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Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management

Abstract

Regime-based approach recently becomes an important strategy while considering aquatic ecosystems in environmental flow management. The key element for supporting this strategy is long streamflow data which is usually not available for determining natural flow regimes. This study uses a back-propagation network to estimate ungauged natural flow regimes. A set of the upper reaches of Taiwan’s 42 flow stations with non-human control streamflow and at least 20 years daily flow data is used to quantify the natural flow regimes using 31 Indicators of Hydrologic Alteration (IHA). Watershed geomorphologic characteristic factors and rainfall parameters are used to classify homogeneous flow regime areas. The results show that there are three types of flow regimes from the flow stations, and each group of indicators in the IHA has different correlations with different geomorphologic characteristic factors and rainfall parameters. The results of using an artificial neural network model to estimate IHA show that the group average percent error fell from 21 % to 8 % and the average correlation coefficient was over 0.7, indicating that the model presented in this study is able to accurately estimate the natural flow regime in ungauged stations. Instead of predicting daily streamflow, this study estimates indicator values for ease of ecological water resources management.

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Acknowledgments

The authors gratefully acknowledge the support for this research provided in part by the National Science Council, Taiwan, under grant numbers NSC 100-2625-M-366-001-MY3 and NSC 102-2221-E-006-246-MY3, and in part by the Headquarters of University Advancement at the National Cheng Kung University, which is sponsored by the Ministry of Education, Taiwan.

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Correspondence to Jian-Ping Suen.

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Yang, HC., Suen, JP. & Chou, SK. Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management. Water Resour Manage 30, 4571–4584 (2016). https://doi.org/10.1007/s11269-016-1437-0

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  • DOI: https://doi.org/10.1007/s11269-016-1437-0

Keywords

  • Natural flow regimes
  • Ecological flow regimes
  • Ungauged station
  • Indicators of hydrologic alteration
  • Artificial neural network