Model-data Fusion Research Articles

An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming

The ratio of transpiration to total terrestrial evapotranspiration (T/ET) plays an important role in the hydrological cycle and in the energy budgets between the land and the atmosphere. Although China has experienced substantial climate warming and vegetation restoration (i.e., greening) over the past decades, the response of T/ET to the changing climate and environmental factors is poorly understood. Here, we apply a model-data fusion method that integrates the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model with multivariate observational datasets (transpiration and evapotranspiration) to quantify the relative contributions of multiple factors to the T/ET trend for the terrestrial ecosystem of China from 1982 to 2015. Validation against the observational data indicates that the PT-JPL model performed well. The multi-year average T/ET was estimated to be 0.56 ± 0.05 in China. The T/ET of the forest ecosystems (0.65–0.72) was generally higher than that of the non-forest ecosystems (0.41–0.60). T/ET increased remarkably at a rate of 0.0019 yr−1 (P < 0.01) during the study period. Leaf area index increased significantly over the period, by 0.0031 m2 m−2 yr−1. It appears that greening and climate change were the most likely causes of the increasing T/ET in China, directly explaining 57.89% and 36.84% of the T/ET trend, respectively. Particularly, in the subtropical-tropical monsoonal region, greening directly contributed 24.43% to the T/ET trend whereas climate change contributed 60.95%. The influences of greening and climate change on T/ET trends are mutually reinforcing. Additionally, partial correlation analyses between the climate-driven T/ET and the climate variables indicate that warming (0.04 °C yr−1, P < 0.01) was the major driving force of the climate-induced interannual variability of T/ET across the whole study area (R = 0.84), especially in the subtropical-tropical monsoonal region (R = 0.89). Our results may help elucidate the interactions between terrestrial ecosystems and the atmosphere within the context of long-term global climate changes.

Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations

Abstract. During the last decade, carbon cycle data assimilation systems (CCDAS) have focused on improving the simulation of seasonal and mean global carbon fluxes over a few years by simultaneous assimilation of multiple data streams. However, the ability of a CCDAS to predict longer-term trends and variability of the global carbon cycle and the constraint provided by the observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask measurement sites from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with independent observations from the period 1982–2010, as well as a third MPI-CCDAS assimilation run using data from the full 1982–2010 period, and an atmospheric inversion covering the same data and time. With 30 years of data, MPI-CCDAS is capable of representing land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. The long-term trend and seasonal amplitude of atmospheric CO2 concentrations at station level over the period 1982 to 2010 is considerably improved after assimilating only the first decade (1982–1990) of observations. After 15–19 years of prognostic simulation, the simulated CO2 mixing ratio in 2007–2010 diverges by only 2±1.3 ppm from the observations, the atmospheric inversion, and the MPI-CCDAS assimilation run using observations from the full period. The long-term trend, phenological seasonality, and interannual variability (IAV) of FAPAR in the Northern Hemisphere over the last 1 to 2 decades after the assimilation were also improved. Despite imperfections in the representation of the IAV in atmospheric CO2, model–data fusion for a decade of data can already contribute to the prognostic capacity of land carbon cycle models at relevant timescales.

Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1

Abstract. Photosynthesis (gross primary production, GPP) and evapotranspiration (ET) are ecosystem processes with global significance for climate, the global carbon and hydrological cycles and a range of ecosystem services. The mechanisms governing these processes are complex but well understood. There is strong coupling between these processes, mediated directly by stomatal conductance and indirectly by root zone soil moisture content and its accessibility. This coupling must be effectively modelled for robust predictions of earth system responses to global change. Yet, it is highly demanding to model leaf and cellular processes, like stomatal conductance or electron transport, with response times of minutes, over decadal and global domains. Computational demand means models resolving this level of complexity cannot be easily evaluated for their parameter sensitivity nor calibrated using earth observation information through data assimilation approaches requiring large ensembles. To overcome these challenges, here we describe a coupled photosynthesis evapotranspiration model of intermediate complexity. The model reduces computational load and parameter numbers by operating at canopy scale and daily time step. Through the inclusion of simplified representation of key process interactions, it retains sensitivity to variation in climate, leaf traits, soil states and atmospheric CO2. The new model is calibrated to match the biophysical responses of a complex terrestrial ecosystem model (TEM) of GPP and ET through a Bayesian model–data fusion framework. The calibrated ACM-GPP-ET generates unbiased estimates of TEM GPP and ET and captures 80 %–95 % of the sensitivity of carbon and water fluxes by the complex TEM. The ACM-GPP-ET model operates 3 orders faster than the complex TEM. Independent evaluation of ACM-GPP-ET at FLUXNET sites, using a single global parameterisation, shows good agreement, with typical R2∼0.60 for both GPP and ET. This intermediate complexity modelling approach allows full Monte Carlo-based quantification of model parameter and structural uncertainties and global-scale sensitivity analyses for these processes and is fast enough for use within terrestrial ecosystem model–data fusion frameworks requiring large ensembles.

Model-data fusion for spatial and statistical characterization of soil parameters from geophysical measurements

A recently developed PDE-constrained stochastic inverse analysis algorithm for spatial and statistical characterization of soil parameters from geophysical measurements, considering uncertainty due to limited measurements and sensor noise, is exemplified and validated. A 60m × 60 m geotechnical site in Garner Valley, CA is used as the validation testbed. Advanced geophysical test measurements – in terms of velocity waveforms at a few locations on the surface due to surface excitations using a mobile shaker – are available for the site. The algorithm inversely analyzes the available measurements to probabilistically estimate the elastic parameters of the soil at the site up to a depth of 40 m. The algorithm relies on (1) hypothesizing the soil parameters to be heterogeneous, anisotropic random fields, (2) making prior assumptions on them, (3) numerically simulating the geophysical experiment using the finite element method in conjunction with a stochastic collocation approach, and (4) fusing simulated measurements with experimental measurements using a minimum variance framework to update the prior assumptions on the soil parameter random fields. The estimated elastic parameters of the soil are presented in terms of marginal mean and marginal standard deviation profiles of the soil's P- and S-wave velocities as well as their correlation structures in the x-, y-, and z-direction. In ascertaining the accuracy of the inverse analysis algorithm, the geophysical experiment is numerically re-simulated with the estimated P- and S-wave velocity profiles and the model predicted velocity waveforms are compared against the field observations at all the measurement locations. Comments are made at appropriate places regarding several aspects of the algorithm in highlighting the lessons learned through this validation effort towards accurate stochastic full waveform inversion of geophysical measurements.

Model-data fusion to assess year-round CO2 fluxes for an arctic heath ecosystem in West Greenland (69°N)

Quantifying net CO2 exchange (NEE) of arctic terrestrial ecosystems in response to changes in climatic and environmental conditions is central to understanding ecosystem functioning and assessing potential feedbacks of the carbon cycle to future climate changes. However, annual CO2 budgets for arctic tundra are rare due to the difficulties of performing measurements during non-growing seasons. It is still unclear to what extent arctic tundra ecosystems currently act as a CO2 source, sink or are in balance. This study presents year-round eddy-covariance (EC) measurements of CO2 fluxes for an arctic heath ecosystem on Disko Island, West Greenland (69 °N) over five years. Based on a fusion of year-round EC-derived CO2 fluxes, soil temperature and moisture, the process-oriented model (CoupModel) has been constrained to quantify an annual budget and characterize seasonal patterns of CO2 fluxes. The results show that total photosynthesis corresponds to -202 ± 20 g C m−2 yr-1 with ecosystem respiration of 167 ± 28 g C m-2 yr-1, resulting in NEE of -35 ± 15 g C m-2 yr-1. The respiration loss is mainly described as decomposition of near-surface litter. A year with an anomalously deep snowpack shows a threefold increase in the rate of ecosystem respiration compared to other years. Due to the high CO2 emissions during that winter, the annual budget results in a marked reduction in the CO2 sink. The seasonal patterns of photosynthesis and soil respiration were described using response functions of the forcing atmosphere and soil conditions. Snow depth, topography-related soil moisture, and growing season warmth are identified as important environmental characteristics which most influence seasonal rates of gas exchange.

Model-data fusion of hydrologic simulations and GRACE terrestrial water storage observations to estimate changes in water table depth

Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom ‘groundwater’ layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management.

Approaching the potential of model-data comparisons of global land carbon storage

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation.

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback.

Is arctic greening consistent with the ecology of tundra? Lessons from an ecologically informed mass balance model

Climate change has been implicated in the widespread ‘greening’ of the arctic in recent decades. However, differences in arctic greening patterns among satellite platforms and recent reports of decreased rate of greening or of browning have made attributing arctic greening trends to a warming climate challenging. Here, we compared MODIS greening trends to those predicted by the coupled carbon and nitrogen model (CCaN); a mass balance carbon and nitrogen model that was driven by MODIS surface temperature and climate. CCaN was parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite based data. We found that, at long temporal and large spatial scales, MODIS greening trends were consistent with ecological and biogeochemical data from arctic tundra. However, at smaller spatial scales, observations and CCaN greening trends differed in the location, extent, and magnitude of greening. CCaN was unable to capture the high rates of MODIS greening in northern wetlands, and the patchy MODIS browning in the southern portion of the North Slope. This model-data disagreement points to disturbance and its legacy impacts on land cover as an important mechanism for understanding greening trends on the North Slope of Alaska.

Agencies Collaborate to Better Monitor and Model the Environment: Interagency Collaborative for Environmental Modeling and Monitoring: Monitoring and Model Data Fusion; Rockville, Maryland, 24-25 April 2018.

Interagency Collaborative for Environmental Modeling and Monitoring: Monitoring and Model Data Fusion; Rockville, Maryland, 24–25 April 2018

Understanding terrestrial water storage variations in northern latitudes across scales

Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid- to high latitudes. To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales. Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid- to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model–data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on inter-annual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short- and long-term variability of water resources.

Using stable isotopes to estimate travel times in a data-sparse Arctic catchment: Challenges and possible solutions.

Use of isotopes to quantify the temporal dynamics of the transformation of precipitation into run‐off has revealed fundamental new insights into catchment flow paths and mixing processes that influence biogeochemical transport. However, catchments underlain by permafrost have received little attention in isotope‐based studies, despite their global importance in terms of rapid environmental change. These high‐latitude regions offer limited access for data collection during critical periods (e.g., early phases of snowmelt). Additionally, spatio‐temporal variable freeze–thaw cycles, together with the development of an active layer, have a time variant influence on catchment hydrology. All of these characteristics make the application of traditional transit time estimation approaches challenging. We describe an isotope‐based study undertaken to provide a preliminary assessment of travel times at Siksik Creek in the western Canadian Arctic. We adopted a model–data fusion approach to estimate the volumes and isotopic characteristics of snowpack and meltwater. Using samples collected in the spring/summer, we characterize the isotopic composition of summer rainfall, melt from snow, soil water, and stream water. In addition, soil moisture dynamics and the temporal evolution of the active layer profile were monitored. First approximations of transit times were estimated for soil and streamwater compositions using lumped convolution integral models and temporally variable inputs including snowmelt, ice thaw, and summer rainfall. Comparing transit time estimates using a variety of inputs revealed that transit time was best estimated using all available inflows (i.e., snowmelt, soil ice thaw, and rainfall). Early spring transit times were short, dominated by snowmelt and soil ice thaw and limited catchment storage when soils are predominantly frozen. However, significant and increasing mixing with water in the active layer during the summer resulted in more damped steam water variation and longer mean travel times (~1.5 years). The study has also highlighted key data needs to better constrain travel time estimates in permafrost catchments.

A test of functional convergence in carbon fluxes from coupled C and N cycles in Arctic tundra

Arctic ecosystems exhibit functional convergence in Net Ecosystem Exchange (NEE’s) of CO2 response to light, air temperature, the Normalized Difference Vegetation Index (NDVI), and Leaf Area Index (LAI), potentially simplifying predictions of climate change impacts on the arctic C cycle over space and time. This convergence is hypothesized to be induced by tightly coupled carbon and nitrogen cycles, but has never been explicitly tested. We used model-data fusion on a mass balance model (i.e. the Coupled Carbon and Nitrogen; CCaN) to determine whether functional convergence in NEE results from tightly coupled carbon (C) and nitrogen (N) cycles. CCaN captured a majority, but not all, of NEE and NDVI observations across eight growing seasons, and MODIS NDVI observations across a tundra latitudinal gradient in Alaska. The hypothesis of temporal functional convergence was challenged by model-data disagreements for NEE during shoulder seasons and low light. The hypothesis of spatial functional convergence was challenged by the underestimation of NDVI in warm tundra. CCaN structure and parameter uncertainty analyses revealed that factors controlling leaf litter rate, the proportion of N in leaves, and net N mineralization rate are critical knowledge gaps in predicting pan-arctic NEE and NDVI in a future warmer climate.

Modelling crops and cropping systems—Evolving purpose, practice and prospects

Abstract Crops and cropping systems are characterised by complexity and variability. Complexity arises from inherently complex plant and soil processes combined in an almost infinite set of permutations and combinations associated with biotic and abiotic drivers that are inherently variable in both space and time. Modelling has evolved over the last 100 years as a means of describing and interpreting complex and variable performance and increasingly as a means of predicting likely performance in prescribed circumstances for better decision making. In this paper we reflect on the evolution of quantitative approaches to describing and predicting crop growth and cropping system performance. We begin with early mathematical descriptions of plant and crop growth and soil processes dating from the 1920′s to 1950′s. We explore the early crop models of the 60′s and 70′s and the more comprehensive crop-soil models of the 1970′s and 80′s. Cropping systems models with comprehensive systems management capabilities underpinned by a modular design began to gain currency in the 1990s. Over this long period, the ambitions held by model-makers’ for model applications grew. We analysed the publication records of major cropping systems models to summarise the broad trends in model applications that emerged in the early 21st Century, based on the 60 years of development in the 20th Century. There was an “explosion” in publications on cropping systems models, with an eight fold increase from 2000 to 2015. In parallel, the application of models greatly expanded from approximately four areas in 2000 (agronomy, model development, climate change and methods) to more than 20 in 2015. However, despite this explosion and expansion there is little evidence in the literature that modelling was having an impact on farmers and policy makers. We conclude with an examination of the forces shaping cropping systems model development and application. Developments in data acquisition and model-data fusion may open the way for cropping systems models to have greater impact in real-world policy or practice settings, making a meaningful contribution to future agricultural productivity and sustainability.

T133. The effective brain connectome: An inverse problem

We give a general overview of recent developments in the study of Brain Connectivity. The essential task is to estimate the “Effective Brain Connectome” (EBC). This is the set of all direct interactions between neural masses. However, these interactions have to be estimated. We propose a formal definition of the EBC as a hypergraph defined on a multidimensional tensor. The simplest version of EBC is defined on a 3-dimensional tensor, with signatures for emitter regions, receiver regions and time delays, taking values for a Wiener-Akaike-Granger-Schweder influence measure. In the more complex case the dimensionality may take into consideration cortical layer, neurotransmitters etc. The support of the hypergraph is defined by the structural connectome. The actual challenge is to estimate the EBC from incomplete, noisy, multimodal neuroimaging data, especially from EEG and fMRI time series. Recognition of the mathematical structure of the underlying model allows the use of new tensor based methods for model inversion and data fusion. These are illustrated with resting state EEG/fMRI data. Finally, open problems in EBC studies are identifies and discussed.

Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion

Process-based ecosystem models are increasingly used to estimate the carbon and water exchanges between ecosystems and the atmosphere. These models inevitably suffer from deficiencies and uncertainties, which should be thoroughly examined to better understand the processes governing the ecosystem dynamics. In this paper, we systematically explored the uncertainties in model predictions of Changbaishan (CBS) broad-leaved Korean pine mixed forest using the SImplified PhotosyNthesis and Evapo-Transpiration (SIPNET) model and eddy flux and meteorological data from 2004 to 2009. We first screened out 21 key parameters from 42 model parameters using Morris global sensitivity analysis method, and then estimated their probability distributions through Markov Chain Monte Carlo technique. Two optimization set-ups, i.e. using observed net ecosystem exchange of CO2 (NEE) only and using observed NEE and evapotranspiration (ET) simultaneously, were conducted to detect the different constraints of different observations on model parameters. Four parameters were well constrained using observed NEE only, including photosynthesis and respiration related parameters. While seven parameters were well constrained using measured NEE and ET simultaneously, four of which were water related parameters. Obviously, more information can be derived from the simultaneous optimization, since there was additional process information in water flux observation. The modeled ET of the NEE and ET optimization set-up had a much better fit to measured values than the NEE only optimization set-up (R2 = 0.70 vs. R2 = 0.30), although the modeled NEE from the two set-ups had a good fit to the observations (R2 = 0.85 vs. R2 = 0.83). This implied that assimilating carbon and water fluxes simultaneously can improve the parameterization and overall performance of the model. Then, we quantified the uncertainties in model predictions using Monte Carlo simulation, and trace them to specific parameter and parameter interactions through Sobol’ variance decomposition method. The uncertainties of five outputs of interest in CBS site, NEE, gross primary productivity (GPP), ecosystem respiration (RE), ET and transpiration (T), were 50.82%, 22.35%, 21.25%, 9.98% and 19.54%, respectively. The uncertainty in predicted NEE was much larger since NEE is a small difference between two large fluxes, i.e. GPP and RE. The maximum net CO2 assimilation rate (Amax) and carbon content of leaves (SLW) were classified as highly sensitive parameters for all outputs of interest in CBS site, contributing more than 70% of the uncertainties in all outputs except NEE. The importance of these two parameters holds for one subtropical evergreen coniferous plantation and one subtropical evergreen broad-leaved forest, too. Therefore, these two parameters and their underlying processes should be a focus of future model research, plant trait data collection and field measurement, at least for the sites in this study. This can help connect the model simulation research and field data collection, making them mutually informative.

Model–data fusion approach to quantify evapotranspiration and net ecosystem exchange across the sagebrush ecosystem at different temporal resolutions

AbstractEvapotranspiration (ET) and net ecosystem exchange (NEE) are driven by both high and slow frequency scalar fluxes. Quantifying the variation of these two processes at different timescales remains a challenge. Bridging this knowledge gap is crucial in order to improve insights of the impact of biotic and abiotic factors modulating these fluxes as well as for accurate estimation of gross primary productivity (GPP) and ecosystem respiration (Re). This issue was addressed using a model–data fusion approach within a Bayesian framework by running the model against ET and NEE observations at three different time steps: subdaily (30 min), daily (1 day), and intermediate (7 days). The model was tested against eddy covariance data collected for a 2‐month period (June and July) from a sagebrush‐steppe ecosystem in the United States. The 95% credible interval (CI) of fast processes such as transpiration and photosynthesis reduced by more than 90% compared with its a priori range when model was run at 30‐min time step. The reduction in CI of the same parameters varied between 30% and 70% when the model was run at 1‐ or 7‐day time step. The 95% CI of slow process such as root respiration reduced by 89% and 73% when model was run at 7‐day and 30‐min time step, respectively. We found strong confidence in predicting ET and NEE at subdaily timescale, whereas uncertainty increased with increase in temporal resolution. GPP and Re varied strongly as the system transitioned from a traditionally wet (June) to a dry (July) month.

Retrieval of High Resolution Surface Net Radiation for Urban Area Using Satellite and CFD Model Data Fusion

Retrieval of High Resolution Surface Net Radiation for Urban Area Using Satellite and CFD Model Data Fusion

Sequential Assimilation of Volcanic Monitoring Data to Quantify Eruption Potential: Application to Kerinci Volcano, Sumatra

Quantifying the eruption potential of a restless volcano requires the ability to model parameters such as overpressure and calculate the host rock stress state as the system evolves. A critical challenge is developing a model-data fusion framework to take advantage of observational data and provide updates of the volcanic system through time. The Ensemble Kalman Filter (EnKF) uses a Monte Carlo approach to assimilate volcanic monitoring data and update models of volcanic unrest, providing time-varying estimates of overpressure and stress. Although the EnKF has been proven effective to forecast volcanic deformation using synthetic InSAR and GPS data, until now, it has not been applied to assimilate data from an active volcanic system. In this investigation, the EnKF is used to provide a “hindcast” of the 2009 explosive eruption of Kerinci volcano, Indonesia. A two-sources analytical model is used to simulate the surface deformation of Kerinci volcano observed by InSAR time-series data and to predict the system evolution. A deep, deflating dike-like source reproduces the subsiding signal on the flanks of the volcano, and a shallow spherical McTigue source reproduces the central uplift. EnKF predicted parameters are used in finite element models to calculate the host-rock stress state prior to the 2009 eruption. Mohr-Coulomb failure models reveal that the shallow magma reservoir is trending towards tensile failure prior to 2009, which may be the catalyst for the 2009 eruption. Our results illustrate that the EnKF shows significant promise for future applications to forecasting the eruption potential of restless volcanoes and hind-cast the triggering mechanisms of observed eruptions.

Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA

In order to generate air-pollutant exposure fields for health studies, a data fusion (DF) approach is developed that combines observations from ambient monitors and simulated data from the Community Multiscale Air Quality (CMAQ) model. These resulting fields capture the spatiotemporal information provided by the air quality model, as well as the finer temporal scale variations from the pollutant observations and decrease model biases. Here, the approach is applied to develop daily concentration fields for PM2.5 total mass, five major particulate species (OC, EC, SO4 2−, NO3 −, and NH4 +), and three gaseous pollutants (CO, NO x , and NO2) from 2006 to 2008 over North Carolina (USA). Several data withholding methods are then conducted to evaluate the data fusion method, and the results suggest that typical approaches may overestimate the ability of spatiotemporal estimation methods to capture pollutant concentrations in areas with limited or no monitors. The results show improvements in capturing spatial and temporal variability compared with CMAQ results. Evaluation tests for PM2.5 led to an R 2 of 0.95 (no withholding) and 0.82 when using 10% random data withholding. If spatially based data withholding is used, the R 2 is 0.73. Comparisons of DF-developed PM2.5 total mass concentration with the spatiotemporal fields derived from two other methods (both use satellite aerosol optical depth (AOD) data) find that, in this case, the data fusion fields have slightly less overall error, with an RMSE of 1.28 compared with 3.06 μg/m3 (two-stage statistical model) and 2.74 (neural network-based hybrid model). Applying the Integrated Mobile Source Indicator (IMSI) method shows that the data fusion fields can be used to estimate mobile source impacts. Overall, the growing availability of chemically detailed air quality model fields and the accuracy of the DF field, suggest that this approach is better able to provide spatiotemporal pollutant fields for gaseous and speciated particulate pollutants for health and planning studies.