In the previous first phase of the Impacts and Vulnerability project, we made substantial progress in assessing climate and land use change impacts across the NCCASC domain. These include: quantifying the rates of land use change in greater wildland ecosystems (GWEs), determining the extent of fragmentation in major ecosystems across GWEs, assessing climate change impacts on public, private, and tribal lands within GWEs, evaluating evaporative demands across hydroclimatic gradients of eight ecoregions across north central U.S., and predicting forest ecosystem responses to climate change. We found that rates of climate and land use change varied across the Great Plains and Rocky Mountains, as did the responses of ecosystems to these changes. We also identified the major locations highly impacted by these changes that call for crafting locally relevant adaptation strategies to cope with these changes. This second phase of the project (FY’17) aimed to generate coproduction of knowledge with a wide range of stakeholders to support decision making for the management and conservation of affected areas. During this FY’17 phase of the project, we worked with various user groups to evaluate potential land use and climate impacts and adaptation strategies for the most affected areas and ecosystem types identified by our previous work. Specifically, we focused on forest and shrubland vegetation and habitat of a selected wildlife species (Gulo gulo) in the Rocky Mountains and Washington Cascade regions. We also designed and produced resource briefs on land use and climate change assessments of selected areas and ecosystem types to provide information to coordinated management. Thirdly, we conducted series of webinars and workshops with federal, private, and NGO stakeholders to draw on all of the science results (e.g., from species distribution models, state and transition models, and mechanistic models) to identify and evaluate vegetation climate adaptation strategies for the Custer Gallatin National Forest Plan Revision that are robust under climate uncertainty.     

Historical and projected suitable habitat of 14 tree and shrub species a under CCSM4 GCMs from 2000 to 2099 was predicted to assess projected climate change impacts in forest communities of North Central U.S. We obtained presence/absence record of each species from Forest Inventory and Analysis (FIA) data. required ata. Historical tme period ranges from 1971 to 2000, and projected time period ranges from 2071 to 2100. Random Forest was used to project historical and future suitable habitat of all species across West U.S. using the Biomod2 software programmed in R environment. We adopted a climate change scenarios generated from the experiments conducted under fifth assessment of Coupled Model Intercomparison Project (CMIP5) for the Intergovernmental Panel on Climate Change. Selected climate change scenarios include high representative concentrative pathway (RCP8.5).

Abstract (from ScienceDirect): Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R2 = 0.75) and gross primary productivity (R2 = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.

Abstract From: (The growth and distribution of plant species in water limited environments is often limited by the atmospheric evaporative demands which us measured in terms of potential evaporation (PET). While PET estimated by different methods have been widely used to assess vegetation response to climate change, species distribution models offer unique opportunity to compare their efficiency in predicting habitat suitability of plant species. In this study, we perform the first multi-species comparison of two widely used metrics of PET i.e., Penman-Monteith and Thornthwaite, and show how they result in similar or different on projected distribution of water limited species and potential consequences on their conservation strategies across North Central U.S. To build species distribution models of eight species, we used two sets of environmental predictors which were identical except for the metric of PET (Penman-Monthith vs Thornthwaite) and projected habitat suitability for historical (2005) and future (0399) periods. We found an excellent model performance with no difference under two sets of predictors (AUC + ~0.93). The relative influence of Thornthwaite PET on habitat prediction was higher than Penman PET for most of the species. We observered that the area of the projected suitable habitat was always higher under Thornthwaite set of predictors which were than Penman set of predictors (ranges from 25% to 941%), with the exception of Pinus contorta for which the reverse was true. In most cases, these differences were non-trivial, indicating that the choice of the PET metric, although both of them are commonly used, can have dramatic consequences on the conservation management decisions. Therefore, the conservation management decisions can be markedly different depending on the choice of the PET metric used for species distribution modeling of water limited species.)

The USA National Phenology Network (USA-NPN) and the North Central Climate Science Center (NC CSC) seek to enhance scientific understanding of how climate trends and variability are linked to phenology across spatial scales, with the ultimate goal of being able to understand and predict climate impacts on natural resources. A key step towards achieving this long-term goal is connecting local observations (individual plants or animals) of phenology with those at regional to continental scales (10 km to 10,000 km), which may ultimately be used to better understand phenology across ecosystems and landscapes and thereby inform natural resource management. The specific shorter-term goals of this effort are to process and distribute phenology camera (or “phenocam”) products, and to develop a plan for how these products can help meet longer-term goals. 

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.

Land surface phenology (LSP) has been widely used as the “footprint” of urbanization and global climate change. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing phenological metrics, e.g. start of season (SOS) and end of season (EOS), between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of SOS and EOS within the urban ecosystem. To fill this knowledge gap, we utilized daily two-band enhanced vegetation index, daily meteorological record, and annual land cover dataset to investigate the respective impacts of urbanization and climate change on temporal shifts of LSP between the post- and the pre-urbanization periods over 196 large cities in the northern mid-latitudes. We found 51% of the cities experienced an advanced SOS with an average of −6.39 ± 5.82 days after urbanization has occurred, while the remaining 49% of the cities had a delayed SOS with an average of 7.56 ± 5.63 days. We also found a later EOS at 53% of the cities and an earlier EOS at 47% of the cities with an average of 8.43 ± 7.59 and −5.57 ± 4.99 days between the post- and pre-urbanization periods, respectively. Multiple linear regression analysis indicates that climate variables (i.e. temperature, precipitation, and insolation) play dominant roles in regulating the temporal shifts of LSP. Furthermore, the earlier SOS and later EOS were significantly correlated with the amplitude of urbanization (i.e. increase of impervious surface area) in cities after controlling for effects of climate factors. These patterns were generally consistent across eight climate zones. Our findings provide critical information in modeling natural and anthropogenic effects on urban ecosystem, with important benefits for urban sustainability and biodiversity conservation.