Prescribing Flood Regimes to Sustain Riparian Ecosystems along Meandering Rivers

Abstract

Abstract: The composition and structure of native riverine ecosystems are tightly linked to natural hydrologic variability. By managing river flows for water supplies and power generation, water management agencies have inadvertently caused considerable degradation of riverine ecosystems and associated biodiversity. New approaches for meeting human needs for water while conserving the ecological integrity of riverine ecosystems are greatly needed. We describe an approach for identifying the natural flooding characteristics that must be protected or restored to maintain riparian ( floodplain) ecosystems along meandering rivers. We developed a computer model to simulate flood‐driven changes in the relative abundance of riparian patch types along the Yampa River in Colorado ( U.S.A.). The model is based on research suggesting that the duration of flooding at or above 209 m 3 per second (125% of bankfull discharge) is particularly important in driving lateral channel migration, which is responsible for initiating ecological succession in the Yampa's riparian forest. Other hydrologic variables, such as the magnitude of annual peak flows, were not as strongly correlated with lateral channel migration rates. Model simulations enabled us to tentatively identify a threshold of alteration of flood duration that could lead to substantial changes in the abundance of forest patch types over time should river flows be regulated by future water projects. Based on this analysis, we suggest an ecologically compatible water management approach that avoids crossing flood alteration thresholds and provides opportunity to use a portion of flood waters for human purposes. Recommended improvements to the Yampa model include obtaining additional low‐elevation aerial photographs of the river corridor to enable better estimation of channel migration rates and vegetation changes. These additional data should greatly improve the model's accuracy and predictive capabilities and therefore its management value.