In the North Central U.S., drought is a dominant driver of ecological, economic, and social stress. Drought conditions have occurred in the region due to lower precipitation, extended periods of high temperatures and evaporative demand, or a combination of these factors. This project aimed to improve our understanding of drought in the North Central region and determine what future droughts might look like over the 21st century, as climate conditions change. Researchers evaluated, with the intent to improve, available and emerging data on climate conditions that influence drought (such as changes in temperature, precipitation, evaporative demand, snow and soil moisture), as well as datasets related to the surface water balance (such as evapotranspiration and streamflow). Researchers sought to use these data to identify a range of plausible future climate conditions for the region, known as “scenarios”, to help land managers better understand the threat posed by drought and to plan for its potential impacts. Researchers aimed to make relevant climate datasets available to ecologists and land managers for modeling ecosystem response under different future climate scenarios. This project team is part of the North Central Climate Science Center’s Foundational Science Area Team, which supports foundational research and advice, guidance, and technical assistance to other NC CSC projects as they address climate science challenges that are important for land managers and ecologists in the region.  

In the North Central U.S., temperatures are rising and precipitation patterns are changing, with consequences ranging from more frequent and severe wildfires to prolonged drought to widespread forest pest outbreaks. As a result, land managers are becoming increasingly concerned about how climate change is affecting natural resources and the essential services they provide communities.   The rates and ecological impacts of changing conditions vary across this diverse region, which stretches from the Great Plains to the High Rockies. The goal of this project was to understand how native grasslands, shrublands, and forests will respond to changing conditions. Researchers first modeled how these vegetation types have changed over the past 50 years, then projected how they might change over the next century under different possible future conditions.   Understanding how these native ecosystems may change is critical, particularly in light of the wildlife and communities that depend on them. Species such as the greater sage-grouse, elk, deer, and grizzly bears could lose important habitat if conditions change. Humans could also be impacted – subalpine forests, for example, control snow accumulation and melt, which in turn affect the water supply. The results of this research are meant to be used to support local stakeholders in developing strategies for coping with and adapting to projected changes in vegetation across the North Central region.   This project team is part of the North Central Climate Science Center’s Foundational Science Area Team, which supports foundational research and advice, guidance, and technical assistance to other NC CSC projects as they address climate science challenges that are important for land managers and ecologists in the region.

The north-central region of the U.S. has experienced a series of extreme droughts in recent years, with impacts felt across a range of sectors. For example, the impacts of a 2002 drought are estimated to have resulted in a $3 billion loss to the agricultural sector in Nebraska and South Dakota. Meanwhile, the ecological impacts of drought in the region have included increased tree mortality, surges in the outbreak of pests, and intensifying forest fires.   Located within this region is the Missouri River Basin, an important agricultural production area home to approximately 12 million people, including 28 Native American tribes. Tribal governments and multiple federal agencies manage land and natural resources in the drought-impacted Basin. The goal of this project was to understand how federal and tribal natural resource managers experience and deal with drought in this landscape. To do this, researchers documented how managers perceive drought impacts, how their decisions are affected by these perceptions, and their capacity to respond to and prepare for drought.   This information is expected to enable researchers to determine the types of climate data and tools that will help managers operating under drought conditions. Locally-specific “drought stories” are being developed, detailing historic trends and future projections of drought, as well as the risk perceptions, decisions, and adaptive capacities of local managers. Understanding the different perceptions and impacts of drought felt by managers can help provide a foundation for fostering more collective resource management across the region in the face of future drought. This project team is part of the North Central Climate Science Center’s Foundational Science Area Team, which supports foundational research and advice, guidance, and technical assistance to other NC CSC projects as they address climate science challenges that are important for land managers and ecologists in the region.  

Natural Resource Technical Report NPS/WICA/NRTR—2014/918

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971–2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981–2000 and projected future distributions to climate scenarios for 2040–2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts.

The potential evapotranspiration (PET) that would occur with unlimited plant access to water is a central driver of simulated plant growth in many ecological models. PET is influenced by solar and longwave radiation, temperature, wind speed, and humidity, but it is often modeled as a function of temperature alone. This approach can cause biases in projections of future climate impacts in part because it confounds the effects of warming due to increased greenhouse gases with that which would be caused by increased radiation from the sun. We developed an algorithm for linking PET to extraterrestrial solar radiation (incoming top-of atmosphere solar radiation), as well as temperature and atmospheric water vapor pressure, and incorporated this algorithm into the dynamic global vegetation model MC1. We tested the new algorithm for the Northern Great Plains, USA, whose remaining grasslands are threatened by continuing woody encroachment. Both the new and the standard temperature-dependent MC1 algorithm adequately simulated current PET, as compared to the more rigorous PenPan model of Rotstayn et al. (2006). However, compared to the standard algorithm, the new algorithm projected a much more gradual increase in PET over the 21st century for three contrasting future climates. This difference led to lower simulated drought effects and hence greater woody encroachment with the new algorithm, illustrating the importance of more rigorous calculations of PET in ecological models dealing with climate change.

Abstract (from  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0111669): Projected climate change at a regional level is expected to shift vegetation habitat distributions over the next century. For the sub-alpine species whitebark pine (Pinus albicaulis), warming temperatures may indirectly result in loss of suitable bioclimatic habitat, reducing its distribution within its historic range. This research focuses on understanding the patterns of spatiotemporal variability for future projected P.albicaulis suitable habitat in the Greater Yellowstone Area (GYA) through a bioclimatic envelope approach. Since intermodel variability from General Circulation Models (GCMs) lead to differing predictions regarding the magnitude and direction of modeled suitable habitat area, nine bias-corrected statistically down-scaled GCMs were utilized to understand the uncertainty associated with modeled projections. P.albicaulis was modeled using a Random Forests algorithm for the 1980-2010 climate period and showed strong presence/absence separations by summer maximum temperatures and springtime snowpack. Patterns of projected habitat change by the end of the century suggested a constant decrease in suitable climate area from the 2010 baseline for both Representative Concentration Pathways (RCPs) 8.5 and 4.5 climate forcing scenarios. Percent suitable climate area estimates ranged from 2-29% and 0.04-10% by 2099 for RCP 8.5 and 4.5 respectively. Habitat projections between GCMs displayed a decrease of variability over the 2010-2099 time period related to consistent warming above the 1910-2010 temperature normal after 2070 for all GCMs. A decreasing pattern of projected P.albicaulis suitable habitat area change was consistent across GCMs, despite strong differences in magnitude. Future ecological research in species distribution modeling should consider a full suite of GCM projections in the analysis to reduce extreme range contractions/expansions predictions. The results suggest that restoration strageties such as planting of seedlings and controlling competing vegetation may be necessary to maintain P.albicaulis in the GYA under the more extreme future climate scenarios. This publication was developed as part of the project,  Informing Implementation of the Greater Yellowstone Coordinating Committee’s (GYCC) Whitebark Pine (WBP) Strategy Based on Climate Sciences, Ecological Forecasting, and Valuation of WBP-Related Ecosystem Services.

Many semi-arid plant communities in western North America are dominated by big sagebrush. These ecosystems are being reduced in extent and quality due to economic development, invasive species, and climate change. These pervasive modifications have generated concern about the long-term viability of sagebrush habitat and sagebrush-obligate wildlife species (notably greater sage-grouse), highlighting the need for better understanding of the future big sagebrush distribution, particularly at the species' range margins. These leading and trailing edges of potential climate-driven sagebrush distribution shifts are likely to be areas most sensitive to climate change. We used a process-based regeneration model for big sagebrush, which simulates potential germination and seedling survival in response to climatic and edaphic conditions and tested expectations about current and future regeneration responses at trailing and leading edges that were previously identified using traditional species distribution models. Our results confirmed expectations of increased probability of regeneration at the leading edge and decreased probability of regeneration at the trailing edge below current levels. Our simulations indicated that soil water dynamics at the leading edge became more similar to the typical seasonal ecohydrological conditions observed within the current range of big sagebrush ecosystems. At the trailing edge, an increased winter and spring dryness represented a departure from conditions typically supportive of big sagebrush. Our results highlighted that minimum and maximum daily temperatures as well as soil water recharge and summer dry periods are important constraints for big sagebrush regeneration. Overall, our results confirmed previous predictions, i.e., we see consistent changes in areas identified as trailing and leading edges; however, we also identified potential local refugia within the trailing edge, mostly at sites at higher elevation. Decreasing regeneration probability at the trailing edge underscores the Schlaepfer et al. Future regeneration potential of big sagebrush potential futility of efforts to preserve and/or restore big sagebrush in these areas. Conversely, increasing regeneration probability at the leading edge suggest a growing potential for conflicts in management goals between maintaining existing grasslands by preventing sagebrush expansion versus accepting a shift in plant community composition to sagebrush dominance.

The TopoWx ('Topography Weather') dataset contains historical 30-arcsec resolution (~800-m) interpolations of daily minimum and maximum topoclimatic air temperature for the conterminous U.S. Using both DEM-based variables and MODIS land skin temperature as predictors of air temperature, interpolation procedures include moving window regression kriging and geographically weighted regression. To avoid artificial climate trends, all input station data are homogenized using the GHCN/USHCN Pairwise Homogenization Algorithm (http://www.ncdc.noaa.gov/oa/climate/research/ushcn/#phas). The interpolation model is open source and information on obtaining model code can be found at http://www.ntsg.umt.edu/project/TopoWx. The following data are available in this archive: 1948-2014 daily minimum and maximum temperature, and 1981-2010 monthly normals for minimum and maximum temperature with corresponding uncertainty (kriging prediction error). Ongoing annual updates will regenerate the entire dataset incorporating both new observations and model enhancements. This will result in a continuously improved dataset. However, different versions of TopoWx will be incompatible. For instance, data from the original 1948-2012 version should not be mixed with data from the new 1948-2014 version.