Cosmolian: High-resolution Integrated OSL and CN modelling Program for Sand Transportation in Eolian Realms (HIPSTER)

2021 ◽  
Author(s):  
Shlomy Vainer ◽  
Yoav Ben Dor
Keyword(s):  
Landscape Evolution ◽  
Sand Dunes ◽  
Integrative Approach ◽  
Model Parameters ◽  
Arid Environments ◽  
Aeolian Transport ◽  
Migration Patterns ◽  
Aeolian Processes ◽  
Simpson Desert

<p>The extensivity of sand dunes in continental interiors makes the understating of their morphodynamical properties valuable for palaeoenvironmental reconstructions and the interpretation of landscape evolution. Nevertheless, the study of aeolian landscape development at the million-years timescale is hampered by the complex interaction of factors determining dune migration and the inherently self-destructive nature of their chronostratigraphy, thus limiting the applicability of traditional luminescence-based dating methods for configuring processes beyond ~300 Ka. In this study, we present a standalone program that simulates aeolian transport based on luminescence-derived chronologies coupled with numerical modelling of cosmogenic nuclides accumulation. This integrative approach reveals ancient phases of sand irruption and provides a data-based scheme facilitating the morphodynamical study of aeolian processes over multiple timescales. We present a case study of the program application by analyzing data from the Australian Simpson Desert, unfolding several phases of aeolian vitality since the late Pliocene. The synchronicity of the results with drastic changes in environmental settings exemplifies the applicability of process-based modelling in constructing a timeframe of key landscape evolution events in arid environments by studying aeolian landforms. Finally, the relationships between model parameters used to determine environmental settings on sand migration patterns make the program a powerful tool to further investigating triggers and mechanisms of aeolian processes.</p>

Intra-Urban Migration Patterns in the Perspective of Improved Radiation Model - A Case Study of Beijing

2013 ◽  
Vol 869-870 ◽  
pp. 110-116 ◽  
Author(s):  
Yu Shi ◽  
Xin Qi Zheng ◽  
Yi Bo Sun ◽  
Zong Ren Jia
Keyword(s):  
Gravity Model ◽  
Internal Migration ◽  
Urban Areas ◽  
Population Migration ◽  
Model Parameters ◽  
Urban Migration ◽  
Economic Data ◽  
Radiation Model ◽  
Migration Patterns

Gravity Model is commonly used in the study of urban internal migration . Filippo Simini etl improve the Gravity Model, thereby create a more realistic radiation model. Radiation model is validated in the U.S., however, isnt sure to be fit in China. According to the actual situations of our country, the study processes Radiation model parameters and simulates internal migration in Beijing based on the socio-economic data (2005-2010). Results show that the Fengtai District and the Tongzhou District are the two largest migration district in the five years. While the Daxing Districts migration increases year by year. Furthermore, by the contrast of population migration radiation line and GDP, this paper points out that the economics is the main driving force of urban internal migration. Finally, from the perspective of new urban areas construction, development of urban functions expansion areas and population migration balance in Beijing, the corresponding suggestions are put forward for urban planning in Beijing.

VALLEY OF THE MASTODONS: CASE STUDY OF AN INTEGRATIVE APPROACH TO RESEARCH COLLABORATION, OUTREACH, AND EXHIBITS

2017 ◽  
Author(s):  
Alton C. Dooley ◽  
◽  
Kathlyn M. Smith ◽  
Brittney Stoneburg ◽  
Darla Radford ◽  
...  
Keyword(s):  
Research Collaboration ◽  
Integrative Approach

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Habitat use and behaviour of cattle in a heterogeneous desert environment in central Australia

2012 ◽  
Vol 34 (3) ◽  
pp. 319 ◽  
Author(s):  
Anke S. K. Frank ◽  
Chris R. Dickman ◽  
Glenda M. Wardle
Keyword(s):  
Habitat Use ◽  
Arid Environments ◽  
Desert Environment ◽  
Point Location ◽  
Dune System ◽  
Grazing Impacts ◽  
Water Point ◽  
Resource Poor ◽  
Central Australia ◽  
Simpson Desert

The activities of livestock in arid environments typically centre on watering points, with grazing impacts often predicted to decrease uniformly, as radial piospheres, with distance from water. In patchy desert environments, however, the spatial distribution of grazing impacts is more difficult to predict. In this study sightings and dung transects are used to identify preferred cattle habitats in the heterogeneous dune system of the Simpson Desert, central Australia. The importance of watering points as foci for cattle activity was confirmed and it was shown that patchily distributed gidgee woodland, which comprises only 16% of the desert environment, is the most heavily used habitat for cattle away from water and provides critical forage and shade resources. By contrast, dune swales and sides, which are dominated by shade- and forage-deficient spinifex grassland and comprise >70% of the available habitat, were less utilised. These results suggest that habitat use by cattle is influenced jointly by water point location and by the dispersion of woodland patches in a resource-poor matrix. The findings were used to build a modified conceptual model of cattle habitat use which was compared with an original piosphere model, and the consequences for wildlife in environments where the model applies are discussed.

scholarly journals Polish Migration’s Socio-Cultural Impact on Wales in the Aftermath of 2004 – Preliminary Findings from Western Wales: An Aberystwyth Case Study

2016 ◽  
Vol 1 (1) ◽  
pp. 91-112 ◽  
Author(s):  
Paweł Wróbel
Keyword(s):  
Eu Enlargement ◽  
Cultural Impact ◽  
Migration Patterns ◽  
Distinctive Features ◽  
Polish Migration ◽  
History Of ◽  
Mutual Relations ◽  
The Uk ◽  
Adaptation Processes

AbstractThis papers looks at the societal and cultural impact of the post-2004 Polish migration to Wales. The history of Polish migration to the UK is introduced together with the relevant statistics and their rationale behind choosing cosmopolitan Wales as their new country of residence. Even though the focus of the paper is rather on the UK as a whole, it is Wales that is central to the investigation. Wales was particularly neglected in the study of migration in the aftermath of the 2004 European Union (EU) enlargement and surprisingly little attention was given to it. Focusing on Polish diaspora is important as it is the most numerous external migration wave to Wales (ONS 2011). The case study of Aberystwyth is introduced as a good example of a semi-urban area to which Poles migrated after 2004. Moreover, the paper elaborates on the characteristics of the Polish newcomers by analysing their distinctive features, migration patterns as well as adaptation processes. Mutual relations between post-1945 and post-2004 immigration waves are investigated, together with Poles’ own image and perception. This paper gives a deeper understanding and provides an insight into the nature of the Polish migrants’ impact on the cultural and societal life of Wales.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

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