scholarly journals Space–Time Trade-Off of Precursory Seismicity in New Zealand and California Revealed by a Medium-Term Earthquake Forecasting Model

2021 ◽  
Vol 11 (21) ◽  
pp. 10215
Author(s):  
Sepideh J. Rastin ◽  
David A. Rhoades ◽  
Annemarie Christophersen
Keyword(s):  
New Zealand ◽  
Spatial Scales ◽  
Order Effect ◽  
Space Time ◽  
Scaling Factor ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Medium Term ◽  
Trade Off ◽  
Temporal And Spatial

The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) medium-term earthquake forecasting model is based on the precursory scale increase (Ψ) phenomenon and associated scaling relations, in which the precursor magnitude is predictive of the mainshock magnitude , precursor time and precursory area . In early studies of Ψ, a relatively low correlation between and suggested the possibility of a trade-off between time and area as a second-order effect. Here, we investigate the trade-off by means of the EEPAS model. Existing versions of EEPAS in New Zealand and California forecast target earthquakes of magnitudes M > 4.95 from input catalogues with M > 2.95. We systematically vary one parameter each from the EEPAS distributions for time and location, thereby varying the temporal and spatial scales of these distributions by two orders of magnitude. As one of these parameters is varied, the other is refitted to a 20-year period of each catalogue. The resulting curves of the temporal scaling factor against the spatial scaling factor are consistent with an even trade-off between time and area, given the limited temporal and spatial extent of the input catalogue. Hybrid models are formed by mixing several EEPAS models, with parameter sets chosen from points on the trade-off line. These are tested against the original fitted EEPAS models on a subsequent period of the New Zealand catalogue. The resulting information gains suggest that the space–time trade-off can be exploited to improve forecasting.

scholarly journals The Effect of Catalogue Lead Time on Medium-Term Earthquake Forecasting with Application to New Zealand Data

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1264
Author(s):  
David A. Rhoades ◽  
Sepideh J. J. Rastin ◽  
Annemarie Christophersen
Keyword(s):  
New Zealand ◽  
Lead Time ◽  
Active Regions ◽  
Earthquake Forecasting ◽  
Scaling Relations ◽  
Lead Times ◽  
Medium Term ◽  
Trade Off ◽  
Forecasting Performance ◽  
Precursory Seismicity

‘Every Earthquake a Precursor According to Scale’ (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.

scholarly journals Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

2019 ◽  
Vol 8 (2) ◽  
pp. 72 ◽  
Author(s):  
Yi Qiang ◽  
Nico Van de Weghe
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Space Time ◽  
Unified Framework ◽  
Temporal Modeling ◽  
Multi Scale ◽  
Space And Time ◽  
Pyramid Model ◽  
Temporal And Spatial ◽  
Spatio Temporal

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

Development and Application of a Storage Model for River Flow Forecasting

1983 ◽  
Vol 14 (3) ◽  
pp. 139-154 ◽  
Author(s):  
Takeshi Hata ◽  
Malcolm G. Anderson
Keyword(s):  
River Flow ◽  
Spatial Scales ◽  
Forecasting Model ◽  
Data Input ◽  
Storage Model ◽  
Topographic Data ◽  
River Flow Forecasting ◽  
Wide Range ◽  
Temporal And Spatial

A lumped sequential river flow forecasting model is outlined. It is shown to be flexible in both temporal and spatial scales, thereby allowing simulations to be undertaken for a wide range of practical purposes. In addition, the required data input is very low, and is restricted to topographic data for only small segments of the entire catchment. The model is successfully applied to the River Avon in England and the River Kako in Japan.

Larval Feeding: Mechanisms, Rates, and Performance in Nature

2018 ◽  
Keyword(s):  
Marine Invertebrates ◽  
Spatial Scales ◽  
Size Spectrum ◽  
Larval Feeding ◽  
Natural Habitats ◽  
Feeding Mechanism ◽  
Temporal And Spatial ◽  
And Performance ◽  
Bioenergetics Models ◽  
Many Particles

Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.

Humans and the Environment

2013 ◽  
Keyword(s):  
Spatial Scales ◽  
Empirical Studies ◽  
Human Action ◽  
Cultural Practice ◽  
Environmental Archaeology ◽  
Archaeological Data ◽  
Temporal And Spatial ◽  
Global And Local ◽  
The Relationship ◽  
The Way

The environment has always been a central concept for archaeologists and, although it has been conceived in many ways, its role in archaeological explanation has fluctuated from a mere backdrop to human action, to a primary factor in the understanding of society and social change. Archaeology also has a unique position as its base of interest places it temporally between geological and ethnographic timescales, spatially between global and local dimensions, and epistemologically between empirical studies of environmental change and more heuristic studies of cultural practice. Drawing on data from across the globe at a variety of temporal and spatial scales, this volume resituates the way in which archaeologists use and apply the concept of the environment. Each chapter critically explores the potential for archaeological data and practice to contribute to modern environmental issues, including problems of climate change and environmental degradation. Overall the volume covers four basic themes: archaeological approaches to the way in which both scientists and locals conceive of the relationship between humans and their environment, applied environmental archaeology, the archaeology of disaster, and new interdisciplinary directions.The volume will be of interest to students and established archaeologists, as well as practitioners from a range of applied disciplines.

An Indigenous Māori Perspective of Rangatahi Personhood

2020 ◽  
Author(s):  
Adreanne Ormond ◽  
Joanna Kidman ◽  
Huia Tomlins-Jahnke
Keyword(s):  
New Zealand ◽  
Multiple Realities ◽  
The Future ◽  
Spatial Dimensions ◽  
Temporal And Spatial ◽  
And Gender ◽  
Colonial Oppression ◽  
Ancestral Landscape

Personhood is complex and characterized by what Avery Gordon describes as an abundant contradictory subjectivity, apportioned by power, race, class, and gender and suspended in temporal and spatial dimensions of the forgotten past, fragmented present, and possible and impossible imagination of the future. Drawing on Gordon’s interpretation, we explore how personhood for young Māori from the nation of Rongomaiwāhine of Aotearoa New Zealand is shaped by a subjectivity informed by a Māori ontological relationality. This discussion is based on research conducted in the Māori community by Māori researchers. They used cultural ontology to engage with the sociohistorical realities of Māori cultural providence and poverty, and colonial oppression and Indigenous resilience. From these complex and multiple realities this essay will explore how young Māori render meaning from their ancestral landscape, community, and the wider world in ways that shape their particular personhood.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

Sign in / Sign up

Export Citation Format

Share Document