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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Modeling streamflow driven by climate change in data-scarce mountainous basins.

Mengtian Fan1, Jianhua Xu1, Yaning Chen2

  • 1Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China; Research Center for East-West Cooperation in China, East China Normal University, Shanghai 200241, China.

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Summary

Climate change impacts mountainous water resources. This study models streamflow in data-scarce basins using Earth system data and advanced AI, finding decreased river flows and identifying key climate drivers.

Keywords:
Climate changeData-scarce mountainous basinsIntegrated modelingStreamflow simulation

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Area of Science:

  • Hydrology
  • Climate Science
  • Artificial Intelligence

Background:

  • Climate change poses significant threats to water resources, particularly in mountainous regions.
  • Data scarcity in mountainous basins hinders accurate streamflow and water resource simulation.
  • Global warming exacerbates water supply challenges in high-altitude areas.

Purpose of the Study:

  • To develop and validate a novel method for modeling streamflow in data-scarce mountainous basins.
  • To identify the primary climatic drivers influencing streamflow in the Tienshan mountains.
  • To assess long-term streamflow trends in the Aksu and Kaidu River headwaters.

Main Methods:

  • Reconstruction of precipitation and temperature dynamics using Earth system data products.
  • Integration of radial basis function artificial neural networks (RBF-ANN) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN-AN).
  • Streamflow modeling in the Aksu and Kaidu River headwaters, data-scarce mountainous basins.

Main Results:

  • The proposed RBF-ANN-CEEMDAN-AN model demonstrated high accuracy in streamflow simulation compared to observed data.
  • El-Niño Southern Oscillation (ENSO), temperature, precipitation, and North Atlantic Oscillation (NAO) were identified as key streamflow drivers.
  • Streamflow in both the Aksu and Kaidu Rivers showed a decreasing trend between 2000 and 2017.

Conclusions:

  • The developed modeling approach effectively addresses streamflow simulation challenges in data-scarce mountainous environments.
  • Climate variability and oscillations significantly influence mountainous water resources.
  • Observed streamflow declines highlight the vulnerability of these regions to ongoing climate change.