• Journal of Geographical Sciences
  • Vol. 30, Issue 12, 2053 (2020)
Yongyong ZHANG1、*, Qiutan CHEN1、2, and Jun XIA1
Author Affiliations
  • 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.1007/s11442-020-1827-3 Cite this Article
    Yongyong ZHANG, Qiutan CHEN, Jun XIA. Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification[J]. Journal of Geographical Sciences, 2020, 30(12): 2053 Copy Citation Text show less
    Locations of the Huaihe River Basin, selected hydrological stations, dams and sluices
    Fig. 1. Locations of the Huaihe River Basin, selected hydrological stations, dams and sluices
    Hierarchical clustering results presented by black, red, yellow, blue and green colors for all the flood events named by station name+ event sequence which is sorted in chronological order (e.g., ZiLS01 means the first event at ZiL station)
    Fig. 2. Hierarchical clustering results presented by black, red, yellow, blue and green colors for all the flood events named by station name+ event sequence which is sorted in chronological order (e.g., ZiLS01 means the first event at ZiL station)
    Classification performance assessment using the Goodman-Kruskal index (GKI), C index (CI) and minimum cluster for different total class numbers
    Fig. 3. Classification performance assessment using the Goodman-Kruskal index (GKI), C index (CI) and minimum cluster for different total class numbers
    Normalized flood hydrographs of individual flood event classes (a-e) and their frequencies of flood events in the pre-flood, flood and post-flood seasons
    Fig. 4. Normalized flood hydrographs of individual flood event classes (a-e) and their frequencies of flood events in the pre-flood, flood and post-flood seasons
    Variations of individual flood behavior metrics among different classes. Median values are defined by the solid dot symbols, respectively. Each black box illustrates the 25th and 75th percentile values, and the vertical line defines the minimum and maximum values without outliers. The white dot means the 50th percentile value and the violin shape means the frequency distribution of flood behavior metric.
    Fig. 5. Variations of individual flood behavior metrics among different classes. Median values are defined by the solid dot symbols, respectively. Each black box illustrates the 25th and 75th percentile values, and the vertical line defines the minimum and maximum values without outliers. The white dot means the 50th percentile value and the violin shape means the frequency distribution of flood behavior metric.
    Spatial variations of different flood event classes
    Fig. 6. Spatial variations of different flood event classes
    Interannual distributions of flood event classes from 2006 to 2015 in the Shaying River, Hongru River, Southern mountainous rivers, Huaihe mainstream and for all the flood events
    Fig. 7. Interannual distributions of flood event classes from 2006 to 2015 in the Shaying River, Hongru River, Southern mountainous rivers, Huaihe mainstream and for all the flood events
    Correlation coefficients between impact factors and flood behavior metrics (the blank column means insignificant impact factor)
    Fig. 8. Correlation coefficients between impact factors and flood behavior metrics (the blank column means insignificant impact factor)
    Contributions of impact categories and their combinations on the regional and interannual variations of individual flood event classes
    Fig. 9. Contributions of impact categories and their combinations on the regional and interannual variations of individual flood event classes
    IDRiversStationsDam regulationCatchment area (km2)Slope length (km)Slope (%)Elevation (m)River density (km/km2)Major land use (%)Flood eventsPrecipitation (mm)Potential evapotranspiration (mm)
    1Shaying RiverZiLS/180089.422.15818.30.027Forest (67.6)1280.0±76.336.9±18.0
    2ZhongT/48541.429.07680.20.019Forest (77.5)1371.0±55.226.7±13.8
    3XiaGSYes35436.919.67471.00.083Forest (45.0)1054.4±36.932.2±21.5
    4RuZYes300573.416.54662.20.016Forest (45.8)1154.0±27.142.8±19.3
    5GaoCYes62749.113.98493.80.055Dryland (58.4)557.2±68.142.5±32.3
    6ZhongM/2106132.31.28144.30.027Dryland (53.4)565.9±51.834.7±8.3
    7JiZ/4610.516.16394.00.023Forest (73.9)554.4±87.718.7±9.8
    8XinZYes107975.55.37268.40.027Dryland (63.6)10134.1±80.665.7±68.1
    9HeKYes2124116.43.32153.10.004Dryland (72.4)957.2±35.438.0±14.8
    10LuoHYes12,150170.07.75316.50.002Dryland (59.7)645.3±34.344.9±12.0
    11ZhouKYes25,800202.14.47214.90.001Dryland (67.5)354.1±52.644.6±41.8
    12Hongru RiverXuT/7013.922.67554.10.430Dryland (61.4)364.1±57.626.9±18.7
    13SuiPYes176096.45.08164.90.030Dryland (59.4)967.5±37.123.0±28.4
    14YangZYes103761.54.23141.90.122Dryland (65.4)512.8±14.026.0±14.8
    15WuGyYes1564107.82.37105.90.054Dryland (73.2)838.3±46.236.7±17.0
    16LiX/7818.06.50175.50.095Dryland (38.0)1039.8±32.924.6±26.3
    17ZhuMD/10417.82.74105.30.113Dryland (75.0)337.5±31.110.2±4.1
    18MiaoWYes266095.21.3981.40.026Dryland (77.8)1243.4±52.447.6±16.8
    19LuZ/39638.49.50214.70.031Forest (56.5)1424.7±29.318.4±9.9
    IDRiversStationsDam regulationCatchment area (km2)Slope length (km)Slope (%)Elevation (m)River density (km/km2)Major land use (%)Flood eventsPrecipitation (mm)Potential evapotranspiration (mm)
    20XinCYes4110178.50.8666.20.043Dryland (79.8)271.1±10.660.7±1.6
    21BanTYes11,280197.61.6988.50.008Dryland (74.2)418.6±26.134.9±28.8
    22GuiLYes105057.63.76133.30.108Dryland (67.0)631.1±38.131.5±10.4
    23Southern mountainous riversTanJH/15224.320.33279.90.040Forest (88.2)1281.2±54.925.7±12.7
    24ZhuGPYes163994.27.11159.80.036Forest (43.6)1475.6±66.051.4±19.9
    25XinXYes27431.519.40286.10.071Dryland (57.7)1679.2±54.142.8±32.2
    26HuangNZ/80548.924.03487.40.006Forest (46.5)1025.1±34.524.5±9.6
    27QiL/18531.426.65531.50.006Forest (58.3)1429.6±31.215.6±6.6
    28HuangCYes2050117.96.76156.70.036Dryland (47.7)652.2±30.184.6±38.7
    29BeiMJ/1710111.62.88101.90.037Paddy (47.7)1673.4±37.756.1±17.2
    30JiangJJYes5930161.211.99246.60.008Forest (36.5)854.5±34.361.8±30.6
    31PeiHYes188.630.98390.30.167Forest (100.0)987.9±54.222.6±12.4
    32Huaihe mainstreamDaPLYes164070.06.68218.20.111Forest (45.9)1358.1±36.644.9±21.5
    33ChangTGYes309078.05.51185.50.031Dryland (41.1)1479.6±45.953.4±22.9
    34XiXYes10,190124.74.86148.20.008Dryland (36.2)1084.5±46.075.8±24.5
    35HuaiBYes16,005138.73.93125.00.005Dryland (43.2)1060.2±43.196.1±46.4
    36LuTZYes88,630232.34.14147.70.001Dryland (54.3)573.6±70.873.7±46.4
    37BengBYes121,330279.33.23123.60.001Dryland (57.2)446.53±39.747.7±31.4
    38WangWQYes20033.20.9163.30.131Dryland (91.4)1228.8±27.524.3±11.9
    39WangJBYes30,630159.82.77104.50.003Dryland (56.6)4107.1±59.1113.6±22.4
    Table 1.

    The general characteristics of controlled catchments, and the selected flood events

    CategoriesBehavior metricsAbbreviationUnitCalculation equation
    MagnitudeTotal amount of floodRsummm${{{R}_{sum}}=86.4\cdot {{10}^{-3}}\cdot {{Q}_{sum}}}/{A}\;={86.4\cdot {{10}^{-3}}\cdot \sum\limits_{t=tbegin}^{tend}{{{Q}_{t}}}}/{A}\;$
    Maximum peak floodQpknone${{{Q}_{pk}}={{{Q}_{t,\max }}}/{{{Q}_{sum}}}\;=\max ({{Q}_{t}})}/{{{Q}_{sum}}}\;$
    DurationFlood event durationTdurationd${{T}_{duration}}={{F}_{end}}-{{F}_{begin}}+1$
    TimingTiming of flood eventFbegind${{{T}_{pk}}=({{F}_{pk,\max }}-{{F}_{begin}}+1)}/{{{T}_{duration}}}\;$
    Timing of maximum peak floodTpknone
    Rate of changesMean rate of positive changesRQrise1/hr$R{{Q}_{rise}}=\frac{({{Q}_{t,\max }}-{{Q}_{Fbegin}})}{[{{Q}_{sum}}\cdot ({{t}_{pk,\max }}-{{F}_{begin}})\cdot 24]}$
    Mean rate of negative changesRQdown1/hr$R{{Q}_{down}}=\frac{({{Q}_{t,\max }}-{{Q}_{Fend}})}{[{{Q}_{sum}}\cdot ({{F}_{end}}-{{t}_{pk,\max }}+1)\cdot 24]}$
    Flood formsNumber of peak floodNpknone$CV={\sigma }/{{{Q}_{av}}}\;$
    Coefficient of variationCVnone
    Table 2.

    Flood behavior metrics used for flood event descriptions

    Factor categoriesFactorsFlood event implications
    GeographyLocationLongitude and latitude (Long and Latt)All the behavior categories
    CatchmentArea (Cat_A, km2), average elevation (Cat_ae, m), slope (Cat_slp, %) and length (Cat_len, km)Magnitude, rate of changes and forms
    RiverSlope (Rch_slp, %) and length (Rch_len, km), with-depth ratio (Rch_wdr, m/m), river density (Rch_den, km/km2)Magnitude, rate of changes and forms
    Land useLand use areaPaddy (Lu_pad, km2), dryland (Lu_dry, km2), forest (Lu_fst, km2), grass (Lu_grs, km2), water (Lu_wat, km2), urban (Lu_urb, km2) and unused land (Lu_uns, km2)Magnitude, rate of changes and forms
    Hydrometeoro- logyPrecipitationCumulative amount in the antecedent three, five and seven days (Pcp_3d, Pcp_5d, Pcp_7d, mm) and during the flood event (Pcp_tot, mm), annual amount (Pcp_ann, mm) and ratio of flood season (R_fldpcp)All the behavior categories
    Potential evapotranspirationCumulative amount in the antecedent three, five and seven days (Pet_3d, Pet_5d and Pet_7d, mm) and during the flood event (Pet_tot, mm), annual amount (Pet_ann, mm) and ratio of flood season (R_fldpet)Magnitude
    BaseflowBaseflow index (BFI)Magnitude, duration and forms
    Human regulationWater storage projectNumber (Num_rsv), total and beneficial capacities (Tot_rsv and Use_rsv, 108 m3), and their ratios of annual average runoff magnitude (R_totrsv and R_usersv)All the behavior categories
    Water diversion projectNumber (Num_wdp) and total capacities (Tot_wdp, 108 m3)Magnitude
    Water pumping projectNumber (Num_wpp) and total capacities (Tot_wpp, 108 m3Magnitude
    Water transferring projectTotal capacity (Tot_wtp, 108 m3)Magnitude
    Table 3.

    Potential impact factor categories used to analyze the space and time variations of flood events

    Yongyong ZHANG, Qiutan CHEN, Jun XIA. Investigation on flood event variations at space and time scales in the Huaihe River Basin of China using flood behavior classification[J]. Journal of Geographical Sciences, 2020, 30(12): 2053
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