Model structures amplify uncertainty in predicted soil carbon responses to climate change

QUANTIFYING PEATLAND CARBON DYNAMICS USING MECHANISTICALLY-BASED BIOGEOCHEMISTRY MODELS

Widespread global changes, including rising atmospheric CO2 concentrations, climate warming and loss of biodiversity, are predicted for this century; all of these will affect terrestrial ecosystem processes like plant litter decomposition. Conversely, increased plant litter decomposition can have potential carbon-cycle feedbacks on atmospheric CO2 levels, climate warming and biodiversity. But predicting litter decomposition is difficult because of many interacting factors related to the chemical, physical and biological properties of soil, as well as to climate and agricultural management practices. We applied 13 C-labelled plant litter to soil at ten sites spanning a 3500-km transect across the agricultural regions of Canada and measured its decomposition over five years. Despite large differences in soil type and climatic conditions, we found that the kinetics of litter decomposition were similar once the effect of temperature had been removed, indicating no measurable effect of soil properties. A two-pool exponential decay model expressing undecomposed carbon simply as a function of thermal time accurately described kinetics of decomposition. (R2 =0.94; RMSE=0.0508). Soil properties such as texture, cation exchange capacity, pH and moisture, although very different among sites, had minimal discernible influence on decomposition kinetics. Using this kinetic model under different climate change scenarios, we projected that the time required to decompose 50% of the litter (i.e. the labile fractions) would be reduced by 1-4months, whereas time required to decompose 90% of the litter (including recalcitrant fractions) would be reduced by 1year in cooler sites to as much as 2years in warmer sites. These findings confirm quantitatively the sensitivity of litter decomposition to temperature increases and demonstrate how climate change may constrain future soil carbon storage, an effect apparently not influenced by soil properties.

Graphical models have been widely applied in distributed network computation problems such as inference in large-scale sensor networks. While belief propagation (BP) based on message passing is a powerful approach to solving such distributed inference problems, one major challenge, in the context of wireless sensor networks, is how to systematically address the trade-off between energy efficiency and inference performance. Although various energy-efficient message passing algorithms based on a given graphical model have been proposed in the literature, little work has been done to optimize the graphical model structure to achieve good energy efficiency and inference performance at the same time. In this paper, we propose an efficient distributed algorithm for optimizing the graphical model structure in order to minimize the communication cost required by the inference algorithm without incurring significant performance loss. We first formulate the problem as a multi-objective constrained problem and prove its NP-hardness. Then, we propose an efficient heuristic to solve the problem in polynomial time. Through extensive simulations, using both real-world sensor network data and synthesized data, we empirically evaluate our proposed graphical model structure optimization framework. The simulation results demonstrate that the optimized graphical model efficiently balances the performance of the inference algorithm (measured by mean squared error) and the energy consumed by the inference algorithm (measured by energy used in communication). These highlight the advantages of our proposed framework.

In vehicular traffic modeling, the effect of link capacity on travel times is generally specified through a delay function. In this paper the Generalized Regression Neural Network (GRNN) method that supports a dynamic network loading (DNL) model is utilized to model delays on an unsignalized highway node. The presented DNL model is constructed with a linear travel time function for link performances and an algorithm written with a set of rules considering the constraints of link dynamics, flow conservation, flow propagation, and boundary conditions. The GRNN method is utilized in the integrated model structure in order to provide a closer functional approximation to pre-defined flow-rate delay function, a conical delay function (CDF). Delays forming as a result of capacity constraint and flow conflicting at an unsignalised node are calculated with selected GRNN configuration after calibrating the neural network component with the CDF formulation. The output of the model structure, run solely with the CDF, is then compared to evaluate the performance of the model supported with GRNN relatively.

A generalized linear model with smoothing effects for claims reserving

Decision support systems (DSS), especially based on the artificial intelligence (AI) techniques are been widely applied in different domains nowadays. In the paper we depict an approach of implementing DSS in to Digital Oil Field (DOF) dynamic model structure in order to reduce the human factor influence, considering the automation of all production processes to be the DOF model clue element. As the basic tool of data handling we propose the hybrid application on artificial neural networks and evolutional algorithms.

A manufacturing model is an information and knowledge model that describes the manufacturing capability of a particular organization. This work contributes to the area of information and knowledge structure to support manufacturing decisions. The structures of the manufacturing model have been defined to achieve suitable access to, and maintenance of, the manufacturing knowledge. Emphasis has been made on investigating a suitable manufacturing model structure in order to readily access manufacturing knowledge related to process planning activities. The aim of this research was to design a manufacturing knowledge model (MKM) and demonstrate its functionality through experimental software. This paper presents requirements for a new MKM, proposes its structure and describes its design.

Abstract. Projections of changes in extreme droughts under future climate conditions are associated with large uncertainties, owing to the complex genesis of droughts and large model uncertainty in the atmospheric dynamics. In this study we investigate the impact of global warming on soil moisture drought severity in west-central Europe by employing pseudo global warming (PGW) experiments, which project the 1980–2020 period in a globally warmer world. The future analogues of present-day drought episodes allow for investigation of changes in drought severity conditional on the historic day-to-day evolution of the atmospheric circulation. The 2018 west-central European drought is the most severe drought in the 1980–2020 reference period in this region. Under 1.5, 2 and 3 ∘C global warming, this drought episode experiences strongly enhanced summer temperatures but a fairly modest soil moisture drying response compared to the change in climatology. This is primarily because evaporation is already strongly moisture-constrained during present-day conditions, limiting the increase in evaporation and thus the modulation of the temperature response under PGW. Increasing precipitation in winter, spring and autumn limits or prevents an earlier drought onset and duration. Nevertheless, the drought severity, defined as the cumulative soil moisture deficit volume, increases considerably, with 20 % to 39 % under 2 ∘C warming. The extreme drought frequency in the 1980–2020 period strongly increases under 2 ∘C warming. Several years without noticeable droughts under present-day conditions show very strong drying and warming. This results in an increase in 2003-like drought occurrences, compounding with local summer temperature increases considerably above 2 ∘C. Even without taking into account a (potentially large) dynamical response to climate change, drought risk in west-central Europe is strongly enhanced under global warming. Owing to increases in drought frequency, severity and compounding heat, a reduction in recovery times between drought episodes is expected to occur. Our physical climate storyline provides evidence complementing conventional large-ensemble approaches and is intended to contribute to the formulation of effective adaptation strategies.

<strong class="journal-contentHeaderColor">Abstract.</strong> Projections of changes in extreme droughts under future climate conditions are associated with large uncertainties, owing to the complex genesis of droughts and large model uncertainty in the atmospheric dynamics. In this study we investigate the impact of global warming on soil moisture drought severity in west-central Europe by employing pseudo-global warming (PGW) experiments, which project the 1980&ndash;2020 period in a globally warmer world. The future analogues of present-day drought episodes allow investigation of changes in drought severity conditional on the historic day-to-day evolution of the atmospheric circulation. The 2018 west-central European drought is the most severe drought in the 1980&ndash;2020 reference period in this region. Under 1.5 &deg;C, 2 &deg;C and 3 &deg;C global warming, this drought episode experiences strongly enhanced summer temperatures, but a fairly modest soil moisture drying response compared to the change in climatology. This is primarily because evaporation is already strongly moisture-constrained during present-day conditions, limiting the increase in evaporation and thus the modulation of the temperature response under PGW. Increasing precipitation in winter, spring and autumn limit or prevent an earlier drought onset and duration. Nevertheless, the drought severity, defined as the cumulative soil moisture deficit volume, increases considerably, with 20 % to 39 % under 2 &deg;C warming. The extreme drought frequency in the 1980&ndash;2020 period strongly increases under 2 &deg;C warming. Several years without noticeable droughts under present-day conditions show very strong drying and warming. This results in an increase in 2003-like drought occurrences, compounding with local summer temperature increases considerably above 2 &deg;C. Even without taking into account a (potentially large) dynamical response to climate change, drought risk in west-central Europe is strongly enhanced under global warming. Owing to increases in drought frequency, severity and compounding heat, a reduction in recovery times between drought episodes is expected to occur. Our physical climate storyline provides evidence complementing conventional large-ensemble approaches, and is intended to contribute to the formulation of effective adaptation strategies.

Unraveling the threshold and interaction effects of environmental variables on soil organic carbon mapping in plateau watershed

Soil organic carbon changes (Δ SOC ) are regulated by climate and land use change. Here, we analyze regional and global Δ SOC from 1861 to 2099 based on five terrestrial biosphere model (TBM) simulations of the Inter‐Sectoral Impact Model Intercomparison Project Phase 2b. The TBMs were driven by harmonized gridded land use change and bias‐adjusted climate forcing data from different general circulation models (GCMs) for climate scenarios RCP 2.6 and RCP 6.0. Between 2005 and the end of this century, we estimated an increase of SOC for two scenarios with large uncertainty, which is dominated by differences between TBMs. We present a new emergent constraint approach to constrain future modeled Δ SOC over natural vegetation from RCP 6.0 simulations using recent observed trends of net primary productivity as a proxy of litter inputs to soil pools. Our results showed that the uncertainties in constrained Δ SOC can be reduced in comparison with the original model ensemble, but constrained values of Δ SOC depend on the choice of a GCM and climate regions. For the reduction of the SOC density in areas where cropland expanded ( Δ soc cropland expansion ) over natural vegetation as a result of land use change, the constrained Δ soc cropland expansion still features large uncertainties due to uncertain observed data. Our proposed emergent constraint approach appears to be valuable to reduce uncertainty on SOC projections, but it is limited here by the small number of models (five) and by the uncertainty in the observational data. Applications to larger ensembles from Earth System Models should be tested for the future.

Quantifying soil carbon dynamics is of utmost relevance in the context of global change because soils play an important role in land–atmosphere gas exchange. Our current understanding of both present and future carbon dynamics is limited because we fail to accurately represent soil processes across temporal and spatial scales, partly because of the paucity of data on the relative importance and hierarchical relationships between microbial, geochemical and climatic controls. Here, using observations from a 3,000-kyr-old soil chronosequence preserved in alluvial terrace deposits of the Merced River, California, we show how soil carbon dynamics are driven by the relationship between short-term biotic responses and long-term mineral weathering. We link temperature sensitivity of heterotrophic respiration to biogeochemical soil properties through their relationship with microbial activity and community composition. We found that soil mineralogy, and in particular changes in mineral reactivity and resulting nutrient availability, impacts the response of heterotrophic soil respiration to warming by altering carbon inputs, carbon stabilization, microbial community composition and extracellular enzyme activity. We demonstrate that biogeochemical alteration of the soil matrix (and not short-term warming) controls the composition of microbial communities and strategies to metabolize nutrients. More specifically, weathering first increases and then reduces nutrient availability and retention, as well as the potential of soils to stabilize carbon. Soil weathering, rather than short-term warming, controls microbial community composition, nutrient availability and soil carbon content, according to observations from a 3-Myr-old soil chronosequence preserved in river terraces in California.

Determining microbial necromass carbon and plant detritus carbon is essential for accurate global soil organic carbon modeling. Yet soil organic carbon precursors are usually ignored in alpine assessments. We examined 363 samples from dominant Himalayan ecosystems and determined the distribution of microbial necromass carbon and plant detritus carbon in 0-30 cm soil and their responses to land use changes. Plant detritus carbon was 6.75 times greater than microbial necromass carbon and dominant across soil depths and ecosystems. Microbial biomass carbon strongly influenced soil organic carbon precursors. Plant detritus carbon exhibited greater sensitivity to land use change than microbial necromass carbon and decomposed preferentially, thus, buffering against fluctuations of the stable soil organic carbon fraction. Our results highlight plant detritus carbon’s key role in maintaining soil organic carbon persistence and suggest that incorporating both residual carbons and land use responses into Earth system models could improve future soil organic carbon projections.

Presented herein are the results of an adaptive control simulation study using metrics and pilot handling qualities on a generic commercial aviation aircraft. For this study, seven model reference adaptive control (MRAC) technologies were identified for investigation. Each technology was tuned using a consistent methodology based on metrics and specific design requirements. Additionally, each adaptive control technology was integrated with a preexisting dynamic-inversion baseline control architecture. Scenarios for evaluation were then designed using Cooper-Harper design criteria. This paper presents the development, execution, and results from the study; additional background material is provided in the references. I. Introduction HE Integrated Resilient Aircraft Control (IRAC) project is part of the Aviation Safety Program under the Aeronautics Research Mission Directorate (ARMD) at NASA. A key focus of this project is to research the use of adaptive control technologies as a risk-mitigating tool for damaged and off-nominal aircraft. In a traditional gain-scheduled design approach, the flight controller is designed by treating the aircraft’s flight envelope as a discrete space. Controls engineers then use traditional linear control techniques to shape the handling qualities of the aircraft at each of these discrete locations. In an off-nominal or damage scenario, this design approach can break down if large model uncertainties are introduced. Even in the case of a relatively robust baseline, gain-scheduled controller, aircraft performance can be severely impacted. The goal of an adaptive flight controller is to then augment the baseline controller such that larger uncertainties can be compensated for instantaneously and without a reduction in the aircraft handling qualities. If successful, these technologies then mitigate overall risk by reducing pilot workload and providing the aircraft with improved handling qualities. The use of adaptive control technologies in manned flight has at its core a component of handling qualities and pilot controller interaction. This is evidenced by the fact that the verification and validation process (VV extremely remote combinations of occurrences are not considered. In the realm of military procurement vehicles, MIL-HDBK-1797A outlines handling qualities specifications for military aircraft. Thus, irrespective of the application, pilot based evaluations of the aircraft are an integral part of maturing any control technology to a real-world platform. Given the above, this paper presents a study to evaluate the strengths and weaknesses of several adaptive control technologies through the design and execution of a handling qualities experiment. The experiment was performed at the NASA Ames Research Center using a full-motion flight simulator and NASA test pilots. In terms of format, Section II of this paper outlines the study design. Section III then presents the simulation environment and the relevant aircraft models. Section IV very briefly reviews adaptive control, the baseline control architecture, and the adaptive control technologies considered for this study; the interested reader will find significantly greater detail on

Coastal wetlands provide many critical ecosystem services including carbon storage. Soil organic carbon (SOC) is the most important component of carbon stock in coastal salt marshes. However, there are large uncertainties when estimating SOC stock in coastal salt marshes at large spatial scales. So far, information on the spatial heterogeneity of SOC distribution and determinants remains limited. Moreover, the role of complex ecological interactions in shaping SOC distribution is poorly understood. Here, we report detailed field surveys on plant, soil and crab burrowing activities in two inter-tidal salt marsh sites with similar habitat conditions in Eastern China. Our between-site comparison revealed slight differences in SOC storage and a similar vertical SOC distribution pattern across soil depths of 0–60 cm. Between the two study sites, we found substantially different effects of biotic and abiotic factors on SOC distribution. Complex interactions involving indirect effects between soil, plants and macrobenthos (crabs) may influence SOC distribution at a landscape scale. Marked differences in the SOC determinants between the study sites indicate that the underlying driving mechanisms of SOC distribution are strongly system-specific. Future work taking into account complex interactions and spatial heterogeneity is needed for better estimating of blue carbon stock and dynamics.