Daily monitoring of retrogressive thaw slumps in the Fosheim Peninsula, Ellesmere Island, Canada

2020 ◽  
Author(s):  
Melissa Ward Jones ◽  
Benjamin Jones ◽  
Wayne Pollard
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Polynomial Regression ◽  
Pearson Correlation ◽  
Daily Basis ◽  
The Arctic ◽  
Regression Equations ◽  
Daily Monitoring ◽  
Sediment Redistribution ◽  
Short Time

<p>Retrogressive thaw slumps (RTS) occur from the mass wasting of ice-rich permafrost. These horseshoe-shaped features have an ablating or retreating ice-rich headwall with fluidized sediment that is transported along the RTS floor. RTS can remain active for up to decades and enlarge as the headwall retreats. With observed increases in RTS number, rates and sizes in recent decades, there is a need to understand these highly dynamic landforms, however there is a general lack of detailed field observations of RTSs. We monitored 3 RTS for over half of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP) and thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat could occur within a single period of increased air temperature. These retreat trends were observed to be largely driven by sediment redistribution in the RTS floor. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. There was a statistically significant correlation between daily mean and mean cumulative retreat with MDAT (p < 0.001) and TDD (p < 0.001 and < 0.0001) but not with TDP. Correlating mean cumulative retreat and cumulative TDD using polynomial regression (quadratic and cubic) generated R<sup>2 </sup>values greater than 0.99 for all 3 sites as these variables account for past and current conditions within the monitoring period, as well as lag responses of retreat. This suggests the potential of accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however this is currently restricted to individual RTSs and only within short time scales. We tested this idea by modelling 2 weeks of cumulative retreat in 2018 for 2 of our sites we monitored using the 2017 regression equations. Percent prediction error was 8% at one site and 16% at the other. Monitoring RTS on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales. With the widespread increased numbers of RTSs being observed around the Arctic, understanding their dynamics is critical as these landforms impact surrounding ecosystems and infrastructure which will be exacerbated with climate change.  </p>

scholarly journals Daily Field Observations of Retrogressive Thaw Slump Dynamics in the Canadian High Arctic

ARCTIC ◽  
2021 ◽  
Vol 74 (3) ◽  
pp. 339-354
Author(s):  
Melissa K. Ward Jones ◽  
Wayne H. Pollard
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Polynomial Regression ◽  
Daily Precipitation ◽  
Pearson Correlation ◽  
High Arctic ◽  
Daily Basis ◽  
Field Observations ◽  
Sediment Distribution ◽  
Retrogressive Thaw Slump

With observed increases in retrogressive thaw slump (RTS) number, rates, and size in recent decades, there is a need to understand these highly dynamic landforms as they impact surrounding ecosystems and infrastructure. There is a general lack of detailed (e.g., daily) field observations of change in RTSs; we help fill this gap by monitoring three RTSs for much of the 2017 thaw period by setting up and tracking survey transects on a near daily basis. We correlated mean daily and cumulative retreat to mean daily air temperature (MDAT), total daily precipitation (TDP), and cumulative thawing degree days (TDD) using various polynomial regressions and Pearson correlation techniques. Our results show that July retreat was highly variable, and periods of increased RTS retreat did not always align with periods of increased air temperature. Also, multiple periods of increased retreat largely driven by sediment distribution in the RTS floor could occur within a single period of increased air temperature. Retreat rates decreased suddenly in early August, indicating a threshold of either air temperature, solar radiation or a combination of both must be reached for increased retreat rates. A statistically significant correlation was found between daily mean and mean cumulative retreat with MDAT (p > 0.001) and TDD (p > 0.001 and > 0.0001) but not with total daily precipitation. Correlating mean cumulative retreat and TDD using polynomial regression generated R2 values greater than 0.99 for all three sites. Both cumulative retreat and TDD account for past and current conditions, as well as lag responses, within the monitoring period. The high R2 values for the correlation of mean cumulative retreat and TDD suggest the potential for accurately modelling RTS retreat with minimal field data (air temperature and headwall position), however modelling is currently restricted to individual RTSs and only within short time scales. Monitoring RTSs on a daily scale allows RTS behaviour and trends to be identified that may be obscured at annual time scales and highlights the importance of all system inputs when considering RTS retreat dynamics. 

Is summer sea surface temperature over the Arctic Ocean connected to winter air temperature over North America?

2016 ◽  
Vol 70 (1) ◽  
pp. 19-27
Author(s):  
M Ogi ◽  
S Rysgaard ◽  
DG Barber ◽  
T Nakamura ◽  
B Taguchi
Keyword(s):  
North America ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Arctic Ocean ◽  
Air Temperature ◽  
Sea Surface ◽  
The Arctic ◽  
The Arctic Ocean ◽  
Winter Air Temperature

The Spread of Pollutants from Harbour Sludge Depots

1984 ◽  
Vol 16 (3-4) ◽  
pp. 623-633
Author(s):  
M Loxham ◽  
F Weststrate
Keyword(s):  
Time Scales ◽  
Molecular Diffusion ◽  
Near Field ◽  
Parameter Analysis ◽  
Migration Patterns ◽  
Long Time ◽  
Dilution Effects ◽  
Short Time ◽  
Harbour Sludge

It is generally agreed that both the landfill option, or the civil techniques option for the final disposal of contaminated harbour sludge involves the isolation of the sludge from the environment. For short time scales, engineered barriers such as a bentonite screen, plastic sheets, pumping strategies etc. can be used. However for long time scales the effectiveness of such measures cannot be counted upon. It is thus necessary to be able to predict the long term environmenttal spread of contaminants from a mature landfill. A model is presented that considers diffusion and adsorption in the landfill site and convection and adsorption in the underlaying aquifer. From a parameter analysis starting form practical values it is shown that the adsorption behaviour and the molecular diffusion coefficient of the sludge, are the key parameters involved in the near field. The dilution effects of the far field migration patterns are also illustrated.

Guest is God

2019 ◽  
Author(s):  
Drew Thomases
Keyword(s):  
Everyday Life ◽  
Sacred Space ◽  
Wide Spectrum ◽  
Daily Basis ◽  
Economic Changes ◽  
Ethnographic Fieldwork ◽  
Northwestern India ◽  
Gathering Place ◽  
Short Time ◽  
The Town

This book is based on ethnographic fieldwork in Pushkar, a Hindu pilgrimage site in northwestern India whose population of 20,000 sees an influx of two million visitors each year. Since the 1970s, the town has also received considerable attention from international tourists, a group with distinctly hippie beginnings but that now includes visitors from a wide spectrum of social positions and religious affiliations. To locals, though, Pushkar is more than just a gathering place for pilgrims and tourists: it is where Brahma, the creator god, made his home; it is where pilgrims feel blessed to stay, if only for a short time; and it is where Hindus would feel lucky to be reborn, if only as an insect. In short, it is their paradise. But even paradise needs upkeep. Thus, on a daily basis the town’s locals, and especially those engaged in pilgrimage and tourism, work to make Pushkar paradise. The book explores this massive enterprise to build “heaven on earth,” paying particular attention to how the articulation of sacred space becomes entangled with economic changes brought on by globalization and tourism. As such, the author not only attends to how tourism affects everyday life in Pushkar but also to how Hindu ideas determine the nature of tourism there; the goal, then, is to show how religion and tourism can be mutually constitutive.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yafei Wang ◽  
Erik Brodin ◽  
Kenichiro Nishii ◽  
Hermann B. Frieboes ◽  
Shannon M. Mumenthaler ◽  
...  
Keyword(s):  
Time Scales ◽  
Constitutive Relations ◽  
Elastic Tissue ◽  
Metastatic Progression ◽  
Patient Death ◽  
Metastatic Growth ◽  
Tumor Parenchyma ◽  
Short Time ◽  
Model Approach

AbstractColorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could “reawaken” after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

scholarly journals Influence of frying parameters and optimization of deep fat frying conditions on the physicochemical and textural properties of chicken nuggets from FUNAAB-Alpha broilers

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
O. R. Faloye ◽  
O. P. Sobukola ◽  
T. A. Shittu ◽  
H. A. Bakare
Keyword(s):  
Polynomial Regression ◽  
Textural Properties ◽  
Quality Attributes ◽  
Sample Thickness ◽  
Regression Equations ◽  
Deep Fat Frying ◽  
Chicken Nuggets ◽  
Frying Temperature ◽  
Frying Time ◽  
Standard Procedures

Abstract Influence of deep fat frying parameters on quality attributes of chicken nuggets from FUNAAB-Alpha broilers and optimization of the process using Box-Behnken experimental design of response surface methodology was investigated. Fried chicken nuggets were obtained using frying temperature (155–175 °C), frying time (3–7 min) and sample thickness (0.5–2.5 cm) as independent variables. Oil and moisture contents, texture (hardness, chewiness, adhesiveness, cohesiveness and springiness) and colour (L*, a* and b*) of samples were analyzed using standard procedures. Significance of each term in polynomial regression equations was evaluated on quality attributes. The accuracy of the regression models varied between 0.727 and 0.939. The effect of frying temperature on quality attributes of fried chicken nuggets was more significant (p > 0.05). The optimum frying temperature, frying time and sample thickness are determined as 175 °C, 3 min, 2.32 cm, respectively. Absolute percent error between optimized and experimental data were within the acceptable limit. Graphic abstract

scholarly journals Variations of indicative dates of ice regime on Lake Onego based on ground air temperature

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Vyacheslav N. Baklagin
Keyword(s):  
Air Temperature ◽  
Regression Equations ◽  
Early Formation ◽  
Air Temperatures ◽  
Ice Regime ◽  
Break Up ◽  
Ice Period ◽  
Ice Phenomena

The paper shows the changes in the dates (complete freeze-up, ±5 days/°C and complete ice clearance, ±3 days/°C) of the ice regime in Lake Onego depending on changes in average air temperature within the preceding two-month periods (autumn and spring). The regression equations for their calculation based on previous three- and four-month periods according to the 2000-2018 data are also provided. Indicative dates of ice regime based on accumulated air temperatures within the ice period of Lake Onego were also established (early formation of ice phenomena, complete freeze-up phase, beginning of the break-up phase and complete ice clearance). Together with the data on expected air temperature above the lake’s surface, these dependencies enable us to predict the indicative dates of the ice regime.

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