• 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

    Abstract

    Flood is one of the severest natural disasters in the world and has caused enormous causalities and property losses. Previous studies usually focus on flood magnitude and occurrence time at event scale, which are insufficient to contain entire behavior characteristics of flood events. In our study, nine behavior metrics in five categories (e.g., magnitude, duration, timing, rates of changes and variability) are adopted to fully describe a flood event. Regional and interannual variations of representative flood classes are investigated based on behavior similarity classification of numerous events. Contributions of geography, land use, hydrometeorology and human regulation on these variations are explored by rank analysis method. Results show that: five representative classes are identified, namely, conventional events (Class 1, 61.7% of the total), low discharge events with multiple peaks (Class 2, 5.3%), low discharge events with low rates of changes (Class 3, 18.1%), low discharge events with high rates of changes (Class 4, 10.8%) and high discharge events with long durations (Class 5, 4.1%). Classes 1 and 3 are the major flood events and distributed across the whole region. Class 4 is mainly distributed in river sources, while Classes 2 and 5 are in the middle and down streams. Moreover, the flood class is most diverse in normal precipitation years (2006, 2008-2010 and 2015), followed by wet years (2007, 2013-2014), and dry years (2011 and 2012). All the impact factor categories explain 34.0%-84.1% of individual flood class variations. The hydrometeorological category (7.2%-56.9%) is the most important, followed by geographical (1.0%-6.3%), regulation (1.7%-5.1%) and land use (0.9%-2.2%) categories. This study could provide new insights into flood event variations in a comprehensive manner, and provide decision-making basis for flood control and resource utilization at basin scale.
    ${{A}_{j}}\text{=}{{({{a}_{1j}},{{a}_{2j}},\ldots ,{{a}_{mj}})}^{\prime }} $(1)

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    $\left\{ \begin{matrix} & a_{_{ij}}^{*}\text{=}{({{a}_{ij}}-{{{\bar{a}}}_{i}})}/{\sqrt{{{\sigma }_{i}}}}\; \\ & A_{_{j}}^{*}\text{=}\left( a_{_{1j}}^{*},a_{_{2j}}^{*},\ldots ,a_{_{mj}}^{*} \right)' \\ \end{matrix} \right. $(2)

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    $PC{{A}_{k}}=\sum\limits_{i=1}^{m}{{{p}_{ki}}\cdot a_{_{ij}}^{*}}=\left( {{p}_{k1}},{{p}_{k2}},\ldots ,{{p}_{km}} \right)\cdot A_{_{j}}^{*} $(3)

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    $R=\left( \begin{matrix} \begin{matrix} {{r}_{11}} & {{r}_{12}} \\ {{r}_{21}} & {{r}_{22}} \\ \end{matrix} & \begin{matrix} \cdots & {{r}_{1m}} \\ \cdots & {{r}_{2m}} \\ \end{matrix} \\ \begin{matrix} \vdots & \vdots \\ {{r}_{m1}} & {{r}_{m2}} \\ \end{matrix} & \begin{matrix} \ddots & \vdots \\ \cdots & {{r}_{mm}} \\ \end{matrix} \\ \end{matrix} \right)$ (4)

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    $\left| \lambda \cdot I-R \right|=0 $(5)

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    ${{R}_{i}}=\frac{{{\lambda }_{i}}}{\sum\limits_{i=1}^{m}{{{\lambda }_{i}}}}\times 100% $(6)

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    $E{{D}_{ij}}=\sqrt{\sum\limits_{k=1}^{l}{{{(PC{{A}_{ki}}-PC{{A}_{kj}})}^{2}}}} $(7)

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    ${{F}_{j}}\text{=}{{\left( {{F}_{1j}},{{F}_{2j}},\ldots ,{{F}_{kj}} \right)}^{\prime }} $(8)

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    ${{y}_{j}}=\text{ }\!\!~\!\!\text{ }\frac{\sum\limits_{i=1}^{k}{{{F}_{ij}}\cdot {{x}_{i}}}}{\sum\limits_{i=1}^{k}{{{F}_{ij}}}} $(9)

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    ${{x}_{j}}=\text{ }\!\!~\!\!\text{ }\frac{\sum\limits_{j=1}^{n}{{{F}_{ij}}\cdot {{y}_{i}}}}{\sum\limits_{j=1}^{n}{{{F}_{ij}}}} $(10)

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    $\sum\limits_{i=1}^{k}{{{F}_{ij}}\cdot {{x}_{i}}}=0\begin{matrix} \begin{matrix} {} & \text{and} \\ \end{matrix} & {} \\ \end{matrix}\sum\limits_{i=1}^{k}{{{F}_{ij}}\cdot x_{_{i}}^{2}}=1$ (11)

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    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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