Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchment

Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices

X-ray measurement of residual stresses in laser surface melted Ti-6Al-4V alloy

In this paper, we report on the residual stresses in laser surface melted Ti-6Al-4V, determined using X-ray diffraction methods. The principal result is that there is an increase in the transverse residual stress with each successive, overlapping laser track. The result can be used to explain the observation of crack formation in overlapping tracks but not necessarily in single tracks produced under identical processing conditions.

Personal and Educational Differences in College Students’ Attitudes Toward Social Justice

Many colleges and universities encourage students to engage with social justice issues in their education and career discernment. However, a variety of individual attributes and life experiences may predict how college students develop an awareness of and attitudes toward social justice, perhaps including ways in which students relate to their own challenging life experiences and encounter others’ experiences of injustice. This study explored the relationship between individual attributes, educational experiences and social justice attitudes among a sample of 347 college students who completed self-report surveys. Specifically, this study examined a) help-seeking attitudes, b) self-compassion, c) prior experience receiving mental health support, and d) prior experience participating in service activities as predictors of social justice attitudes. As hypothesized, higher willingness to seek help in times of personal distress and higher levels of self-compassion were positively correlated with awareness and concern for social justice issues, with help-seeking attitudes being the stronger predictor. Significant differences were also observed across gender, help-seeking history, and service experience. Furthermore, the association between help-seeking attitudes and social justice attitudes was moderated by gender and by prior service experience and mental health support. Implications of these findings for social justice education and college student well-being are discussed

Cosmic distance-duality as probe of exotic physics and acceleration

In cosmology, distances based on standard candles (e.g. supernovae) and standard rulers (e.g. baryon oscillations) agree as long as three conditions are met: (1) photon number is conserved, (2) gravity is described by a metric theory with (3) photons travelling on unique null geodesics. This is the content of distance-duality (the reciprocity relation) which can be violated by exotic physics. Here we analyse the implications of the latest cosmological data sets for distance-duality. While broadly in agreement and confirming acceleration we find a 2-sigma violation caused by excess brightening of SN-Ia at z > 0.5, perhaps due to lensing magnification bias. This brightening has been interpreted as evidence for a late-time transition in the dark energy but because it is not seen in the d_A data we argue against such an interpretation. Our results do, however, rule out significant SN-Ia evolution and extinction: the "replenishing" grey-dust model with no cosmic acceleration is excluded at more than 4-sigma despite this being the best-fit to SN-Ia data alone, thereby illustrating the power of distance-duality even with current data sets.Comment: 6 pages, 4 colour figures. Version accepted as a Rapid Communication in PR

Cosmological Parameters Degeneracies and Non-Gaussian Halo Bias

We study the impact of the cosmological parameters uncertainties on the measurements of primordial non-Gaussianity through the large-scale non-Gaussian halo bias effect. While this is not expected to be an issue for the standard LCDM model, it may not be the case for more general models that modify the large-scale shape of the power spectrum. We consider the so-called local non-Gaussianity model and forecasts from planned surveys, alone and combined with a Planck CMB prior. In particular, we consider EUCLID- and LSST-like surveys and forecast the correlations among $f_{\rm NL}$ and the running of the spectral index $\alpha_s$, the dark energy equation of state $w$, the effective sound speed of dark energy perturbations $c^2_s$, the total mass of massive neutrinos $M_\nu=\sum m_\nu$, and the number of extra relativistic degrees of freedom $N_\nu^{rel}$. Neglecting CMB information on $f_{\rm NL}$ and scales $k > 0.03 h$/Mpc, we find that, if $N_\nu^{\rm rel}$ is assumed to be known, the uncertainty on cosmological parameters increases the error on $f_{\rm NL}$ by 10 to 30% depending on the survey. Thus the $f_{\rm NL}$ constraint is remarkable robust to cosmological model uncertainties. On the other hand, if $N_\nu^{\rm rel}$ is simultaneously constrained from the data, the $f_{\rm NL}$ error increases by $\sim 80$. Finally, future surveys which provide a large sample of galaxies or galaxy clusters over a volume comparable to the Hubble volume can measure primordial non-Gaussianity of the local form with a marginalized 1--$\sigma$ error of the order $\Delta f_{\rm NL} \sim 2-5$, after combination with CMB priors for the remaining cosmological parameters. These results are competitive with CMB bispectrum constraints achievable with an ideal CMB experiment.Comment: 17 pages, 1 figure added, typos corrected, comments added, matches the published versio

Environment and Rural Affairs Monitoring & Modelling Programme - ERAMMP Year 1 Report 21: GMEP outstanding analysis part 2 - Revisiting trends in topsoil carbon from CS2007 to GMEP 2013-2016

New analysis was carried out to explore the reported loss of topsoil-C between 2007 and 2016 in the ‘Habitat’ category in the final GMEP report. This ‘Habitat’ category is defined as all habitats except woodlands, arable and improved grassland. The GMEP survey squares were selected using Countryside Survey protocols stratified according to Land Classes. The final GMEP survey sample from 2012-2016 consists of 7% previously surveyed Countryside Survey squares. Further analysis was needed to explore, and account for, unintended shifts in environmental variables which could have contributed to the reported topsoil carbon decline. The results indicate: 1. The reported change in the ‘Habitat’ category is driven by trends in upland habitats (median elevation of 400m). 2. In upland habitats, soil carbon is positively associated with dwarf shrub cover (particularly ericoid e.g. heather cover), Sphagnum, presence of peat, elevation and moisture conditions. 3. The coverage of dwarf shrubs was lower in GMEP than in Countryside Survey 2007, mostly due to lower cover of ericoids i.e. heather. This is consistent with decreasing soil carbon in upland habitats. Other variables (i.e. potential drivers) did not differ between surveys, or direction of change was inconsistent with reported C trends. 4. Re-analysis of Countryside Survey data (1978-2007) provides evidence that shifts over time from dwarf shrub to grass-dominated habitats are associated with a decline in topsoil carbon. 5. Overall, this suggest a potential role of ongoing vegetation change in upland habitats (i.e. conversion of dwarf shrub to grass-dominated) contributing to topsoil carbon loss. Further work is needed to: • Confirm recent vegetation change in upland habitats using independent data e.g. satellite data; • Explore relationships between specific plant species and topsoil carbon in Countryside Survey where we have a high number of true repeat samples; This work highlights the importance of the findings of the next ERAMMP survey, which will be more powerful than the combined CS-GMEP approach reported here