9:30 9:50 10:10 10:30Dec. 8 Kevin Laura AliDec. 10 Jonathan Sohaib

Dec. 15 ChristinaDec. 17 Joel

Presentation Schedule

Lecture 20Watersheds Plus Case

Studies

Watershed

•An area that contributes flow to a point on the landscapeWater falling anywhere in the upstream area of a watershed will pass through that point.

•Many be small or large

•Identified from a flow direction surface

Drainage network

•A set of cells through which surface water flows

•Based on the flow direction surface

DEM_UTM83

FlowDir

SinksFilled

FlowDir2

FlowAcc

Reclass

Net

Source

Watershed

Con

Stream Link

Hydrological Modeling

From ArcGIS 10 Desktop Help

Flow Direction

Sinks• A sink is a cell or set of spatially connected cells

whose flow direction cannot be assigned one of the eight valid values in a flow direction raster.

• This can occur when all neighboring cells are higher than the processing cell or when two cells flow into each other, creating a two-cell loop.

• To create an accurate representation of flow direction and, therefore, accumulated flow, it is best to use a dataset that is free of sinks. A digital elevation model (DEM) that has been processed to remove all sinks is called a depressionless DEM.

From ArcGIS 10 Desktop Help

High pass filters

Return: •Small values when smoothly changing values.•Large positive values when centered on a spike•Large negative values when centered on a pit

High Pass Filter

• Raster data may also contain “noise”; values that are large or small relative to their spatial context.

• A mean kernal is used to reduce the difference between a cell and surrounding cells. (done by average across a group of cells)

• The identified spikes or pits can then be corrected or removed by editing

35.7

Flow Accumulation

From ArcGIS 10 Desktop Help

Conditional

• The results of Flow Accumulation can be used to create a stream network by applying a threshold value to select cells with a high accumulated flow.

• For example, the procedure to create a raster where the value 1 represents the stream network on a background of NoData could use one of the following:

• Perform a conditional operation with the Con tool with the following settings: – Input conditional raster : Flowacc– Expression : Value > 100– Input true raster or constant : 1

From ArcGIS 10 Desktop Help

Stream Link• Assigns unique values to

sections of a raster linear network between intersections.

• Links are the sections of a stream channel connecting two successive junctions, a junction and the outlet, or a junction and the drainage divide.

• Links are the sections of a stream channel connecting two successive junctions, a junction and the outlet, or a junction and the drainage divide.

From ArcGIS 10 Desktop Help

14

Case Studies Part 2

http://pro.arcgis.com/en/analysis/case-studies/case-studies-

overview.htm

Case 3

Which County Has the Most Private Land for a Golf Resort?

Problem• A retired golf pro has teamed up with a national land

development company to build a world-class golf resort in his home state.

• The resort will have three 18-hole courses, a hotel and lodge, and several restaurants, and will also offer horseback riding.

• The pro will assist in the design of the courses and the resort will bear his name.

• The development team is considering an area in the central part of the state, in one of three adjacent counties.

• There are already a number of golf courses in the area and it is becoming known as a vacation destination—anticipating the retirement of the baby boomers, the team believes there will be a market for more golf resorts in the region.

• The development team is being courted by the chambers of commerce and government officials in all three counties.

• Much of the land in the area is forest or rangeland, with large tracts in public ownership.

• The resort will require at least 500 acres for the courses, buildings, and riding trails.

• The developers want to show the pro and his people which county has the most private land, giving them the greatest range of options in selecting a site for the resort.

• They believe the most effective way to present this information is a map showing the three counties with the public land removed, leaving only the private land displayed.

Find and display the portion of each county that is privately owned land

Step 1Use Select By Attributes to select the three counties of interest.

The three counties of interest

Step 2Use Select By Attributes to select the publically owned land.

Publically owned land in the three counties

Step 3Use the Erase tool to create a layer containing the portions of the three counties that are privately owned.

Calculate the amount of private land in each county, in acres.

Step 4

Add a field to the layer containing the private land to hold the area values.

Step 5Right-click the Acres field and use Calculate Geometry to calculate the amount of private land for each county, in acres.

Map of the counties with the area that is public lands removed using Erase, leaving the portion of each county that is privately owned. The popup and the table display the amount of private land in a selected county. Locations of existing golf courses are also shown.

Conclusion – Economics vs Vision

• The developers believe it would be best to locate in the southwestern county, due to the presence of the main city in the region and the cluster of existing golf courses and clubs.

• While this county has the least amount of private land, there is still more than enough land to provide ample options for a site for the resort.

• They argue that even though the existing courses represent some competition, they will also attract golfers to the immediate area.

• Plus, the county—having a larger population and tax base—has offered more generous tax breaks.

• However, the golf pro insists the resort not be near the existing golf courses and clubs.

• He wants the resort to stand alone and envisions it as a retreat that would be away from other development.

• He believes with his name attached to the resort it will be a big draw even if it is located some distance from the current golfing center of the region.

• After a lengthy discussion, the developers agree to move forward with analyzing the two counties favored by the pro.

• For now they will keep the county they favor as an option should site analysis and market analysis show that the other counties do not have any viable sites.

• The next step will be to use the layer of private land in these analyses.

Case 4Exploring Spatial and Temporal

Patterns of Invasive Aquatic Species

Problem• Two exotic aquatic invaders, the zebra mussel (Dreissena

polymorpha) and the quagga mussel (Dreissena rostriformis bugensis), have the potential to inhabit most of the fresh waters of the United States and could have negative impacts on a variety of native aquatic species and eventually entire ecosystems.

• The zebra mussel was originally native to the streams of southern Russia, while the quagga mussel is indigenous to the Ukraine.

• Both these Dreissena mussels are now considered an invasive species in the United States.

• Ecologically, both mussel species can interfere with feeding, growth, movement, respiration, and reproduction of native mussels.

• Economically, their impact is felt as they can attach themselves to pipelines and impact water movement through hydroelectric turbines as well as intake structures for drinking water and irrigation systems.

• In the United States, researchers estimated that zebra mussels cost the power industry $3.1 billion in the 1993-1999 period, with their impact on industries, businesses, and communities exceeding $5 billion.

The distribution of mussels across the USA

• Since an established population of zebra mussels was first found in Lake St. Clair, located between Lake Huron and Lake Erie, they have spread throughout the country and have been found in 30 states.

• Quagga mussels have been reported in 16 states. • Zebra mussels were first reported in 1988 and

quagga mussels was first sighted in late 1989. • The spatial distribution varies by species with only

quagga mussels being seen in western states, however, it is not clear if this distribution simply reflects the timing and location of introduction.

Do both zebra and quagga mussels pose the same risk?

• Although both these Dreissena mussels have some similarities and have been confused in the past, they do have a number of differences that may impact how they spread across the country.

• The life-span of these two Dreissena mussels varies, although most live 3-9 years (USGS 2008).

• Zebra mussels can inhabit warm waters, however, quagga mussels are unable to survive water temperatures higher than 30 degrees Celsius.

• Zebra mussels are found in warm shallow nutrient rich waters whereas quagga mussels inhabit both shallow warm and deep cold waters that are nutrient poor.

• In time, quagga mussels may become the dominant species in some areas, such as the Great Lakes (MacIsaac, 1994).

• In southern Lake Michigan, quagga mussels were reported to spawn earlier in the season and at greater depths then zebra mussels (Nalepa et al. 2010).

• Although quagga mussels spawn fewer eggs than zebra mussels over a season, their ability to inhabit colder deeper waters may allow them to build larger populations over time.

• Adult zebra mussels start to reproduce in the spring, when water temperatures rise to about 12 degrees Celsius, but it is thought that they may reproduce continuously in habitats where the water stays warm year-round.

• Using the 16 years of data it is possible to investigate whether the number of mussels, by specie type, changes by month across different regions of the USA.

• Do quagga mussels always spawn earlier in the season than zebra mussels?

• Do zebra mussels reproduce continuously in regions where water temperatures tend to remain warmer year-round?

• Each reported location of the zebra mussels or quagga mussels also has an associated date and can be mapped by month, for example.

• In this case, the pattern by year is not clear on a static map, but including animation can help to show the spread of mussels from Lake St. Clair to California.

• This information, however, is still difficult to express clearly without a more detailed mapping method.

• To evaluate overall patterns, individual mussel reports need to be aggregated to areas.

• USGS hydrologic units that divide the United States into 21 major geographic areas, or regions provide reasonable boundaries for this task.

• These geographic areas contain either the drainage area of a major river, such as the Missouri region, or the combined drainage areas of a series of rivers, such as those that drain into the Gulf of Mexico.

• In order to evaluate changes in mussel numbers over a season, which will be linked to spawning patterns, the total number, by specie type, can be aggregated by month.

• A coxcomb chart allows you to visualize the monthly totals by region.

• In a Coxcomb chart (also known as a rose diagram), each category is represented by a segment, each of which has the same angle.

• The area of a segment represents the value of the corresponding category.

• They are, essentially, multivariate proportional symbols. Coxcomb charts are best used to demonstrate patterns rather than for subtle differences shown by exact numbers as these can be difficult to see.

Finding the number of mussels by type and watershed

Step 1

Intially you need to find the hydrologic unit each mussel has been reported in. To do this you should join the hydrologic units to the mussel reports using Spatial Join.

Step 2

You can now summarize the number of mussels to the hydrologic unit (HUC2) in which they were located, by species, name, and month using Summary Statistics. Your data is now a table with a row for each unique hydrologic unit, species and month.

Creating the coxcomb features

Steps 3 & 4Using the table of data, created by following the above steps, add a new text field to store month names. A text field must be used because these will eventually become field names.

In this new month field you should calculate the relevant month name based on the known month number so, for example Month 1 becomes January. This can be done by selecting the months by number (using Select by Attribute) and doing a field calculation, which honors the selected features. In some cases, the month was unknown and was entered as month = 0. We can not use these data so for some records the new text field be blank.

A short Python script can be used to automate these two processes (monthNames.py). A prefix should be added to the month names to ensure they are in alphabetical order when pivoted. This means that the coxcomb features are displayed clockwise from January to December.

Steps 5 & 6You now want to divide the data by species. Use Select By Attribute on the table to select where the species is polymorpha and only select those rows where the month is known species = 'polymorpha' AND month > 0.

Export the selected features to a new table showing the number of polymorpha (zebra) mussels by hydrologic unit and month.

Repeat steps 3 and 4 above but this time select the rostriformis species (species = 'rostriformis' AND month > 0). This exported table shows the number of rostriformis (quagga) mussels by hydrologic unit and month.

Step 7

Next, you must pivot the data so that the total number of mussel species is stored by watershed and month using Pivot Table. Repeat this for both the polymorpha and the rostriformis data tables.

Step 8The final process is to link our pivoted data tables to a location in the hydrologic unit. The coxcombs are displayed at a point location and you can use Feature To Point to obtain the hydrologic unit centroids. Use the Inside option.

Step 9n both pivoted data tables we have a field showing the hydrologic unit (HUC2) which can be used in an Attribute Join to join the table to the point features you have just created. This step should be done twice, firstly with the polymorpha table and secondly, with the rostriformis table. Export each joined dataset to a new point feature class.

Step 10

You can now run the Coxcomb Features tool with each point feature class. A coxcomb will be created for each unique location, identified by the hydrologic unit (HUC2). A segment is created for each field that is input in the List Fields box so only month fields should be selected.

The Coxcomb scale value allows you to change the scale of the coxcomb features. All coxcombs are rescaled proportionally, ensuring the largest coxcomb radius is this input. The Measure tool can be used to find a suitable value.

Automating the Process

Case 5Selecting an Appropriate Kriging

ModelUsing Soil Contamination Data

Introduction

• Since the Chernobyl accident in 1986, contamination levels have been collected in nearby areas.

• The half-life of the major contaminant, cesium-137, is approximately 30 years, and it is likely that contamination will spatially vary across the region; however, it is not feasible to take samples everywhere.

• Using interpolation, values at locations that have not sampled can be estimated using the collected data (Krivoruchko, 2012).

• Due to the complex meteorological conditions following the accident, contamination is likely a combination of both large-scale and small-scale variations.

• It was known, for example, to have rained several days following the nuclear accident in the area to the northeast of the sample area.

• Sample sites closer the Chernobyl nuclear power plant site are likely to have the highest cesium-137 levels.

• Some measurement error is also inevitable given that radioactive decay is a stochastic process.

• Using kriging to predict values, both large- and small-scale variations can be reconstructed, and if the data is not precise, in most cases the method can filter out measurement error.

• With some understanding of the area and the processes that took place, such as the wind conditions and rainfall, looking at the data distribution can reveal more information.

• Exploring the data will, in part, help determine which kriging model is appropriate for this data. It also gives you knowledge of effective parameters for use in the modeling process.

• Exploratory spatial data analysis is a crucial first step in statistical analysis.

• The Geostatistical Analyst extension is required for this analysis

Data Stationarity• Kriging assumes that data exhibits stationarity. • Stationarity means that statistical properties do not depend

on exact locations; i.e. there is no trend in the data.• The mean of a sample at one location is equal to the mean at

any other location; data variance is constant in the area being analyzed, and the correlation (covariance or semivariogram) between any two locations depends only on the distance between them, not their exact locations.

• A number of steps can be followed to identify whether your data meets these assumptions, and in cases where it doesn't, it may be possible to use some approaches as part of the analysis.

Examining the Distribution of Data

• Use the Histogram tool under Explore Data (ESDA tools) with the sample values of cesium-137.

• Look for a bell-shaped distribution and check the reported diagnostics for the following: – Similar mean and median values– Skewness around 0– Kurtosis value around 3

Examining the Distribution of Data

• Plotting the cesium-137 data as a histogram shows the frequency distribution of the data along with some summary statistics.

• To meet the assumption of data normality, the distribution in the histogram should be bell-shaped; the mean and median values should be similar, the skewness value should be around 0, and the kurtosis value around 3.

• None of these are evident in the cesium-137 data. • This data can still be used in a kriging model if a

transformation can be applied so that the transformed values have a normal distribution.

Histogram of Cs-137 and with its log transformation.

Histograms

• The data shows a skewed data distribution with very few larger values.

• A logarithmic transformation may bring the data closer to a normal distribution.

• Therefore, lognormal kriging will likely produce more accurate predictions than using kriging without the data transformation option. For further validation, the normal QQ plot (probability plot) can be used to graph the data distribution against the standard normal distribution (shown as a line).

• For further data exploration, with the knowledge that a lognormal transformation will likely produce more accurate predictions, adding a new field with log values can be helpful. – Use Add Field to create a double field and Calculate Field to

add the lognormal values of cesium-137.

QQ Plot

Exploring Spatial Structure

• A value that is significantly higher than others can cause problems when choosing the spatial correlation model (semivariogram).

• A high value located close to Chernobyl is evident in all the above approaches.

• This could possibly be removed from the model building process but then included when the data is used for prediction, and should be investigated at the modeling stage.

• Stationarity is evident when any two locations a similar distance and direction apart also have a similar difference squared.

• Using pairs of sample locations, the spatial autocorrelation in the data can be examined and plotted as a semivariogram cloud.

• If spatial correlation exists, pairs of points that are close together should have less difference and so be low on the y-axis as distance increases, so the difference squared should increase and the values will be higher on the y-axis.

• The semivariogram values are very different for pairs with the largest cesium value near Chernobyl.

• For this high value near Chernobyl, at different distances to other points, the differences squared are always large (high on the y-axis) rather than varying from small to large.

• Furthermore, this structure, which is evidence of spatially dependence in data, can also been seen in the semivariogram surface.

Looking for Global Trends

• The Trend Analysis tool provides a three-dimensional perspective of the data, and polynomials are fit through the scatterplots on the projected planes.

• The stick heights reflect the cesium-137 value at each sample location, and rotating the data to 315 degrees so that Chernobyl is located in the back corner of the grid shows you a strong trend is visible in two perpendicular directions, the north-northeast and south-southeast directions.

Simple Kriging

Examining Local Variation

The local-scale variability can be evaluated using Voronoi polygons. Voronoi (or Thiessen) polygons show the areas of influence around each sample point.

• Dividing the data into five categories shows some distinct areas of local data variability in the northeast and south of the sampling region.

• These patterns are more evident with lognormal data and at a local scale, before detrending, there is clearly an issue with local data stationarity in the data.