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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Leaf Trait Plasticity Alters Competitive Ability and Functioning of Simulated Tropical Trees in Response to Elevated Carbon Dioxide

The response of tropical ecosystems to elevated carbon dioxide (CO 2 ) remains a critical uncertainty in projections of future climate. Here, we investigate how leaf trait plasticity in response to elevated CO 2 alters projections of tropical forest competitive dynamics and functioning. We use vegetation demographic model simulations to quantify how plasticity in leaf mass per area and leaf carbon to nitrogen ratio alter the responses of carbon uptake, evapotranspiration, and competitive ability to a doubling of CO 2 in a tropical forest. Observationally constrained leaf trait plasticity in response to CO 2 fertilization reduces the degree to which tropical tree carbon uptake is affected by a doubling of CO 2 (up to -14.7% as compared to a case with no plasticity; 95% confidence interval [CI95%] -14.4 to -15.0). It also diminishes evapotranspiration (up to -7.0%, CI95% -6.4 to -7.7), and lowers competitive ability in comparison to a tree with no plasticity. Consideration of leaf trait plasticity to elevated CO 2 lowers tropical ecosystem carbon uptake and evapotranspirative cooling in the absence of changes in plant-type abundance. However, “plastic” responses to high CO 2 which maintain higher levels of plant productivity, many of which fall outside of the observed range of response, are potentially more competitively advantageous, thus, including changes in plant type abundance may mitigate these decreases in ecosystem functioning. Models that explicitly represent competition between plants with alternative leaf trait plasticity in response to elevated CO 2 are needed to capture these influences on tropical forest functioning and large-scale climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A data-driven global soil heterotrophic respiration dataset and the drivers of its inter-annual variability

Soil heterotrophic respiration (SHR), one of the primary carbon fluxes from terrestrial ecosystems to the atmosphere, is important for carbon-climate feedbacks because of its sensitivity to available litter and soil carbon, climatic conditions, and nutrient availability. However, until recently limited SHR data were available, and most published global SHR estimates have either a short time span, coarse spatial resolution, or reply on overly-simple model formulations. To better understand and quantify the global distribution of SHR and its sensitivity to climate variability, we produced a new global SHR dataset using Random Forest algorithms, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity as explanatory variables. We estimated a global total SHR of 46.8 Pg C yr-1 over 1985-2013 (95% confidence interval: 38.6-56.3 Pg C yr-1), with a significant increasing trend of 0.03 Pg C yr-2 during this period. We found that the choice of soil moisture datasets contributes more to the difference among these data-driven SHR members rather than that of productivity, temperature and precipitation data sources. We also analyzed the influence of climatic variables on the inter-annual variability (IAV) of our SHR product. Water availability was the dominant driver of IAV at global scales, although the inferred sensitivity depends on the choice of the soil moisture gridded dataset. At the ecosystem scale, temperature strongly controls the IAV of SHR in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning gridded SHR dataset and outputs from process-based land surface models (TRENDYv6) show agreement for a strong association between water variability and SHR IAV at the global scale, but the two approaches lead to different temporal trend globally and different controlling variables for IAV at the ecosystem scale. Our study provides evidence for the pervasive and important role of water availability in driving SHR, indicating both a direct effect limiting decomposition rates and an indirect effect through the amount of fresh organic matter made available to SHR from productivity. In consideration of potential limitations and uncertainties remaining in our data-driven SHR datasets, we call for a more scientifically designed observation network for SHR, more observation data compilation, and increased use of deep learning methods making maximum use of observation data in hand. This will benefit process-based models, and improve our understanding of SHR response to future anomalous environmental conditions.

Yao, Yitong↗
DOE OSTI2021

Close Observation of Hydraulic Fracturing at EGS Collab Experiment 1: Fracture Trajectory, Microseismic Interpretations, and the Role of Natural Fractures

Abstract Despite the wide application of hydraulic fracturing in enhanced geothermal system (EGS) development and unconventional hydrocarbon production, our understanding of the many factors affecting the propagation of hydraulic fractures has relied on circumstantial evidence. This is partly due to the scarcity of direct observations in the subsurface. The EGS Collab project attempts to address these issues in the context of EGS research by performing intermediate‐scale (∼10 m) hydraulic stimulation experiments in a thoroughly characterized and heavily instrumented underground testbed. This paper analyzes the data collected from the first suite of hydraulic fracturing tests in this testbed, consisting of seven stimulation episodes. High‐quality microseismic data delineate five planar features very clearly. Combining fracture‐wellbore intersection observations from distributed temperature sensing (DTS) and visual observations from an open‐hole well, as well as prior in‐situ stress measurements, we conclude with high certainty that the four larger planes were hydraulic fractures. The growth of the hydraulic fractures was temporarily halted by a prominent, open natural fracture in the testbed but they eventually crossed and slightly reoriented under continued stimulation. Mineral‐filled (i.e., healed) natural fractures, though prevalent in the testbed, did not have perceivable effects on hydraulic fracture propagation. The high‐quality, mutually corroborating data sets allowed conclusions to be drawn with high confidence and attests to the advantage of intermediate‐scale experiments in subsurface research.

58 GEOSCIENCES↗
DOE OSTI2021

Meteorological environments associated with California wildfires and their potential roles in wildfire changes during 1984-2017

California has been seeing more wildfires in recent years, resulting in huge economic losses and threatening human health. Clarifying the meteorological environments of wildfires is foundational to improving the understanding and prediction of wildfires and their impacts. Here, 1535 California wildfires during 1984-2017 are systematically investigated. Based on two key meteorological factors - temperature and moisture anomalies - all wildfires are classified into four groups: hot-dry, hot-wet, cold-dry, cold-wet. Most (~60%) wildfires occurred on hot-dry days. Compositing the large-scale environments of the four groups shows that persistent high pressure and strong northeasterly wind descending from inland favor hot-dry conditions for wildfires. This analysis also reveals an important role of anomalies in southerly onshore flow that supports stronger convection, accompanied by more lightning flashes that provide a triggering mechanism for wildfires under hot-wet conditions. Self-organizing map analysis lends confidence in the large-scale meteorological pattern for dominating hot-dry wildfires in California. Besides wildfire occurrence, wildfire size is also influenced by meteorological anomalies through their magnitudes. Among them, moisture anomaly explains the largest fraction (~69%) of variability in wildfire sizes. Large-scale meteorological anomalies are found to play an important role in the devastating 2018 wildfire season in California. During 1984-2017, wildfire burned area has significantly increased by ~3.6% per year, indicating a doubling of burned area in 2017 relative to 1984, with the trend dominated by hot-dry wildfires in summer. Drying and warming in conjunction with strengthening of the high pressure in summer support more frequent and larger wildfires in California.

58 GEOSCIENCES↗
DOE OSTI2021

A Distinct Atmospheric Mode for California Precipitation

The hydrologic cycle in California is strongly influenced by wet-season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet-season precipitation variability over both northern and southern California. This mode, named as the “California precipitation mode” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM's strong association with California wet-season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Integrating Arctic Plant Functional Types in a Land Surface Model Using Above‐ and Belowground Field Observations

Abstract Accurate simulations of high‐latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO 2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Many such models represent high‐latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine Arctic‐specific PFTs. The newly developed PFTs include: (1) nonvascular mosses and lichens, (2) deciduous and evergreen shrubs of various height classes, including an alder PFT, (3) graminoids, and (4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario and constant PFT fractional areas showed alder‐dominated plant communities gaining more biomass and lichen‐dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Linking Flood Frequency With Mesoscale Convective Systems in the US

Mesoscale convective systems (MCSs) with larger rain areas and higher rainfall intensity than non-MCS events can produce severe flooding. Flooding occurrences associated with MCS and non-MCS rainfall in the US east of 110°W are examined by linking a high-resolution MCS data set and reported floods in the warm season (April-August) between 2007 and 2017. MCSs account for the majority of slow-rising and hybrid floods, while non-MCS rainfall explains about half of flash floods in July and August as individual thunderstorms occur frequently in the Rocky Mountains and Appalachian Mountains. The event-total rainfall area of MCSs is the dominant factor of flood occurrences: MCSs with greater rainfall areas tend to produce more floods. While not related to flood frequency, propagating MCSs tend to produce flash floods with longer durations. These established links can improve our confidence in interpreting flood risks and their future changes due to changes in MCS characteristics with warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Measuring Fault Zone and Host Rock Hydraulic Properties Using Tidal Responses

Pore pressure diffusion along faults influences induced seismicity and rupture mechanics. In situ hydraulic diffusivity measurements along faults are rare and generally lower than inferred from seismicity migration. Here we use the tidal response of deep geothermal boreholes to measure fault diffusivity and permeability. Initial interpretations of the observation with a homogeneous confined aquifer model result in diffusivities of 10 -3 –10 -1 m 2 /s. However, this model mixes signals from both the conduit and the host rock. We develop a model for tidal response with a fault passing through the aquifer based on the fault-guided fracture network and solve for hydraulic properties in both the fault and the host rock. The resulting fault permeability is 2 × 10 -14 –7 × 10 -14 m 2 (90% confidence interval[CI]) and fault diffusivity is 0.08–0.33 m 2 /s (90% CI), which is 2 orders of magnitude higher than the host rock diffusivity in some wells, thus highlighting the role of faults as fluid conduits.

58 GEOSCIENCES↗
DOE OSTI2021

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Abstract Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave

Abstract Daily maximum temperatures during the 2021 heatwave in the Pacific Northwest United States and Canada shattered century old records. Multiple causal factors, including anthropogenic climate change, contributed to these high temperatures, challenging traditional methods of attributing human influence. We demonstrate that the observed 2021 daily maximum temperatures are far above the bounds of Generalized Extreme Value distributions fitted from historical data. Hence, confidence in Granger causal inference statements about the human influence on this heatwave is low. Alternatively, we present a more conditional hindcast attribution study using two regional models. We performed ensembles of simulations of the heatwave to investigate how the event would have changed if it had occurred without anthropogenic climate change and with future warming. We found that global warming caused a ∼0.8°C–1°C increase in heatwave temperatures. Future warming would lead to a ∼5°C increase in heatwave temperature by the end of the 21st century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi‐Layered Systems for Permanent Geologic Storage of CO 2 at the Gigatonne Scale

Abstract The effectiveness of Carbon Capture and Storage (CCS) as an imperative decarbonization technology relies on the sealing capacity of a fine‐grained caprock to permanently store CO 2 deep underground. Uncertainties in assessing the caprock sealing capacity increase with the spatial and temporal scales and may delay CCS deployment at the gigatonne scale. We have developed a computationally efficient transport model to capture the dynamics of basin‐wide upward CO 2 migration in a multi‐layered setting over geological time scales. We find that massive capillary breakthrough and viscous flow of CO 2 , even through pervasively fractured caprocks, are unlikely to occur and compromise the storage security. Potential leakage from the injection reservoir is hampered by repetitive layering of overlying caprocks. This finding agrees with geologic intuition and should be understandable by the public, contributing to the development of climate policies around this technology with increased confidence that CO 2 will be indefinitely contained in the subsurface.

58 GEOSCIENCES↗
DOE OSTI2023

Energy Surplus and an Atmosphere‐Land‐Surface “Tug of War” Control Future Evapotranspiration

Abstract The 21st century evapotranspiration (ET) trends over the continental U.S. are assessed using innovative, energy‐based principles. Annual ET is projected to increase with high confidence at the rate of 20 mm for every 1℃ of rise in near‐surface air temperature, or 0.45 or 0.98 mm/year/year, depending on the emission scenario. The ET trajectory is dominated (58%) by the increase of land‐surface net radiative energy. An enhancement of the fraction of energy taken up by ET becomes a more important controller (53%) in late 21st century, under the high emission scenario. This increase is explained by the “tug of war” between atmospheric vapor demand and land‐surface ability to supply water. An assessment of future water availability (precipitation minus ET) shows no significant changes at the continental scale. This outcome nevertheless hides strong spatial variability, emphasizing the role of ET in shaping the distribution of water availability among human populations.

Xu, Donghui↗
DOE OSTI2023

The Post‐Perovskite Transition in Fe‐ and Al‐Bearing Bridgmanite: Effects on Seismic Observables

Abstract The primary phase of the Earth’s lower mantle, (Al, Fe)‐bearing bridgmanite, transitions to the post‐perovskite (PPv) phase at Earth’s deep mantle conditions. Despite extensive experimental and ab initio investigations, there are still important aspects of this transformation that need clarification. Here, we address this transition in (Al 3+ , Fe 3+ )‐, (Al 3+ )‐, (Fe 2+ )‐, and (Fe 3+ )‐bearing bridgmanite using ab initio calculations and validate our results against experiments on similar compositions. Consistent with experiments, our results show that the onset transition pressure and the width of the two‐phase region depend distinctly on the chemical composition: (a) Fe 3+ ‐, Al 3+ ‐, or (Al 3+ , Fe 3+ )‐alloying increases the transition pressure, while Fe 2+ ‐alloying has the opposite effect; (b) in the absence of coexisting phases, the pressure‐depth range of the Pv‐PPv transition is likely too broad to cause a sharp D” discontinuity (<30 km); (c) the average Clapeyron slope of the two‐phase regions are consistent with previous measurements, calculations in MgSiO 3 , and inferences from seismic data. In addition, (d) we observe a softening of the bulk modulus in the two‐phase region. The consistency between our results and experiments gives us the confidence to proceed and examine this transition in aggregates with different compositions computationally, which will be fundamental for resolving the most likely chemical composition of the D" region by analyses of tomographic images.

Geochemistry & Geophysics↗
DOE OSTI2022

Observations of Mantle Seismic Anisotropy Using Array Techniques: Shear-Wave Splitting of Beamformed SmKS Phases

Shear-wave splitting measurements are commonly used to resolve seismic anisotropy in both the upper and lowermost mantle. Typically, such techniques are applied to SmKS phases that have reflected (m-1) times off the underside of the core-mantle boundary before being recorded. Practical constraints for shear-wave splitting studies include the limited number of suitable phases as well as the large fraction of available data discarded because of poor signal-to-noise ratios (SNRs) or large measurement uncertainties. Array techniques such as beamforming are commonly used in observational seismology to enhance SNRs, but have not been applied before to improve SmKS signal strength and coherency for shear wave splitting studies. Here, we investigate how a beamforming methodology, based on slowness and backazimuth vespagrams to determine the most coherent incoming wave direction, can improve shear-wave splitting measurement confidence intervals. Through the analysis of real and synthetic seismograms, we show that (a) the splitting measurements obtained from the beamformed seismograms (beams) reflect an average of the single-station splitting parameters that contribute to the beam; (b) the beams have (on average) more than twice as large SNRs than the single-station seismograms that contribute to the beam; (c) the increased SNRs allow the reliable measurement of shear wave splitting parameters from beams down to average single-station SNRs of 1.3. Beamforming may thus be helpful to more reliably measure splitting due to upper mantle anisotropy. Moreover, here we show that beamforming holds potential to greatly improve detection of lowermost mantle anisotropy by demonstrating differential SKS–SKKS splitting analysis using beamformed USArray data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil Carbonyl Sulfide (OCS) Fluxes in Terrestrial Ecosystems: An Empirical Model

Measurements of carbonyl sulfide (OCS) enable independent estimates of regional stomatal conductance provided that non-stomatal OCS fluxes are well constrained. OCS is taken up through plant leaves, following the same pathway as CO 2 ; in contrast to CO 2 , OCS is irreversibly destroyed in plant leaves and plants do not typically exhibit OCS emissions. Ecosystem uptake of OCS can indicate changes in stomatal opening. Here we present an empirical model to assess the potential impact of soil OCS exchange, the non-Stomatal OCS exchange Empirical Model (SOCSEM, version 0). We created biome-specific response curves characterizing soil OCS exchange and restricted the model design to require only knowledge of soil moisture and surface temperature because remote sensing observations are available for these two features. Comparing the model to field-based chamber observations reveal deviations that can be attributed to missing complexity of the ground surface (having excluded litter and plants without regulated stomata), shortwave radiation, or the soil environment. For agricultural regions with known net emissions, we use remotely-sensed surface temperature data and demonstrate that data resolution can affect anticipated fluxes. We further investigate the influence of regions with unknown soil OCS responses, for example, Arctic tundra. We compare our model to a process-based and respiration-based soil OCS exchange model that has been implemented in a land surface model. Further field study of tropical and arctic ecosystems in conjunction with studies of non-stomatal surfaces in addition to soil (e.g., bryophytes) will increase confidence in applying OCS as a regional tracer for stomatal conductance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗