Sediment accumulation threatens the viability and hydrologic functioning of many naturally formed depressional wetlands across the interior regions of North America. These wetlands provide many ecosystem services and vital habitats for diverse plant and animal communities. Climate change may further impact sediment accumulation rates in the context of current land use patterns. We estimated sediment accretion in wetlands within a region renowned for its large populations of breeding waterfowl and migrant shorebirds and examined the relative roles of precipitation and land use context in the sedimentation process. We modeled rates of sediment accumulation from 1971 through 2100 using the Revised Universal Soil Loss Equation (RUSLE) with a sediment delivery ratio and the Unit Stream Power Erosion Deposition model (USPED). These models predicted that by 2100, 21–33 % of wetlands filled completely with sediment and 27–46 % filled by half with sediments; estimates are consistent with measured sediment accumulation rates in the region reported by empirical studies. Sediment accumulation rates were strongly influenced by size of the catchment, greater coverage of tilled landscape within the catchment, and steeper slopes. Conservation efforts that incorporate the relative risk of infilling of wetlands with sediments, thus emphasizing areas of high topographic relief and large watersheds, may benefit wetland-dependent biota.
The Landscape Conservation Cooperatives (LCCs) are a network of partnerships throughout North America that are tasked with integrating science and management to support more effective delivery of conservation at a landscape scale. In order to achieve this integration, some LCCs have adopted the approach of providing their partners with better scientific information in an effort to facilitate more effective and coordinated conservation decisions. Taking this approach has led many LCCs to begin funding research to provide the information for improved decision making. To ensure that funding goes to research projects with the highest likelihood of leading to more integrated broad scale conservation, some LCCs have also developed approaches for prioritizing which information needs will be of most benefit to their partnerships. We describe two case studies in which decision analytic tools were used to quantitatively assess the relative importance of information for decisions made by partners in the Plains and Prairie Potholes LCC. The results of the case studies point toward a few valuable lessons in terms of using these tools with LCCs. Decision analytic tools tend to help shift focus away from research oriented discussions and toward discussions about how information is used in making better decisions. However, many technical experts do not have enough knowledge about decision making contexts to fully inform the latter type of discussion. When assessed in the right decision context, however, decision analyses can point out where uncertainties actually affect optimal decisions and where they do not. This helps technical experts understand that not all research is valuable in improving decision making. But perhaps most importantly, our results suggest that decision analytic tools may be more useful for LCCs as way of developing integrated objectives for coordinating partner decisions across the landscape, rather than simply ranking research priorities.
The Eastern Shoshone and Northern Arapaho Tribes on the Wind River Indian Reservation in Wyoming are preparing for drought and other climate fluctuations with help from a broad coalition of scientists, including groups at the University of Nebraska-Lincoln. Read More: http://drought.unl.edu/NewsOutreach/NDMCNews.aspx?id=204
The Eastern Shoshone and Northern Arapaho Tribes on the Wind River Indian Reservation in Wyoming are preparing for drought and other climate fluctuations with help from a broad coalition of scientists. Read More: https://www.drought.gov/drought/sites/drought.gov.drought/files/media/whatisnidis/Newsletter/October%202015%20v4.pdf
The HPRCC has an established partnership with the North Central Climate Science Center (NC CSC) and has enjoyed collaborating on regional projects since its inception. Housed at Colorado State University in Fort Collins, the NC CSC is one of eight such centers that were established in 2010 within the U.S. Department of the Interior. The mission of the Climate Science Centers is to help meet the changing needs of land and resource managers across the U.S. (For more information on the Climate Science Centers, please visit: https://www.doi.gov/csc/about.) The NC CSC collaborates with a consortium of nine institutions that provide expertise in climate science and sectors impacted by climate. The University of Nebraska-Lincoln, where HPRCC is housed, is a member of this consortium. Read More: http://hprcc.unl.edu/hprccquarterly/HPRCCQuarterly-Fall2015.pdf
In the coming century, climate change is projected to impact precipitation and temperature regimes worldwide, with especially large effects in drylands. We use big sagebrush ecosystems as a model dryland ecosystem to explore the impacts of altered climate on ecohydrology and the implications of those changes for big sagebrush plant communities using output from 10 Global Circulation Models (GCMs) for two representative concentration pathways (RCPs). We ask: 1) What is the magnitude of variability in future temperature and precipitation regimes among GCMs and RCPs for big sagebrush ecosystems and 2) How will altered climate and uncertainty in climate forecasts influence key aspects of big sagebrush water balance? We explored these questions across 1980-2010, 2030-2060, and 2070-2100 to determine how changes in water balance might develop through the 21st century. We assessed ecohydrological variables at 898 sagebrush sites across the western US using a process-based soil water model, SOILWAT to model all components of daily water balance using site-specific vegetation parameters and site-specific soil properties for multiple soil layers. Our modeling approach allowed for changes in vegetation based on climate. Temperature increased across all GCMs and RCPs, while changes in precipitation were more variable across GCMs. Winter and spring precipitation was predicted to increase in the future (7% by 2030-2060, 12% by 2070-2100), resulting in slight increases in soil water potential (SWP) in winter. Despite wetter winter soil conditions, SWP decreased in late spring and summer due to increased evapotranspiration (6% by 2030-2060, 10% by 2070-2100) and groundwater recharge (26% and 30% increase by 2030-2060 and 2070-2100). Thus, despite increased precipitation in the cold season, soils may dry out earlier in the year, resulting in potentially longer drier summer conditions. If winter precipitation cannot offset drier summer conditions in the future, we expect big sagebrush regeneration and survival will be negatively impacted, potentially resulting in shifts in the relative abundance of big sagebrush plant functional groups. Our results also highlight the importance of assessing multiple GCMs to understand the range of climate change outcomes on ecohydrology, which was contingent on the GCM chosen.
NOTICE: Given the large size of the MACAv2METDATA dataset, and a known issue with the data server being used to host it, initial load times may take a very long time and / or time out. Subsequent requests should be faster due to caching, but the cache clears periodically and the dataset must be rescanned prior to access. We are working on a fix for this issue. In the mean time, please use the dataset with care and make sureyou've reviewed the GDP scalability guidelines. https://my.usgs.gov/confluence/display/GeoDataPortal/Geo+Data+Portal+Scalability+Guidelines This archive contains daily downscaled meteorological and hydrological projections for the Conterminous United States at 1/24-deg resolution utilizing the Multivariate Adaptive Constructed Analogs (MACA, Abatzoglou, 2012) statistical downscaling method with the METDATA (Abatzoglou,2013) training dataset. The downscaled meteorological variables are maximum/minimum temperature(tasmax/tasmin), maximum/minimum relative humidity (rhsmax/rhsmin)precipitation amount(pr), downward shortwave solar radiation(rsds), eastward wind(uas), northward wind(vas), and specific humidity(huss). The downscaling is based on the 365-day model outputs from different global climate models (GCMs) from Phase 5 of the Coupled Model Inter-comparison Project (CMIP3) utlizing the historical (1950-2005) and future RCP4.5/8.5(2006-2099) scenarios. Leap days have been added to the dataset from the average values between Feb 28 and Mar 1 in order to aid modellers. See: http://maca.northwestknowledge.net/ for more information.
This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.