Surface Runoff Differences in California during El Niño periods

Background
     What kind of effects can warming sea temperatures in the Pacific bring to California? The answer: a lot.... First the warming of ocean water in the Pacific leads to a climatic event know as El Niño. El Niño is caused by the warming of ocean water near the South American coast which limits upwelling of cold water further increasing the ocean temperature. The warm water then evaporates and causes warmer climatic conditions throughout North American. Winters in California, during El Niño events, are significantly wetter and warmer. Essentially, El Niño brings a greater amount of precipitation to all regions of North America.
     Can the increase in precipitation caused by El Niño be observed by different methods? This study was completed to answer that very question.

Methodology
      We analyzed the runoff amounts measured by stream gauges in California to find an increase in runoff during El Niño events. Data for surface runoff was downloaded from the USGS Water Watch data archive for 1991-1998. The data is given in millimeters of runoff per watershed. The watersheds for California were extracted. Known El Niño years runoff amounts were subtracted by the previous year to show the change in runoff amount.

Results

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Conclusions
      Overall, for all of the El Niño years, there has been an increase in observed surface runoff amounts. This in large part is due to the higher precipitation amounts, but when you start factoring in the change in air temperature El Niño brings, there are more players in the game of runoff. California has glaciers in the Sierra Nevada, Cascade, and Trinity Alps mountain ranges. The warmer weather during the winter may not allow for the correct accumulation of snow and melts the ice contributing the further surface runoff.

NOAA SLOSH Model predictions for Hurricane Sandy, 2012

Background
     Hurricanes, or Atlantic tropical storms, are rapidly-rotating storm systems that all have a low-pressure center, strong winds, and a spiral arrangement. These meteorological beasts generally form over large bodies of warm water. The energy that drives hurricanes is derived from the evaporation of water from the ocean surface that cools and condenses into clouds. The strong winds and rotating storm is the result of partial conservation of angular momentum from Earth's rotation and the actual air flow. These storms bring heavy rains and storm surges where ever they decided to pass over.
     Ultimately (with no more physics thrown into the mix), hurricanes have the potential to leave a wake of destruction following their path. Because hurricanes cause so much damage and can displace so many people from their home, it is vital to prepare in the event that a hurricane forms.
      Luckily, our friends at the National Oceanic and Atmospheric Administration have a model that analyzes the potential storm surge effects of a hurricane. This grand model used for emergency preparation is called Sea, Lake, and Overland Surges from Hurricanes, or SLOSH (so fitting, right?). This model has three data ouputs: Maximum of Maximums (MoM's), Maximum Envelop of Water (MEOW), and Probabilistic Storm Surge. Each data output is used at different times before the hurricanes landfall.  MoM's is used over 120 hours before landfall and this is a historic record of all the largest envelops of water. The MEOW output is used 48-120 hours before landfall and is a estimate of how high the water will rise based on the incoming hurricane. Probabilistic Storm Surge is used less than 48 hours before landfall and give an areas probability of experiencing a storm surge of x amount.


Methodology
      Hurricane Sandy was used a a case study in analyzing SLOSH model outputs. The data was downloaded from the National Hurricane Center SLOSH website. Each output from the SLOSH model was displayed to show how each can be used in emergency preparation.

Results

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Conclusions
     The SLOSH model proves to be a valuable tool in analyzing how an incoming hurricane can affect a basin. As the time approaches landfall, the data becomes more and more detailed which makes preparation more feasible.

Population and Property Risk of Earthquakes in California

Background
     Earthquakes are the result of a sudden release of energy from the Earth's crust. Earthquakes occur naturally at the fault lines within the Earth's crust. The San Andreas fault in California is a major transform fault formed by the convergence/passing of the North American and Pacific Plate Tectonics. A transform fault is a type of strike-slip fault, where strike-slip faults are known to cause many and very strong earthquakes.
     The strength of earthquakes are measured by Richter Scale magnitude or just magnitude. The greater the earthquake, the higher the magnitude values. For instance, the strongest magnitude earthquake ever record along the San Andreas fault was 8.1 on the Richter Scale. This high frequency of strong earthquakes have the capability of causing large amounts of property damage and sometimes deaths within the population.
     The United States Geological Survey produces map called ShakeMaps that analyze the ground motion and shaking intensity from earthquakes in near-real time. These ShakeMaps do not analyze the populations risk to earthquakes, which is what was done in this study.

Methodology
     For this analysis we downloaded data from the USGS earthquake archive for the whole of California for the years of 2000 and 2010. County level census data for California was also downloaded from the American Factfinder application provided by the U.S. Census Bureau. Both data sets were imported into ArcGIS 10.1 geospatial software for analysis. The geographic locations of earthquakes events were fist displayed and exported as its own file. The information for the earthquakes was then spatially joined with the county level census data.
      The first analysis comprised of change in risk of population density to the mean magnitude of the county. Population density was calculated by normalizing population with area. The second analysis performed was on property damage based on magnitude by depth. The magnitude by depth was calculated by dividing individual earthquakes magnitude by the depth of occurrence and then averaging the values over the county.

Results

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Conclusions
      The map shows a high concentration of risk in areas where major cities are located in. Overall there seems to be more of an increase in risk from 2000 to 2010 in large part due to population growth.
       When completing the study a couple of assumptions were made. For the first analysis the first assumption is that population density would be uniform throughout the county and second that all of the individuals within the county would be affected at the same scale. The second analysis assumed that there is a correlation between how an earthquake affects materials on the surface and its depth.

Renewable Energy Production for Brazil

Background
     Green Energy. That is today's large push and "the way to the future". Although a large portion of our energy is produced from fossil fuels, a large amount of resources are being invested in renewable energy sources. There are two main sources of renewable energy that are dependent on environmental parameters; these energy sources are solar and wind energy. Wind energy is growing 20% annually and is widely used in the Asia, Europe, and the United States. Many other countries use wind power on a commercial basis. Solar energy is rapidly growing as well. With new technology being developed continuously for both solar and wind energy the potential for renewable energy grows as well.
     . There is a need to pinpoint geographic locations where it would be profitable to establish a renewable energy infrastructure first before this transition into renewable energy become feasible. Many countries possess the correct area for renewable energy but have not had those areas outline just yet. Brazil is a country that is developing renewable energy infrastructure. It's large coastline and large input of solar radiative energy make it a well situated area for renewable energy.

Methods Used
     Open Energy Information (OpenEI) is a website for decision makers and researchers where energy data is available on a county basis. Wind and solar energy for Brazil was downloaded at a 40 km resolution for this analysis from the OpenEI database. The wind energy data came formatted in units of Watts per meter squared. The solar energy data used was formatted in units average Kilowatt hours per m squared per day. This needed to be converted to watts per meter squared. Once converted to the correct units, an assumption that 50% of the area will be used by infrastructure or unusable and was accounted for.
     Areas that produced over 300 watts per meter squared were deemed beneficial to build renewable energy infrastructure for Brazil. These areas are shown below.

Results

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Conclusions
     Brazil has a large amount of area that can be used for solar energy, but a large part of that area is the Amazon rain forest. Brazil also has a considerable amount of area that is beneficial to build wind farms, a lot of the area beneficial for wind lie near the coast. This makes sense because off-shore winds can be very powerful.
     Compared to Brazil's total energy consumption, there will not be enough energy produced if the country were to just rely on renewable energy. This is not to say that statement will still hold true with the development of new and better technology. These values for energy are grossly oversimplificated and may not be entirely accurate as well.

Cholera Cases in Asia from 1950-2011

Background
     Cholera is an infectious disease of the small intestines that is caused by the bacterium Vibrio cholerae. This disease is a waterborne and foodborne illness, where water and food is contaminated by feces of an infected  individual. The symptoms of cholera include:  watery diarrhea and vomiting. These symptoms cause dehydration and an imbalance in electrolytes for the infected individual. Primary treatment for the disease is rehydration therapy. Severe case of the disease can lead to hospitalization and even death.
    The cholera disease is monitored because of the potentially dangerous effects of cholera to populations and its ability to be spread easily. The World Health Organization (WHO) collects data for diseases such as cholera. This data can be used to analyze the trends in cholera occurrences throughout space and time, and this exact analysis was done!

Methodology
     Data provided by the WHO for the continent of Asia was downloaded. This data contained the number of cholera occurrences, cholera deaths, and the death rate for each country form 1950-2011. This data is provided as tabular form. To analyze geographic trends, the before mentioned data was combined by year to provide total occurrences, total deaths, and average death rate for each country and then joined with county shapefiles. The time analyses combined the cholera data for total occurrences, total deaths, and average death rate for the entire continent to display the trends through time.

Results

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Conclusions
     Cholera still affects populations in developing countries or countries that have poor sanitation practices. Logically thinking, cholera was a big problem in the past. That is clearly seen in the graph where the trend has a negative slope approaching current times. Geographically, India is shown to be consistent in high total occurrences, total deaths, and death rate. This could potentially be because India is over populated and lacks proper sanitation in some portions of the county.
      The data provided by the WHO is all reported data from individual countries. There could possibly be missing data and the whole picture for cholera in Asia is not displayed correctly. Regardless, the fight against cholera is shown to be improving and that is a good outlook.

Side Note
     Cholera and GIS have a little history together...one the first GIS studies completed was done by an English physician named John Snow in 1854. There was a large cholera outbreak in London and he sought to figure out why (cholera back then was thought to be spread by "bad" air). He created a dot map of cholera cases around a water pump to illustrate the clustering of cases. He also used statistics to link cholera infection to water quality. John Snow's cholera map can be found here.

Sea Level Rise Along the United States Gulf Coast

Background
     Climate change is a ever present aspect of our ever changing world. With climate change comes a multitude of effects. One, namely, is the melting of ice. According to the United States Geological Survey, 69% of all the Earth's freshwater which equals to about 1.75% of all Earth's water is located in the ice caps, glaciers, and permanent snow. The melting of these reservoirs will result in a significant increase of the worlds water supply. So, the question is, where would all of that water go? The ocean that is. If the ice reservoirs begin to melt, even a small amount, all of the excess water will flow into the ocean.
     Global oceans temperatures are on the rise along with air temperatures. This raise in water temperature changes the density and volume of the water, making it take up more space. This chemical phenomenon along with the input of extra water will raise the sea level.
     This study analysis (1) the extent to which the sea with grow to in the event of a 1 m, 3 m, and 6 m rise along the Gulf Coast of the United States and (2) the number of people per state along the Gulf Coast that will be displaced from their homes.

Methodology
     Two main data sets were used for this analysis, sea level rise and population. The sea level rise data used was provided and downloaded from Center for Remote Sensing of Ice Sheets (CReSIS) sea level rise maps. Population data was acquired from the United States Census Bureau's fact finder application.  Population data for affected area was calculated by first, extracting the counties that will be affected by 1 m, 3 m, and 6 m sea level rise. The population density was calculated for each county by normalizing population of the county with total county area. Number of individuals affected by each level of sea rise was found by multiplying the population density of each county by the area affected for the county by sea level rise, assuming that population density was uniform throughout the entire county.
     
Results

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Conclusions
     The land affected by each level of sea rise is displayed. Using visual interpretation, Florida has the greatest amount of land affected by sea rise, but a significant amount of the land lost is in the Everglades national park. Florida was also found the have the largest amount of people displaced by sea level rise. This is partly in large part due to the fact that a good amount of Florida's large cities, such as Miami, Panama City, and Pensacola, are located near the shore.

Roads and Lands Affect by 100 and 500 year Floods in Suffolk County, MA

Background
     Flood zones are geographic areas defined by the Federal Emergency Management Agency (FEMA) by varying levels of flood risk and flooding type. Special flood hazard areas are areas subject to a 1% annual chance to floods, or 100 year flood zones. Moderate and Minimal risk areas have a 0.2% chance of an annual flood and are also know as 500 year flood zones.
     Boston, MA is located within Suffolk County. Its close proximity to the ocean make the Boston area more vulnerable to flooding. Mapping areas such as roads that are found within flood zones can help prepare for such events.

Methodology
     Data for the analysis was provided and downloaded from MassGIS Datalayer webpage. The data for 100 year and 500 year flood zones were dissolved and clipped to Suffolk County. The area of land and road length affected by the two flood zones was then calculated.

Results

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Conclusions
Suffolk County has a greater scale of road length with a 1% chance of annual flooding but a greater scale of land has a 0.2% chance of annual flooding. The Boston Harbor area of Suffolk County has a greater extent of 500 year flood zones than any other area in the county. This is in large part due to its close proximity to the ocean.

Human Appropriation as a Percent of Net Primary Production for the World

Background
     Net Primary Production (NPP) is a measure of rate at which plants in an ecosystem produce useful chemical energy. This measurement is used to monitor the productivity of an ecological system. The units for NPP in terrestrial ecosystems is generally mass of carbon per unit area per year (g C m ^-2 yr^-1).
     Human Appropriation of Net Primary Production (HANPP) is a measure of the consumption of NPP by people. The global HANPP is 23.8% of potential vegetation. The disproportionate use limits the energy availability to other species, having impacts on biodiversity, carbon flows, and water and energy.

Methodology
     The methodology used for this analysis includes simple calculations from available data. Data was downloaded from Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. The datasets used include:

• Human Appropriation of Net Primary Production
• Net Primary Production

The Human Appropriation as a Percent of Net Primary Production was calculated by dividing the two data sets.

Results

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Conclusions
     The areas the have the highest Human Appropriation of Net Primary Production include areas the either have high HANPP or a low NPP, these areas are shown in red. The most notable areas are the eastern part of China, northern India, and western part of the Arabian Peninsula. All of these areas have high population density's and relatively low NPP.