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

In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions.

Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.