The mathematical representation of large data sets of electronic energies has seen substantial progress in the past 10 years. The so-called Permutationally Invariant Polynomial (PIP) representation ...is one established approach. This approach dates from 2003, when a global potential energy surface (PES) for CH5 + was reported using a basis of polynomials that are invariant with respect to the 120 permutations of the five equivalent H atoms. More recently, several approaches from “machine learning” have been applied to fit these large data sets. Gaussian Process (GP) regression is such an approach. Here, we consider the implementation of the (full) GP due to Krems and co-workers, with a modification that renders it permutationally invariant, which we denote by PIP-GP. This modification uses the approach of Guo and co-workers and later extended by Zhang and co-workers, to achieve permutational invariance for neural-network fits. The PIP, GP, and PIP-GP approaches are applied to four case studies for fitting data sets of electronic energies: H3O+, OCHCO+, and H2CO/cis-HCOH/trans-HCOH with the goal of assessing precision, accuracy in normal-mode analysis and barrier heights, and timings. We also report an application to (HCOOH)2, where the full PIP approach is possible but where the PIP-GP one is not feasible. However, by replicating data, which is feasible in this case, the GP approach is able to represent the data with precision comparable to that of the PIP approach. We examine these assessments for varying sizes of data sets in each case to determine the dependence of properties of the fits on the training data size. We conclude with some comments on the different aspects of computational effort of the PIP, GP, and PIP-GP approaches and also challenges these methods face for more “rugged” PESs, exemplified here by H2CO/cis-HCOH/trans-HCOH.
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IJS, KILJ, NUK, PNG, UL, UM
Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source ...of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We present a machine-learning method for predicting sharp transitions in a Hamiltonian phase diagram by extrapolating the properties of quantum systems. The method is based on Gaussian process ...regression with a combination of kernels chosen through an iterative procedure maximizing the predicting power of the kernels. The method is capable of extrapolating across the transition lines. The calculations within a given phase can be used to predict not only the closest sharp transition but also a transition removed from the available data by a separate phase. This makes the present method particularly valuable for searching phase transitions in the parts of the parameter space that cannot be probed experimentally or theoretically.
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CMK, CTK, FMFMET, IJS, NUK, PNG, UL, UM
Climate reanalyses complement traditional surface-based measurements and offer unprecedented coverage over previously inaccessible or unmonitored regions. Even though these have improved the ...quantification of the global water cycle, their varying performances and uncertainties limit their applicability. Herein, we discuss how a framework encompassing precipitation, evaporation, their difference, and their sum could further constrain uncertainty by unveiling discrepancies otherwise overlooked. Ahead, we physically define precipitation plus evaporation to describe the global water cycle fluxes in four reanalysis data sets (20CR v3, ERA-20C, ERA5, and NCEP1). Among them, we observe four different responses to the temperature increase between 1950-2010, with ERA5 showing the best agreement with the water cycle acceleration hypothesis. Our results show that implementing the framework proposed can improve the evaluation of reanalyses' performance and enhance our understanding of the water cycle changes on a global scale.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Average net ecosystem exchange (NEE) and CH4 exchange in grams of carbon per square meter across different plant phenological phases in this temperate salt marsh dominated by grass species. Plant ...phenological phases are: Greenup (˜April to June), when grasses start to grow, Maturity (˜July to September), when grasses reach their peak of growth and greenness, Senescence (˜September to October), when grasses start to decrease in greenness, and Dormancy (˜November to March), when grasses are inactive. Solid blue arrows represent carbon uptake by the ecosystem; solid red arrows represent carbon emissions from CO2; and red dashed arrows carbon emissions from CH4. This salt marsh was a net source of carbon during the annual cycle.
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•Plant phenological phases influence NEE and CH4 exchange•CO2 and CH4 emissions during senescence and dormancy overshadowed annual carbon uptakes•Light availability partially explained NEE variability•Lower water table level increased ecosystem-scale CH4 emissions•This temperate tidal salt marsh was a net source of carbon to the atmosphere
Salt marshes are large carbon reservoirs as part of blue carbon ecosystems. Unfortunately, there is limited information about the net ecosystem (NEE) and methane (CH4) exchange between salt marshes and the atmosphere to fully understand their carbon dynamics. We tested the influence of biophysical drivers by plant phenological phases (i.e., Greenup, Maturity, Senescence and Dormancy) on NEE and CH4 exchange in a grass-dominated temperate tidal salt marsh. We used three years of data derived from eddy covariance, PhenoCam (to measure vegetation phenology), and ancillary meteorological and water/soil variables. Overall, NEE showed significant differences among all phenological phases (p < 0.05), while CH4 exchange had significant differences among all phases except for Greenup and Dormancy. Net CO2 uptake was higher across Maturity (-61 g C-CO2 m2), while CO2 emissions were higher during Dormancy (182 g C-CO2 m2). The lower but constant CO2 emissions during Dormancy overshadowed the CO2 uptake during the growing season and contributed to >72% of the annual CO2 emissions in this ecosystem. Net CH4 emissions were higher during Maturity (3.7 g C-CH4 m2) and Senescence (4.2 g C-CH4 m2). Photosynthetically active radiation (PAR) substantially influenced (r2 > 0.57) daytime NEE across phenological phases, but a combination of variables including water table level (WTL), water temperature and atmospheric pressure were relevant to explain CH4 exchange. The study site was an overall net carbon source to the atmosphere with annual emissions of 13-201 g C-CO2 m−2yr−1 and 8.5-15.2 g C-CH4 m−2yr−1. Our findings provide insights on: a) the role of plant phenological phases on ecosystem-scale CO2 and CH4 fluxes; b) challenges for modeling ecosystem-scale CO2 and CH4 fluxes in salt marshes; and c) the potential net loss of carbon to the atmosphere that should be considered for carbon management and accounting in these ecosystems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A current challenge is to understand what are the legacies left by disturbances on ecosystems for predicting response patterns and trajectories. This work focuses on the ecological implications of a ...major hurricane and analyzes its influence on forest gross primary productivity (GPP; derived from the moderate-resolution imaging spectroradiometer, MODIS) and soil CO2 efflux. Following the hurricane, there was a reduction of nearly 0.5 kgC m−2 yr−1, equivalent to ∼15% of the long-term mean GPP (∼3.0 ± 0.2 kgC m−2 yr−1; years 2003-8). Annual soil CO2 emissions for the year following the hurricane were > 3.9 ± 0.5 kgC m−2 yr−1, whereas for the second year emissions were 1.7 ± 0.4 kgC m−2 yr−1. Higher annual emissions were associated with higher probabilities of days with extreme soil CO2 efflux rates ( > 9.7 μmol CO2 m−2 s−1). The variance of GPP was highly variable across years and was substantially increased following the hurricane. Extreme soil CO2 efflux after the hurricane was associated with deposition of nitrogen-rich fresh organic matter, higher basal soil CO2 efflux rates and changes in variance of the soil temperature. These results show that CO2 dynamics are highly variable following hurricanes, but also demonstrate the strong resilience of tropical forests following these events.
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global ...predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Nonstructural carbon in woody plants Dietze, Michael C; Sala, Anna; Carbone, Mariah S ...
Annual review of plant biology,
01/2014, Volume:
65
Journal Article
Peer reviewed
Open access
Nonstructural carbon (NSC) provides the carbon and energy for plant growth and survival. In woody plants, fundamental questions about NSC remain unresolved: Is NSC storage an active or passive ...process? Do older NSC reserves remain accessible to the plant? How is NSC depletion related to mortality risk? Herein we review conceptual and mathematical models of NSC dynamics, recent observations and experiments at the organismal scale, and advances in plant physiology that have provided a better understanding of the dynamics of woody plant NSC. Plants preferentially use new carbon but can access decade-old carbon when the plant is stressed or physically damaged. In addition to serving as a carbon and energy source, NSC plays important roles in phloem transport, osmoregulation, and cold tolerance, but how plants regulate these competing roles and NSC depletion remains elusive. Moving forward requires greater synthesis of models and data and integration across scales from -omics to ecology.
The fitting of physical models is often done only using a single target observable. However, when multiple targets are considered, the fitting procedure becomes cumbersome, there being no easy way to ...quantify the robustness of the model for all different observables. Here, we illustrate that one can jointly search for the best model for each desired observable through multi-objective optimization. To do so, we construct the Pareto front to study if there exists a set of parameters of the model that can jointly describe multiple, or all, observables. To alleviate the computational cost, the predicted error for each targeted objective is approximated with a Gaussian process model as it is commonly done in the Bayesian optimization framework. We applied this methodology to improve three different models used in the simulation of stationary state cis-trans photoisomerization of retinal in rhodopsin, a significant biophysical process. Optimization was done with respect to different experimental measurements, including emission spectra, peak absorption frequencies for the cis and trans conformers, and energy storage. Advantages and disadvantages of previously proposed models are exposed.
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