A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

Classical and quantum cosmology in f(T)-gravity theory: A Noether symmetry approach

In the framework of [Formula: see text]-gravity theory, classical and quantum cosmology has been studied in this work for Friedmann Lemaitre Robertson Walker Metric (FLRW) space-time model. The Noether symmetry, a point-like symmetry of the Lagrangian, is used to the physical system and a specific functional form of [Formula: see text] is determined. A point transformation in the 2D augmented space restricts one of the variables to be cyclic so that the Lagrangian as well as the field equations are simplified so that they are solvable. Lastly, for quantum cosmology, the WD equation is constructed and a possible solution has been evaluated.

Space-Time Model to Predict The Evolution of the Regional Subsidence in The Lacustrine Zone of Mexico City

In several scientific and engineering disciplines, models have been used to understand the behavior of dynamic processes that evolve in space and in time by providing a probabilistic framework to analyzing the available information. The geostatistical tools used to analyze space-time data are based on established statistical methods, where time is considered as an additional dimension. These models have become very useful in fields such as meteorology, hydrology, ecology, geosciences, and environmental sciences, among others. Subsidence generated by the intense extraction of groundwater in a region is a dynamic phenomenon that manifests itself through the sinking of the ground surface, leading to significant settling in buildings and public utilities as well as cracks in roads. Since the regional subsidence of Mexico City is one of the most representative cases of this type in the world, in this work a model with a full grid space-time layout (STF) is used to analyze and predict the evolution of this phenomenon in the city, taking into account a monitoring system composed of 1931 surface benchmarks. Results show that the separable variogram model was the one that best represented the spatial and temporal correlation of the phenomenon in the area of study. In addition, the differences between the registered ground elevation made in 2016 and those estimated by the space-time model for the same year, were less than 1.00 m. This implies that in general accurate ground elevation values and subsidence rates can be obtained from the proposed space-time model during the time period 2010-2030 for the lacustrine zone of Mexico City.

A Brief Insight on Simulation and Space-Time

Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–Time analysis seeks to understand when and where (and sometimes why) things occur. Ever since Einstein introduced this topic in his “General Theory of Relativity” (a remarkable feat) in 1915, many explanations, assumptions about Space-Time have been published. The authentic archives of Space-Time have helped us to predict and express the ongoing spatial phenomena of the Universe. The strangeness of Space-Time forces Young Researchers and Physicists to study upon the current predictions and theories, sink into the ocean of Space-Time mystery and come up with their own predictions. Besides Space-Time, the predictions about us being in a Simulation is a recent concept. Nick Bostrom’s trilemma “the Simulation argument”, published in 2003 led to the commencement of further oddly satisfying, real life evidence enriched, research papers. “Two possibilities exist: either we are alone in the Universe or we are not. Both are equally terrifying.”- by Andrew C Clarke. Throughout the paper, the Loaf Space-Time model and brief insights on other hypotheses about the structure of the mysterious space-time will be presented with insightful examples, authentic research outcomes, which will be linked with the Simulation Hypothesis.

A Brief Insight on Simulation and Space-Time

Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space–Time analysis seeks to understand when and where (and sometimes why) things occur. Ever since Einstein introduced this topic in his “General Theory of Relativity” (a remarkable feat) in 1915, many explanations, assumptions about Space-Time have been published. The authentic archives of Space-Time have helped us to predict and express the ongoing spatial phenomena of the Universe. The strangeness of Space-Time forces Young Researchers and Physicists to study upon the current predictions and theories, sink into the ocean of Space-Time mystery and come up with their own predictions. Besides Space-Time, the predictions about us being in a Simulation is a recent concept. Nick Bostrom’s trilemma “the Simulation argument”, published in 2003 led to the commencement of further oddly satisfying, real life evidence enriched, research papers. “Two possibilities exist: either we are alone in the Universe or we are not. Both are equally terrifying.”- by Andrew C Clarke. Throughout the paper, the Loaf Space-Time model and brief insights on other hypotheses about the structure of the mysterious space-time will be presented with insightful examples, authentic research outcomes, which will be linked with the Simulation Hypothesis.

African Swine Fever and Its Epidemiological Course in Lithuanian Wild Boar

African swine fever (ASF) has been present in Lithuania since 2014. It is mainly the wild boar population that is affected. Currently, little is known about the epidemiological course of ASF in Lithuania. In the present study, ASF surveillance data from 2016–2021 were analyzed. The numbers of samples taken from hunted wild boar and wild boar found dead per year and month were recorded and the prevalence was estimated for each study month and administrative unit. A Bayesian space–time model was used to calculate the temporal trend of the prevalence estimates. In addition, population data were analyzed on a yearly basis. Most samples were investigated in 2016 and 2017 and originated from hunted animals. Prevalence estimates of ASF virus-positive wild boar decreased from May 2019 onwards. Seroprevalence estimates showed a slight decrease at the same time, but they increased again at the end of the study period. A significant decrease in the population density was observed over time. The results of the study show that ASF is still present in the Lithuanian wild boar population. A joint interdisciplinary effort is needed to identify weaknesses in the control of ASF in Lithuania and to combat the disease more successfully.

A space-time Bayesian hierarchical modeling framework for projection of seasonal streamflow extremes

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.