Vegetation phenology is one of the most sensitive indicators to environmental and climate changes. In order to characterize the seasonal variation in relatively pure or homogenous vegetation types, fine spatial resolution satellite data (≤ 30 m), such as Landsat, Sentinel-2, PlanetScope, or Harmonized Landsat and Sentinel-2 (HLS), have been increasingly applied to detect land surface phenology (LSP). However, the most critical challenge in LSP detections is the gaps in temporal satellite observations caused by noise and persistent cloud/snow cover. Therefore, this study presented a novel algorithm for generating synthetic gap-free time series at the field scale (30 m) for LSP detections. Specifically, we first developed a framework to establish a large collection of temporal shapes of vegetation growth with as many as 100 grid-based Green Chromatic Coordinate (GCC) time series in a single PhenoCam site. For a given HLS pixel, the two-band Enhanced Vegetation Index (EVI2) time series was matched and fused with the most comparable temporal GCC shape selected from the collection of PhenoCam GCC time series to generate a synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to detect the 30 m phenometrics. The detected phenometrics were evaluated using manually selected and spatially matched GCC observations as well as phenology detections from HLS alone. The result indicates that the HLS-PhenoCam phenometrics are very close to the observations from PhenoCam network with a correlation coefficient (R) of 0.82–0.97, a mean absolute difference (MAD) of 2.8–3.5 days, a root mean squared error (RMSE) of 3.5–4.0 days, and a mean systematic bias (MSB) of 0.1–2.2 days. The HLS-PhenoCam detections are significantly improved relative to the HLS phenometrics that have a statistic accuracy of R = 0.57–0.78, MAD = 6.4–9.3 days, RMSE = 8.8–13.9 days, MSB = -5.2–5.9 days. The difference between HLS-PhenoCam and HLS alone LSP detections over a HLS tile could be on average larger than two weeks if high-quality observation (HQO) proportion in the annual HLS time series is <10%, which exponentially reduces with the increase of HQO in HLS observations. The analyses in this study suggest that the gap-free HLS-PhenoCam time series is able to be generated for producing high-quality phenology datasets across a local and regional scale, to bridge near-surface PhenoCam observations with satellite observations data at various scales, and to be used as a scalable phenology dataset for the validation of global MODIS and VIIRS LSP products.

Climate change is expected to alter the distribution and abundance of tree species, impacting ecosystem structure and function. Yet, anticipating where this will occur is often hampered by a lack of understanding of how demographic rates, most notably recruitment, vary in response to climate and competition across a species range. Using large-scale monitoring data on two dry woodland tree species (Pinus edulis and Juniperus osteosperma), we develop an approach to infer recruitment, survival, and growth of both species across their range. In doing so, we account for ecological and statistical dependencies inherent in large-scale monitoring data. We find that drying and warming conditions generally lead to declines in recruitment and survival, but the strength of responses varied between species. These climate conditions point to geographic regions of high vulnerability for particular species, such as Pinus edulis in northern Arizona, where both survival and recruitment are low. Our approach provides a path forward for leveraging emerging large-scale monitoring and remotely sensed data to anticipate the impacts of global change on species distributions.      

This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the projections, climate and time since fire varies by year but other predictors stay constant at fixed values. Distance to seed source was set at 50 m and fire severity, measured as the differenced normalized burn ratio (dNBR), was set at 400 for all projections. Because we hold distance to seed source and fire severity constant, the post-fire recruitment index is interpreted as the climate suitability for post-fire recruitment, under the given scenario. We recognize that post-fire recruitment is also influenced by other local factors that are unaccounted for in our models, including biotic interactions, such as herbivory and competition, and abiotic factors, such as substrate, topography and soil moisture.

The sagebrush ecosystem is home to diverse wildlife, including charismatic species such as pronghorn, pygmy rabbits, mule deer and Greater Sage-Grouse. Historic and contemporary land-uses, large wildfires, non-native (introduced) plant invasions, climate cycles with droughts, and long-term climate trends characterize the list of widespread threats to this heavily used landscape. Semi-arid habitats, such as sagebrush ecosystems, present challenges for management due to natural limitations and variability in growing conditions across the landscape and over time. Differences in environmental conditions lead to differences in plant composition, fuel accumulation, resilience to stress and disturbance and restoration/management outcomes. Much variability is created by the combination of soil and climate conditions that create a water availability gradient. Therefore, climate-soil-plant relations describe a fundamental ecosystem function with direct implications for management. Until now, lack of accurate representation of the spatial and temporal heterogeneity in soil moisture and temperature have restricted use of soil-climate in ecological applications. We created a simulation model that accounts for soil water content over the course of a year using precipitation, temperature, soil water capacity, evapotranspiration, and related environmental characteristics (such as topography) across large landscapes. We modeled soil-climate using averaged climate conditions (30-years) across 313 million hectares (774 million acres) of the western U.S., encompassing most of the sagebrush biome (approximately 17 million hectares were excluded along the southern extent, but sagebrush cover is very low there). The model produced spatially independent cell-by-cell soil-climate classification and estimates of soil moisture across the entire region. Models were tested using climate station data, qualitative comparison with soil data, and correlation with ecological patterns using shrub and annual grass cover, exposed soil, and fire frequency. Analyses indicated strong connections between our results and landscape patterns: 66% of variation (deviance explained) in exposed bare ground (inverse of plant cover) was explained by spring soil moisture alone, and 51% of the variability in sagebrush cover was explained by combination of spring and winter soil moisture. Importantly, soil moisture combined with burn frequency explained 69% of the deviance in annual herbaceous grasses across the region (this is very strong). These results improve understanding of ecosystem patterns (demonstrated by plant cover here) and management risks (such as, fire and invasion) and provide useful data for management and research applications. 

Developing scientific information that is used in policy and practice has been a longstanding challenge in many sectors and disciplines, including climate change adaptation for natural resource management. One approach to address this problem encourages scientists and decision-makers to co-produce usable information collaboratively. Researchers have proposed general principles for climate science co-production, yet few studies have applied and evaluated these principles in practice. In this study, climate change researchers and natural resource managers co-produced climate-related knowledge that was directly relevant for on-going habitat management planning. We documented our methods and assessed how and to what extent the process led to the near-term use of co-produced information, while also identifying salient information needs for future research. The co-production process resulted in: 1) an updated natural resource management plan that substantially differed from the former plan in how it addressed climate change, 2) increased understanding of climate change, its impacts, and management responses among agency staff, and 3) a prioritized list of climate-related information needs that would be useful for management decision-making. We found that having a boundary spanner—an intermediary with relevant science and management expertise that enables exchange between knowledge producers and users—guide the co-production process was critical to achieving outcomes. Central to the boundary spanner’s role were a range of characteristics and skills, such as knowledge of relevant science, familiarity with management issues, comfort translating science into practice, and an ability to facilitate climate-informed planning. By describing specific co-production methods and evaluating their effectiveness, we offer recommendations for others looking to co-produce climate change information to use in natural resource management planning and implementation.

The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. All survey questions were optional to complete. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust). Results of the survey are presented as summary tables in order to protect personal identifiable information of the respondents. Summary information is in the form of tables and word cloud graphics to communicate results of open ended questions.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|instrumental|tree ring with a geographic location of North America. The time period coverage is from 1150 to -65 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Welcome to the North American Seasonal Precipitation Atlas.  This web application provides access to cool- and warm-season reconstructions of total precipitation and the standardized precipitation index on a 0.5° latitude/longitude grid centered over North America from AD 0000-2016.  Maps and time series can be created using tools under the "Maps" and "Time Series" menus, respectively.  For details on how to create maps and time series, see the "Help" menu.  An animation that runs through each year of the reconstruction from AD 0000-2016 is available via the web application. Questions, comments, or suggestions may be sent to Dorian J. Burnette. The entire dataset can be downloaded from NOAA Paleoclimatology.

This project examines how key institutional and emotional factors shape management decisions about changing the social science of ecological resources. We use interviews and focus groups to study how the culture and policy of individual parks, and the psychological and emotional experiences of managers responding to landscape changes, influence decisions.

To cope with complex environmental impacts in a changing climate, researchers are increasingly being asked to produce science that can directly support policy and decision making. To achieve such societal impact, scientists are using climate services to engage directly with stakeholders to better understand their needs and inform knowledge production. However, the wide variety of climate-services outcomes—ranging from establishing collegial relationships with stakeholders to obtaining specific information for inclusion into a pre-existing decision process—do not directly connect to traditional methods of measuring scientific impact (e.g., publication citations, journal impact factor). In this paper, we describe how concepts from the discipline of evaluation can be used to examine the societal impacts of climate services. We also present a case study from climate impacts and adaptation research to test a scalable evaluation approach. Those who conduct research for the purposes of climate services and those who fund applied climate research would benefit from evaluation from the beginning of project development. Doing so will help ensure that the approach, data collection, and data analysis are appropriately conceived and executed.