nyuuzyou/wikis huggingface dataset:
Abstract IAMG 2009

= The CSDMS project and submission standards for model source code =
S.D. Peckham, E.W.H. Hutton and J.P.M. Syvitski
CSDMS Integration Facility, INSTAAR, University of Colorado, Boulder CO, 80309-0545
== Abstract ==
 300px | right
The Community Surface Dynamics Modeling System (CSDMS) is an NSF-funded project that represents an effort to bring together a diverse community of surface dynamics modelers and model users. Key goals of the CSDMS project are to
# promote open-source code sharing and re-use, 
# to develop a review process for code contributions,
# promote recognition of contributors, 
# develop a library of low-level software tools and higher-level models that can be linked as easily as possible into new applications and
# provide resources to simplify the efforts of surface dynamics modelers.
CSDMS builds upon the software standards and tools of the Common Component Architecture (CCA) which have been specifically designed to support component-based, high-performance, scientific computing. Babel, one of the key tools in the CCA toolchain, provides interoperability between components that are written in different languages. It currently supports C, C++, Fortran (all years), Java and Python. In addition, CSDMS utilizes model interface standards such as OpenMI to help accomodate other differences between models, such as dimensionality, units and computational mesh type.
In order for a model to be usable as a plug-and-play component, it must provide some type of standard component interface. Typical surface dynamics models advance values forward in time on a grid or mesh and have a similar internal structure. This structure consists of some lines of code before the beginning of a time loop, some lines of code inside the time loop and then some additional lines after the end of the time loop. Virtually all component-based modeling efforts have recognized the utility of moving these lines of code into three separate functions, with names such as Initialize, Update and Finalize. This very simple refactoring is a first important step towards allowing a model or process module to be used either as a component within a larger application or as a stand-alone model. It provides a calling program with fine-grained access to the model's capabilities and the ability to take over the management of time. Another key step is to annotate the model source code using special keywords within comment blocks to provide basic metadata for a model and its variables. For example, metadata for a variable would include its units, valid range of values and whether it is used for input or output. This type of annotation makes it possible to write utilities that parse the source code, extract the metadata and then automatically generate whatever component interface is required for compatibility with other models. The purpose of this talk is to explain these ideas with examples and to propose a set of model code submission standards that may be adopted by code repositories such as CSDMS or Computers & Geosciences. Though relatively simple, standards such as these will help to facilitate more widespread use of open-source models.
Abstract UCSB 2009

= Hyperpycnal flows and the generation of continental shelf-traversing turbidity currents =
James P.M. Syvitski, Eric W.H. Hutton, Albert Kettner
CSDMS Integration Facility, INSTAAR, University of Colorado, Boulder CO 
and John D. Milliman
Virginia Inst of Marine Science, College of William and Mary, Williamsburg, VA
== Abstract ==
Hyperpycnal discharge into the world’s ocean is limited to small and medium-sized rivers that drain mountainous terrain capable of generating hyper-elevated sediment concentrations during high-energy floods. The paper will explore the frequency of occurrence of “hyperpycnal sensitive” active continental margins:
# Taiwan and New Zealand, where the generating conditions include rain-intense cyclones, set up by their earthquake sensitive landscape, and in the New Zealand case major deforestation;
# California, where ENSO + PDO conditions align to produce high intensity frontal rainfall, again in a tectonically active setting set up with deforestation activities; and
# mountainous Mediterranean and Black Sea rivers where high intensity convective rainfall produces extremely short lived yet turbid flood events.
In all of these site examples, the critical threshold to overcome the buoyancy set up of coastal-ocean salinity has either been documented through in-situ observations, or with established hydrological rating curves, or hydrological modeling. 
When these flows enter the ocean, the immediate slope of the seafloor becomes the controlling factor. If the slope is gentle, model simulations suggest that the flow enters a depositional state immediately. Modeled deposit characteristics are very similar to a deposit from deposition under a surface plume. If the seafloor slope is steep enough, erosion of a near shore channel may take place. The transit across the continental shelf is then controlled by the strength of along-shelf currents in competition with the slope of the shelf. Hyperpycnal flows, at least in models, may sometimes not complete their transit across the continental shelf, where the rate of deposition is high, and the transit length is long, and flows dilute through entrainment. Here the role of wave support of the deposition rate becomes important, and thus the coherency of an ocean storm with the river flood. Flows may move down a continental slope, at least in models, as a line source, if along shelf-currents are strong.
Abstract WPC 2010 a

= Hyperpycnal Current-Sensitive Continental Margins =
James P.M. Syvitski, Albert J. Kettner, Eric W.H. Hutton
Community Surface Dynamics Modeling System (CSDMS), Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder CO, USA. 
Hyperpycnal (HP) flows generated by rivers discharging into marine continental shelves have received limited study. Theory is well advanced of field observations that remain limited and very hard to obtain. The geophysical importance of HP events has also been speculative, particularly on the nature of whether such flows are erosive or depositional upon leaving the coastal zone, and whether sediment can be carried directly into the deep ocean. Numerical models, calibrated on flume experiments, have provided perhaps the most insight into the frequency and magnitude of HP events, and which margins are likely to be greatly impacted by these events. Theory suggests hyperpycnal flows begin with coastal concentrations of tens of grams of muddy sediment per liter, with flow velocities of <3 m/s. The flows are long body quasi-steady flows lasting hours to weeks. HP discharge into the world’s ocean is mostly limited to small and medium-sized rivers that drain mountainous terrain capable of generating hyper-elevated sediment concentrations during high-energy floods. HP sensitive continental margins include:
# Taiwan and New Zealand, where the generating conditions include rain-intense cyclones, set up by their earthquake sensitive landscape, and in the New Zealand case major deforestation;
# California, where ENSO + PDO conditions align to produce high intensity frontal rainfall, again in a tectonically active setting set up with deforestation activities; and
# mountainous Mediterranean and Black Sea rivers where high intensity convective rainfall produces extremely short lived yet turbid flood events.
In all of these site examples, the critical threshold to overcome the buoyancy set up of coastal-ocean salinity has either been documented through in-situ observations, or with established hydrological rating curves, or hydrological modeling. When these flows enter the ocean, the immediate slope of the seafloor becomes the controlling factor. If the slope is gentle, model simulations suggest that the flow enters a depositional state immediately. Modeled deposit characteristics are very similar to a deposit from deposition under a surface plume. If the seafloor slope is steep enough, erosion of a near shore channel may take place. The transit across the continental shelf is then controlled by the strength of along-shelf currents in competition with the slope of the shelf. Hyperpycnal flows, at least in models, may sometimes not complete their transit across the continental shelf, where the rate of deposition is high, and the transit length is long, and flows dilute through entrainment. Here the role of wave support of the deposition rate becomes important, and thus the coherency of an ocean storm with the river flood. Flows may move down a continental slope, at least in models, as a line source, if along shelf currents are strong.
Abstract WPC 2010 b

= Observing Coastal-Resuspension associated with Tropical Cyclones =
James P.M. Syvitski, Albert J. Kettner, Eric W.H. Hutton
Community Surface Dynamics Modeling System (CSDMS), Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Boulder CO, USA. 
The CSDMS Integration Facility is developing an integrated workflow that involves model and satellite data to characterize sediment flux within the coastal zone. The Work Flow includes:
# Identify and track cyclone to landfall (e.g. MODIS);
# Obtain cyclone winds & pressure (e.g. GFS);
# Obtain wave history (e.g. NOAA WAVEWATCH IIITM);
# Determine cyclone storm surge (e.g. ADCIRC, AMSR-E);
# Obtain rainfall before and during landfall (e.g. TRMM);
# Obtain evapotranspiration (e.g. MOD16) and soil moisture conditions (e.g. ASAR);
# Create a coastal DEM to be used for the routing of runoff (e.g. SRTM, LIDAR);
# Determine the routing of water off of the land surface (e.g. TopoFlow);
# Determine the flux of sediment off the landscape (e.g. HydroTrend-sed);
# Obtain visible to near infrared imagery of coastal waters (e.g. MODIS);
# Model sediment dispersal patterns for water column fluxes (e.g. ROMS, SWAN).
MODIS satellites can track the landfall of tropical cyclones, provide for patterns of sediment plumes emanating from river mouths, and from resuspension of shoreline sediment. MODIS can also be used to observe the dimensions of delta flooding and delta-plain aggradation. ROMS coupled with the wave model SWAN provides estimates for bedload and suspended load transport along with evolution of bottom morphology within estuarine, shelf, and nearshore environments. ROMS can be coupled with river models such as HydroTrend or TopoFlow to complete the land-ocean coupling.
AnalystExecutorBlanca

Instructions for installing and configuring a WMT executor on blanca.
------
==Set build environment on blanca==
Login to summit.
 ssh mapi8461@login.rc.colorado.edu
Load the correct slurm for blanca.
 module load slurm/blanca
Login to a compute node.
 sinteractive
Build everything on the compute node.
==Set install and conda directories==
The install directory for this executor is /work/csdms/wmt/analyst.
 install_dir=/work/csdms/wmt/analyst
 mkdir -p $install_dir
Make sure read and execute bits are set on this directory. 
 chmod 0775 $install_dir
An Anaconda distribution is located at /work/csdms/anaconda3.
This distribution is used by all WMT executors on blanca.
Store this location and add it to your path.
 conda_dir=/work/csdms/anaconda3
 PATH=$conda_dir/bin:$PATH
WMT executors are installed as environments within this distribution.
==Install the CSDMS software stack==
Load blanca's `git` module.
 module load git
Clone the `wmt-executor-config-files` repo.
 mkdir -p $install_dir/opt && cd $install_dir/opt
 git clone https://github.com/mdpiper/wmt-executor-config-files
 cd wmt-executor-config-files
Install the CSDMS software stack,
including the `babelizer` for components that need to be built from source,
in a new environment.
 conda env create -f wmt-analyst.yml
The stack will be installed in $conda_dir/envs/wmt-analyst.
Note that the `netcdf-fortran` package needs to be wound back so that HDF5 1.8.18 is used
because of problems with HDF5 1.10.1 and ESMF, used in PyMT
(see discussion here).
Also note I used a tagged version of PyMT because there have been a lot of changes to it 
since I built the conda recipes for the permamodel models.
===Install executor software===
Install the `wmt-exe` package from source.
 mkdir -p $install_dir/opt && cd $install_dir/opt
 git clone https://github.com/csdms/wmt-exe
 cd wmt-exe
 python setup.py develop
Create a site configuration file that describes the executor and symlink it to the executor's etc/ diectory.
 work_dir='/rc_scratch/$USER/wmt/analyst' # note single quotes
 python setup.py configure --wmt-prefix=$install_dir --launch-dir=$work_dir --exec-dir=$work_dir
 ln -s "$(realpath wmt.cfg)" $conda_dir/envs/wmt-analyst/etc
Check that `$USER` didn't get expanded in the file. Lines 10-11 should be:
 exec_dir = /rc_scratch/$USER/wmt/analyst
 launch_dir = /rc_scratch/$USER/wmt/analyst
Note that we're using /rc_scratch for the launch and execution directories instead of the default ~/.wmt.
Also note that we needed an SbatchLauncher class
for wmt-exe because blanca uses Slurm
instead of Torque for job control.
==Install and test CSDMS components==
Each section below
describes how to install and test a particular CSDMS component.
Currently installed components:
# BRaKE
# CHILD
# CMIP
# CRUAKTemp
# FrostNumberGeoModel
# FrostNumberModel
# Hydrotrend
# KuGeoModel
# KuModel
# PyDeltaRCM
# Sedflux3D
Note that when running IPython remotely it's helpful to set
 export MPLBACKEND=Agg
==Note: 2019-02-11==
I deleted the wmt-analyst environment and reinstalled it with Hydrotrend as the only component.
It works.
(It did not work when I also installed the permamodel components.)
AnalystExecutorSiwenna

Instructions for installing and configuring a WMT executor on siwenna.
--Mpiper (talk) 15:40, 8 October 2018 (MDT)
==Set install and conda directories==
The install directory for this executor is /home/csdms/wmt/analyst.
 install_dir=/home/csdms/wmt/analyst
 mkdir -p $install_dir
Make sure read and execute bits are set on this directory. 
 chmod 0775 $install_dir
==Install Python==
==Install the CSDMS software stack==
Clone the `wmt-executor-config-files` repo.
 mkdir -p $install_dir/opt && cd $install_dir/opt
 git clone https://github.com/mdpiper/wmt-executor-config-files
 cd wmt-executor-config-files
Install the CSDMS software stack,
including the `babelizer` for components that need to be built from source,
in a new environment.
 conda install --file wmt-analyst.txt -c csdms-stack -c defaults -c conda-forge
Note that the `netcdf-fortran` package needs to be wound back so that HDF5 1.8.18 is used
because of problems with HDF5 1.10.1 and ESMF, used in PyMT
(see discussion here).
Also note I used a tagged version of PyMT because there have been a lot of changes to it 
since I built the conda recipes for the permamodel models.
===Install executor software===
Install the `wmt-exe` package from source.
 mkdir -p $install_dir/opt && cd $install_dir/opt
 git clone https://github.com/csdms/wmt-exe
 cd wmt-exe
 python setup.py develop
Create a site configuration file that describes the executor and symlink it to the executor's etc/ diectory.
 work_dir='/scratch/$USER/wmt/analyst' # note single quotes
 python setup.py configure --wmt-prefix=$install_dir --launch-dir=$work_dir --exec-dir=$work_dir
 ln -s "$(realpath wmt.cfg)" $conda_dir/envs/wmt-analyst/etc
Note that we're using /scratch for the launch and execution directories instead of the default ~/.wmt.
==Install and test CSDMS components==
Each section below
describes how to install and test a particular CSDMS component.
Currently installed components:
# BRaKE
# CHILD
# CMIP
# CRUAKTemp
# FrostNumberGeoModel
# FrostNumberModel
# Hydrotrend
# KuGeoModel
# KuModel
# PyDeltaRCM
# Sedflux3D
Update (2018-10-08): CHILD is not working, so I've pulled it.
Note that when running IPython remotely it's helpful to set
 export MPLBACKEND=Agg
Annualmeeting-numbers

==CSDMS annual meetings==
Year 	# of registrants	# of submitted poster abstracts	# of submitted Epub abstracts
2025 	 	 	
2024 	 	 	
2023 	 	 	
2022 	 	 	N/A
2021 	 	 	N/A
2020 	 	 	N/A
2019 	 	 	N/A
2018 	 	 	N/A
2017 	 	 	N/A
Data of previous years will be added shortly (June 22nd, '24)
Arctic 2007

===Arctic Coasts at Risk===
Date: October 1 to 3, 2007 	
Location: Tromso, Norway 	
Sponsors: Land-Ocean Interaction In The Coastal Zone (LOICZ), International Arctic Science Committee (IASC), Arctic Monitoring and Assessment Programme (AMAP)	
 	
 	
 	
 	
Goals:
# Assess the current state of the art knowledge on the impact of Global Climate Change reflecting in the Arctic coast: What do we know about the physical, biological, ecological, socio-economic status? Which models/scenarios for predicting trends do we have? What kind of human responses/governance systems are in place and how well established are the science policy links for informed decision making?
# Suggest new approaches and recommendations on the physical, biological-chemical coupling, integrated assessment and modeling, and governance and adaptation aimed to improve and strengthen science policy interfaces. 
Participants:
Invitation was done through the workshop organizers.
Scientific Committee: 	
* Chris Cogan (IASC)
* Nicole Couture (IASC)
* Louwrens Hacquebord (IASC)
* Volker Rachold (IASC)
* Gotz Floser (LOICZ)
* Hartwig Kremer (LOICZ)
* Josef Pacyna (LOICZ)
* James Syvitski (LOICZ & CSDMS)
* Georg Hansen (local)
* Lars-Otto Reiersen (AMAP)
* Leslie King (IHDP)
* Oran Young (IHDP) 
Description:
The Workshop report will comprise assessment, prognosis, and human responses of/to Arctic Change and thus be a link between and coastal focus update of the existing ACIA (Arctic Climate Impact Assessment) and AHDR (Arctic Human Development Report) reports, focused on Arctic coasts and people. In the working groups, physical, biological-ecological as well as socio-economic perspectives will be addressed. The physical perspectives of Global Change comprise changes in environmental forcing (by sea ice, wind and waves, rising sea level etc.), in permafrost stability, morphodynamics and river discharge into the Arctic Sea. The biological-ecological perspective covers biodiversity issues, biogeochemical cycles including pollution, ecosystem functioning and thresholds, and ecosystem goods and services. The socio-economic perspectives look upon new forms of land and sea use, governance systems linked to decision-making in the coastal zone (scales and participation), effectiveness of management, and adaptation strategies and frameworks.
'''
Workshop Structure:
Plenary keynote lectures, including introduction to the working groups:
# Physical perspectives;
# Biological-ecological perspectives;
# Socio-economic perspectives 
and reconfigured working groups:
# Physical, biological-ecological, socioeconomic coupling;
# Prognosis, integrated assessment and modeling; and
# Human responses: governance and adaptation.
Available Model related publications conditions

=Available Model related publication conditions=
The model related publication repository is stored on several databases. This as there are various ways to enter a reference into the system (automatically, based on the DOI, or manually enter the fields). The tables below contain all possible reference metadata query conditions that can be used, using the Cargo query style. See more at Web APIs on how to include these conditions in an API query.
Database: RefsExt (automatically populated fields, based on DOI)
Fields 	Definition
Firstname	First name author(s)
Lastname 	Last name author(s)
Database: RefsExt2 (automatically populated fields, based on DOI)
Fields 	Definition
DOIentry 	The input DOI, of a reference to populate all the below parameters from online sources
ItemType 	Type of medium of publication
Title 	Title of publication
PublicationTitle 	Journal or other medium that holds the publication
Booktitle 	Title of the book
JournalAbbreviation	Abbreviation of journal (if any)
Volume 	Volume number that holds the publication
Issue 	Issue number that holds the publication
Url 	The web address of the publication
DOI 	The Digital Object Identifier (DOI) of the publication (same as the input to get this information)
Pagenumbers 	Page numbers of the publication
Publicationdate 	Date of publication
AccessDate 	Date of access of online information
Database: AuthorsMan (manual populated fields)
Fields 	Definition
Firstname	Capturing the full name (so first name and given name) of authors and co-authors)
Database: BibEntryCargo (manual populated fields)
Fields 	Definition
BibType 	Type of medium of publication
Title 	Title of publication
Editors 	Editors of the issue or volume
Year 	Year of publication
Journal 	Journal that holds the publication
Booktitle	Title of the book
Volume 	Volume number that holds the publication
Pages 	Page numbers of the publication
URL 	The web address of the publication
DOI 	The Digital Object Identifier (DOI) of the publication
ISBN 	The International Standard Book Number (ISBN) of the publication when available
Note 	Any relevant comments
Database: RefsInt1 (manual populated fields)
Fields 	Definition
PublicationClusterID 	Unique Reference ID number that links the number of citations to the paper
PublicationCitationsNr 	The actual number of citations for a reference
PublicationWhatKindOf 	Type of medium of publication
PublicationNrofModels 	If the publication describes or applies a single or numerous modules
PublicationHPCCYesno 	If the CSDMS HPC is used for the publication
PublicationHPCCprojectCargo	If the reference has a link to one or more HPC projects
PublicationAnimationsCargo 	If the reference has a link to one or more animations
Database: RefsInt2 (manual populated fields)
Fields 	Definition
PublicationMultipleModelsCargo	If the reference has a link to one or more modules or models in the Model repository
Available data repository conditions

=Available Data repository conditions=
The table below contain all possible Data metadata query conditions that can be using within the SMW query style. See more at Web APIs on how to include these conditions in an API query.
Condition 	Definition
Data:+ 	Notice: Always use this condition to ensure you are querying the data metadata database. Within the Data repo, select all records (+); or replace + by a dataset name to select data of a specific dataset. CSDMS has grouped all data in a separate Namepace, named "Data".
Upload image dataset 	Image, graphically illustrating the dataset
Caption dataset image 	Describing the image of the dataset
One-line data description	Short dataset description
Extended data description	Extended dataset description
Data type 	Indicate in which category the dataset will fit best
Data origin 	Indicate the origin of the data
Data format 	Indicate in what format the data can be obtained
Other data format 	Indicate additional formats
Data resolution 	Indicate spatial data resolution
Data projection 	Indicate that data projection the dataset has
Data spatial coverage 	What is the spatial coverage of the data
Data temporal coverage 	For the temporal domain, is the presented data a time series, snap shot or time average?
Data time period covered 	Indicate what temporal period is covered by the dataset
Data download link 	Provide the URL of where the data can be downloaded
Data source link 	Provide a URL to the data description (if any)
Key references dataset 	Indicate one or more references that describe the dataset