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With an increasing number of continental‐scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be “properly” calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash‐Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture‐and‐energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture‐and‐energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation.

Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions.

Effective conservation of freshwater biodiversity requires spatially explicit investigations of how dams and hydroclimatic alterations among climate regions may interact to drive species to extinction. We investigated how dams and hydroclimatic alterations interact with species ecological and life history traits to influence past extirpation probabilities of native freshwater fishes in the Upper and Lower Colorado River (CR), Alabama‐Coosa‐Tallapoosa (ACT), and Apalachicola‐Chattahoochee‐Flint (ACF) basins. Using long‐term discharge data for continuously gaged streams and rivers, we quantified streamflow anomalies (i.e., departure “expected” streamflow) at the sub‐basin scale over the past half‐century. Next, we related extirpation probabilities of native fishes in both regions to streamflow anomalies, river basin characteristics, species traits, and non‐native species richness using binomial logistic regression. Sub‐basin extirpations in the Southwest (n = 95 UpperCR,n = 130 LowerCR) were highest in lowland mainstem rivers impacted by large dams and in desert springs. Dampened flow seasonality, increased longevity (i.e., delayed reproduction), and decreased fish egg sizes (i.e., lower parental care) were related to elevated fish extirpation probability in the Southwest. Sub‐basin extirpations in the Southeast (ACTn = 46,ACFn = 22) were most prevalent in upland rivers, with flow dependency, greater age and length at maturity, isolation by dams, and greater distance upstream. Our results confirm that dams are an overriding driver of native fish species losses, irrespective of basin‐wide differences in native or non‐native species richness. Dams and hydrologic alterations interact with species traits to influence community disassembly, and very high extirpation risks in the Southeast are due to interactions between high dam density and species restricted ranges. Given global surges in dam building and retrofitting, increased extirpation risks should be expected unless management strategies that balance flow regulation with ecological outcomes are widely implemented.