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applications:applications [2022/03/23 20:59] – joetulenkoapplications:applications [2023/02/16 05:11] (current) – gregbalco
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 Here are some examples we have come up with so far. Please email any of us on the project [[balcs@bgc.org|Greg Balco]], [[benjamin.laabs@ndsu.edu|Ben Laabs]], and/or [[jtulenko@bgc.org|Joe Tulenko]] with your ideas so we can add them to the list! Here are some examples we have come up with so far. Please email any of us on the project [[balcs@bgc.org|Greg Balco]], [[benjamin.laabs@ndsu.edu|Ben Laabs]], and/or [[jtulenko@bgc.org|Joe Tulenko]] with your ideas so we can add them to the list!
- 
-**1) Analysis layer examples: the ICE-D X OCTOPUS web application** 
-The [[http://octopus.x.ice-d.org|ICE-D X OCTOPUS]] web application is not very much like the rest of the ICE-D focus area applications because it doesn't rely on an ICE-D-maintained back end database. Instead, its data layer is the [[https://earth.uow.edu.au|OCTOPUS database of cosmogenic-nuclide data used for erosion rate estimates]]. Also, the web application accesses those data by interacting with a Geoserver web feature server rather than a MySQL database. However, it does use ICE-D middle layer applications (the web service implementation of the online erosion rate calculator), so it's a good example of an analysis-layer application that uses both data-layer and middle-layer services. Also, it highlights the idea that you can mix and match data- and middle-layer servives from various places to do what you need to do. Finally, it's a pretty simple web application with a fairly minimal amount of code.  
- 
-The source code for the ICE-D X OCTOPUS web app can be viewed [[https://github.com/balcs/ice-d-x-octopus|here]]. It is written in Python 2.7, uses the [[https://webapp2.readthedocs.io/en/latest/|webapp2]] application framework, and runs on Google App Engine. Unfortuately this is kind of obsolete because GAE has been migrating to Python 3 and different web app frameworks (so I am not sure if you could install and run it on a newly created GAE project), but it is a nice simple example of how a transparent-middle-layer application can work.  
  
 ---- ----
  
-**2) Data-model comparison between LGM and penultimate moraine ages, and model output from simulations over multiple glaciations** (ie the ice sheet influence on regional climate example). **Hypothesis:** if the impact from ice sheets on re-arranging large-scale atmospheric circulation and thus modulating regional climate is actually significant, we should be able to use climate model output to predict the geospatial patterns of moraine preservation over multiple glacial cycles. This could be tested by comparing moraine ages and geospatial patterns from the database with model output to see where there is good fit between model output and data and where there isn't. +**1) Data-model comparison between LGM and penultimate moraine ages, and model output from simulations over multiple glaciations** (ie the ice sheet influence on regional climate example). **Hypothesis:** if the impact from ice sheets on re-arranging large-scale atmospheric circulation and thus modulating regional climate is actually significant, we should be able to use climate model output to predict the geospatial patterns of moraine preservation over multiple glacial cycles. This could be tested by comparing moraine ages and geospatial patterns from the database with model output to see where there is good fit between model output and data and where there isn't. 
  
 See the example output figure below and {{ :applications:ice_sheet_influence_exercise.pdf |find a link to the tutorial here.}} See the example output figure below and {{ :applications:ice_sheet_influence_exercise.pdf |find a link to the tutorial here.}}
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 ----  ---- 
  
-**3) Testing global expression of Younger Dryas**+**2) Testing global expression of Younger Dryas**
 Please find a tutorial and some matlab scripts used to generate some of the plots found in a recent paper from Greg Balco ([[https://www.annualreviews.org/doi/abs/10.1146/annurev-earth-081619-052609|Balco, 2021]]) in this example. Please find a tutorial and some matlab scripts used to generate some of the plots found in a recent paper from Greg Balco ([[https://www.annualreviews.org/doi/abs/10.1146/annurev-earth-081619-052609|Balco, 2021]]) in this example.
  
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 ----  ---- 
  
-**4) Post-Glacial Greenland ice-sheet retreat time-distance diagram** following up on a workshop at the University at Buffalo, we attempt to generate a time-distance diagram of SW Greenland Ice Sheet retreat and you can find the matlab script here to generate the following results:+**3) Post-Glacial Greenland ice-sheet retreat time-distance diagram** following up on a workshop at the University at Buffalo, we attempt to generate a time-distance diagram of SW Greenland Ice Sheet retreat and you can find the matlab script here to generate the following results:
  
 {{:applications:westgreenland_tdd_fig1.png?nolink&275|}}{{:applications:westgreenland_tdd_fig2.png?nolink&675|}} {{:applications:westgreenland_tdd_fig1.png?nolink&275|}}{{:applications:westgreenland_tdd_fig2.png?nolink&675|}}
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 ---- ----
  
-**5) Determining if measurement precision has gotten better through time**+**4) Determining if measurement precision has gotten better through time**
 This is a somewhat simple and fun exercise to investigate whether or not we as a community have been making progressively better measurements (ie improvements to field sample techniques, lab extraction procedures, AMS measurements, etc that should hopefully be leading to more precise cosmo measurements). This is a somewhat simple and fun exercise to investigate whether or not we as a community have been making progressively better measurements (ie improvements to field sample techniques, lab extraction procedures, AMS measurements, etc that should hopefully be leading to more precise cosmo measurements).
  
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-**6) Is there a correlation between Al/Be ratios and sample elevation?**+**5) Is there a correlation between Al/Be ratios and sample elevation?**
 This example is based off a recent publication ([[https://www.mdpi.com/2076-3263/11/10/402|Halsted et al., 2021]]) that found there is a correlation between Al / Be ratios and elevation that likely needs to be taken into account. This example is based off a recent publication ([[https://www.mdpi.com/2076-3263/11/10/402|Halsted et al., 2021]]) that found there is a correlation between Al / Be ratios and elevation that likely needs to be taken into account.
  
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 % assuming you set up an ODBC connection: % assuming you set up an ODBC connection:
  
-dbc = database('ICED-ALPINE','reader','beryllium-10');+dbc = database('ICED2','reader','beryllium-10');
  
 % here is some code from Greg Balco for connecting to the database from a % here is some code from Greg Balco for connecting to the database from a
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 % dictating marker size). % dictating marker size).
  
-q1 = ['select samples.sample_name, samples.elv_m, Be10_Al26_quartz.N10_atoms_g, Be10_Al26_quartz.delN10_atoms_g, Be10_Al26_quartz.N26_atoms_g, '... +%% Query to extract all 10Be Al26 pairs from the alpine database
-' Be10_Al26_quartz.delN26_atoms_g, calculated_ages.t_LSDn '... +
-' from samples, Be10_Al26_quartz, calculated_ages '... +
-' where samples.sample_name = Be10_Al26_quartz.sample_name '... +
-' and samples.sample_name = calculated_ages.sample_name '... +
-' and samples.elv_m is not null and Be10_Al26_quartz.N10_atoms_g is not null and Be10_Al26_quartz.N26_atoms_g is not null '... +
-' and Be10_Al26_quartz.N26_atoms_g > 1000 and Be10_Al26_quartz.N26_atoms_g not like 0 '];+
  
-% this will store all data in a table that we can format into cell arrays +q1 = ['select iced.base_sample.name, ' ... 
-% for plotting with the following code:+' iced.base_sample.elv_m, ' ... 
 +' iced._be10_al26_quartz.N10_atoms_g, ' ... 
 +' iced._be10_al26_quartz.delN10_atoms_g, ' ... 
 +' iced._be10_al26_quartz.N26_atoms_g, ' ... 
 +' iced._be10_al26_quartz.delN26_atoms_g, ' ... 
 +' iced.base_calculatedages.t_LSDn ' ... 
 +' from iced.base_sample, iced._be10_al26_quartz, iced.base_calculatedages, iced.base_site, iced.base_application_sites ' ... 
 +' where iced.base_sample.id = iced._be10_al26_quartz.sample_id ' ... 
 +' and iced.base_sample.id = iced.base_calculatedages.sample_unique_id ' ... 
 +' AND iced.base_sample.site_id = iced.base_site.id ' ... 
 +' and iced.base_site.id = iced.base_application_sites.site_id ' ... 
 +' and iced.base_sample.elv_m is not NULL ' ... 
 +' and iced._be10_al26_quartz.N10_atoms_g is not NULL ' ... 
 +' and iced._be10_al26_quartz.N26_atoms_g is not null ' ... 
 +' and iced._be10_al26_quartz.N26_atoms_g > 1000 ' ... 
 +' and iced._be10_al26_quartz.N26_atoms_g not like 0 ' ... 
 +' and iced.base_application_sites.application_id = 2 ']; 
 + 
 +%% If you would like to try the entire database (not just the alpine database) use this query instead of the first one 
 +% Worth noting that in this query, we have not isolated the samples that 
 +% have strictly simple exposure histories so there are probably a lot of 
 +% samples in the dataset that are below the simple exposure ratio line 
 + 
 +%q1 = ['select iced.base_sample.name, ' ... 
 +%' iced.base_sample.elv_m, ' ... 
 +%' iced._be10_al26_quartz.N10_atoms_g, ' ... 
 +%' iced._be10_al26_quartz.delN10_atoms_g, ' ... 
 +%' iced._be10_al26_quartz.N26_atoms_g, ' ... 
 +%' iced._be10_al26_quartz.delN26_atoms_g, ' ... 
 +%' iced.base_calculatedages.t_LSDn ' ... 
 +%' from iced.base_sample, iced._be10_al26_quartz, iced.base_calculatedages ' ... 
 +%' where iced.base_sample.id = iced._be10_al26_quartz.sample_id ' ... 
 +%' and iced.base_sample.id = iced.base_calculatedages.sample_unique_id ' ... 
 +%' and iced.base_sample.elv_m is not NULL ' ... 
 +%' and iced._be10_al26_quartz.N10_atoms_g is not NULL ' ... 
 +%' and iced._be10_al26_quartz.N26_atoms_g is not null ' ... 
 +%' and iced._be10_al26_quartz.N26_atoms_g > 1000 ' ... 
 +%' and iced._be10_al26_quartz.N26_atoms_g not like 0 ' ... 
 +% and iced.base_calculatedages.t_LSDn not like 0 ' ... 
 +%' AND iced.base_sample.name NOT LIKE "MH12-47"']; 
 + 
 +%% gather the data and organize it into cell arrays that can be used for plotting
  
 samples.table = fetch(dbc,q1); samples.table = fetch(dbc,q1);
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 samples.error = double((samples.cell_array(:,4)./samples.cell_array(:,2)).*(((samples.cell_array(:,3)./samples.cell_array(:,2)).^2)+((samples.cell_array(:,5)./samples.cell_array(:,4)).^2)).^0.5); samples.error = double((samples.cell_array(:,4)./samples.cell_array(:,2)).*(((samples.cell_array(:,3)./samples.cell_array(:,2)).^2)+((samples.cell_array(:,5)./samples.cell_array(:,4)).^2)).^0.5);
  
-%now time to plot it all!+%% The code to maka a figure plotting up all of the samples by elevation vs Al/Be ratio
  
 +fig1 = figure(1)
 errorbar(samples.cell_array(:,1), (samples.cell_array(:,4)./samples.cell_array(:,2)), samples.error, 'vertical', 'LineStyle','none') errorbar(samples.cell_array(:,1), (samples.cell_array(:,4)./samples.cell_array(:,2)), samples.error, 'vertical', 'LineStyle','none')
 hold on hold on
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     'MarkerEdgeColor',[0 0 0], 'MarkerFaceColor', [0 .7 .7], 'SizeData', (samples.cell_array(:,6)./1000),...     'MarkerEdgeColor',[0 0 0], 'MarkerFaceColor', [0 .7 .7], 'SizeData', (samples.cell_array(:,6)./1000),...
     'LineWidth', 0.5)     'LineWidth', 0.5)
 +ylim([0 15])
 xlabel('Elevation (m)','FontSize',16) xlabel('Elevation (m)','FontSize',16)
 ylabel('[Al-26] / [Be-10]','FontSize',16) ylabel('[Al-26] / [Be-10]','FontSize',16)
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 ---- ----
  
-**7) Heinrich Stadials aridity drives glacier retreat in the Mediterranean?**+**6) Heinrich Stadials aridity drives glacier retreat in the Mediterranean?**
 This example is a follow up to a paper recently published in Nature Geoscience ([[https://www.nature.com/articles/s41561-021-00703-6|Allard et al., 2021]]) that found that glaciers in the region may have been retreating during Heinrich Stadials due to more arid conditions.  This example is a follow up to a paper recently published in Nature Geoscience ([[https://www.nature.com/articles/s41561-021-00703-6|Allard et al., 2021]]) that found that glaciers in the region may have been retreating during Heinrich Stadials due to more arid conditions. 
  
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-**8) Identifying regions of possible heavy moraine degradation** (using the moraine ages and land degradation models incorporated into the middle layer of calculations) and **comparing identified areas of high degradation to geohazards** (plate boundaries and areas of high seismic activity). +**7) Identifying regions of possible heavy moraine degradation** (using the moraine ages and land degradation models incorporated into the middle layer of calculations) and **comparing identified areas of high degradation to geohazards** (plate boundaries and areas of high seismic activity). 
 **Hypothesis:** if geohazards actually present a notable obstacle to moraine dating through moraine degradation, then we should be able to find instances of high moraine degradation coinciding with high seismic activity. **Hypothesis:** if geohazards actually present a notable obstacle to moraine dating through moraine degradation, then we should be able to find instances of high moraine degradation coinciding with high seismic activity.