Assessing Flood Risk Zones in India

Introduction & Study Area

A monsoon is a seasonal change of winds in several tropical areas of the world.

Monsoons are usually accompanied by precipitation because it is a change of wind

direction from ocean to land instead of land to ocean as it is normally. (McGregor, 121)

One of the more known monsoons is the Indian monsoon. The Indian monsoon brings a

lot of rain to the country but is always variable in the amount and distribution of rain for

the country. The monsoons variability can affect Indiaʼs people and economy because

many people rely on this water supply for their agriculture which will be devastated if

they do not get it. High amounts of rain can also bring flood risks to several areas in

India. (BBC)

Assessing possible flood risk areas can be helpful for these people so that they

can take safety precautions to avoid damages and death. People living near streams

and rivers may have higher chances of being affected which is not good because more

people tend to live here due to the availability of water, a required resource for survival.

India is located in Asia and has the worlds second largest population. (Population of All

Countries of the World Largest to Smallest) India also contains several rivers and

streams, the largest and more known of which is the Ganges River located in the North.

The Southern portion of India is surrounded by the ocean while the northern portion

borders several countries and part of the Himalaya Mountains.

Methodology

In order to preform an analysis on possible flood risk zones in India I first took a

look at several aspects which may affect the risk. First I decided to do an interpolation of

the average precipitation values of July from 1961-1990. The first step for my

interpolation was finding data for precipitation in India. Once I found the data, I decided

to use the averages for July because it seems to be the month with the most activity in

terms of rain. I made an excel sheet for several cities throughout India trying to reach

the farthest extents I could. I then researched their latitudes and longitudes in decimal

degrees to add the data into arc-map. Finally I added in the averaged precipitation

values. Once I added the data in the X,Y format, I used the Spatial Analyst tool to

perform the interpolation using the IDW method.

My second step in assessing possible flood zones was looking at the elevation of

India. I downloaded a raster DEM from Diva-GIS which I displayed using the symbology

tab under properties. I decided to display the DEM under 6 classes in the geometric

classification to better view the elevation and understand the elevation of areas. Areas

with lower elevation would be at higher risk than those in lower elevations because

water flows downhill. This will be taken into account later on in the analysis.

The third aspect I took a look at was the accumulation flow of streams and rivers

in India. To preform this I used the symbology classified tab and used the flow

accumulation of streams as the value to be classified. I used a total of 9 classes ranging

from blue for low accumulation to red for high accumulation with yellow as the

intermediate. I then found the threshold (120,000 cells) for which the accumulation

starts to be significant. I then selected these lines and created a shape-file to display the

Med-High Accumulation so that I could later overlay them on other maps.

The fourth step I took in assessing flood risk zones was analyzing the drainage

basins to see which areas may be affected. I decided to first merge all the basins to see

the relationship between the merged basins and the rivers. I then overlaid the shape-file

I made for the significant accumulation flow streams on the layer of individual basins to

later do a select by location. I selected the individual basins that intersect with the

accumulation layer and exported them into another shape-file for the High Risk Basin

Accumulation layer.

The final step of my analysis was creating a map displaying the population and

which areas are at a higher risk for floods. I obtained a population raster data-set which

I classified into 7 classes in the geometric classification. I then overlaid the significant

flow accumulation layer followed by the High Risk Basin Accumulation layer which I

hollowed out so we can see the relative population within these areas. I then used the

extract by mask spatial analyst tool to extract the population information of the areas

within the High Risk Basin Accumulation layer to find out statistics of the population

living within the risk zones.

Results

Below are the five maps I produced showing the different information I derived

from my data sets. A brief analysis on the information displayed is given for each map.

The first map is the interpolation of the rain averages for July from 1961-1990. As

noted before, I chose this month because it shows the most activity during the monsoon

season of India. The map shows us that there is more rain in the southwestern portion

of India which makes sense because this is where a portion of the winds come, bringing

the rain with it since it is located next to the ocean.(McGregor, 125) Here we can see

the streams that are located within the areas of higher precipitation and how they may

affect the flood risk based on their location.

The next map is the elevation map with the Med-High Flow Accumulation

streams layer. Here we can see the areas that have a higher chance of being flooded

based on the accumulation flow and the elevation. Areas in lower elevations are more

susceptible to flooding because water flows downhill. Here we can see that the streams

with the higher possible accumulations are located in the lower elevations partly

because of this reason. We can also see how the elevation changes gradually getting

lower as it moves towards the ocean, the same direction that the rivers flow.

 The third map shows the flow accumulation of the streams. Here we can see

which streams have a higher accumulation of water flow as they flow downstream. The

largest river with a significant amount of flow accumulation is the Ganges River in the

North which makes sense because it has many tributaries flowing into it. One of the

streams connected to the Ganges also has a significant amount of flow accumulation

risk, the Ghaghara. To the West of the Ganges, the Brahmaputra is another river which

may pose a threat for flooding in India as well as a small portion of the Luni located on

the eastern coast of India. Moving South from the Ganges, the Mahanadi, the Godavari,

and the Krishna also have a significant amount of accumulation flow which may pose a

threat of flooding for these areas as well.

The fourth map shows us the individual drainage basins that are at a higher risk

of flooding during the monsoon season because they are within the path of these

streams and rivers. Most of the areas at risk are located on the Northern and Western

part of India which when looking at the fifth map, population, is where a majority of the

population lives.

According to the statistics calculated of the population living within the basins

with higher flood risk is approximately 1,309,897 people. This makes sense because a

lot of people live nearby water systems because they depend on the water for survival.

Conclusion

In conclusion, according to the data and my analysis, several areas in India have

a risk of flooding due to their elevation, flow accumulation, and drainage basins.

Together, these areas have a population of about 1,309,897 people which are at greater

risk of having damage to their property, injuries, and even death due to flooding during

the monsoon season. Many of these people live near these bodies of water because

they depend on the water for agriculture and other needs so something should be done

to better protect these people from flooding hazards. Relocation of the population

seems like a good idea, but these people would just over extract ground water or create

more environmental degradation in other areas. More dams or better management of

dams would also be a good idea, but it may also bring more conflicts because the

people upstream would have more control over the water in dryer periods and the water

would not be equally distributed for those living downstream where the danger is. At the

moment, it seems that the best thing to do is just have better management of the

existing dams.

References

"Geography Facts about India." Geography Facts about India. N.p., n.d. Web. 19 Sept. 2012.

<http://www.facts-about-india.com/Geography-facts-about-india.php>.

"India Monsoon Floods Kill 34 in Uttarakhand." BBC News. BBC, 08 June 2012. Web. 19 Sept.

2012. <http://www.bbc.co.uk/news/world-asia-india-19144580>.

McGregor, Glenn R., and S. Nieuwolt. Tropical Climatology: An Introduction to the Climates of

the Low Latitudes. New York: Wiley, 1998. Print.

"Population of All Countries of the World Largest to Smallest." Population of All Countries of

the World Largest to Smallest. N.p., n.d. Web. 19 Sept. 2012.

<http://www.worldatlas.com/aatlas/populations/ctypopls.htm>.

"Providing Science and Imagery to Better Understand Our Earth." USGS/EROS âÿº -Home.

N.p., n.d. Web. 19 Sept. 2012. <http://eros.usgs.gov/>.

"Spatial Data Download." DIVA-GIS. N.p., n.d. Web. 19 Sept. 2012.

<http://www.diva-gis.org/datadown>.

"World Weather Information Service - India." World Weather Information Service - India. N.p.,

n.d. Web. 19 Sept. 2012. <http://www.worldweather.org/066/m066.htm>.

Drainage Basin Information & Shapefile, Instructor Provided, Jida Wang Sept. 2012

Station Fire Research

  

So. CA Station Fire 2009: Effect on Water Systems, Drains, and Debris

The Station Fire of Southern California was a big fire in Los Angeles County that started on August 26, 2009 in the Angeles National Forest near La Canada Flintridge. The fire destroyed  approximately 161,000 acres in all, 154,000 acres of which served as Forest Service systems. (ANGELES NF - STATION FIRE Burned Area Emergency Response) This fire burned a span of just over a week but damaged much of the Angeles National Forest and nearby water systems which may have caused danger of major floods, mud slides, and debris flows. My thematic map shows the extent of the fire on August 26, 2009, and September 1, 2009. The map also displays poly-lines and polygons of the bodies of water in the area and the direction the water flows, as well as the debris basin and storm drain points nearby. This will help us analyze and describe  the effects the fire had on the nearby water systems and debris. USGS also has interactive maps which shows how the fire has affected the area and the possibility of these problems occurring. “Much of the watershed was severely burned by the Station Fire of October, 2009. Because of this, downstream areas are at high risk for flooding or debris flows.” (USGS California Water Science Center)

Fire affects everything in its path, including the plants and soils, which in turn can have an effect on the water. In this case, the fire destroyed much of the plants and trees that were in its path, but it also opened up many seeds that need fire to open. This allows these trees to grow again, but the environment the fire created may not be the best for the plants. When a fire burns through an area, it can make the soil more fertile by adding many nutrients, but it can also have the opposite effect where it destroys the soil. In the case of the Station Fire, some areas of soil have become “hydrophobic” which can have a negative effect not only for the plants, but for humans as well. “Due to the lack of ground cover in Station Fire burn areas, as well as this hydrophobic layer, soils in the burn area are substantially more unstable in post-fire conditions than pre-fire. This change in the affected environment introduces increased risk to water quality, as well as risk to human life, property, and infrastructure associated with potential flood and debris flow damage.” (4.7.2, Hydrology and Water Quality,) The effect the fire had on the soil can be one cause of the many floods and debris flows we have seen since the fire burned in 2009. 

As we can see in the slope map, the area south of the final fire extent seems to be the steepest, this may cause the water in the Big Tujunga Canyon, Tujunga Wash, and the nearby streams to run in the direction of Hansen Lake in the West, and the West Fork San Gabriel River and nearby streams to run into the Cogswell Reservoir on the East. We can also see the greater amount of storm drains and debris basins in the southernmost part of the fire extent. The effect of the fire on the soil may have caused it to give way easier, affecting the nearby bodies of water to overflow or jam with either water (floods), mud, or debris during the rain season. Depending on the extent of the fire, this problem could be dealt with quickly and easily, or over a long period of time. Unfortunately, since the Station Fire caused a great amount of damage, this problem will have long term effects on the nearby areas. “The Forest Service and LA County are looking at the full scope and scale of the situation. The increased potential for floods and debris flows are not just a one-year concern; affected communities could be impacted 3-5 years until the burned area fully recovers.” (ANGELES NF - STATION FIRE Burned Area Emergency Response) One manner in which the county is trying to fix this is with debris basins all along the Southern part of the fire. 

Debris basins are an essential part of the clean up of this fire since they help collect all the debris while allowing water to flow to prevent floods. “Debris Basins are key components of the Los Angeles County Flood Control District’s flood control system. Typically located at the mouths of canyons, debris basins capture sediment, gravel, boulders, and vegetative debris that are washed out of the canyons during storms but allow water to flow into the downstream storm drain system, thereby protecting drainage systems and communities in lower-lying watershed areas from possible flooding and property damage.” (County of Los Angeles Department of Public Works) The debris collects in these areas until the county collects and disposes it. Since the fire was very bug, the amount of debris it has created is also quite large. In fact, “The District removed close to 1.3 million cubic yards of material from its debris retention facilities. According to storm season projections, the District will need to dispose of an estimated 1.2 million cubic yards of material each storm season for the next 3-5 years, as watersheds affected by the recent wildfires recover.” (Department of Public Works Sediment Management) Debris is just one of the many problems the fire has created for the residents and the county to deal with. 

In conclusion, the Southern California Station Fire in the summer of 2009 has greatly affected the surrounding systems of water by creating unstable soils which led to mud flows, debris, and floods. The county is doing as much as it can to help clean up the mess by cleaning out the debris basins which have collected much of the debris. This problem, unfortunately, will take time to fully clear up since the size of the fire was really big. The slope in the southernmost part of the fire extent is also contributing to much of the debris flowing into nearby streams and bodies of water, but there is nothing the county can do about it. As for the future, we will have to wait and see how the storms affect the amount of floods and debris in the area.

Map shape-files and Information obtained through:

Debris Basin: Hydrography “Debris Basin (Point Locations)” Los Angeles County Enterprise GIS: Public Works Data  http://egis3.lacounty.gov/eGIS/ 

Flow Direction: Hydrography “Watershed Sub Basins- Water Flow Directions” Los Angeles County Enterprise GIS: Public Works Data  http://egis3.lacounty.gov/eGIS/ 

Intermittent Streams/Creeks: Los Angeles County Rivers and Streams (Polyline, 2008) UCLA Mapshare http://gis.ats.ucla.edu

Lakes/Bodies of Water: U.S. National Atlas Water Feature Areas for Los Angeles County (Polygon, 2006) UCLA Mapshare http://gis.ats.ucla.edu

Storm Drains: Utilities “Storm Drain Annotation Points” Los Angeles County Enterprise GIS: Public Works Data  http://egis3.lacounty.gov/eGIS/

Streams/Rivers: U.S. National Atlas Water Feature Lines for Los Angeles County (Polyline, 2006) UCLA Mapshare http://gis.ats.ucla.edu/ 

Works Cited

Burned Area Emergency Response. ANGELES NF - STATION FIRE Burned Area Emergency Response · BAER   Implementation. Rep., 04 Nov. 2009. Web. 7 June 2011. <http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev3_020019.pdf>.

"Sediment Management." Dpw.lacounty.gov. Los Angeles County. Web. 07 June 2011. <http://dpw.lacounty.gov/lacfcd/sediment/dspFeedbackResponse.aspx>.

Tehachapi Renewable Transmission Project , comp. Hydrology and Water Quality . Issue brief no. 4.7. 2011. Web. 05 June 2011. <http://docs.cpuc.ca.gov/environ/tehachapi_renewables/TRTP-SDSEIS/SDEIS/4.7_Hydro-WQ.pdf>.

USA. LA County. Department of Public Works. Debris Basin Point Locations. 2011. Web. 7 June 2011. <http://gis.dpw.lacounty.gov/oia/metadata.cfm?path=debrisbasin_pt.htm&zip=Debris%20Basin%20(Point%20Locations).zip>.

"USGS California Water Science Center Information Requests." USGS California Water Science Center. Ed. USGS. 04   Oct. 2010. Web. 07 June 2011. <http://ca.water.usgs.gov/webcams/>.

Site Analysis

This lab consisted of preforming spatial analysis to find the easiest route

for access to a new proposed school in the town of Stowe, Vermont. The town of Stowe,

Vermont has had an increase of population due to many families with children moving in

due to all the recreational activities it offers. The great influx of children is causing

overpopulation in the current schools and therefore a new school is being proposed.

Our first step is to find the best location for this new school. We must first gather our

data and open arc-catalog to use the spatial analyst tool functions. Once we have

displayed our data we can begin to manipulate the data to do a suitable analysis in the

model-builder feature under the spatial analyst tools.

Model builder allows us to display our data in an organized manner and later

calculate and run functions such as distances, etc. Model builder also allows us to

import and change information from raster to vector data-sets so we can make better

use of them in arc-map. We will locate the most suitable place for the new school based

on our land-use, elevation, recreational sites, schools, slope, and relative distances.

Since we do not have all these data-sets, we will have to use model builder to derive

some such as the slope and distances between points. After deriving this information we

will be able to give them a value from 1 to 10 according to their influence on the best

new location. After doing so, we will end up with a map displaying the suitable locations

for the new school. After carefully viewing the most suitable locations and selecting the

one spot that is best, we will then have to find the best access route to get to the new

school.

In order to find the most accessible way to the new school we must preform a

cost distance analysis using the cost distance tool. To preform the cost distance

analysis I will use the cost and source data-sets. I will assign values to the land types

according to its accessibility, for instance water will be 10 while barren land will be 2

because it is easier to build a road through there. When completed, we will end up with

a raster data-set of the least costly route which you will then have to convert to a poly

line feature.

Once all these steps are complete I can proceed to making my final map. In my

final map I have added the location of the new school site, along with the new route. We

can see the types of land the new route traverses along with the nearby roads. We can

see that this new route uses the shortest distance through the roads to save people

time and it will also save money because no new extensive road work will have to be

done. Spatial analysis in terms of suitability analysis and cost analysis is very useful

when planning out new locations for both public and private places. It also allows us to

find the most efficient and accessible way to get from one point to another. Although

spatial analysis has many advantages, it can also cause some problems because it

cannot cover environmental things such as land depths, or account for what may

happen if that area is changed. For instance, if a gas company was looking for a place

to add a gas station they may find a good location but it may have an underground

water system which can be ruined if the gas station was placed there. Another example

is if a mall was looking for a place and the best suitable place was in a section of the

forest because of space, but if the forest is changed it can bring problems to the animals

and ecosystem. We must also consider how up to date the data is that we are using to

create the analysis because things do change over time. Despite these factors,

suitability analysis seems to be good for planning and development.

Spatial Interpolation

This lab uses the process of spatial interpolation and its possible uses. As part of the lab I used two methods of spatial interpolation to help in the comparison and assessment of precipitation level changes between the season to date and the normal precipitation. Spatial interpolation helps us find out the unknown values of rainfall with the already known values. I decided I would use the method of Inverse Distance Weighted (IDW) and Kriging since both follow different paths when interpolating. IDW uses points to calculate weights and distances for the unknown areas of precipitation we are trying to calculate. Kriging has a more statistical approach using relationships in samples to calculate

the weighted average. Although both help us find the unknown values with those that we do, they give us different results due to the different methods they preform.

The first map shown displays the results of the IDW. Here we can see gradual changes from high to low going towards the West. The second map shows us the results of the Kriging method and how the changes are more gradual since it has slight change. When comparing the overall changes, there is more change in the Kriging method than in the IDW method probably because there is more change in the Season to Date map compared to the Normal Precipitation map, thus establishing a greater change when taking the difference between the two. Overall we can see how there tends to be more rain in the eastern part of the county than in the western portion, and there are about equal amounts of rain

going North and South from there. We can also see that there has been less rain this year than average possibly signifying a drought because there are a lot of areas in green. This may, however, change once we reach the wet months closer to the winter. 

Spatial Analysis

This tutorial went through methods of how to work through a research question. In this case, the objective was to find potential wastewater plant sites. We had to evaluate several characteristics such as elevation and distance to nearby areas to avoid flood problems, etc.

Geocoding

This lab consisted of using our new geocoding skills in a research question. I decided to

research the 25 closest libraries to my elementary, Third Street Elementary School, and

then compare the amount of libraries to the population of children 5-17 in that area. I

first made a spreadsheet of the 25 closest Los Angeles public libraries closest to 201 S.

June St. 90004. I then went through the process of geocoding and overlaying the points

in my map on the streets layer. I noticed that there were a lot more libraries towards the

East but not as many in the West. I then decided to obtain some census information to

see the demographics of the area. I mapped amount of children ages 5-17 per polygon

and discovered that there are more children living in the East than there are living in the

West. Although there are more libraries in the East because there are more children in

the East, the majority of the libraries are in the Northern region, but more children are in

the Southern region. This may have to do partly with the income of the area. Areas

more South tend to be of lower income than those in the North. Geocoding allowed me

to display where each library is located along with labeling each library.

Cartography: Natural Disasters

This is a final map of some natural disasters in the US. After obtaining my shapefiles I was able to manipulate the data through the joins and relates feature in Arcmap to better display and understand the statistics. Here we can see that almost every region of the US suffers from some sort of natural disaster whether it is earthquakes and volcanoes or hurricanes and tornadoes.

Importance of Normalization: Worldwide Alcohol Consumption

This lab consisted of mapping worldwide alcohol consumption data. The first of the two maps shows the worldwide total consumption of alcohol in millions of liters per country. The second map is normalized per person in each country, calculated in liters per person. Each map displays different colors per class, varying from lighter colors for the lower values to darker colors for the higher values. I used the “Color Brewer” website (http://colorbrewer2.org/) for the first map and chose a 5 class, sequential, multi-hue CMYK scheme which ranges from yellow, to green, to blue. The second uses the standard colors which arcmap had set for the Jenks classification. I decided to keep it since it ranges from a yellow, to an orangish-tan, to brown, the typical colors of beer, an alcoholic drink. Both color schemes have colors greatly distributed to clearly show the differences between classes which can be important when looking at a world map with countries varying in size. 

In creating my maps, I had to choose a classification to map the data in a way that makes sense. When choosing a classification, I took the type of data and the type  of distribution into account. I decided that the type of classification that would best distribute the intervals in each class was the Natural Breaks aka Jenks classification because “the purpose of natural breaks is to minimize differences between data values in the same class and maximize differences between classes.” (Slocum: Data Classification, pg. 84)  Jenks is typically a good type of classification for many types of distributions and data types and helps tell a “good” story of the information being mapped, this is probably why arcmap has it as the default classification. 

Taking these things into consideration, I can conclude that both maps are true for what they are trying to display, but they can also be misleading depending on the information the author is trying to convey to the reader. For example, if someone were to look at the first map and tried to figure out which country drinks the most, they would assume it was China because it is the darkest country on the map. This is true because as a whole, they drink the most alcohol, but this is only because they have a big population. However, if we take a look at the second map we will see that China is not the darkest country on the map anymore. This is because the second map is a normalized map of alcohol consumption per capita. The same can be said for India due to its big population. Personally I prefer the second map because I believe it tells a better story of alcohol consumption per country. When people think of alcohol consumption, they usually think of how much alcohol is being consumed per person not so much the country because we generally do not really know how big or small the population is per country. Another reason I’d prefer the second map is because it is easier to understand liters/person as opposed to the millions of liters/country in the first map. Not everyone enjoys math which would make the first map’s units a nightmare to try to visualize. Overall, the second map is just easier to understand and probably easier to relate to when trying to understand the data. 

Graphic Design: Brazil

All text and information from Brazil obtained from Wikipedia

http://en.wikipedia.org/wiki/Brazil