4.7 Article

Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region, China

Journal

CATENA
Volume 206, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.catena.2021.105489

Keywords

Flood response; Data-driven analysis; Small and medium sized basins; The Yangtze River Delta

Funding

  1. National Key Research and Development Program of China [2018YFC1508201]
  2. National Natural Science Foundation of China [41771032]
  3. Office of Science, Office of Biological and Environmental Research of the US Department of Energy [DE-AC02-05CH11231]

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The study found that floods are becoming more frequent and harder to control due to the lack of water conservancy projects in small and medium-sized basins, especially in developing regions. By analyzing observational data and using models, it was determined that physical geographic characteristics and rainfall play key roles in influencing flood characteristics.
Floods are becoming more frequent and are hard to control due to the shortage of water conservancy projects in small and medium sized basins, especially in developing regions. Understanding the hydrologic responses and estimated flood characteristics to storm events can help to predict flood disasters and form better effective mitigation and adaptation strategies. Using observations of several representative watersheds and Artificial Neural Networks (ANN) and Principal Component Regression (PCR) models, we conducted data-driven analyses to examine the flood responding characteristics. We quantified the relative contributions of the influencing factors to the variation of each flood characteristic. Statistical analysis of the observations shows that the drainage area plays a key role in determining the distribution of lag time and peak discharge. Rainstorm variability has direct influence on floods, and typhoon-induced rainstorms with high total rainfall and rainfall intensity generate higher lag time, flood peak, unit discharge and runoff depth, but lower runoff coefficient. The ANN and PCR models accurately predicted the variations of flood features using the driving factors including physical geographical characteristics, rainstorm features, and antecedent condition. Physical geographic characteristics are key influential factors of lag time, flood peak and runoff coefficient, while the rainstorm features control the magnitude of unit discharge and runoff depth. These results indicate that floods are mainly affected by rainstorm features and physical geographic characteristics in the Yangtze River delta, and it might become more damaging with the increasing rainfall extremes and sprawling impervious surfaces in a changing environment.

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