modified: m101-wsim-gldas.qmd

	modified:   references/wsim-gldas-references.bib

m101-wsim-gldas.qmd

@@ -26,9 +26,9 @@ After completing this lesson, you should be able to:
 
 ## Introduction
 
-The water cycle is the process of circulation of water on, above, and under the Earth's surface [@NOAA2019]. Human activities produce greenhouse gas emissions, land use changes, dam and reservoir development, and groundwater extraction have affected the natural water cycle in recent decades [@intergovernmentalpanelonclimatechange2023]. The influence of these human activities on the water cycle has consequential impacts on oceanic, groundwater, and land processes, influencing phenomena such as droughts and floods [@Zhou2016].
+The water cycle is the process of circulation of water on, above, and under the Earth's surface [@NOAA2019] . Human activities produce greenhouse gas emissions, land use changes, dam and reservoir development, and groundwater extraction have affected the natural water cycle in recent decades [@intergovernmentalpanelonclimatechange2023]. The influence of these human activities on the water cycle has consequential impacts on oceanic, groundwater, and land processes, influencing phenomena such as droughts and floods [@Zhou2016].
 
-Precipitation deficits, or periods of below-average rainfall, can lead to drought, characterized by prolonged periods of little to no rainfall and resulting water shortages. Droughts often trigger environmental stresses and can create cycles of reinforcement impacting ecosystems and people [@Rodgers2023]. For example, California frequently experiences drought but the combination of prolonged dry spells and sustained high temperatures prevented the replenishment of cool fresh water to the Klamath River, which led to severe water shortages in 2003 and again from 2012 to 2014. These shortages affect agricultural areas like the Central Valley, which grows almonds, one of California's most important crops, with the state producing 80% of the world's almonds. These severe droughts, coupled with competition for limited freshwater resources, resulted in declining populations of [Chinook salmon](https://www.fisheries.noaa.gov/species/chinook-salmon) due to heat stress and gill rot disease disrupting the food supply for Klamath basin tribal groups [@guillen2002; @Bland2014].
+Precipitation deficits, or periods of below-average rainfall, can lead to drought, characterized by prolonged periods of little to no rainfall and resulting water shortages. Droughts often trigger environmental stresses and can create cycles of reinforcement impacting ecosystems and people [@Rodgers2023; @CDC2024]. For example, California frequently experiences drought but the combination of prolonged dry spells and sustained high temperatures prevented the replenishment of cool fresh water to the Klamath River, which led to severe water shortages in 2003 and again from 2012 to 2014. These shortages affect agricultural areas like the Central Valley, which grows almonds, one of California's most important crops, with the state producing 80% of the world's almonds. These severe droughts, coupled with competition for limited freshwater resources, resulted in declining populations of [Chinook salmon](https://www.fisheries.noaa.gov/species/chinook-salmon) due to heat stress and gill rot disease disrupting the food supply for Klamath basin tribal groups [@guillen2002; @Bland2014].
 
 ![](docs\images\watercycle_rc.jpg)[^1]
 
@@ -72,7 +72,7 @@ The **Water Security (WSIM-GLDAS) Monthly Grids, v1 (1948 - 2014)** The Water Se
 The **Water Security (WSIM-GLDAS) Monthly Grids dataset** used in this lesson is hosted by [NASA's Socioeconomic Data and Applications Center (SEDAC](https://sedac.ciesin.columbia.edu/)), one of several [Distributed Active Archive Centers (DAACs)](https://www.earthdata.nasa.gov/eosdis/daacs). SEDAC hosts a variety of data products including geospatial population data, human settlements and infrastructure, exposure and vulnerability to climate change, and satellite-based land use, air, and water quality data. In order to download data hosted by SEDAC, you are required to have a free NASA EarthData account. You can create an account here: [NASA EarthData](https://urs.earthdata.nasa.gov/users/new).
 :::
 
-For this lesson, we will work with the **WSIM-GLDAS dataset Composite Anomaly Twelve-Month Return Period NetCDF** file. This contains water deficit, surplus, and combined "Composite Anomaly" variables with an integration period of 12 months. Integration period represents the time period at which the anomaly values are averaged over. The 12-month integration averages water deficits (droughts), surpluses (floods), and combined (presence of droughts and floods) over a 12-month period ending with the month specified. We begin with the 12-month aggregation because this is a snapshot of anomalies for the entire year making it useful to get an understanding of a whole year; once we identify time periods of interest in the data, we can take a closer look using the 3-month or 1-month integration periods.
+For this lesson, we will work with the **WSIM-GLDAS dataset Composite Anomaly Twelve-Month Return Period NetCDF** file. This contains water deficit, surplus, and combined "Composite Anomaly" variables for an integration period of 12 months. Integration period represents the time period at which the anomaly values are averaged over. The 12-month integration averages water deficits (droughts), surpluses (floods), and combined (presence of droughts and floods) over a 12-month period ending with the month specified. We begin with the 12-month aggregation because this is a snapshot of anomalies for the entire year making it useful to get an understanding of a whole year; once we identify time periods of interest in the data, we can take a closer look using the 3-month or 1-month integration periods.
 
 We'll start by downloading the file directly from the SEDAC website. The [dataset documentation](https://sedac.ciesin.columbia.edu/downloads/docs/water/water-wsim-gldas-v1-documentation.pdf) describes the composite variables as key features of WSIM-GLDAS that combine "the return periods of multiple water parameters into composite indices of overall water surpluses and deficits [@isciences2022a]". The composite anomaly files present the data in terms of how often they occur; or a "return period." For example, a deficit return period of 25 signifies a drought so severe that we would only expect it to happen once every 25 years. Please go ahead and download the file.
 
@@ -268,7 +268,7 @@ From here we can clip the WSIM-GLDAS raster stack by using the stored boundary o
 ::: {.callout-tip style="color: #5a7a2b;"}
 ## Drought in the News
 
-Texas experienced a severe drought in 2011 that caused rivers to dry up and lakes to reach historic low levels [@StateImpact]. 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 [@NOAA2023].
+Texas experienced a severe drought in 2011 that caused rivers to dry up and lakes to reach historic low levels [@StateImpact; @carver2022]. 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 [@NOAA2023].
 
 It is estimated that the drought cost farmers and ranchers about \$8 billion in losses.[@Roeseler2011] 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 [@Roeseler2011].
 :::
@@ -435,7 +435,7 @@ We can get a more detailed look at these drought events by using the 1-month com
 ::: {.callout-tip style="color: #5a7a2b;"}
 ## Drought in the News
 
-The California drought of 2012-2014 was the worst in 1,200 years [@WHOI2014]. This drought caused problems for homeowners, and even conflicts between farmers and wild salmon! Governor Jerry Brown declared a drought emergency and called on residents to reduce water intake by 20%. Water use went up by 8% in May of 2014 compared to 2013, in places like coastal California and Los Angeles. Due to the water shortages, the state voted to fine water-wasters up to \$500 dollars. The drought also affected residents differently based on economic status. For example, in El Dorado County, located in a rural area east of Sacramento, residents were taking bucket showers and rural residents reported wells, which they rely on for fresh water, were drying up. The federal government eventually announced a \$9.7 million emergency drought aid for those areas [@Sanders2014].
+The California drought of 2012-2014 was the worst in 1,200 years [@WHOI2014]. This drought caused problems for homeowners, and even conflicts between farmers and wild salmon! Governor Jerry Brown declared a drought emergency and called on residents to reduce water intake by 20%. Water use went up by 8% in May of 2014 compared to 2013, in places like coastal California and Los Angeles. Due to the water shortages, the state voted to fine water-wasters up to \$500 dollars. The drought also affected residents differently based on economic status. For example, in El Dorado County, located in a rural area east of Sacramento, residents were taking bucket showers and rural residents reported wells, which they rely on for fresh water, were drying up. The federal government eventually announced a \$9.7 million emergency drought aid for those areas [@Sanders2014; @tortajada2017].
 
 Additionally, there were thousands of adult salmon struggling to survive in the Klamath River in Northern California, where water was running low and warm due to the diversion of river flow into the Central Valley, an agricultural area that grows almond trees. Almonds are one of California's most important crops, with the state producing 80% of the world's almonds. However, salmon, which migrate upstream, could get a disease called gill rot, which flourishes in warm water and already killed tens of thousands of Chinook in 2002. This disease was spreading through the salmon population again due to this water allocation, affecting local Native American tribes that rely on the fish [@Bland2014].
 :::
@@ -582,7 +582,7 @@ The histograms start to quantify what we saw in the time series maps. Whereas th
 
 To this point we've described the 2014 California drought by examining the state as a whole. Although we have a sense of what's happening in different cities or counties by looking at the maps, they do not provide quantitative summaries of local areas.
 
-Zonal statistics are one way to summarize the cells of a raster layer that lie within the boundary of another data layer (which may be in either raster or vector format). For example, aggregating deficit return periods with another raster depicting land cover type or a vector boundary (shapefile) of countries, states, or counties, will produce descriptive statistics by that new layer. These statistics could include the sum, mean, median, standard deviation, and range.
+[Zonal statistics](https://docs.qgis.org/2.18/en/docs/user_manual/plugins/plugins_zonal_statistics.html) are one way to summarize the cells of a raster layer that lie within the boundary of another data layer (which may be in either raster or vector format). For example, aggregating deficit return periods with another raster depicting land cover type or a vector boundary (shapefile) of countries, states, or counties, will produce descriptive statistics by that new layer. These statistics could include the sum, mean, median, standard deviation, and range.
 
 For this section, we begin by calculating the mean deficit return period within California counties. First, we retrieve a vector dataset of California counties from the geoBoundaries API. Since geoBoundaries does not attribute which counties belong to which states, we utilize a spatial operation called intersect to select only those counties in California.
 
@@ -975,4 +975,4 @@ Congratulations! Now you should be able to:
 
 In this lesson we explored the California drought of 2014. In our next lesson, we will examine near real-time flood data in California using the MODIS data product.
 
-[Lesson 2: Moderate Resolution Imaging Spectroradiometer (MODIS) Near-Real Time (NRT) flood data](https://ciesin-geospatial.github.io/TOPSTSCHOOL-water/m102-lance-modis-nrt-global-flood.html){.btn .btn-primary .btn role="button"}
+[Lesson 2: Moderate Resolution Imaging Spectroradiometer (MODIS) Near-Real Time (NRT) flood data](https://ciesin-geospatial.github.io/TOPSTSCHOOL-water/m102-lance-modis-nrt-global-flood.html){.btn .btn-primary .btn role="button"}

references/wsim-gldas-references.bib

@@ -223,30 +223,32 @@ @book{goodell2017
   edition = {Hardcover},
   langid = {en}
 }
-@article{tortajada2017,
-	title = {The California drought: Coping responses and resilience building},
-	author = {Tortajada, Cecilia and Kastner, Matthew J. and Buurman, Joost and Biswas, Asit K.},
-	year = {2017},
-	month = {12},
-	date = {2017-12},
-	journal = {Environmental Science & Policy},
-	pages = {97--113},
-	volume = {78},
-	doi = {10.1016/j.envsci.2017.09.012},
-	url = {http://dx.doi.org/10.1016/j.envsci.2017.09.012},
+
+@misc{CDC2024,
+	title = {Climate and Health: Precipitation Extremes},
+	author = {The Centers for Disease Control and Prevention (CDC)},
+	year = {2024},
+	month = {03},
+	date = {2024-03},
+	url = {https://www.cdc.gov/climate-health/php/effects/precipitation-extremes.html?CDC_AAref_Val=https://www.cdc.gov/climateandhealth/effects/precipitation_extremes.htm},
 	langid = {en}
 }
 
-@article{tortajada2017a,
-	title = {The California drought: Coping responses and resilience building},
-	author = {Tortajada, Cecilia and Kastner, Matthew J. and Buurman, Joost and Biswas, Asit K.},
-	year = {2017},
-	month = {12},
-	date = {2017-12},
-	journal = {Environmental Science & Policy},
-	pages = {97--113},
-	volume = {78},
-	doi = {10.1016/j.envsci.2017.09.012},
-	url = {http://dx.doi.org/10.1016/j.envsci.2017.09.012},
-	langid = {en}
+@article{carver2022,
+  title        = {West Texas farmers and ranchers fear the worst as drought, heat near 2011 records},
+  author       = {Jayme Lozano Carver},
+  year         = 2022,
+  month        = {June},
+  journal      = {The Texas Tribune},
+  langid= {en}
 }
+
+@article{tortajada2017,
+  title={The California drought: Coping responses and resilience building},
+  author={Cecilia Tortajada , Matthew J. Kastner , Joost Buurman , Asit K. Biswas},
+  journal={Environmental Science & Policy},
+  volume={78},
+  pages={97-113},
+  year={2017},
+  doi={https://doi.org/10.1016/j.envsci.2017.09.012},
+}