Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. 

Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. For further details please see summary sheet below. 

Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. 

Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. 

Abstract from Ecosphere: The Prairie Pothole Region, situated in the northern Great Plains, provides important stopover habitat for migratory shorebirds. During spring migration in the U.S. Prairie Potholes, 7.3 million shorebirds refuel in the region's myriad small, freshwater wetlands. Shorebirds use mudflats, shorelines, and ephemeral wetlands that are far more abundant in wet years than dry years. Generally, climate change is expected to bring warmer temperatures, seasonality shifts, more extreme events, and changes to precipitation. The impacts to wetland habitats are uncertain. In the Prairie Potholes, earlier spring onset and warmer temperatures may advance drying of wetlands or, alternately, increased spring precipitation may produce abundant shallow‐water habitats. To look at the availability of habitats for migratory shorebirds under different climate regimes, we compared habitat selection between a historic wet year and a dry year using binomial random‐effects models to describe local and landscape patterns. We found that in the dry year shorebirds were distributed more northerly and among more permanent wetlands, whereas in the wet year shorebirds were distributed more southerly and among more temporary wetlands. However, landscape‐scale variation played a larger role in the dry year. At the local wetland scale, shorebirds selected similarly between years—for shallower wetlands and wetlands in croplands. Overall, while shorebirds were sensitive to local habitat conditions, they exhibited a degree of adaptive capacity to climate change impacts by their ability to shift on the landscape. This indicates an avenue through which management decisions can enhance climate change resilience for these species given an uncertain future—by preserving shallow‐water wetlands in croplands throughout the landscape.

The Capacity Building Project increased the North Central Climate Science Center (NC CSC) constituents’ abilities to gather and use climate data through formation of the Indigenous Phenology Network (IPN), collaboration with AmericaView to join the PhenoCam network, partnership with the National Conservation Training Center (NCTC) to offer free regional climate smarts courses, and mentoring of students. 

Managing natural resources is fraught with uncertainties around how complex social-ecological systems will respond to management actions and other forces, such as climate. Modeling tools have emerged to help grapple with different aspects of this challenge, but they are often used independently. The purpose of this project is to link two types of commonly-used simulation models (agent-based models and state-and-transition simulation models) and streamline the handling of model inputs and outputs. This innovation will provide researchers with the capability to simulate the interactions of wildlife, vegetation, management actions, and other drivers, and thus answer questions and inform decisions about how best to manage natural resources.