Transport processes and pathways through many hydrodynamic systems vary over time, often driven by variations in total water storage. This paper develops a very general approach to modeling unsteady transport through an arbitrary control volume (such as a watershed) that accounts for temporal variability in the underlying transport dynamics. Controls on the selection of discharge from stored water are encapsulated in probability distributions ΩQ(ST,t) of age-ranked storage ST (the volume of water in storage ranked from youngest to oldest). This framework is applied to a long-term record of rainfall and streamflow chloride in a small, humid watershed at Plynlimon, UK. While a time-invariant gamma distribution for ΩQ produced a good fit to data, the fit was significantly improved when the distribution was allowed to vary with catchment storage. However, the variation was inverse to that of a “well-mixed” system where storage has a pure dilution effect. Discharge at high storage was predicted to contain a larger fraction of recent event water than at low storage. The effective volume of storage involved in transport was 3411 mm at mean catchment wetness, but declined by 71 mm per 1 mm of additional catchment storage, while the fraction of event water in discharge increased by 1.4%. This “inverse storage effect” is sufficient to reproduce the observed long-memory 1∕f fractal spectral structure of stream chloride. Metrics quantifying the strength and direction of storage effects are proposed as useful signatures, and point toward a unified framework for observing and modeling coupled watershed flow and transport.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014WR015707

Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed

Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data center to develop such capabilities. First, we search for repeatable workload patterns by exploring cross-VM workload correlations resulted from the dependencies among applications running on different VMs. Treating workload data samples as time series, we develop a co-clustering technique to identify groups of VMs that frequently exhibit correlated workload patterns, and also the time periods in which these VM groups are active. Then, we introduce a method based on Hidden Markov Modeling (HMM) to characterize the temporal correlations in the discovered VM clusters and to predict variations of workload patterns. The experimental results show that our method can not only help better understand group-level workload characteristics, but also make more accurate predictions on workload changes in a cloud.

Workload characterization and prediction in the cloud: A multiple time series approach

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here, we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data-such as microevolutionary estimates of selection parameters and paleontological timeseries-allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

/pdf/a-novel-bayesian-method-for-inferring-and-interpreting-the-e3pe5odrzw.pdf

A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data.

The complex interactions of runoff generation processes underlying the hydrological response of streams remain incompletely understood at the catchment scale. Extensive research has demonstrated the utility of tracers for both inferring flow paths distributions and constraining model parameterizations. While useful, the common use of linearity assumptions, i.e. time-invariance and complete mixing, in these studies provides only partial understanding of actual process dynamics. Here we use long term (< 20 yr) precipitation, flow and tracer (chloride) data of three contrasting upland catchments in the Scottish Highlands to inform integrated conceptual models investigating different mixing assumptions. Using the models as diagnostic tools in a functional comparison, water and tracer fluxes were tracked with the objective of characterizing water age distributions in the three catchments and establishing the wetness-dependent temporal dynamics of these distributions. The results highlight the potential importance of partial mixing which is dependent on the hydrological functioning of a catchment. Further, tracking tracer fluxes showed that the various components of a model can be characterized by fundamentally different water age distributions which may be highly sensitive to catchment wetness, available storage, mixing mechanisms, flow path connectivity and the relative importance of the different hydrological processes involved. Flux tracking also revealed that, although negligible for simulating the runoff response, the omission of processes such as interception evaporation can result in considerably biased water age distributions. Finally, the modeling indicated that water age distributions in the three study catchments do have long, power-law tails, which are generated by the interplay of flow path connectivity, the relative importance of different flow paths as well as by the mixing mechanisms involved. In general this study highlights the potential of customized integrated conceptual models, based on multiple mixing assumptions, to infer system internal transport dynamics and their sensitivity to catchment wetness states.

/pdf/what-can-flux-tracking-teach-us-about-water-age-distribution-25oxtblwcg.pdf

What can flux tracking teach us about water age distribution patterns and their temporal dynamics

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the Italian government has adopted containment measures to control the virus’s spread, including limitations to the practice of physical activity (PA). The aim of this study was to estimate the levels of PA, expressed as energy expenditure (MET–minute/week), among the physically active Sicilian population before and during the last seven days of the COVID-19 quarantine. Furthermore, the relation between this parameter and specific demographic and anthropometric variables was analyzed. Methods: 802 Sicilian physically active participants (mean age: 32.27 ± 12.81 years; BMI: 23.44 ± 3.33 kg/m2) were included in the study and grouped based on gender, age and BMI. An adapted version of the International Physical Activity Questionnaire—short form (IPAQ-SF) was administered to the participants through an online survey. The Wilcoxon signed-rank test and the Kruskal-Wallis rank-sum test were used for statistical analyses. Results: As expected, we observed a significant decrease of the total weekly energy expenditure during the COVID-19 quarantine (p < 0.001). A significant variation in the MET–min/wk in the before quarantine condition (p = 0.046) and in the difference between before and during quarantine (p = 0.009) was found for males and females. The male group decreased the PA level more than the female one. Moreover, a significant difference in the MET–min/wk was found among groups distributions of BMI (p < 0.001, during quarantine) and of age (p < 0.001, both before and during quarantine). In particular, the highest and the lowest levels of PA were reported by the young and the elderly, respectively, both before and during quarantine. Finally, the overweight group showed the lowest level of PA during quarantine. Conclusion: Based on our outcomes, we can determine that the current quarantine has negatively affected the practice of PA, with greater impacts among males and overweight subjects. In regards to different age groups, the young, young adults and adults were more affected than senior adults and the elderly.

Physical activity levels and related energy expenditure during COVID-19 quarantine among the sicilian active population: A cross-sectional online survey study

We suggest the use of Poisson hidden Markov models (PHMMs) in non life insurance. PHMMs are an extension of the well-known mixture models and we use them to model the dynamics of overdispersed data, in particular of the claim number. PHMMs allow us to explicitly consider unobserved factors influencing the dynamics of the claim number. This has an immediate impact on the value of the pure risk premium: the expected claim number is given by a weighted average of the intensity parameters of a PHMM. We show how the maximum likelihood estimators of the parameters of PHMMs may be suitably obtained using the EM algorithm and apply PHMMs t,o model the daily frequencies of injuries in the work place in Lombardia (Italy).

Poisson Hidden Markov models for time series of overdispersed insurance counts

Lezioni di Statistica

A new approach to simulating stream isotope dynamics using Markov switching autoregressive models

We deal with one-layer feed-forward neural network for the Bayesian analysis of nonlinear time series. Noises are modeled nonlinearly and nonnormally, by means of ARCH models whose parameters are all dependent on a hidden Markov chain. Parameter estimation is performed by sampling from the posterior distribution via Evolutionary Monte Carlo algorithm, in which two new crossover operators have been introduced. Unknown parameters of the model also include the missing values which can occur within the observed series, so, considering future values as missing, it is also possible to compute point and interval multi-step-ahead predictions.

Bayesian Inference and Forecasting in Dynamic Neural Networks with Fully Markov Switching ARCH Noises

An evolutionary Monte Carlo method for the analysis of turbidity high-frequency time series through Markov switching autoregressive models

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