Changing forest water yields in response to climate warming: results from long-term experimental watershed sites across North America

Climate warming is projected to affect forest water yields but the effects are expected to vary. We investigated how forest type and age affect water yield resilience to climate warming. To answer this question, we examined the variability in historical water yields at long-term experimental catchments across Canada and the United States over 5-year cool and warm periods. Using the theoretical framework of the Budyko curve, we calculated the effects of climate warming on the annual partitioning of precipitation (P) into evapotranspiration (ET) and water yield. Deviation (d) was defined as a catchment’s change in actual ET divided by P [AET/P; evaporative index (EI)] coincident with a shift from a cool to a warm period – a positive d indicates an upward shift in EI and smaller than expected water yields, and a negative d indicates a downward shift in EI and larger than expected water yields. Elasticity was defined as the ratio of inter annual variation in potential ET divided by P (PET/P; dryness index) to inter annual variation in the EI – high elasticity indicates low d despite large range in drying index (i.e., resilient water yields), low elasticity indicates high d despite small range in drying index (i.e., non-resilient water yields). Although the data needed to fully evaluate ecosystems based on these metrics are limited, we were able to identify some characteristics of response among forest types. Alpine sites showed the greatest sensitivity to climate warming with any warming leading to increased water yields. Conifer forests included catchments with lowest elasticity and stable to larger water yields. Deciduous forests included catchments with intermediate elasticity and stable to smaller water yields. Mixed coniferous/deciduous forests included catchments with highest elasticity and stable water yields. Forest type appeared to influence the resilience of catchment water yields to climate warming, with conifer and deciduous catchments more susceptible to climate warming than the more diverse mixed forest catchments

Diagnosing a distributed hydrologic model for two high-elevation forested catchments based on detailed stand- and basin-scale data

This study evaluates the performance and internal structure of the distributed hydrology soil vegetation model (DHSVM) using 1998-2001 data collected at Upper Penticton Creek, British Columbia, Canada. It is shown that clear-cut snowmelt rates calculated using data-derived snow albedo curves are in agreement with observed lysimeter outflow. Measurements in a forest stand with 50% air crown closure suggest that the fraction of shortwave radiation transmitted through the canopy is 0.18-0.28 while the hemispherical canopy view factor controlling longwave radiation fluxes to the forest snowpack is estimated at 0.81 ± 0.07. DHSVM overestimates shortwave transmittance (0.50) and underestimates the view factor (0.50). An alternative forest radiation balance is formulated that is consistent with the measurements. This new formulation improves model efficiency in simulating streamflow from 0.84 to 0.91 due to greater early season melt that results from the enhanced importance of longwave radiation below the canopy. The model captures differences in canopy rainfall interception between small and large storms, tree transpiration measured over a 6-day summer period, and differences in soil moisture between a dry and a wet summer. While the model was calibrated to 1999 snow water equivalent (SWE) and hydrograph data for the untreated control basin, it successfully simulates forest and clear-cut SWE and streamflow for the 3 other years and 4 years of preharvesting and postharvesting streamflow for the second basin. Comparison of model states with the large array of observations suggests that the modified model provides a reliable tool for assessing forest management impacts in the region.Mark Thyer, Jos Beckers, Dave Spittlehouse, Younes Alila, and Rita Winkle

Changing forest water yields in response to climate warming: results from long-term experimental watershed sites across North America

Climate warming is projected to affect forest water yields but the effects are expected to vary. We investigated how forest type and age affect water yield resilience to climate warming. To answer this question, we examined the variability in historical water yields at long‐term experimental catchments across Canada and the United States over 5‐year cool and warm periods. Using the theoretical framework of the Budyko curve, we calculated the effects of climate warming on the annual partitioning of precipitation (P) into evapotranspiration (ET) and water yield. Deviation (d) was defined as a catchment's change in actual ET divided by P [AET/P; evaporative index (EI)] coincident with a shift from a cool to a warm period – a positive d indicates an upward shift in EI and smaller than expected water yields, and a negative d indicates a downward shift in EI and larger than expected water yields. Elasticity was defined as the ratio of interannual variation in potential ET divided by P (PET/P; dryness index) to interannual variation in the EI – high elasticity indicates low d despite large range in drying index (i.e., resilient water yields), low elasticity indicates high d despite small range in drying index (i.e., nonresilient water yields). Although the data needed to fully evaluate ecosystems based on these metrics are limited, we were able to identify some characteristics of response among forest types. Alpine sites showed the greatest sensitivity to climate warming with any warming leading to increased water yields. Conifer forests included catchments with lowest elasticity and stable to larger water yields. Deciduous forests included catchments with intermediate elasticity and stable to smaller water yields. Mixed coniferous/deciduous forests included catchments with highest elasticity and stable water yields. Forest type appeared to influence the resilience of catchment water yields to climate warming, with conifer and deciduous catchments more susceptible to climate warming than the more diverse mixed forest catchments

Interaction of elevation and climate change on fire weather risk

Most wildfire studies are regional to global in scale; however, many of the values of interest and the weather are local phenomenon that may give rise to large spatial variability in risk. We assessed the interaction of elevation and climate on fire weather for the Penticton Creek watershed in south-western Canada for historic weather, and five climate change scenarios. 100-year records of daily temperature and precipitation were generated using the LARS-WG5 weather generator, and used to calculate the Fire Weather Indices of the Canadian Forest Fire Danger Rating System. Fire season length, restricted activity season and fire season severity are all projected to increase by the 2050s and in some scenarios to increase further by the 2080s. Low and mid-elevations had substantially worsening risks, whereas at the highest elevations risks were mitigated by the continuation of the snowpack. Increasing temperatures lengthened the fire season while decreasing (increasing) precipitation exacerbated (ameliorated) the intensity of the fire risk. These results indicated more variable climate change effects than in the literature. Over 24 million kmThe accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data

Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.

<p>Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.</p

Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.

<p>Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.</p

The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.

<p>The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.</p

Proportions of variance explained (<i>R</i> square values) by local linear regression in total variation among the nine neighbor pixels for monthly minimum and maximum temperatures, and monthly precipitation across the entire North America.

<p>The extent of the box indicates the 25^th and 75^th percentiles. The horizontal solid lines inside the boxes indicate the medians.</p

Distribution of 4891 weather stations and the baseline data sources (PRISM and WorldClim) within the coverage of ClimateNA.

<p>Distribution of 4891 weather stations and the baseline data sources (PRISM and WorldClim) within the coverage of ClimateNA.</p