Briana Seapy
UEP 232; Parmenter
5/6/2011
Final Project Description: Water Resource Vulnerability
For my culminating project, I undertook a vulnerability analysis on water resources within the Gunnison
River Basin (USGS HUC Codes 140200-01 through 06), a sub-watershed of the Colorado River Basin. The
goal of the analysis was to explore anthropogenic and environmental stressors on the quantity of
freshwater resources in this sub-watershed and to investigate GIS techniques for combining
vulnerability criteria. The project attempts to identify water districts within the Gunnison River Basin
that are prone to shortcomings in water resource quantity (i.e. Drinking water, irrigation water,
industrial water, etc.) ; Water districts are generally defined by micro watersheds or drainage systems.
Being able to identify specific water districts as highly vulnerable to insufficient water quantity can
efficiently direct future water-management planning and allow existing populations to appropriately
and spatially focus their water-management strategies in order to avoid water shortages in the case of
disasters, low precipitation years, or booming populations. This analysis integrates a series of 6
vulnerability indicators (a.k.a. stressors) in order to predict which districts face the highest water
resource vulnerability: precipitation, potential evapotranspiration, population change, water demand,
reservoir storage, and canopy cover. Each district received a vulnerability indicator score (1best<->5worst) for each factor, and the culmination of these scores into one final weighted average
distinguishes levels of water resource vulnerability between Gunnison River Basin water districts.
Data Sources
(Projection: UTM, NAD 83, Zone 13, Meters)
Data layer
Source
Scale/
Context
Year
(ground
condition)
Web Sites
Canopy Cover (raster)
MRLC/NLCD
30m:30m grid
2001
http://www.mrlc.gov/
http://www.epa.gov/esd/uwr_browser/p
ages/uwr_metadata_nlcd.htm
Dams (point)
Water Use (Tabular)
Reservoir Capacity (Tabular)
Lakes/Rivers (shape)
USGS/Colorado
Division of
Water
Resources
2000
2000
2000
1996
http://water.state.co.us/DataMaps/GISan
dMaps/Pages/GISDownloads.aspx
1:100,000
Potential Evapotranspiration (considers
heat, relative humidity, and wind)
(raster)
CGIAR
Mean Annual
PET (mm/year)
(1950-2000)
http://csi.cgiar.org/Aridity/
http://csi.cgiar.org/Aridity/Global_Aridity
_PET_Methodolgy.asp
CO Water Divisions(polygon)
CO Water Districts(polygon)
Streamgages
Colorado
Division of
Water
Resources
Based Primarily
on Macro and
Micro stream
drainage
systems
2011
http://water.state.co.us/Home/Pages/def
ault.aspx
Precipitation
(raster)
PRISM
Mean
Annual
(1971-2000)
http://www.prism.oregonstate.edu/
http://www.prism.oregonstate.edu/docs/
1
Precipitation
(mmx100/year)
Colorado Counties
Colorado Block Groups
Population Centroids
2000, 2010
Census--TIGER;
CO Department
of Local Affairs
meta/ppt_30s_meta.htm
2000, 2010
http://dola.colorado.gov/gis/tiger2010.ht
ml
http://factfinder2.census.gov/main.html
Data Preparation & Methodology
General Plan of Action
In order to consider each indicator in a final analysis, it was first necessary to aggregate all tabular and
relevant data to the water district level. To do so each water resource stressor was spatially defined in
ArcGIS and then designated an appropriate rating on scale of 1-5. Each district received a number for
each of the 6 indicator categories, and all those numbers were weighted and summed to determine a
final vulnerability rating that was then spatially displayed in ArcMap (1=lowest vulnerability, 5=highest
vulnerability).
1) Download all necessary data layers & tabular information
2) Ensure that all layers are spatially defined; re-project layers to NAD 1983 UTM zone 13 (meters)
3) Define water-resource stressors as they correspond with GIS layers
4) Process and join tabular data to ARC GIS layers (See ‘Data Processing’ below)
5) Use GIS tools to spatially define each stressor (See ‘GIS Analysis’ below)
6) Decide how much weight to assign to each stressor
7) Standardize all stressors into vector layers at the Water District level and reclassify stressors based on
a vulnerability scale of 1-5 (1=lowest vulnerability, 5=highest vulnerability)
8) Calculating a weighted average of all vulnerability scores for each water district in order to visually
display level of overall vulnerability
Data Processing
1) Ensure that all layers are spatially defined; Re-project layers to NAD 1983 UTM zone 13 (meters)
2) Clip larger layers to the Gunnison River Watershed and data-export as individual layers
3) Aggregate dams by water district and calculate reservoir storage capacity:
Add dam count as new field to Water District attribute table and calculate reservoir capacity for each
district using the summation tool → divide total normal reservoir storage by area of district to create a
standardized statistic and add as field to Water District attribute table
4) Aggregate USGS stream-gage stations by water district:
Add count as new field to Water District attribute table → determine existence of any non-USGS water2
monitoring stations and add these to the tabular count → divide number of stations by area of district
to create a standardized statistic and add as field to Water District attribute table
5) Aggregate block population centroids to water district level:
Use spatial join (points to polygons) → add population as new field to Water District attribute table
6) Join CO Water Demand tabular information to counties layer using FIPS field
7) Recalculate water demand to determine demand per capita by water district:
Using excel and Arc GIS, calculate the population proportion of each county (using population centroids
and polygon intersects (see image below) between county and water district) that fall within each water
district → multiply these population proportions by entire county water-usage and then sum by water
district to determine approximate water district usage → finally divide total district usage by water
district population (as previously counted) to determine overall water usage per capita by district → add
field to water district attribute table
8) Recalculate %Population change by water district
Use similar process as in step 7 → sum populations by water district for beginning year and end year →
perform percent change calculations [(end year-beginning year)/ beginning year]--> add as a new field
to water district attribute table
GIS Analysis (by vulnerability factors)
1) Clip raster layers to watershed using the raster calculator → perform zoning statistics on all raster
layers to aggregate information to the water district level (this is an intermediate step to get to final
analysis--it is not visually displayed)
2) For final calculation purposes, reclassify raster layers (Canopy Cover, Precipitation, and Potential
Evapotranspiration) into quantiles (sparse, sparse-moderate, moderate, moderate-dense, dense or low,
low-mid, medium, mid-high, high) with a rating of 1-5 (1 being low vulnerability [high canopy coverage,
high precipitation, low evapotranspiration], 5 being high vulnerability [low canopy coverage, low
precipitation, high evapotranspiration])--> assign each district a number based on the quantile in which
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it falls (use zonal statistics visuals to help assign numbers quickly).
3) Create population density map to visually display areas of high population. Employ density mapping
tool on 2000 and 2010 block population centroids.
5) Reclassify (in quantiles) and spatially display by water district all vector layers determined in data
processing (water-demand per capita, reservoir storage/district area, and percent population
change).The values necessary to these maps were already added to the attribute table for water districts
after data processing and only need to be classified into 5 breaks.
6) Final Map: calculate a final weighted average for the 6 vulnerability scores in excel to determine the
‘final weighted vulnerability score’ for each district and choose a color ramp to represent vulnerability
for each district polygon.
Difficulties Encountered
Among the many difficulties I encountered during the analysis were endless error messages, partially
completed outputs, erroneously projected layers, flaws in methodology, and over-loaded spatial joins.
Generally speaking, hydrological boundaries, like water districts or microwatersheds, do not coincide
with standardized political boundaries, like counties. This made aggregating data to the district level
difficult when the data was only available at only the county level. The solution here was to make my
‘best guess’ for the population of water districts by finding proportions of county populations in each
water district (using population centroids at the block level) and then multiplying these proportions by
county level-data. In terms of methodology, I ran into a number of questions that simply were never
addressed given the time-frame and scale of this project. What if water resources used in one district
were in fact coming from a different water district? Will there be visible and adequate spatial variability
for each indicator amongst water districts to merit its status as vulnerability indicator in the analysis?
What about water storage in lakes, not just reservoirs? How do I decide my indicator-weighting without
any precedent?
In terms of GIS software, I came across some issues when I tried to join many tables and layers of
information to one layer, so instead I made copies of the layer and only joined one or two tables to each
copy. Another problem arose when I performed zonal statistics on raster layers and my outputs were
incomplete and generally incorrect. To solve this issue, I looked to the tools-->options--> geoprocessing-> environments-->general settings and changed the settings in this interface to match the layer on which
I was performing the zonal statistics. I did this for each separate raster layer. I’m not sure it was always
necessary, but it cleared up my difficulties with zonal statistics. Overall, ArcMap itself is a difficulty; it is
finicky and unpredictable. I learned to deal. Patience is crucial, and becoming familiar with the nuances
of the many moods of ArcMap is an acquired skill and rite of passage that will allow you to understand
when to try it again, when to CLEAN OUT YOUR H: DRIVE (always), when to shut the program off and
restart, and when to quit and leave the lab for a drink and some fresh air.
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Concluding Thoughts
This spatial approach to assessing water resource vulnerability, albeit frustrating at moments, worked
pretty much as planned. The poignancy of the results, however, may be lacking. The analysis is based on
water resource vulnerability as determined by a limited, and no-where-near-exhaustive list of
vulnerability factors; the technique used does not account for water transfers between districts, neither
does it consider natural lake water storage, nor does it account for the many other factors aside from
tree coverage that play a role in the green cycle, nor does it account for population density sub the
water district-level, and so on. Essentially, this analysis is crude at best and is intended to demonstrate
an analytical approach rather than a thorough and infallible analysis of water resource vulnerability.
That said the technique, I believe, serves a solid analytical purpose. It is important to employ an
integrated approach when it comes to water resource management that considers environmental
components (climate dynamics, geophysical factors, etc.) in addition to anthropogenic influence
(population dynamics, water infrastructure, etc).
It is also important to combine science and policy on a level that informs policy-makers yet satisfies
scientists. For example, this analysis aggregates vulnerability indicators to the water district level-- a
scientifically and politically important level. Water districts align with natural micro-watersheds and
changes in water management are often decided at the water district level. That said, it follows logically
that the data and science behind water-management decisions should consider spatial variability and
focus on localized needs and regionally pertinent stressors. If I were to further belabor this project, I
would try to determine the basic movement of water throughout the state in order to better
understand what water resources were available to each region. Tracing water’s movement, both
naturally and via man-made infrastructure (dams, hydropower stations, water mains, et.), could be a
project in itself. Knowing the movement of water would allow a more thorough understanding of water
supply in a water districts. On a final note, I would strongly encourage looking at available water-related
datasets for a region/watershed before committing to any specific analysis. USGS is a great data source
and has endless tabular data, but if you do not narrow your focus based on available data, you will likely
waste time trying to find data that does not exist, or try to integrate all the data you’ve found into an
entirely-too-big project that will eventually need to be trimmed.
Influencing Analyses
Biswas, S., S. Sudhakar, V.R. Desai. 2002. Remote Sensing and Geographic Information System Based
Approach for Watershed Conservation. Journal of Surveying Engineering. 128(3): 108-124.
This study uses GIS analysis to prioritize watersheds for conservation. The study took place in the
Mindapore district of the West Bengal State in eastern India and focused on microwatersheds.
Researchers used morphometric parameters to help analyze microwatersheds and rank them in order of
conservation-priority. The authors suggest that remote sensing and GIS are important for improving soil
and water conservation and watershed management, and that prioritization of microwatersheds is
crucial to this process. This study offers insight on appropriate spatial scales on which to conduct
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conservation analyses.
Depeweg, H., E.R. Urquieta. 2004. GIS Tools and the Design of Irrigation Canals. Irrigation and Drainage.
53: 301-314.
This study concentrates on the optimization of irrigation-canal location. Researchers consider
topographic data, soil data, and natural drainage data using GIS in locating the most suitable area for an
irrigation canal. The authors found that using DEM’s to model surface variation in GIS is more accurate
than designing a canal by hand. Along these lines, it is easy to defer how GIS can be used for the
opposite; identifying areas least suitable for irrigation. This analysis demonstrates how spatial analysis
can help improve water resource planning and conserve waters.
Maantay, Juliana, and John Ziegler. Water Resource Protection. GIS for the Urban Environment.
Redlands, CA: ESRI, 2006.
This ‘Natural Habitat and Open Space Assessment’ (p.352) case-study spatially categorizes land areas as
being of High to Low value in terms of water resources. The study uses and weights the following as
positive value criteria: wetland proximity, riparian areas, floodplains, steep slope, watersheds with less
than 10% impervious landcover, and soils important to groundwater recharge. The study provides
suggested numeric distance and percentage cutoffs (i.e. wetland buffers, slope and impervious cover
percentages, etc.). The goal of the analysis is to identify areas of high-value land that should be of high
conservation priority in order to protect water resources.
Hurd, B., C.Brown, J.Greenlee, A. Granados, M. Hendrie. 2006. Assessing Water Resource Vulnerability
for Arid Watersheds: GIS -based Research in the Paso del Norte Region. New Mexico Journal of Science.
44:1-23.
This study analyzes USGS HUC watersheds in the US-Mexico border watersheds in light of water
resource vulnerability. Researchers compiled a series of watershed vulnerability indicators and looked at
the geo-spatial data from these indicators using GIS in order to determine water resource vulnerability
in terms of water quality, ecosystem viability, and socio/economic development activities. The following
is the list of indicators used in the study: evapotranspiration, groundwater potential, groundwater
salinity, population change, salinity and sodicity for row and tree crops, standardized precipitation
index, biochemical oxygen demand, fecal coliforms, BOD/FC due to wastewater discharge. This study
offers up essential vulnerability indicators for both water quantity and quality.
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