Observational Uncertainties Research Articles

Waveform accuracy and systematic uncertainties in current gravitational wave observations

Waveform accuracy and systematic uncertainties in current gravitational wave observations

ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks

Aims. Characterizing the interior structure of exoplanets is essential for understanding their diversity, formation, and evolution. As the interior of exoplanets is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observable parameters such as mass and radius. This is a highly degenerate problem whose solution often relies on computationally expensive and time-consuming inference methods such as Markov chain Monte Carlo. Methods. We present ExoMDN, a machine-learning model for the interior characterization of exoplanets based on mixture density networks (MDN). The model is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. We employ log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle. Results. Given mass, radius, and equilibrium temperature, we show that ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer in under a second on a standard Intel i5 CPU. Observational uncertainties can be easily accounted for through repeated predictions from within the uncertainties. We used ExoMDN to characterize the interiors of 22 confirmed exoplanets with mass and radius uncertainties below 10 and 5%, respectively, including the well studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss the inclusion of the fluid Love number k2 as an additional (potential) observable, showing how it can significantly reduce the degeneracy of interior structures. Utilizing the fast predictions of ExoMDN, we show that measuring k2 with an accuracy of 10% can constrain the thickness of core and mantle of an Earth analog to ≈13% of the true values.

Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework.

Deep learning based image enhancement models have largely improved the readability of fundus images in order to decrease the uncertainty of clinical observations and the risk of misdiagnosis. However, due to the difficulty of acquiring paired real fundus images at different qualities, most existing methods have to adopt synthetic image pairs as training data. The domain shift between the synthetic and the real images inevitably hinders the generalization of such models on clinical data. In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. The student network uses synthetic pairs for supervised enhancement, and regularizes the enhancement model to reduce domain-shift by enforcing teacher-student prediction consistency on the real fundus images without relying on enhanced ground-truth. Moreover, we also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network. Our MAGE-Net utilizes multi-stage enhancement module and retinal structure preservation module to progressively integrate the multi-scale features and simultaneously preserve the retinal structures for better fundus image quality enhancement. Comprehensive experiments on both real and synthetic datasets demonstrate that our framework outperforms the baseline approaches. Moreover, our method also benefits the downstream clinical tasks.

Missing Giants: Predictions on Dust-obscured Galaxy Stellar Mass Assembly Throughout Cosmic Time

Due to their extremely dust-obscured nature, much uncertainty still exists surrounding the stellar mass growth and content in dusty, star-forming galaxies (DSFGs) at z > 1. In this work, we present a numerical model built using empirical data on DSFGs to estimate their stellar mass contributions across the first ∼10 Gyr of cosmic time. We generate a dust-obscured stellar mass function that extends beyond the mass limit of star-forming stellar mass functions in the literature, and predict that massive DSFGs constitute as much as 50%–100% of all star-forming galaxies with M ≥1011 M ⊙ at z > 1. We predict the number density of massive DSFGs and find general agreement with observations, although more data is needed to narrow wide observational uncertainties. We forward-model mock massive DSFGs to their quiescent descendants and find remarkable agreement with observations from the literature demonstrating that, to first order, massive DSFGs are a sufficient ancestral population to describe the prevalence of massive quiescent galaxies at z > 1. We predict that massive DSFGs and their descendants contribute as much as 25%–60% to the cosmic stellar mass density during the peak of cosmic star formation, and predict an intense epoch of population growth during the ∼1 Gyr from z = 6 to 3 during which the majority of the most massive galaxies at high-z grow and then quench. Future studies seeking to understand massive galaxy growth and evolution in the early universe should strategize synergies with data from the latest observatories (e.g., JWST and the Atacama Large Millimeter/submillimeter Array) to better include the heavily dust-obscured galaxy population.

Is there an excess of black holes around 20 M⊙? Optimizing the complexity of population models with the use of reversible jump MCMC.

ABSTRACT Some analyses of the third gravitational wave catalogue released by the LIGO-Virgo-KAGRA collaboration (LVK) suggest an excess of black holes around $15\!-\!20 \, {\rm M}_{\odot }$. In order to investigate this feature, we introduce two flexible population models, a semiparametric one and a non-parametric one. Both make use of reversible jump Markov chain Monte-Carlo to optimise their complexity. We also illustrate how the latter can be used to efficiently perform model selection. Our parametric model broadly agrees with the fiducial analysis of the LVK, but finds a peak of events at slightly larger masses. Our non-parametric model shows this same displacement. Moreover, it also suggests the existence of an excess of black holes around $20 \, {\rm M}_{\odot }$. We assess the robustness of this prediction by performing mock injections and running simplified hierarchical analyses on those (i.e. without selection effects and observational uncertainties). We estimate that such a feature might be due to statistical fluctuations, given the small number of events observed so far, with a 5 per cent probability. We estimate that with a few hundreds of observations, as expected for O4, our non-parametric model will be able to robustly determine the presence of this excess. It will then allow for an efficient agnostic inference of the properties of black holes.

HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery

Abstract. Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth observation (EO) data, several gaps and uncertainties remain with developing models to be operational at the global scale. The lack of a high-resolution globally distributed and event-diverse dataset for landslide segmentation poses a challenge in developing machine learning models that can accurately and robustly detect landslides in various regions, as the limited representation of landslide and background classes can result in poor generalization performance of the models. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD), a high-resolution (HR) satellite dataset (PlanetScope, 3 m pixel resolution) for landslide mapping composed of landslide instances from 10 different physiographical regions globally in South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall-triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions in the form of standardized image patches containing four PlanetScope image bands (red, green, blue, and NIR) and a binary mask for landslide detection. The HR-GLDD can be accessed through this link: https://doi.org/10.5281/zenodo.7189381 (Meena et al., 2022a, c). HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, three recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results when testing the HR-GLDD at individual test sites, thereby indicating the robustness of the dataset for such purposes. The HR-GLDD is open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

The chemical evolution of the solar neighbourhood for planet-hosting stars

ABSTRACT Theoretical physical-chemical models for the formation of planetary systems depend on data quality for the Sun’s composition, that of stars in the solar neighbourhood, and of the estimated ’pristine’ compositions for stellar systems. The effective scatter and the observational uncertainties of elements within a few hundred parsecs from the Sun, even for the most abundant metals like carbon, oxygen and silicon, are still controversial. Here we analyse the stellar production and the chemical evolution of key elements that underpin the formation of rocky (C, O, Mg, Si) and gas/ice giant planets (C, N, O, S). We calculate 198 galactic chemical evolution (GCE) models of the solar neighbourhood to analyse the impact of different sets of stellar yields, of the upper mass limit for massive stars contributing to GCE (Mup) and of supernovae from massive-star progenitors which do not eject the bulk of the iron-peak elements (faint supernovae). Even considering the GCE variation produced via different sets of stellar yields, the observed dispersion of elements reported for stars in the Milky Way (MW) disc is not reproduced. Among others, the observed range of super-solar [Mg/Si] ratios, sub-solar [S/N], and the dispersion of up to 0.5 dex for [S/Si] challenge our models. The impact of varying Mup depends on the adopted supernova yields. Thus, observations do not provide a constraint on the Mup parametrization. When including the impact of faint supernova models in GCE calculations, elemental ratios vary by up to 0.1–0.2 dex in the MW disc; this modification better reproduces observations.

A surrogate-assisted uncertainty-aware Bayesian validation framework and its application to coupling free flow and porous-medium flow

Existing model validation studies in geoscience often disregard or partly account for uncertainties in observations, model choices, and input parameters. In this work, we develop a statistical framework that incorporates a probabilistic modeling technique using a fully Bayesian approach to perform a quantitative uncertainty-aware validation. A Bayesian perspective on a validation task yields an optimal bias-variance trade-off against the reference data. It provides an integrative metric for model validation that incorporates parameter and conceptual uncertainty. Additionally, a surrogate modeling technique, namely Bayesian Sparse Polynomial Chaos Expansion, is employed to accelerate the computationally demanding Bayesian calibration and validation. We apply this validation framework to perform a comparative evaluation of models for coupling a free flow with a porous-medium flow. The correct choice of interface conditions and proper model parameters for such coupled flow systems is crucial for physically consistent modeling and accurate numerical simulations of applications. We develop a benchmark scenario that uses the Stokes equations to describe the free flow and considers different models for the porous-medium compartment and the coupling at the fluid–porous interface. These models include a porous-medium model using Darcy’s law at the representative elementary volume scale with classical or generalized interface conditions and a pore-network model with its related coupling approach. We study the coupled flow problems’ behaviors considering a benchmark case, where a pore-scale resolved model provides the reference solution. With the suggested framework, we perform sensitivity analysis, quantify the parametric uncertainties, demonstrate each model’s predictive capabilities, and make a probabilistic model comparison.

Melding wildlife surveys to improve conservation inference.

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced surveyeffort.

Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation

Assimilating remotely sensed observation into crop model solves the model parameterization in regional scale by considering the uncertainty of observation and simulation. However, a fixed value of observation uncertainty could not describe the spatio-temporal heterogeneity of observation error. This study developed a method to estimate wheat yield in China by assimilating remotely sensed observation into WheatGrow model. A dynamic background value of WheatGrow and a theoretical uncertainty of remotely sensed observation were proposed, and a strategy assimilating phenology and LAI hierarchically was applied at county and plot scales. At the county scale, phenology was estimated using the time sequence of PlanetScope images, sowing date was estimated using one-class support vector classification, heading and maturity date were estimated using function-fitting method. The revisiting cycle of PlanetScope was used to determine the theoretical uncertainty of remotely sensed observed sowing date (RS-sowing date), while the uncertainty in RS-heading and RS-maturity dates were calculated using field measurements. LAI was estimated using Sentinel-2 images. The theoretical uncertainty of remotely sensed observed LAI (RS-LAI) was developed using a synthetic dataset. At the plot scale, RS-heading date and RS-LAI were estimated using the same method as for the county scale based on images obtained with a RedEdge-MX camera carried by drone. The results indicated that the theoretical uncertainty sufficiently estimated the trend of LAI error with canopy density. The assimilation strategies based on theoretical uncertainty relieved the error propagation caused by poor parameterization of model background value and the RS observation error in dense canopy, outperforming the results based on constant uncertainty. After hierarchical assimilation based on theoretical uncertainty, the root mean square errors of phenology, LAI and yield were 4.3 d, 0.75, and 825 kg/ha at the county scale, and 4.1 d, 0.57 and 969 kg/ha at the plot scale. These results showed that the hierarchical assimilation based on theoretical uncertainty could improve the accuracy of yield estimation.

Accounting for bias in prevalence estimation: The case of a globally emerging pathogen

Abstract Accurate quantification of infection parameters is necessary to ensure effective surveillance, investigation and mitigation of infectious diseases. However, hosts and pathogens are often imperfectly observed and key epidemiological parameters, such as infection prevalence, can be biased if this observational uncertainty is not properly accounted for. Here, we evaluated the combined effects of imperfect pathogen detection and host pseudoreplication on the estimation of infection prevalence of the pathogen Batrachochytrium dendrobatidis (Bd) in the southern Darwin's frog (Rhinoderma darwinii). This pathogen causes amphibian chytridiomycosis, a panzootic disease responsible for the greatest documented loss of biodiversity due to an infectious disease. From November 2018 to March 2019, we made 1085 captures of 641 R. darwinii individuals in two areas of Southern Chile. Captured frogs were individually identified to eliminate host pseudoreplication, skin‐swabbed twice in sequence, and each swab was analysed in duplicate using a real‐time polymerase chain reaction (qPCR) assay to detect Bd. To provide a robust estimate of period prevalence, we used a Bayesian multiscale occupancy model that considers pathogen imperfect detection arising from both sampling and diagnostic testing processes. Finally, using a deterministic matrix population model, we illustrated how the method chosen to estimate prevalence influenced our conclusions regarding the impact of Bd infection on host population trajectories. Our results showed that Bd prevalence could be underestimated by 55% if false negatives and host pseudoreplication were not accounted for. Host pseudoreplication had a greater impact on prevalence underestimation than pathogen imperfect detection in our study. This underestimation in prevalence changed our interpretation of the impacts of Bd infections on our model species, from a nearly stable population using the naïve period prevalence to a declining one using our robust estimate. Synthesis and applications. These results highlight the importance of using robust inferences to inform disease risk assessments and to efficiently allocate limited resources during mitigation strategies of infectious diseases. The methods used here can be applied to a wide range of host–pathogen systems, and will be of interest to both researchers and practitioners aiming to investigate and mitigate the impacts of infectious diseases on free‐ranging populations.

FREE-ENERGY MODEL OF SENSE OF AGENCY FOR HUMAN-MACHINE INTERFACE DESIGN BASED ON COMPARATOR MODEL

AbstractSense of agency is the sense that one is causing an action. The increase in machine or system autonomy leads to an increase in the loss of sense of agency for the operation causing the loss of pleasure in the operation or sense of responsibility for the consequences of operations. Designing a sense of agency is necessary, especially in the context of machine autonomy. This calls for the control of the sense of agency, which requires the construction of a model to predict the sense of agency and establishing a design methodology to manipulate the factors of sense of agency. We propose the mathematical model that predicts the sense of agency in a human-machine system based on the comparator model and free-energy principle and what to design to enhance the sense of agency. Proposed model explains the effects of prediction error, prediction uncertainty, and observation uncertainty for body, machine, and environment feedback on the sense of agency. The model generally reveals the interaction effect between prediction error and prediction uncertainty and between prediction error and observation uncertainty. The model prediction can be widely applied as a design guide for enhancing sense of agency of human-machine interfaces.

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

Abstract Satellite observations show a near-zero trend in the top-of-atmosphere global-mean net cloud radiative effect (CRE), suggesting that clouds did not further cool nor heat the planet over the last two decades. The causes of this observed trend are unknown and can range from effective radiative forcing (ERF) to cloud feedbacks, cloud masking, and internal variability. We find that the near-zero NetCRE trend is a result of a significant negative trend in the longwave (LW) CRE and a significant positive trend in the shortwave (SW) CRE, cooling and heating the climate system, respectively. We find that it is exceptionally unlikely (<1% probability) that internal variability can explain the observed LW and SW CRE trends. Instead, the majority of the observed LWCRE trend arises from cloud masking wherein increases in greenhouse gases reduce OLR in all-sky conditions less than in clear-sky conditions. In SWCRE, rapid cloud adjustments to greenhouse gases, aerosols, and natural forcing agents (ERF) explain a majority of the observed trend. Over the northeast Pacific, we show that ERF, hitherto an ignored factor, contributes as much as cloud feedbacks to the observed SWCRE trend. Large contributions from ERF and cloud masking to the global-mean LW and SW CRE trends are supplemented by negative LW and positive SW cloud feedback trends, which are detectable at 80%–95% confidence depending on the observational uncertainty assumed. The large global-mean LW and SW cloud feedbacks cancel, leaving a small net cloud feedback that is unconstrained in sign, implying that clouds could amplify or dampen global warming.

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.

Quantifying uncertainties related to observational datasets used as reference for regional climate model evaluation over complex topography — a case study for the wettest year 2010 in the Carpathian region

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901.The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper.

A Robust and Fast Method to the Perspective-n-Point Problem for Camera Pose Estimation

This paper investigates the prospective n-point (PnP) problem and provides an effective, dependable, and fast optimal solution. The challenging pose estimation problem is changed into the ideal circumstance for solving the transition matrix by estimating the position and orientation of the camera based on known reference points. This method meets both the geometric optimality and the statistical optimal solution by accounting for observation and propagation uncertainty in the solution process. In addition, in order to further optimize the error caused by the mapping process, this paper introduces the L-BFGS algorithm for optimal processing. It saves time and cost because it can iterate the optimal transition matrix and systematic error synchronously. In this paper, the algorithm simulation tests are carried out in the standard case, the quasi-singular case, and the planar case. The results of the experiments demonstrate that the strategy suggested in this paper can successfully address the issue of camera position estimation. Compared with the existing advanced technology, the accuracy is improved by about 8% - 13%, and the time cost is low.

Integrated analysis shows how the effects of extreme flooding events propagate through fish communities to impact amphibians.

Research Highlight: Davis, C. L., Walls, S. C., Barichivich, W. J., Brown, M. E., & Miller, D. A. (2022). Disentangling direct and indirect effects of extreme events on coastal wetland communities. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13874. Catastrophic events such as floods, hurricanes, winter storms, droughts and wildfires increasingly touch our lives either directly or indirectly. These events draw our attention to the seriousness of changes in climate not only to human well-being but also to the integrity of ecological systems upon which we depend. Understanding the impacts of extreme events on ecological systems requires the ability to characterize the cascading effects of environmental changes on the environments in which organisms live and the altered biological interactions produced. This scientific ambition represents no small challenge for the study of animal communities, which are typically difficult to census as well as dynamic in time and space. Davis et al. (2022) in a recent study in the Journal of Animal Ecology examined the amphibian and fish communities found in depressional coastal wetlands to better understand how they respond to major rainfall and flooding events. Data from the U.S. Geological Survey's Amphibian Research and Monitoring Initiative provided an 8-year record of observations as well as environmental measurements. For this study, the authors integrated techniques for assessing the dynamics of animal populations with a Bayesian implementation of structural equation modelling. Using their integrated methodological approach permitted the authors to reveal the direct and indirect effects of extreme weather events on co-occurring amphibian and fish communities while accounting for observational uncertainty and temporal variation in population-level processes. Their findings indicate that the most prominent effects of flooding on the amphibian community were caused by changes in the fish community that led to increased predation and resource competition. In their conclusions, the authors emphasize the importance of understanding networks of abiotic and biotic effects if we are to predict and mitigate the influence of extreme weather events.

The use of crowdsourced social media data to improve flood forecasting

Reliable flood forecasting systems are essential for predicting and mitigating the impact of flooding worldwide. However, minimising flood forecast uncertainties remains a challenging task due to many sources of uncertainty in underlying flood simulation modelling. Such uncertainties can be reduced by employing data assimilation techniques to dynamically incorporate the most recent available observations into the system while accounting for existing uncertainties in both models and observations. However, traditional observations often lack the necessary temporal or spatial resolution, limiting the adoption of data assimilation methods for real-time applications. In contrast, data collection through crowdsourcing has grown in popularity with the potential to provide high spatiotemporal resolution data, especially in urban areas. Nevertheless, the use of crowdsourcing is still impacted by validation uncertainties and data quality, which makes it a complementing data to traditional observations rather than an alternative data source. This paper presents a novel methodology for assimilating crowdsourced social media data to improve a 2D flood forecasting model through various update strategies. The methodology was tested against a real case flood event of the 2017 Phetchaburi flood (Thailand), and the performance of different update strategies was evaluated with reference to the calibrated model output obtained from a particle swarm optimisation algorithm. Empirical results demonstrate that global state updates suffer from inconsistencies in predicted water levels, whereas topographically based local state updates provide encouraging results. Specifically, the improvement due to the local state update alone is short-lived, and findings indicate that a longer lasting improvement in flood forecasting performance can be achieved through a combination of both state and boundary updates. Overall, the results indicate the feasibility of utilising crowdsourced social media data to improve the performance of flood forecasting systems for urban environments.

Reconciling the conflicting extent of overriding plate deformation before and during megathrust earthquakes in South America, Sunda and northeast Japan

SUMMARYWe aim to better understand the overriding plate deformation during the megathrust earthquake cycle. We estimate the spatial patterns of interseismic GNSS velocities in South America, Southeast Asia and northern Japan and the associated uncertainties due to variations in network density and observation uncertainties. Interseismic velocities with respect to the overriding plate generally decrease with distance from the trench with a steep gradient up to a ‘hurdle’, beyond which the gradient is distinctly lower and velocities are small. The hurdle is located 500–1000 km away from the trench for the trench-perpendicular velocity component, and either at the same distance or closer for the trench-parallel component. Significant coseismic displacements were observed beyond these hurdles during the 2010 Maule, 2004 Sumatra–Andaman, and 2011 Tohoku earthquakes. We hypothesize that both the interseismic hurdle and the coseismic response result from a mechanical contrast in the overriding plate. We test our hypothesis using physically consistent, generic, 3-D finite element models of the earthquake cycle. Our models show a response similar to the interseismic and coseismic observations for a compliant near-trench overriding plate and an at least five times stiffer overriding plate beyond the contrast. The model results suggest that hurdles are more prominently expressed in observations near strongly locked megathrusts. Previous studies inferred major tectonic or geological boundaries and seismological contrasts located close to the observed hurdles in the studied overriding plates. The compliance contrast probably results from thermal, compositional and thickness contrasts and might cause the observed focusing of smaller-scale deformation like backthrusting.

Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations

SUMMARY We report on the analysis of M2 ocean tide loading (OTL) kinematic GPS vertical displacement and tidal gravity measurements using 26 GPS and four gravimetric sites across the Canary Islands archipelago. In this region, the standard deviation among recent ocean tide models is lower than 0.4 cm in amplitude and 0.3° in phase, which are suitably accurate for displacement modelling. However, for gravity we need to model regional ocean tides to achieve enough accuracy in the loading calculations. Particularly, this study improves the predicted OTL gravity variations when global ocean models are replaced with the regional model CIAM2 which assimilates local tide gauge data. These small ocean tide model errors allow us to use the differences between observed and predicted OTL values to study the elastic and anelastic properties of the solid Earth around the Canary Islands. In the prediction of OTL, we first used the recent elastic STW105 and S362ANI seismic models, obtaining average observed minus predicted residuals of 1.2–1.3 mm for vertical displacement and 3 nm s−2 for gravity. After the STW105 and S362ANI models were adjusted for anelasticity, by considering a constant quality factor Q at periods ranging from 1 s to 12.42 hr, the average misfit between observations and predicted OTL values reduced to 0.7–0.8 mm for vertical displacement and to 1 nm s−2 for gravity. However, the average vertical displacement misfit is made up from site misfits less than 0.5 mm in western islands but for the easternmost islands of Lanzarote and Fuerteventura, they still reach up to nearly 2 mm at some sites, which still exceeds the uncertainty in the GPS observations. It is hypothesized that mantle upwelling underneath the Canary Islands, creating spatial variations in the elastic properties, causes the large residuals observed in the eastern islands. We reduced the shear modulus by up to 35 per cent in the upper mantle layer of 24.4–220 km depth. This produced residual observed minus model differences of about 0.7 mm for the sites on Lanzarote and Fuerteventura, comparable to the results obtained for the GPS sites across the rest of the archipelago, whose residuals in turn were also slightly reduced through the VS velocity and shear modulus reductions (by 0.2 mm on average).