Showing papers by "Mathew Williams published in 2021"

Optimal model complexity for terrestrial carbon cycle prediction

Abstract: . The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

Structural diversity and tree density drives variation in the biodiversity-ecosystem function relationship of woodlands and savannas.

Abstract: Positive biodiversity-ecosystem function relationships (BEFRs) have been widely documented, but it is unclear if BEFRs should be expected in disturbance-driven systems. Disturbance may limit competition and niche differentiation, which are frequently posited to underlie BEFRs. We provide the first exploration of the relationship between tree species diversity and biomass, one measure of ecosystem function, across southern African woodlands and savannas, an ecological system rife with disturbance from fire, herbivores and humans. We used >1000 vegetation plots distributed across 10 southern African countries, and structural equation modelling, to determine the relationship between tree species diversity and aboveground woody biomass, accounting for interacting effects of resource availability, disturbance by fire, tree stem density and vegetation type. We found positive effects of tree species diversity on aboveground biomass, operating via increased structural diversity. The observed BEFR was highly dependent on organismal density, with a minimum threshold of c. 180 mature stems ha-1. We found that water availability mainly affects biomass indirectly, via increasing species diversity. The study underlines the close association between tree diversity, ecosystem structure, environment and function in highly disturbed savannas and woodlands. We suggest that tree diversity is an under-appreciated determinant of wooded ecosystem structure and function.

Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation

Abstract: Chinese forests cover most of the representative forest types in the Northern Hemisphere and function as a large carbon (C) sink in the global C cycle The availability of long-term C dynamics observations is key to evaluating and understanding C sequestration of these forests The Chinese Ecosystem Research Network has conducted normalized and systematic monitoring of the soil-biology-atmosphere-water cycle in Chinese forests since 2000 For the first time, a reference dataset of the decadal C cycle dynamics was produced for 10 typical Chinese forests after strict quality control, including biomass, leaf area index, litterfall, soil organic C, and the corresponding meteorological data Based on these basic but time-discrete C-cycle elements, an assimilated dataset of key C cycle parameters and time-continuous C sequestration functions was generated via model-data fusion, including C allocation, turnover, and soil, vegetation, and ecosystem C storage These reference data could be used as a benchmark for model development, evaluation and C cycle research under global climate change for typical forests in the Northern Hemisphere

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Abstract: . Identification of terrestrial carbon (C) sources and sinks is critical for understanding the Earth system as well as mitigating and adapting to climate change resulting from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts, we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 ∘ spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood ( NPPwood ) and mean residence time of wood ( MRTwood ). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood . Due to the coupling between these variables and C stock dynamics being bi-directional, we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions, necessitating a greater understanding of wood litter C cycling than is typically considered in large-scale TEMs.

Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Abstract: Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

Does Economic Optimisation Explain LAI and Leaf Trait Distributions Across an Amazon Soil Moisture Gradient

Abstract: Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates

Abstract: . Land-atmosphere carbon and water exchanges have large uncertainty in land surface and biosphere models. Using observations to reduce land biosphere model structural and parametric errors is a key priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARDAMOM model-data fusion framework have yielded key insights into ecosystem carbon and water cycling. CARDAMOM analyses—informed by co-located C and H2O flux observations—have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. While CARDAMOM model configurations (namely CARDAMOM-compatible biogeochemical model structures) have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints, there has so far been no concerted effort to globally and systematically validate CARDAMOM performance across individual model-data fusion configurations. Here we use the FLUXNET-2015 dataset—an ensemble of 200+ eddy covariance flux tower sites—to formulate a concerted benchmarking framework for CARDAMOM carbon (GPP, NEE) and water (ET) flux estimates (CARDAMOM-FLUXVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET-2015 GPP, NEE and ET data. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land cover types, (ii) monthly, annual and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system—along with CARDAMOM driver files provided—can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development and testing of new CARDAMOM model structures.

The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties

Abstract: Identification of yield deficits early in the growing season for cereal crops (e.g., Triticum aestivum) could help to identify more precise agronomic strategies for intervention to manage production. We investigated how effective crop canopy properties, including leaf area index (LAI), leaf chlorophyll content, and canopy height, are as predictors of winter wheat yield over various lead times. Models were calibrated and validated on fertiliser trials over two years in fields in the UK. Correlations of LAI and plant height with yield were stronger than for yield and chlorophyll content. Yield prediction models calibrated in one year and tested on another suggested that LAI and height provided the most robust outcomes. Linear models had equal or smaller validation errors than machine learning. The information content of data for yield prediction degraded strongly with time before harvest, and in application to years not included in the calibration. Thus, impact of soil and weather variation between years on crop phenotypes was critical in changing the interactions between crop variables and yield (i.e., slopes and intercepts of regression models) and was a key contributor to predictive error. These results show that canopy property data provide valuable information on crop status for yield assessment, but with important limitations.

A New Field Protocol for Monitoring Forest Degradation

Abstract: Forest degradation leads to the gradual reduction of forest carbon stocks, function, and biodiversity following anthropogenic disturbance. Whilst tropical degradation is a widespread problem, it is currently very under-studied and its magnitude and extent are largely unknown. This is due, at least in part, to the lack of developed and tested methods for monitoring degradation. Due to the relatively subtle and ongoing changes associated with degradation, which can include the removal of small trees for fuelwood or understory clearance for agricultural production, it is very hard to detect using Earth Observation. Furthermore, degrading activities are normally spatially heterogeneous and stochastic, and therefore conventional forest inventory plots distributed across a landscape do not act as suitable indicators: at best only a small proportion of plots (often zero) will actually be degraded in a landscape undergoing active degradation. This problem is compounded because the metal tree tags used in permanent forest inventory plots likely deter tree clearance, biasing inventories towards under-reporting change. We have therefore developed a new forest plot protocol designed to monitor forest degradation. This involves a plot that can be set up quickly, so a large number can be established across a landscape, and easily remeasured, even though it does not use tree tags or other obvious markers. We present data from a demonstration plot network set up in Jalisco, Mexico, which were measured twice between 2017 and 2018. The protocol was successful, with one plot detecting degradation under our definition (losing greater than 10% AGB but remaining forest), and a further plot being deforested for Avocado (Persea americana) production. Live AGB ranged from 8.4 Mg ha-1 to 140.8 Mg ha-1 in Census 1, and from 0 Mg ha-1 to 144.2 Mg ha-1 Census 2, with four of ten plots losing AGB, and the remainder staying stable or showing slight increases. We suggest this protocol has great potential for underpinning appropriate forest plot networks for degradation monitoring, potentially in combination with Earth Observation analysis, but also in isolation.

Long‐term outcomes after transcatheter aortic valve replacement with minimal contrast in chronic kidney disease

Abstract: BACKGROUND Patients with renal insufficiency have poor short-term outcomes after transcatheter aortic valve replacement (TAVR). METHODS Retrospective chart review identified 575 consecutive patients not on hemodialysis who underwent TAVR between September 2014 and January 2017. Outcomes were defined by VARC-2 criteria. Primary outcome of all-cause mortality was evaluated at a median follow-up of 811 days (interquartile range 125-1,151). RESULTS Preprocedural glomerular filtration rate (GFR) was ≥60 ml/min in 51.7%, 30-60 ml/min in 42.1%, and < 30 ml/min in 6.3%. Use of transfemoral access (98.8%) and achieved device success (91.0%) did not differ among groups, but less contrast was used with lower GFR (23 ml [15-33], 24 ml [14-33], 13 ml [8-20]; p < .001). Peri-procedural stroke (0.7%, 2.1%, 11.1%; p < .001) was higher with lower GFR. Core lab analysis of preprocedural computed tomography scans of patients who developed a peri-procedural stroke identified potential anatomic substrate for stroke in three out of four patients with GFR 30-60 ml/min and all three with GFR <30 ml/min (severe atheroma was the most common subtype of anatomical substrate present). Compared to GFR ≥60 ml/min, all-cause mortality was higher with GFR 30-60 ml/min (HR 1.61 [1.00-2.59]; aHR 1.61 [0.91-2.83]) and GFR <30 ml/min (HR 2.41 [1.06-5.48]; aHR 2.34 [0.90-6.09]) but not significant after multivariable adjustment. Follow-up echocardiographic data, available in 63%, demonstrated no difference in structural heart valve deterioration over time among groups. CONCLUSIONS Patients with baseline renal insufficiency remain a challenging population with poor long-term outcomes despite procedural optimization with a transfemoral-first and an extremely low-contrast approach.

Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

Abstract: Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarc...

The Role of the Biomass Mission in Carbon Cycle Science and Politics

Abstract: The European Space Agency's 7th Earth Explorer mission, BIOMASS, was proposed in 2005 and since then there have been major changes in the scientific and political conditions within which it was conceived, and also within the technology and methodology both of the mission itself and of the complementary systems that BIOMASS will work with. This paper describes some of the most important recent developments in this overall environment of the mission, and how they affect the likely use of data from BIOMASS mission after its launch in 2023 and over its nominal five-year lifetime.