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Davis College of Agriculture, Natural Resources and Design


Agricultural & Extension Education


Determining environmental flow requirements to sustain aquatic ecosystem health remains a challenge. The purpose of this research was to quantify the extent of current flow alterations relative to baseline hydrologic conditions of a simulated historic flow regime prior to anthropogenic flow disturbance (i.e., pre-settlement flows). Results allowed assessment of the efficacy of environmental flow targets based on pre-settlement land cover in a contemporary mixed-land-use catchment (i.e., urban, agricultural, and forested). Pre-settlement flows were simulated using the Soil and Water Assessment Tool (SWAT). Pre-settlement land cover, based on soil physical characteristics, was used to simulate pre-settlement flows with the SWAT model. Environmental flow targets were calculated for each flow element of a historic flow regime (magnitude, frequency, duration, timing, and rate of change). Urban (20% of watershed area) and agricultural development (42% of watershed area) were correlated to decreased median daily stream flow by 0.8 m3 s−1 (percent difference = −115%), increased maximum daily flow by 22 m3 s−1 (percent difference = 13%), and a 34% increase in daily flow variability. High flow frequency increased by 45–76% following development. Results highlight a need for consideration of environmental flow targets appropriate for watersheds already modified by existing land use, and point to a need for long-term, broad-scale, and persistent efforts to develop achievable environmental flow recommendations, particularly in rapidly urbanizing mixed-land-use watersheds.

Source Citation

Zeiger, S., & Hubbart, J. (2018). Assessing Environmental Flow Targets Using Pre-Settlement Land Cover: A SWAT Modeling Application. Water, 10(6), 791.


  1. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (



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