10 Jan 2022
10 Jan 2022
Status: a revised version of this preprint was accepted for the journal HESS.

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Stephen Adams1, Brian Bledsoe2, and Eric Stein3 Stephen Adams et al.
  • 1Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80525, USA
  • 2Institue for Resilient Infrastructure Systems, College of Engineering, University of Georgia, Athens, GA, 30602, USA
  • 3Southern California Coastal Water Research Project, Costa Mesa, CA, 92626, USA

Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow-ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning a gradient of urbanization, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this “Hydrologic Model-based Classification” (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins, while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall-runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins, and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the south coast region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an Average Cluster Error metric, results show HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic Model-based Classification is relatively complex and time-consuming to implement, but it shows potential for advancing ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches, and suggests that Hydrologic Model-based Classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

Stephen Adams et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-553', Anonymous Referee #1, 20 Mar 2022
    • AC1: 'Reply on RC1', Stephen Adams, 12 Jun 2022
  • RC2: 'Comment on hess-2021-553', Anonymous Referee #2, 31 Mar 2022
    • AC2: 'Reply on RC2', Stephen Adams, 12 Jun 2022

Stephen Adams et al.

Stephen Adams et al.


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Short summary
Managing for environmental streamflows involve the practice of prioritizing healthy stream ecosystems while distributing water resources. Classifying similar streams is a useful step in developing environmental streamflows. Environmental streamflows are often needed on streams that must be modeled because they do not contain flow data. This paper has developed a new method of classification that prioritizes model accuracy. The new method advances environmental streamflow management.