Environmental flows (e‐flows) are powerful tools for sustaining freshwater biodiversity and ecosystem services, but their widespread implementation faces numerous social, political, and economic barriers. These barriers are amplified in water‐limited systems where strong trade‐offs exist between human water needs and freshwater ecosystem protection. We synthesize the complex, multidisciplinary challenges that exist in these systems to help identify targeted solutions to accelerate the adoption and implementation of environmental flows initiatives. We present case studies from three water‐limited systems in North America and synthesize the major barriers to implementing environmental flows. We identify four common barriers: (a) lack of authority to implement e‐flows in water governance structures, (b) fragmented water governance in transboundary water systems, (c) declining water availability and increasing variability under climate change, and (d) lack of consideration of non‐biophysical factors. We then formulate actionable recommendations for decision makers facing these barriers when working towards implementing environmental flows: (a) modify or establish a water governance framework to recognize or allow e‐flows, (b) strive for collaboration across political jurisdictions and social, economic, and environmental sectors, and (c) manage adaptively for climate change in e‐flows planning and recommendations.This article is categorized under: Water and Life > Conservation, Management, and Awareness Human Water > Water Governance Engineering Water > Planning Water

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin‐wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach‐scale geomorphic channel types using publicly available geospatial data. A bottom‐up machine learning approach selects the most accurate and stable model among 20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three‐tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred‐meter reaches. Performance in statistical learning is reasonable with a 61% median cross‐validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large‐scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy‐based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors.

Streamflow patterns are shifting with climate change, and these shifts pose increasing risk to freshwater ecosystems. These emerging changes must be linked with ecological functions of river systems to understand how climate change may affect freshwater biota. In this study we used a functional flows approach to analyze the ecological effects of changing streamflow patterns in snowmelt‐dominated watersheds of the Sierra Nevada mountains of California. Our climate change modeling method combined ensemble Global Climate Models (GCMs) and decision scaling methods to incorporate the effects of GCM‐projected changes in precipitation variability on streamflow patterns. Of climate parameters explored, air temperature causes the most change in functional flows, although precipitation variability compounds changes driven by air temperature. The greatest changes in ecologically‐relevant streamflow patterns manifest as longer and drier dry season conditions, earlier snowmelt recession, and higher‐magnitude peak flows. Although the directions of changes in functional flow metrics are largely consistent across models and watersheds, the magnitude of change depends strongly on human emissions levels. The analytical approach used in this study can serve as a model for integrating multiple approaches in hydroclimatic assessment of ecological change, and the results can help prioritize specific aspects of the flow regime for restoration efforts.

Sandoval-Solis, S., S. Paladino, L. E. Garza-Diaz, L. F. Nava, J. R. Friedman, J. Ortiz-Partida, S. Plassin, G. Gomez-Quiroga, J. A. Koch, J. Fleming, B. A. Lane, S. Wineland, A. Mirchi, R. Saiz-Rodriguez, and T. Neeson. 2022. Environmental flows in the Rio Grande - Rio Bravo basin. Ecology and Society 27(1):20. https://doi.org/10.5751/ES-12944-270120