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This talk goes into the algorithmic work done under the Forest project in order to solve expensive power grid models. We explore multiple fidelities of models that balance accuracy and computational expense. We use bundling strategies and progressive hedging in order to parallelize large stochastic programs.
Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon-climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental US, and two Machine Learning approaches [Random Forest (RF) and Generalized Additive Modeling (GAM)] to (1) select important environmental predictors of SOC stocks, (2) derive empirical relationships between environmental factors and SOC stocks, and (3) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy as the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon-climate feedbacks.
We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.
Grasslands provide many important ecosystem services globally, and projecting grassland productivity in the coming decades will provide valuable information to land managers. Productivity models can be well calibrated at local scales but generally have some maximum spatial scale in which they perform well. Here we evaluate a grassland productivity model to find the optimal spatial scale for parameterization and thus for subsequently applying it in future productivity projections for North America. We also evaluated the model on new vegetation types to ascertain its potential generality. We find the model most suitable when incorporating only grasslands, as opposed to also including agriculture and shrublands, and only in the Great Plains and eastern temperate forest ecoregions of North America. The model was not well suited to grasslands in North American deserts or northwest forest ecoregions. It also performed poorly in agriculture vegetation, likely due to management activities, and shrubland vegetation, likely because the model lacks representation of deep water pools. This work allows us to perform long-term projections in areas where model performance has been verified, with gaps filled in by future modeling efforts.
Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.
Tropical forest photosynthesis can decline at high temperatures due to (1) biochemical responses to increasing temperature and (2) stomatal responses to increasing vapor pressure deficit (VPD), which is associated with increasing temperature. It is challenging to disentangle the influence of these two mechanisms on photosynthesis in observations, because temperature and VPD are tightly correlated in tropical forests. Nonetheless, quantifying the relative strength of these two mechanisms is essential for understanding how tropical gross primary production (GPP) will respond to climate change, because increasing atmospheric CO 2 concentration may partially offset VPD-driven stomatal responses, but is not expected to mitigate the effects of temperature-driven biochemical responses. We used two terrestrial biosphere models to quantify how physiological process assumptions (photosynthetic temperature acclimation and plant hydraulic stress) and functional traits (e.g., maximum xylem conductivity) influence the relative strength of modeled temperature versus VPD effects on light-saturated GPP at an Amazonian forest site, a seasonally dry tropical forest site, and an experimental tropical forest mesocosm. By simulating idealized climate change scenarios, we quantified the divergence in GPP predictions under model configurations with stronger VPD effects compared with stronger direct temperature effects. Assumptions consistent with stronger direct temperature effects resulted in larger GPP declines under warming, while assumptions consistent with stronger VPD effects resulted in more resilient GPP under warming. Furthermore, our findings underscore the importance of quantifying the role of direct temperature and indirect VPD effects for projecting the resilience of tropical forests in the future, and demonstrate that the relative strength of temperature versus VPD effects in models is highly sensitive to plant functional parameters and structural assumptions about photosynthetic temperature acclimation and plant hydraulics.
Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.
Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.The LANDIS-II model shows different annual estimates of forest C storage (g/m2) across the MCBCL landscape, based on the user selecting different forest management scenarios typically used in the southeastern US. LANDIS-II is a model with several extensions capable of simulating different landscape and ecosystem-level dynamics.
We report satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate global terrestrial ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to vegetation photosynthetic light use efficiency (LUE), GPP and canopy water-stress. Here in this paper, we use the NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) based PRI with eddy covariance CO2 flux measurements and meteorological observations from 20 tower sites representing major plant functional type (PFT) classes within the continental USA (CONUS) to assess GPP sensitivity to soil moisture related water stress. The sPRI (scaled PRI) metric derived using MODIS band 13 as a reference channel (sPRI13) shows generally higher correspondence with tower GPP estimates than other potential MODIS reference bands. The sPRI13 observations were used as a proxy for soil moisture related water supply constraints to LUE within a satellite data driven terrestrial carbon flux model to estimate GPP (GPPPRI). The GPPPRI calculations show generally favorable correspondence with tower GPP estimates (0.457 ≤ R2 ≤ 0.818), except for lower GPPPRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI13 as a water supply proxy indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in drier climate areas where atmospheric moisture deficits (VPD) alone are insufficient to represent both atmosphere demand and water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI13 to represent soil moisture related water supply controls influencing photosynthesis, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations. Lastly, these observations may provide an effective tool for characterizing sub-grid spatial heterogeneity in soil moisture related controls that inform coarser scale observations and estimates determined from other satellite observations and earth system models.
The Suits bidirectional reflectance model (1972) for vegetation canopies was used to calculate reflectance values for the four Landsat spectral bands under a variety of canopy and observation conditions. Relationships between characteristics of developing wheat fields and associated reflectances were studied. Coincident measurements of crop reflectance and Landsat signals were compared using regression analysis techniques. The model was also applied to forest situations both to help establish the extent to which Landsat may provide direct information about conditions occurring beneath forest overstories and to help develop methods for reducing the effects of terrain slope and aspect in the processing of Landsat data. Finally, a relationship based on model calculations was used to adjust Landsat signal values and was found to reduce topography-related variability and improve subsequent classification performance.
There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.
A new study, led by Los Alamos National Laboratory's Nathan McDowell, suggests that widespread loss of a major forest type, the pine-juniper woodlands of the Southwestern U.S., could be wiped out by the end of this century due to climate change, and that conifers throughout much of the Northern Hemisphere may be on a similar trajectory. New results, reported in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. McDowell and his large international team strove to provide the missing pieces of understanding tree death at three levels: plant, regional and global. The team rigorously developed and evaluated multiple process-based and empirical models against experimental results, and then compared these models to results from global vegetation models to examine independent simulations. They discovered that the global models simulated mortality throughout the Northern Hemisphere that was of similar magnitude, but much broader spatial scale, as the evaluated ecosystem models predicted for in the Southwest.
Spatial and temporal variation in wet canopy conditions following precipitation events can influence processes such as transpiration and photosynthesis, which can be additionally enhanced as upper canopy leaves dry more rapidly than the understory following each event. As part of a larger study aimed at improving land surface modelling of evapotranspiration processes in wet tropical forests, we compared transpiration among trees with exposed and shaded crowns under both wet and dry canopy conditions in central Costa Rica, which has an average 420 mm annual rainfall. Transpiration was estimated for 5 months using 43 sap flux sensors in eight dominant, ten midstory and eight suppressed trees in a mature forest stand surrounding a 40-m tower equipped with micrometeorological sensors. Dominant trees were 13% of the plot's trees and contributed around 76% to total transpiration at this site, whereas midstory and suppressed trees contributed 18 and 5%, respectively. After accounting for vapour pressure deficit and solar radiation, leaf wetness was a significant driver of sap flux, reducing it by as much as 28%. Under dry conditions, sap flux rates (J s ) of dominant trees were similar to midstory trees and were almost double that of suppressed trees. On wet days, all trees had similarly low J s . As expected, semi-dry conditions (dry upper canopy) led to higher J s in dominant trees than midstory, which had wetter leaves, but semi-dry conditions only reduced total stand transpiration slightly and did not change the relative proportion of transpiration from dominant and midstory. Thus, models that better capture forest stand wet–dry canopy dynamics and individual tree water use strategies are needed to improve accuracy of predictions of water recycling over tropical forests.
Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R2 of the organic layer depth reduction models was 0.60 and 0.55 (pb0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R2 to 0.80 and 0.79 (pb0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (N200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.
Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.
Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.
In this study, the authors used the relationship between mean annual rainfall (MAR) and net primary production (NPP) (MAR–NPP) observed in tropical forests to evaluate the performance (twentieth century) and predictions (twenty-first century) of tropical NPP from 10 earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Over the tropical forest domain most of the CMIP5 models showed a positive correlation between NPP and MAR similar to observations. The GFDL, CESM1, CCSM4, and Beijing Normal University (BNU) models better represented the observed MAR–NPP relationship. Compared with observations, the models were able to reproduce the seasonality of rainfall over areas with long dry seasons, but NPP seasonality was difficult to evaluate given the limited observations. From 2006 to 2100, for representative concentration pathway 8.5 (RCP8.5) (and most RCP4.5 simulations) all models projected increases in NPP, but these increases occurred at different rates. By the end of the twenty-first century the models with better performance against observed NPP–MAR projected increases in NPP between ~2% (RCP4.5) and ~19% (RCP8.5) relative to contemporary observations, representing increases of ~9% and ~25% relative to their historical simulations. When climate and CO 2 fertilization are considered as separate controls on plant physiology, the current climate yields maximum productivity. Yet, as future climate changes become detrimental to productivity, CO 2 fertilization becomes the dominant response, ending in an overall increase in NPP toward the end of the twenty-first century. Thus, the way in which models represent CO 2 fertilization affects their performance. Additional studies addressing the individual and simultaneous effect of other climate variables on NPP are needed.
An integrated multi-feedstock bioenergy (i.e., biofuel, biopower, or bioproduct) supply system has potential to reduce biomass supply system uncertainties and costs. This study identifies optimal configurations of multi-feedstock biomass-to-biorefinery supply chains and pertinent feedstock combinations based on spatial distribution of feedstock and lowest delivered cost to the biorefinery. We used the Supply Characterization Model (SCM) to allocate feedstock supplies to candidate biorefinery facilities. Model runs were performed for herbaceous energy crops, agriculture residue, and woody biomass available in 2017, 2022, 2025, and 2030 as estimated by the Policy Analysis System (POLYSYS) and Forest Sustainable and Economic Analysis Model (ForSEAM) models. Three feedstock supply scenarios were compared: (a) an herbaceous scenario: switchgrass, miscanthus, biosorghum, and corn stover; (b) a woody scenario: coppice wood, noncoppice wood, whole trees, and forestry residues, and (c) a mixed scenario: a combination of all feedstocks in herbaceous and woody scenarios. By 2030 the analyses predicted that 323, 168, and 473 biorefineries were sited in the herbaceous, woody, and mixed scenario, respectively, in the conterminous USA. Feedstock mixes supplied to the biorefineries were mostly dominated by a single feedstock. The most prominent feedstock mixes identified were: (1) switchgrass and miscanthus; (2) coppice and noncoppice wood; and (3) coppice wood, noncoppice wood, switchgrass and miscanthus. Biorefineries using multi-feedstock would be beneficial for growth of bioeconomy, however flexible and cost-effective conversion platforms should be developed to efficiently utilize multiple feedstocks. This analysis identifies biorefinery locations and feedstock supply mixes while minimizing delivered feedstock costs based on spatial and temporal feedstock availability.