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Climate change is altering biogeochemical, metabolic, and ecological functions in lakes across the globe. Historically, mountain lakes in temperate regions have been unproductive because of brief ice‐free seasons, a snowmelt‐driven hydrograph, cold temperatures, and steep topography with low vegetation and soil cover. We tested the relative importance of winter and summer weather, watershed characteristics, and water chemistry as drivers of phytoplankton dynamics. Using boosted regression tree models for 28 mountain lakes in Colorado, we examined regional, intraseasonal, and interannual drivers of variability in chlorophyll a as a proxy for lake phytoplankton. Phytoplankton biomass was inversely related to the maximum snow water equivalent (SWE) of the previous winter, as others have found. However, even in years with average SWE, summer precipitation extremes and warming enhanced phytoplankton biomass. Peak seasonal phytoplankton biomass coincided with the warmest water temperatures and lowest nitrogen‐to‐phosphorus ratios. Although links between snowpack, lake temperature, nutrients, and organic‐matter dynamics are increasingly recognized as critical drivers of change in high‐elevation lakes, our results highlight the additional influence of summer conditions on lake productivity in response to ongoing changes in climate. Continued changes in the timing, type, and magnitude of precipitation in combination with other global‐change drivers (e.g., nutrient deposition) will affect production in mountain lakes, potentially shifting these historically oligotrophic lakes toward new ecosystem states. Ultimately, a deeper understanding of these drivers and pattern at multiple scales will allow us to anticipate ecological consequences of global change better. [ABSTRACT FROM AUTHOR]

Land managers often need to predict watershed-scale erosion rates after disturbance or other land cover changes. This study compared commonly used hillslope erosion models to simulate post-fire sediment yields (SY) at both hillslope and watershed scales within the High Park Fire, Colorado, U.S.A. At hillslope scale, simulated SY from four models— RUSLE, AGWA/KINEROS2, WEPP, and a site-specific regression model—were compared to observed SY at 29 hillslopes. At the watershed scale, RUSLE, AGWA/KINEROS2, and WEPP were applied to simulate spatial patterns of SY for two 14–16 km2 watersheds using different scales (0.5–25 ha) of hillslope discretization. Simulated spatial patterns were compared between models and to densities of channel heads across the watersheds. Three additional erosion algorithms were implemented within a land surface model to evaluate effects of parameter uncertainty. At the hillslope scale, SY was only significantly correlated to observed SY for the empirical model, but at the watershed scale, sediment loads were significantly correlated to observed channel head densities for all models. Watershed sediment load increased with the size of the hillslope sub-units due to the nonlinear effects of hillslope length on simulated erosion. SY's were closest in magnitude to expected watershed-scale SY when models were divided into the smallest hillslopes. These findings demonstrate that current erosion models are fairly consistent at identifying areas with low and high erosion potential, but the wide range of predicted SY and poor fit to observed SY highlight the need for better field observations and model calibration to obtain more accurate simulations. • Evaluated performance of erosion models at hillslope and watershed scales. • Simulated hillslope sediment yields did not correlate with measured values. • Simulated watershed sediment yields were correlated with observed rill densities. • Longer hillslopes within watershed simulations led to greater sediment loads. • Parameter uncertainty caused >2 orders of magnitude variability in sediment yields. [ABSTRACT FROM AUTHOR]

Erosion is one of the primary land management concerns following wildfire. This study examines controls on post-fire hillslope-scale erosion for the 2012 High Park Fire in northern Colorado, develops simple empirical models for predicting post-fire sediment yields, and evaluates model performance on several nearby fires. From 2013 to 2015 we collected ground cover, rainfall, topographic, and sediment yield measurements from 29 convergent hillslopes; eight of these hillslopes had varying amounts of mulch applied to reduce erosion. From these data we examined correlations between annual sediment yield and three categories of predictor variables (ground cover, precipitation, and topography). Percent bare soil was the single largest control on sediment yield, followed by rainfall variables. Sediment yield generally decreased with flow path length, but the correlation was weak. The empirical models each predicted sediment yield with three variables: percent bare soil, one precipitation variable, and one topographic variable. The models had similar accuracy for the High Park Fire using varying combinations of precipitation and topographic variables (Nash-Sutcliffe coefficients 0.70–0.84). An empirical model predicting annual sediment yields as a function of percent bare soil, June–October precipitation, and the maximum flow path length had variable performance when applied to other fires in the same region, with predictions ranging from poor to good for individual fires and Nash-Sutcliffe coefficients of 0.26–0.32 for all fires combined. These tests show some promise for applying the empirical model to fires in the study region, but further model testing is needed to determine the range of conditions under which the model can be applied. [ABSTRACT FROM AUTHOR]

A small but growing number of watershed investment programs in the western United States focus on wildfire risk reduction to municipal water supplies. This paper used return on investment (ROI) analysis to quantify how the amounts and placement of fuel treatment interventions would reduce sediment loading to the Strontia Springs Reservoir in the Upper South Platte River watershed southwest of Denver, Colorado following an extreme fire event. We simulated various extents of fuel mitigation activities under two placement strategies: (a) a strategic treatment prioritization map and (b) accessibility. Potential fire behavior was modeled under each extent and scenario to determine the impact on fire severity, and this was used to estimate expected change in post-fire erosion due to treatments. We found a positive ROI after large storm events when fire mitigation treatments were placed in priority areas with diminishing marginal returns after treating >50–80% of the forested area. While our ROI results should not be used prescriptively they do show that, conditional on severe fire occurrence and precipitation, investments in the Upper South Platte could feasibly lead to positive financial returns based on the reduced costs of dredging sediment from the reservoir. While our analysis showed positive ROI focusing only on post-fire erosion mitigation, it is important to consider multiple benefits in future ROI calculations and increase monitoring and evaluation of these benefits of wildfire fuel reduction investments for different site conditions and climates. [ABSTRACT FROM AUTHOR]