lit review updates to wsim-gldas-acquisition

docs/wsim-gldas-acquisition.html

@@ -4133,12 +4133,10 @@ <h2 class="anchored" data-anchor-id="introduction">Introduction</h2>
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 </div></div><p>The <strong>Water Security (WSIM-GLDAS) Monthly Grids, v1 (1948 - 2014)</strong> dataset can be download from the <a href="https://sedac.ciesin.columbia.edu/data/set/water-wsim-gldas-v1">NASA SEDAC</a> website <span class="citation" data-cites="isciences2022">(<a href="#ref-isciences2022" role="doc-biblioref">ISciences and Center For International Earth Science Information Network-CIESIN-Columbia University 2022b</a>)</span>. The dataset abstract describes these data saying that WSIM-GLDAS “identifies and characterizes surpluses and deficits of freshwater, and the parameters determining these anomalies, at monthly intervals over the period January 1948 to December 2014.”</p>
-<p>Downloads are organized by a combination of thematic variables (composite surplus/deficit, temperature, PETmE, runoff, soil moisture, precipitation) and integration periods (a temporal aggregation) (1, 3, 6, 12 months). Each variable-integration combination consists of a NetCDF <strong>raster</strong> file with a time dimension that contains a raster layer for each of the 804 months between January, 1948 and December, 2014. Some variables also contain multiple attributes each with their own time series. Hence, this is a large file that can take a lot of time to download and may cause computer memory issues on certain systems. This is considered BIG data.</p>
+<p>Downloads are organized by a combination of thematic variables (composite surplus/deficit, temperature, PETmE, runoff, soil moisture, precipitation) and integration periods (a temporal aggregation) (1, 3, 6, 12 months). Each variable-integration combination consists of a <strong>NetCDF raster</strong> (.nc) file ( with a time dimension that contains a raster layer for each of the 804 months between January, 1948 and December, 2014. Some variables also contain multiple attributes each with their own time series. Hence, this is a large file that can take a lot of time to download and may cause computer memory issues on certain systems. This is considered BIG data.</p>
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 <h2 class="anchored" data-anchor-id="acquiring-the-data">Acquiring the Data</h2>
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@@ -4152,7 +4150,7 @@ <h2 class="anchored" data-anchor-id="acquiring-the-data">Acquiring the Data</h2>
 <p>The <strong>Water Security (WSIM-GLDAS) Monthly Grids dataset</strong> used in this lesson is hosted by <a href="https://sedac.ciesin.columbia.edu/">NASA’s Socioeconomic Data and Applications Center (SEDAC</a>), one of several <a href="https://www.earthdata.nasa.gov/eosdis/daacs">Distributed Active Archive Centers (DAACs)</a>. SEDAC hosts a variety of data products including geospatial population data, human settlements and infrastructure, exposure and vulnerability to climate change, and satellite-based data on land use, air, and water quality. In order to download data hosted by SEDAC, you are required to have a free NASA EarthData account. You can create an account here: <a href="https://urs.earthdata.nasa.gov/users/new">NASA EarthData</a>.</p>
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-</div></div><p>For this lesson, we will work with the WSIM-GLDAS data set <strong>Composite Anomaly Twelve-Month Return Period</strong> NetCDF file. This represents the variable “Composite Anomaly” for the integration period of twelve-month. Let’s download the file directly from the SEDAC website. The <a href="https://sedac.ciesin.columbia.edu/downloads/docs/water/water-wsim-gldas-v1-documentation.pdf">data set documentation</a> describes the composite variables as key features of WSIM-GLDAS which combine “the return periods of multiple water parameters into composite indices of overall water surpluses and deficits <span class="citation" data-cites="isciences2022a">(<a href="#ref-isciences2022a" role="doc-biblioref">ISciences and Center For International Earth Science Information Network-CIESIN-Columbia University 2022a</a>)</span>”. The composite anomaly files represent these model outputs in terms of the rarity of their return period, or how often they occur. Please go ahead and download the file.</p>
+<p>For this lesson, we will work with the WSIM-GLDAS data set <strong>Composite Anomaly Twelve-Month Return Period</strong> NetCDF file. This represents the variable “Composite Anomaly” for the integration period of twelve-month. Let’s download the file directly from the SEDAC website. The <a href="https://sedac.ciesin.columbia.edu/downloads/docs/water/water-wsim-gldas-v1-documentation.pdf">data set documentation</a> describes the composite variables as key features of WSIM-GLDAS which combine “the return periods of multiple water parameters into composite indices of overall water surpluses and deficits <span class="citation" data-cites="isciences2022a">(<a href="#ref-isciences2022a" role="doc-biblioref">ISciences and Center For International Earth Science Information Network-CIESIN-Columbia University 2022a</a>)</span>”. The composite anomaly files represent these model outputs in terms of the rarity of their return period, or how often they occur. Please go ahead and download the file.</p>
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 <li><p>First, go to the SEDAC website at <a href="https://sedac.ciesin.columbia.edu/" class="uri">https://sedac.ciesin.columbia.edu/</a>. You can explore the website by themes, data sets, or collections. We will use the search bar at the top to search for “water security wsim”. Find and click on the Water Security (WSIM-GLDAS) Monthly Grids, v1 (1948 – 2014) data set. Take a moment to review the dataset’s Overview, and Documentation pages.</p></li>
 <li><p>When you’re ready, click on the Data Download tab. You will be asked to sign in using your NASA EarthData account.</p></li>
@@ -4292,7 +4290,7 @@ <h2 class="anchored" data-anchor-id="spatial-selection">Spatial Selection</h2>
 <p>Built by the community and <a href="https://github.com/wmgeolab">William &amp; Mary geoLab</a>, the geoBoundaries Global Database of Political Administrative Boundaries is an online, open license (CC BY 4.0 / ODbL) resource of information on administrative boundaries (i.e., state, county) for every country in the world. Since 2016, this project has tracked approximately 1 million spatial units within more than 200 entities, including all UN member states.</p>
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-</div></div><p>In this example we use a vector boundary to accomplish the geoprocessing task of clipping the data to an administrative or political unit. First we acquire the data in GeoJSON format for the United States from the geoBoundaries API. (Note it is also possible to download the vectorized boundaries directly from <a href="https://www.geoboundaries.org/">https://www.geoboundaries.org/</a> in lieu of using the API).</p>
+</div></div><p>In this example we use a vector boundary to accomplish the geoprocessing task of clipping the data to an administrative or political unit. First we acquire the data in GeoJSON format for the United States from the geoBoundaries API. (Note it is also possible to download the vectorized boundaries directly from <a href="https://www.geoboundaries.org/" class="uri">https://www.geoboundaries.org/</a> in lieu of using the API).</p>
 <p>To use the geoBoundaries’ API, the root URL below is modified to include a 3 letter code from the International Standards Organization used to identify countries (ISO3), and an administrative level for the data request. Administrative levels correspond to geographic units such as the Country (administrative level 0), the State/Province (administrative level 1), the County/District (administrative level 2) and so on:</p>
 <p>“https://www.geoboundaries.org/api/current/gbOpen/<strong>ISO3</strong>/<strong>LEVEL</strong>/”</p>
 <p>For this example we adjust the bolded components of the sample URL address below to specify the country we want using the ISO3 Character Country Code for the United States (<strong>USA</strong>) and the desired Administrative Level of State (<strong>ADM1</strong>).</p>
@@ -4378,8 +4376,8 @@ <h2 class="anchored" data-anchor-id="spatial-selection">Spatial Selection</h2>
 Drought in the News
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-<p>Texas experienced a severe drought in 2011 that caused rivers to dry up and lakes to reach historic low levels <span class="citation" data-cites="StateImpact">(<a href="#ref-StateImpact" role="doc-biblioref">StateImpact 2014</a>)</span>. Climate experts discovered that the drought was produced by “La Niña”, a weather pattern that causes the surface temperature of the Pacific Ocean to be cooler than normal. This, in turn, creates drier and warmer weather in the southern United States. La Niña can occur for a year or more, and returns once every few years. The drought was further exacerbated by high temperatures related to climate change in February of 2013 <span class="citation" data-cites="NOAA2023">(<a href="#ref-NOAA2023" role="doc-biblioref">NOAA 2023</a>)</span>.</p>
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+<p>Texas experienced a severe drought in 2011 that caused rivers to dry up and lakes to reach historic low levels <span class="citation" data-cites="StateImpact">(<a href="#ref-StateImpact" role="doc-biblioref">StateImpact 2014</a>)</span>. The drought was further exacerbated by high temperatures related to climate change in February of 2013. Climate experts discovered that the drought was produced by “La Niña”, a weather pattern that causes the surface temperature of the Pacific Ocean to be cooler than normal. This, in turn, creates drier and warmer weather in the southern United States. La Niña can occur for a year or more, and returns once every few years <span class="citation" data-cites="NOAA2023">(<a href="#ref-NOAA2023" role="doc-biblioref">NOAA 2023</a>)</span>.</p>
 <p>It is estimated that the drought cost farmers and ranchers about $8 billion in losses. Furthermore, the dry conditions fueled a series of wildfires across the state in early September of 2011, the most devastating of which occurred in Bastrop County, where 34,000 acres and 1,300 homes were destroyed <span class="citation" data-cites="Roeseler2011">(<a href="#ref-Roeseler2011" role="doc-biblioref">Roeseler 2011</a>)</span>.</p>
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