Projected warming will have significant impacts on snowfall accumulation and melt, with implications for water availability and management in snow-dominated regions. Changes in snowfall extremes are confounded by projected increases in precipitation extremes. Downscaled climate projections from 20 global climate models were bias-corrected to montane Snowpack Telemetry stations across the western United States to assess mid-21st century changes in the mean and variability of annual snowfall water equivalent (SFE) and extreme snowfall events, defined by the 90th percentile of cumulative 3 day SFE amounts. Declines in annual SFE and number of snowfall days were projected for all stations. Changes in the magnitude of snowfall event quantiles were sensitive to historical winter temperature. At climatologically cooler locations, such as in the Rocky Mountains, changes in the magnitude of snowfall events mirrored changes in the distribution of precipitation events, with increases in extremes and less change in more moderate events. By contrast, declines in snowfall event magnitudes were found for all quantiles in warmer locations. Common to both warmer and colder sites was a relative increase in the magnitude of snowfall extremes compared to annual SFE and a larger fraction of annual SFE from snowfall extremes. The coefficient of variation of annual SFE increased up to 80% in warmer montane regions due to projected declines in snowfall days and the increased contribution of snowfall extremes to annual SFE. In addition to declines in mean annual SFE, more frequent low-snowfall years and less frequent high-snowfall years were projected for every station.
Water resources in the western United States are contingent on interannual variations in snowpack. Interannual snowpack variability has been attributed to large-scale climate patterns including the El Niño-Southern Oscillation (ENSO), however, the contribution of snowfall frequency and extreme snowfall events to this variability are less well quantified. Long-term records from Snowpack Telemetry and Cooperative Observer Program stations in the 11 western states were used to investigate these relationships by considering the number of snowfall days and snowfall water equivalent (SFE) of extreme snowfall events. The top decile of snowfall events contributed 20-38% of annual SFE, depending on the region. An average of 65% and 69% of the interannual variability in annual SFE was explained by snowfall days and SFE of top decile snowfall events, respectively, with extreme events being a more significant predictor at most stations. The latitudinal dipole in SFE during ENSO phases results from changes in snowfall frequency and extreme events. In the Pacific Northwest, above normal SFE during La Niña winters was a product of both larger contributions from extremes and more snowfall days, while below normal SFE during El Niño winters was primarily associated with a substantial reduction in extremes. Conversely, annual SFE during ENSO phases in the mountains of Arizona was more closely linked to fluctuations in snowfall days than extremes. Results indicate the importance of extreme snowfall events in shaping interannual variability in water resources and suggest that improved predictive ability may inform better water resource management now and in the coming decades.
Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long‐term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi‐population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub‐models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi‐pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi‐population viability analysis bridges the gap between site‐level field observations and population‐level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.
The near-surface air temperature lapse rate is the predominant source of spatial temperature variability in mountains and controls snowfall and snowmelt regimes, glacier mass balance, and species distributions. Lapse rates are often estimated from observational data, however there is little guidance on best practices for estimating lapse rates. We use observational and synthetic datasets to evaluate the error and uncertainty in lapse rate estimates stemming from sample size, dataset noise, covariate collinearity, domain selection, and estimation methods. We find that lapse rates estimated from small sample sizes (<5) or datasets with high noise or collinearity can have errors of several C km −1. Uncertainty in lapse rates due to non-elevation related largescale temperature variability was reduced by correcting for spatial temperature gradients and restricting domains based on spatial clusters of stations. We generally found simple linear regression to be more robust than multiple linear regression for lapse rate estimation. Finally, lapse rates had lower error and uncertainty when estimated from a sample of topoclimatically self-similar stations. Motivated by these results, we outline a set of best practices for lapse rate estimation that include using quality controlled temperature observations from as many locations as possible within the study domain, accounting for and minimizing non-elevational sources of climatic gradients, and calculating lapse rates using simple linear regression across topoclimatically self-similar samples of stations which are roughly 80% of the station population size.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.