Cumulative Impacts of Stream Restoration on Watershed-Scale Flood Attenuation, Floodplain Inundation, and Nitrate Removal

Severe flooding and excess nutrient pollution, exacerbated by heightened anthropogenic pressures (e.g., climate change, urbanization, land use change, unsustainable agricultural practices), have been detrimental to riverine systems and their estuaries. The degradation of riverine systems can negatively impact human and environmental health, as well as local, regional, and even global economies. Floods provide beneficial ecosystem services (e.g., processing pollutants, transferring nutrients and sediment, supporting biodiversity), but they can also damage infrastructure and result in the loss of human life. Meanwhile, eutrophication can cause anoxic dead zones, harming aquatic ecosystems and public health. To address the issues facing riverine systems, focus has shifted to watershed-scale management plans. However, it can prove challenging to quantify the cumulative impacts of multiple stream restoration projects within a single watershed on flooding and nutrient removal. Previous studies have quantified the effects of stream restoration on flood attenuation. However, our first study fills a substantial knowledge gap by evaluating the impacts of different floodplain restoration practices, varied by location and length, on flood attenuation and floodplain inundation dynamics at the watershed scale during more frequent storm recurrence intervals (i.e., 2-year, 1-year, 0.5-year, and monthly). We created a 1D HEC-RAS model to simulate the effects of Stage 0 restoration within a 4th-order generic watershed based on the Chesapeake Bay watershed. By varying the percent river length restored and location, we found that Stage 0 restoration, especially in 2nd-order rivers, can be particularly effective at enhancing flood attenuation and floodplain inundation locally and farther downstream. We addressed the water quality component by using a random forest machine learning approach coupled with artificial neural networks to find trends and predict nitrate removal rates associated with spatial, temporal, hydrologic, and restoration features. Our results showed that hydrologic conditions were the most important variable for predicting actual nitrate removal rates. Overall, both studies demonstrate the importance of hydrologic connectivity for flood attenuation, channel-floodplain exchange, and nutrient processing.