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Bark beetles have impacted over 58 million acres of coniferous forest in the Western US since 2000. Most beetle impacted forests are in snow dominated, water limited headwater basins, which generate a disproportionate fraction of water supplies. Previous studies show mixed impacts of bark beetle outbreaks on streamflow with the potential to cause increased or decreased flows, but these studies either predate long‐term snowpack data, are model‐based, or examine only mountain pine beetle outbreaks. Ours is the first study to use an empirical, climate‐normalized paired catchment approach to quantify streamflow response to spruce beetle kill. Using 27 years of climate and streamflow observations from southwest Colorado, we show that in three of the six beetle impacted study basins, annual climate‐normalized streamflow increased by 22%–37% for at least three to 6 years after the beetle outbreak. Impacted basins exhibited no decreased flows and flows in unimpacted control basins remained unchanged. Among impacted basins, no single basin characteristic clearly explained variation of streamflow response. Higher runoff ratios during snowmelt contribute anywhere from 9% to 64% of streamflow increases, implying the importance of both snow and growing season processes in driving streamflow increases. These findings show variable, sometimes substantial streamflow increases in critical water supply basins following beetle kill in subalpine spruce forests, and contrast with evidence of unchanged or decreased streamflow following beetle kill in lower elevation pine forests in colder northern Colorado basins, highlighting the importance of climate and forest composition in refining hydrologic predictions following mountain forest disturbances. Plain Language Summary: Since 2000, bark beetles have damaged over 58 million acres of conifer forest in the Western US. Forest canopy has large but complex effects on snow and water use in mountain areas, which are important sources of streamflow. Previous studies exploring how beetle kill affects streamflow show mixed results, with some suggesting that beetle kill causes increased streamflow and others finding no significant effects or decreases. We use measurements of streamflow and climate in six beetle impacted basins and two unaffected basins in southwest Colorado to determine if streamflow changed after a spruce beetle outbreak. We found that streamflow increased by 22%–37% in three out of six study basins after beetle kill, while streamflow in unaffected basins did not change. Streamflow increased both during and before/after the snowmelt season. These findings suggest that beetle kill can substantially increase streamflow in ways that are not accounted for in water resource management and planning, but variable response between basins and forest types remains challenging to predict. Key Points: Streamflow increased in three of six mountain basins after a spruce beetle outbreak, while streamflow in control basins remained unchangedAmong impacted basins, no single factor or basin characteristic fully explained variation in streamflow response to beetle killHigher streamflow after spruce beetle contrasts with reported unchanged or lower streamflow after pine beetle in other Colorado basins [ABSTRACT FROM AUTHOR]

Long-term prediction of groundwater dynamics is important for assessing water resources and their impacts on biogeochemical cycling. However, estimating future groundwater dynamics is challenging due to the wide range of spatiotemporal scales in hydrological processes and uncertainty in future climate conditions. In this study, we develop a Bayesian model to combine small-scale historical hydrological data with large-scale climate information to estimate groundwater dynamics at a floodplain site in Rifle, Colorado. Although we have only a few years of groundwater elevation measurements, we have 47 years of streamflow data from a gaging station approximately 43 km upstream and long-term climate prediction on the Upper Colorado River Basin. To estimate future daily groundwater dynamics, we first develop a time series model to downscale the monthly streamflow derived from climate information to daily streamflow, and then transform the daily streamflow to groundwater dynamics at the downstream floodplain site. We use Monte Carlo methods to estimate future groundwater dynamics at the site through sampling from the joint posterior probability distribution. The results suggest that although future groundwater levels are expected to be similar to the current levels, the timing of the high groundwater levels is predicted to occur about 1 month earlier. The developed framework is extendable to other sites to estimate future groundwater dynamics given disparate data sets and climate projections. Additionally, the obtained estimates are being used as input to a site-specific watershed reactive transport models to predict how climate-induced changes will influence future biogeochemical cycling relevant to a variety of ecosystem services. [ABSTRACT FROM AUTHOR]

In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in seasonal snowpack to support local and regional water management. Previous studies have shown that assimilating satellite-station blended snow depth data sets can lead to improved snow predictions, which however do not always translate into improved streamflow predictions, especially in complex mountain regions. In this study, we explore how an existing optimal interpolation-based blending strategy can be enhanced to reduce biases in satellite snow depth products for improving streamflow predictions. Two major new considerations are explored, including: (1) incorporating terrain aspect and (2) incorporating areal snow coverage information. The methodology is applied to the bias reduction of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth estimates, which are then assimilated into the Noah land surface model via the ensemble Kalman Filtering (EnKF) for streamflow predictions in the Upper Colorado River Basin. Our results indicate that using only observations from low-elevation stations such as the Global Historical Climatology Network (GHCN) in the bias correction can lead to underestimation in streamflow, while using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) network) along with terrain aspect is critically important for achieving reliable streamflow predictions. Additionally incorporating areal snow coverage information from the Moderate Resolution Imaging Spectroradiometer (MODIS) can slightly improve the streamflow results further. [ABSTRACT FROM AUTHOR]

Spatial and temporal trends in stream chemistry were investigated in a large (1600 km2) alpine watershed in the southern Rocky Mountains of Colorado to help understand mechanisms of streamflow generation. We observed linear increases of concentrations of chemical constituents in streamflow as accumulated drainage area increased along the main channel of Saguache Creek. We tested two conceptual models of streamflow generation against our stream chemistry observations. One model is essentially two-dimensional and treats streamflow generation at the large watershed scale as the aggregation of runoff responses from individual hillslopes, primarily surface and shallow subsurface flow paths. Alternatively, a fully three-dimensional conceptual model treats streamflow generation as being controlled by a distribution of large-scale groundwater flow paths as well as surface and shallow subsurface flow paths. The structure and magnitude of groundwater contributions in streamflow as a function of increasing scale provided a key distinction between these two conceptual models. End-member mixing analysis and measurements of hydraulic head gradients in streambeds were used to quantify basin-scale groundwater contributions to streamflow with increasing spatial scale in the Saguache Creek watershed. Our data show that groundwater contributions are important in streamflow generation at all scales and, more importantly, that groundwater contributions to streamflow do increase with increasing watershed scale. These results favor the three-dimensional conceptual model in which long groundwater flow paths provide a streamflow generation process at large scales that is not operative at smaller scales. This finding indicates that large watersheds may be more than simply the aggregation of hillslopes and small catchments. [ABSTRACT FROM AUTHOR]

The scaling properties of channel-forming flows are investigated using a regional flow frequency model developed for snowmelt-dominated streams in Colorado. The model is derived from analyses of daily flow records at 32 gauging stations where we have independent measurements of the bankfull discharge. The study sites are located in alpine/subalpine basins with drainage areas ranging from 4 to 3700 km2. The frequency distribution of daily flows at these locations can be reproduced with a broken power law (BPL) function described by two free parameters. Both parameters are strongly correlated with drainage area, and based on these correlations, a regional model capable of predicting the frequency of daily flows above the mean annual flow was formulated. The applicability of the model was tested using daily flow records from 32 similar-size basins in Idaho. The frequency distributions of daily flows in snowmelt-dominated streams in Colorado and Idaho with highly predictable hydrographs (i.e., 1 year autocorrelation above 0.7) are well fitted by the BPL function. According to the model, the frequency of flows greater than bankfull decreases downstream from about 15 d/yr in headwater reaches to about 6 d/yr in downstream reaches. These results imply that the basin response to precipitation and runoff is nonlinear. This multiscaling behavior can be physically interpreted as the result of scale-dependent variations in runoff and sediment supply, which influence downstream trends in the bankfull channel geometry and intensity of sediment transport. [ABSTRACT FROM AUTHOR]