zhai X Y, Guo L, Zhang Y Y, 2021a. Flash flood type identification and simulation based on flash flood behavior indices in China. Science China: Earth Sciences, 64(7): 1140–1154.

    Article  Google Scholar 

  • Zhai X Y, Zhang Y Y, Zhang Y Q et al., 2021b. Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management. Science of the Total Environment, 763: 142977.

    Article  Google Scholar 

  • Zhang J Y, Hall M J, 2004. Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology, 296(1–4): 98–117.

    Google Scholar 

  • Zhang R H, Ni Y Q, Liu L P et al., 2011. South China heavy rainfall experiments (SCHeREX). Journal of the Meteorological Society of Japan, 89A: 153–166.

  • Zhang Y, Wang Y, Chen Y et al., 2019. Assessment of future flash flood inundations in coastal regions under climate change scenarios: A case study of Hadahe River basin in northeastern China. Science of the Total Environment, 693: 133550.

    Article  Google Scholar 

  • Zhang Y Y, Chen Q T, Xia J, 2020. Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification. Journal of Geographical Sciences, 30(12): 2053–2075.

    Article  Google Scholar 

  • Zhang Y Y, Xia J, Shao Q X et al., 2021. Uncertainty analysis for integrated water system simulations using GLUE with different acceptability thresholds. Science China Technological Sciences, 64(8): 1791–1804.

    Article  Google Scholar 

  • Zhao R J, 1992. The Xinanjiang model applied in China. Journal of Hydrology, 135(1–4): 371–381.

    Google Scholar 

  • Zheng Y C, Li J Z, Rong Y T et al., 2022. Quantification of rainfall spatial and temporal distribution characteristics on the flood hydrograph and its application in flood type classification. Journal of Hydraulic Engineering, 53(5): 560–573. (in Chinese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Xuemei Wang, Xiaoyan Zhai & Liang Guo

  2. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Yongyong Zhang

Authors
  1. Xuemei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyan Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongyong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyan Zhai.

Additional information

Foundation: National Natural Science Foundation of China, No.42171047, No.42071041

Author: Wang Xuemei (1998–), PhD Candidate, specialized in hydrology and water resources. E-mail: wang_xmww@163.com

Electronic Supplementary Material

11442_2023_2188_MOESM1_ESM.pdf

Evaluating flash flood simulation capability with respect to rainfall temporal variability in a small mountainous catchment

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Zhai, X., Zhang, Y. et al. Evaluating flash flood simulation capability with respect to rainfall temporal variability in a small mountainous catchment. J. Geogr. Sci. 33, 2530–2548 (2023). https://doi.org/10.1007/s11442-023-2188-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-023-2188-5

Keywords

Search

Navigation