In part 1 of this blog, we downloaded data from the Fishnet website and made a web map on ArcGIS.com. Part 2 showed how the data could be prepared with the ArcGIS Field Calculator tool and some simple Python functions to improve the pop-ups for better presentation of the data. In this blog we use the data that was processed in part 2 to build a new web map and configure the pop-ups with a custom attribute display.

Create the Web Map

To create the web map we opened a new map in ArcGIS.com and saved it. We then added the data by dragging and dropping the file on the map (we could have also used the add button at the top of the map and selected “Add Layer from File”).

Configure Pop-up

To configure the pop-up we opened the “Configure Pop-up” window in the ArcGIS.com web map.

Figure 1: Open the “Configure Pop-up” window.

We then changed the “Display” drop down to “A Custom Attribute Display”.

 

Figure 2: Set the pop-up contents to “A custom attribute display”.

We entered the following using the “+” button to add field names (field names are surrounded by set braces) and the “B” and “I” buttons to set the format.

 

Figure 3: Setting up the custom attribute display.

We then clicked “Format Fields” in the pop-up configuration window and set the precision of the Latitude and Longitude to 3.

At this point we saved the pop-up and the web map then tested the pop-up.

Figure 4: The original and new pop-ups.

When we were satisfied with the pop-up, we added a description, tags, and credits to the web map and shared it with everyone. As a final step we published the web map as a hosted application.

Conclusion

Rich new sources of data such as the Fishnet website and the powerful tools available in the ArcGIS system allow the creation of useful web maps that provide important insights into the distribution of organisms.
A few simple steps that organize your data can produce a neater and more presentable web map application. By having a clear vision of what you want your audience to see can help simplify the process of data preparation. Two things that we find useful to remember when using the Field Calculator are:

1. Be sure the correct parser is selected – in this case Python. The Field Calculator defaults to VB when first opened and Python-based calculation fields will not run if VB is checked.
2. When working with text files, beware of result strings that are longer than 254 characters. If your field calculation results in strings longer than 254 characters the field calculation will fail. If some of the output is less than 254 characters long the script may run until it hits a record that is longer than 254 characters then fail.

Speckled Dace - Rhinichthys osculus Version 2.0
Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

http://www.arcgis.com/home/item.html?id=2e0946bbb6834366a31044c05e3201d4

Special Thanks to:

The Fishnet data portal data portal and the following institutions for providing the data used in this application:

• US National Museum of Natural History
• North Carolina Museum of Natural Sciences
• University of Kansas Natural History Museum
• Canadian Museum of Nature
• Harvard University Museum of Comparative Zoology
• University of Washington Fish Collection
• Natural History Museum Los Angeles County
• Royal Ontario Museum
• Tulane University Museum of Natural History
• University of Michigan Museum of Zoology
  

And to Rich Nauman and Michael Dangermond for providing the post. For more information on using biodiversity data in ArcGIS.com web maps and applications contact Richard Nauman (rnauman@esri.com).

Prepare the Data

In part 1 of this blog, we showed you how to take species observation data such as museum records with an x, y coordinate pair and drag and drop it into ArcGIS.com map to make a web map. We used data we downloaded from the Fishnet website to make a web mapping application showing records for Speckled Dace.

Let's say you click on a point in this map, what do you see? You see a simple list of attributes. It's a start. But you have to scroll way down to see the important things like the collection date and the collector. Other values could use some clean up on the database side to make the map more presentable. For example, values for counties include "Benton County", "Catron", “DOUGLAS” and “Kittitas Co.” while other records have no value for county at all.

 

Figure 1: The original pop-up window from our Speckled Dace map.

In version 2.0 of our Speckled Dace map we used the field calculator in ArcMap to format the data a bit first before it goes into a pop-up window. To build this new map we downloaded the most recent version of the Fishnet query for Rhinichthys osculus and deleted records without coordinates. These data records sometimes contain commas in their fields, so we chose to download the tab-delimited format, *.txt. We imported the table to a geodatabase and added it to a new ArcMap document for editing.

Specimen

The catalog number and museum code are used to refer to a particular specimen held by a specific institution in scientific publications. The catalog number is the key value that unlocks all sorts of information about a specimen, including notes kept by the collector and other related information.

The original pop-up displayed the institution and catalog number separately because they are in two different fields. We think our map reader would like to see them together because they make more sense to our audience when read together like this:

UWFC 029568

The CatalogNumber field in the Speckled Dace dataset contains a variety of data: numbers, text plus numbers, and combinations of text, numbers and special characters. A second field, InstitutionCode, is relatively straightforward. InstitutionCode provides the standard code used to identify the museum that owns the specimen. This field is fully populated and needs no modification.

 

Figure 2: Data in the CatalogNumber field are in different formats and need to be standardized for the pop-up.

To standardize the catalog number we first added a new field, CatNum, to the table as text format field and set its length to 20.

As we mentioned the CatalogNumber field has data formatted different ways, including some records with their museum codes still attached. To remove the museum codes from the catalog numbers first right-click the CatNum field and then select field calculator.  Then we use a simple Python function to split the CatalogNumber field and populate the CatNum field with the museum code removed.

 

Figure 3: Screen shot of the field calculator with Split_CatalogNumber.cal calculation file loaded.

Note that the python parser radio button has been selected and there are two windows into which to place code, the pre-logic script code window, and the window below it where the field calculation is defined. Be sure when you do your calculation to check the Show Codeblock box, otherwise you will not see the pre-logic script code window.

 

Figure 4: The data table after performing the calculation using the Split_CatalogNumber.cal calculation file.

CatNum is now populated with the catalog number of each specimen and its museum code removed.

We would like to provide a link from each record to the web page of the institution that provided the data. We can do that by joining a table of with hyperlinks in it to our original table. To add links we made a new table with fields that will contain the museum codes and hyperlinks.

Figure 5: Lookup table for adding links to museum web pages.

Join the museum links table to the fishnet records using InstitutionCode field from the original table and MuseumCode from the museum link table.  Next, export the joined table to make the join permanent. The extra MuseumCode and OBJECTID_1 fields may be deleted from the new table.

We then made a new text field, Specimen, and set its length to 200. To create this field we will use field calculator (Specimen_html.cal) to merge the InstitutionCode, CatNum, and Link fields and add a hyperlink.

The Specimen_html.cal calculation file does not use a python function so it only has the following code in the field calculation window:

"<a href=\"" + !Link! + "\"> " + !InstitutionCode! + "</a> " + !CatNum!

This concatenates the InstitutionCode, CatNum, and Link fields and adds a hyperlink with an HTML link tag.

The new Specimen field is populated with values that look like this:

<a href="http://www.museum.tulane.edu/"> TU</a> 93821

Because ArcGIS.com understands the meaning of the HTML link tag, the field will display in the pop-up like this:

TU 93821

The museum code is now linked to the museum’s webpage! The specimen field is now ready for the web map. Once this is all working properly you can place hyperlinks in your arcgis.com map pop-ups and have all kinds of information available to your map viewer without the needless clutter of dumping things unformatted into the pop-up window.

 

Locality

In the original version of the Speckled Dace map, information about location of the record was split amongst three fields, StateProvince, County, and Locality.

Raw values in the StateProvince field are both capitalized and un-capitalized. We could run a simple capitalization script, however some states such as North Dakota and New Mexico have two words in their names that need to be capitalized, so we recommend using a script to calculate this field so that everything comes out the way it should.  This script is called State_caps.cal and is the following:

State_caps.cal

Pre-logic Script Code:

    def function(State):   
        final = "" 
        split =  State.split()
        for word in split:
            caps = word.capitalize()
            final += caps + " "
        final = final.strip()
        return final

Field Calculation:
    StateProvince = function(!StateProvince!)

To run the calculation script we right-clicked the StateProvince field, selected field calculator, loaded and then ran the State_caps.cal script.

Counties are even more complicated, some values end in “Co.” while others end in “county”.  Still others are in Canada where the county field is not populated.  The following County_Cleanup.cal script traps all these cases and standardizes the field:

County_Cleanup.cal
 
Pre-logic Script Code:
    def function(county, state):
        if county[-3:] == "Co.":
            return county[:-3] + " County"
        else:
            if county.find("County") == -1 and state != "British Columbia":
                return county + " County"
            else:
                return county

Field Calculation:
    County = function(!County!, !StateProvince!)

 

Figure 6: The County field before and after running the County_Cleanup.cal script. Note that the counties still have irregular capitalization.

The last step for preparing the counties is to standardize capitalization. To do this we ran the County_caps.cal script which is very similar to the State_caps.cal script.

County_caps.cal

Pre-Logic Script Code:
    def function(CountyCap):   
        final = "" 
        split =  CountyCap.split()
        for word in split:
            caps = word.capitalize()
            final += caps + " "
        final = final.strip()
        return final

Field Calculation:
    County = function( !County! )

To standardize the capitalization we used a second script (County_caps.cal) that is similar to the one used to fix the capitalization of the StateProvince field.
 

Figure 7: Final County field ready for use in web map.

To organize the locality information contained in the StateProvince, County, and Locality fields into a single field we first need to tackle the case of records with no locality data. First we sorted the table so the fields with NULL values in their locality field are all in a row. Then we highlighted these records and used field calculator to populate them with the value “x” which a later script will change into “No locality information reported.”

We then cleared the selection and added a text field LocHTML and set its length to 250. We ran a field calculator script (Locality.cal) to assemble the three fields for the web map.

Locality.cal

Pre-logic Script Code:
    def function(loc):
        length = len(loc)
        if length > 200:
            loc = loc[:200] + "..."
        else:
           if loc[-1:] != ".":
                loc = loc + "."
        return loc

    def countyFunction(county):
        if county == "x":
            county = " "
        else:
            county = " " + county + ", "      
        return county

Field Calculation:
        function( !Locality! ) + countyFunction(!County!) + ( !StateProvince!) + "."

Because fields in a dbf file cannot be longer than 254 characters, the Locality.cal script trims Locality values greater than 200 characters, leaving space for the county and state. If the locality is clipped an ellipsis is added (“…”) to indicate that more information is associated with the original record. Next if the Locality does not end in a period one is added. If the County field is = “x” a space is added to the end of the locality string otherwise a space followed by the County value then a comma and space are appended to the locality string. Lastly the state and a period are added yielding values that look like this:

    New Fork River 1.5 mi. W of Pinedale, US Hwy. 187. Sublette County, Wyoming.

    No locality information reported. Idaho.

    North Fork John Day River. Oregon.

Collection Date

In our original map the collection date is displayed in three rows, in a number format.  Because using a number for the month can be confusing to international audiences, we wanted to convert the MonthCollected field to a text field instead of a number field.

First we made a lookup table which contains the name of each month and the number of each month in two fields. We are going to concatenate some of these fields together, so we need to limit the lengths of each of the fields for right now. The Month field should be an integer, and the MonthText field should be a text field with a length of 40.

 

Figure 8: Lookup table used to convert MonthCollected values from number to text.

We then joined the new table’s Month field to the original tables MonthCollected field. We exported the joined table to make the join permanent and then deleted the Month and OBJECTID_1 fields. We sorted the new table on the MonthText field and select null values. We used the field calculator to set the null values to "Collection date not provided" (this is why we made the field length = 40).
We then added a new text field named Full_Date to the table and set its length to 40. We used the field calculator script Date.cal to assemble the month, day, and year for the web map.
Date.cal

Pre-logic Script Code:
    def function(month, day, year):
        if month == "None":
            date = "Collection date not provided"
        else:
            date = month + " " +  str(day) + ", " + str(year)
        return date

Field Calculation:
    function( !MonthText! , !DayCollected! , !YearCollected! )

Collector

We sorted the Collector field and selected the null values. We then used the field calculator to set the null values to “Unknown”.

Now the data are ready for use in our web map. Right click the table in ArcMap and export the table to a text file (*.txt).

Next week

Next week we will show you how to configure a custom pop-up format that we used to make our new speckled dace web application.

Download the Field Calculator Scripts

We posted the field calculation scripts on the ArcGIS Hydro Resource Center for you to download, use, and modify.

County_caps.cal
County_Cleanup.cal
Date.cal
Locality.cal
Specimen_html.cal
Split_CatalogNumber.cal
State_caps.cal

Special Thanks to:

The Fishnet data portal data portal and the following institutions for providing the data used in this application:

• US National Museum of Natural History
• North Carolina Museum of Natural Sciences
• University of Kansas Natural History Museum
• Canadian Museum of Nature
• Harvard University Museum of Comparative Zoology
• University of Washington Fish Collection
• Natural History Museum Los Angeles County
• Royal Ontario Museum
• Tulane University Museum of Natural History
• University of Michigan Museum of Zoology
  

And to Rich Nauman and Michael Dangermond for providing the post. For more information on using biodiversity data in ArcGIS.com web maps and applications contact Richard Nauman (rnauman@esri.com).

Cross sections are important and common data products used for characterizing the subsurface and for building 3D subsurface models. Cross sections can display the vertical dimensions of hydrogeologic units, and portray different physical entities such as faults, well locations, and water properties like water quality.

The Subsurface Analyst toolset, available as part of the Arc Hydro Groundwater tools, makes it easy to create cross sections in ArcMap. Users can utilize a wide array of tools to “project” standard GIS datasets onto a cross section plane which is displayed as a separate data frame in ArcMap.

Below are some examples of how Subsurface Analyst tools were used to create cross sections:

Example 1 – displaying water quality probabilities on a cross section
In a recent project, Bob Reedy from the Bureau of Economic Geology created maps displaying the probability of water quality exceeding the Maximum Concentration Limit (MCL) for different aquifers in Texas. As part of this process, Bob used the Subsurface Analyst tools to show the changes in probabilities on top of a vertical cross section.

Bob started with a set of rasters defining the top and bottom of each aquifer unit. He then used the Cross Section Wizard from Subsurface Analyst to create a new cross section data frame and used the cross section editing tools to add the raster elevations onto the cross section.

Once the units were defined on the cross section, he projected a probability raster, showing the probability to exceed any of the major MCLs, onto the cross section. This process created a set of lines that were then connected to create a set of polygons displaying the different probability categories.

Map and cross section showing the probability of having a primary MCL violation in the Gulf Coast Aquifer, Texas.

 

Example 2 – Displaying well construction and water quality
These sample cross sections were developed as part of a project done by Daniel Lupton of Intera Geosciences and Engineering. During the project, oil and gas resistivity and spontaneous potential logs were used to evaluate the distribution of water quality. A script was written to interpolate the average depth to the base of fresh water and to the base of slightly saline water. The results were projected onto cross sections for visualization of the fresh and saline water on top of the hydrogeologic units.

 

Cross section of water quality derived from oil and gas resistivity and spontaneous potential logs.  The water quality is displayed on top of hydrogeologic unit boundaries.

 

In addition cross sections were created to display the location and construction elements of water wells used by the oil and gas industry to acquire water for hydrofracing. Subsurface Analyst is used to create plots that show the conceptual model for the pumping situation and to parsimoniously display the interpretation results to the local groundwater district managers, and to evaluate localized effects of the high intensity pumping on the local land owners.

 

Cross section showing the location and construction elements of water wells.

 

As part of this project, automation was achieved by building a model in the Model Builder application in ArcGIS. The model included several tools and was used to automate the process of interpolating data, rasterizing, clipping and masking. Subsurface Analyst geoprocessing tools were included in the model to automate the process of updating the cross sections.

 

Subsurface Analyst geoprocessing tools were included in models to automate the update of cross sections.

 

Thanks to Bob Reedy (bob.reedy@beg.utexas.edu) and Daniel Lupton (dlupton@intera.com) for contributing the material for this post. For more information on the Arc Hydro Groundwater tools visit the Aquaveo website (www.aquaveo.com/archydro-groundwater). 

The National Hydrography Dataset (NHD) contains detailed geospatial information about the Nation's surface water. Ensuring the accuracy of these data is an ongoing task as the landscape changes and users demand greater accuracy.

Just as building the NHD required a large partnership across the nation, maintaining the NHD also requires an extensive partnership, and can best be accomplished by those closest to the hydrography. Updates to the NHD are made by NHD stewards, transmitted to the USGS, processed, and made available to the general public in the national dataset distribution.

Stewardship is facilitated through an update process that has previously been performed through a suite of tools known as the NHD GeoEdit Tools. These tools have been particularly difficult to install, cumbersome to use, and the process is so complicated that stewardship tasks were often limited to a designated full-time NHD expert.

In order to support and grow the NHD stewardship program, the USGS has reinvented both the process and tools involved in maintaining the NHD. This new process is known as the NHD Update Process. Also, with travel restrictions in place due to budget restrictions, training new stewards is a challenge that is being solved by the creation of online tutorials, video demos, and user guides. The new update process will be released in mid to late January and will include enhancements in the tool installation process, data management process, quality control process, editing environment, utilities, user interface, and help portal. The new tools and process are being released initially for ArcGIS version 9.3.1 with high priority immediately following the January release to develop a version compatible with ArcGIS version 10. These improvements will enable the USGS to expand and strengthen the Stewardship program by providing stewards with a simplified and streamlined process, making it easier to update the NHD.

Special thanks to Kathryn Isham for providing this post. Questions for Kathy: krisham@usgs.gov

Background

The Global Runoff Data Centre  (GRDC) operating under the auspices of the World Meteorological Organization (WMO) is one of the few data centers tasked to collect and archive in-situ Earth observations globally. GRDC is dedicated to collect river discharge data, which is the most accurately measured component of the water cycle (Grabs, 1997; Gutowski Jr., Chen, & Otles, 1997; Hagemann & Dümenil, 1998)⁠. In-situ monitoring is a largely underappreciated element of the Earth observations primarily due to difficulties in collecting and archiving data operationally from diverse institutions globally.

Data centers like GRDC are tasked with enormous responsibilities, while operating under severely limited resources and constraints in their ability to pursue donor agencies to submit their data. With the exception of exchanging meteorological observations, which is governed by international treaties, the collection and submission of in-situ observations largely relies on volunteer efforts. GRDC and other data centers are continuously searching for new data and establishing partnerships with national agencies and institutions, which can provide updates to their existing archives.

History

The Global Runoff Data Centre and its data archive can trace back its origin to the International Hydrological Decade initiated by the United Nations Educational, Scientific and Cultural Organization (UNESCO) in 1965. Perhaps, the most lasting result of this effort was the compilation of a comprehensive global river discharge database “The Discharge of Selected Rivers of the World” which was published in book form from 1969 through 1984 with some updates in 1988. The digitized version of this data compendium served as the basis of the GRDC data archive.

Originally, GRDC was started by the Institute for Bioclimatology and Applied Meteorology at the University of Munich in 1982, but it was transferred to the Federal Institute of Hydrology (Bundesanstalt für Gewässerkünde, BfG), Koblenz, Germany in 1984. After four years of initial preparation, GRDC was officially in operation in 1988.

The initial database had monthly average discharge records for over 3500 stations with varying temporal coverage that was expanded to over 8000 stations in the past two decades and initial monthly data was partially replaced and recalculated with daily discharge records.

GRDC's Data Holding

The 8000 discharge gauges in the current GRDC data archive offer a reasonably complete coverage for discharge spatially (Figure 1), but the temporal updates are lagging in many part of the world.

Figure 1: Discharge Gauges in the data archive from the Global Runoff Data Centre.

This lag is more apparent on the temporal evolution of the number of observing stations over time (Figure 2). According to the GRDC archive the “birthplace” of the modern discharge monitoring is Sweden that has discharge records dating back to 1807, followed by Germany in the 1820s and the former Astro-Hungarian Monarhy in 1860s. The rest of Europe and North America started discharge monitoring in the in the later part of the 19th Century, while most of the stations in Siberia started to operate after World War II, which probably coincided with the forced exile of the entire staff of the Federal Hydrological Institute of Hydrology in Leningrad (now St. Petersburg) during the last years of the Stalin era.

Figure 2: Availability of historical discharge data in the GRDC database by year.

The sharp decrease in the number of stations after the 1980s is often mistaken as a sign of the declining discharge monitoring network. Although numerous in-situ observing stations (not only discharge) were discontinued in the last two decades ironically coinciding with the elevated interest in climate change, but the drop in observations according to the GRDC archive is primarily the result of the lag between the observation and the time it takes to get it in the GRDC database. The decline of in-situ monitoring in the last two decades is partially attributed to the collapse of the Soviet Union and civil wars in developing countries, but even rich nations like the United States and Canada cut back on their monitoring programs due to budgetary constraints on the operating agencies.

Lack of international cooperation, and unwillingness to share data are typically sited as the main reasons for the lack of more up to date and comprehensive global data holding. The scientific community often expressed strong criticism regarding the data distribution policies that GRDC and other data centers adopted primarily to adhere to their agreements with data donors.

Perhaps, if the scientific community was just as vocal expressing the needs for free and open access to comprehensive in-situ Earth observation and convinced policy makers to set aside resources and make international binding agreements as they promoted immediate action on climate change, data centers like GRDC would be in a better position to serve their needs.

Summary

GRDC holds comprehensive records of observed discharge globally, but the data records are not always up-to-date particularly for the developing world. GRDC distributes its data freely upon requests from end users. Unfortunately, GRDC cannot share its entire data archive and only able to provide data for finite number of stations. Their discharge gauge catalog is available from their web-site (http://grdc.bafg.de) that includes all the station informations (name, country, river, catchment area, long-term average discharge, etc.) to make station selections easier. Requests to data should be sent to grdc@bafg.de.

GRDC is continuously seeking for new data and open to work with new partners to establish steady incorporation of newly recorded discharge data. GRDC has a preference to accept data from the original source on order to minimize the data degradation.

References

Grabs, W. (1997). Report on the third meeting of the {GRDC Steering Committee}.

Gutowski Jr., W. J., Chen, Y., & Otles, Z. (1997). Atmospheric water vapour transport in NCEP-NCAR reanalyses: comparison with river discharge in the central United States. Bulletin of the American Meteorological Society, 78, 1957-1969.

Hagemann, S., & Dümenil, L. (1998). A parameterization of the lateral waterflow for the global scale. Climate Dynamics, 14, 17-31.

 

Special thanks to Balázs M. Fekete for providing this post. Questions for Balázs: bfekete@ccny.cuny.edu

 

By:  Fernando Rios (University at Buffalo, jfernand@buffalo.edu)
Ming Ye (Florida State University, mye@fsu.edu)
Paul Z. Lee (Florida Department of Environmental Protection, paul.lee@dep.state.fl.us)

1.0 What does it do?

Nitrate is a commonly identified groundwater and surface-water pollutant; it poses serious threats to human health and the environment. One important source of nitrate in the environment is due to wastewater treatment using Onsite Sewage Treatment and Disposal Systems (OSTDS) (a.k.a., septic systems). To facilitate water resources and environmental management (e.g., the calculation of total maximum daily loads, TMDL), we developed an ArcGIS-Based Nitrate Load Estimation Toolkit (ArcNLET) to simulate nitrate transport from septic systems in groundwater and to estimate nitrate load from septic systems to surface water bodies such as lakes and rivers. It is a screening tool based on a simplified conceptual model of groundwater flow and nitrate transport.

We have used it to estimate nitrate load from thousands of septic systems to surface water bodies in two neighborhoods located in Jacksonville, FL, USA, where nitrate due to septic systems is believed to be one of the reasons of nutrient enrichment and an isotope study indicates that denitrification is significant. While our study includes local and global sensitivity study to identify critical parameters to nitrate concentration and load estimates as well as uncertainty analysis to quantify uncertainty in model parameters, we only describe below the basic steps of using the software for nitrate load estimation in a deterministic manner.

2.0  Using ArcNLET to Predict Nitrate Distribution and Load

A typical ArcNLET workflow consists of four steps:

1. Calculating groundwater flow velocity and direction with the Groundwater Flow module.
2. Generating flow lines using the particle tracking function.
3. Calculating nitrate plumes via the Transport module.
4. Estimate load with the load estimation function.
   

For illustration purposes only, a test site consisting of a neighborhood in the Lakeshore area located in Jacksonville, Florida is presented.  This neighborhood has many septic tanks that are close to a river.  It is desired to estimate the nitrate load to the river from these 265 septic tanks as well as visualize the nitrate plumes.  The data used for this example is available on the ArcNLET webpage http://people.sc.fsu.edu/~mye/ArcNLET/

 

2.1  Calculate Groundwater Flow

The first step is to calculate groundwater flow velocity and direction.  As inputs, a 1/3 arcsecond (approximately 10 m) digital elevation model (DEM) of the area was obtained from the publicly available USGS National Elevation Dataset.  The location of the river was obtained from the National Hydrography Dataset.  Raster files representing hydraulic conductivity and porosity were created and were set to be constant values (2.113 m/day and 0.25, respectively) over the entire neighborhood. However, ArcNLET supports spatially variable conductivity and porosity values, if available.

After loading all the input data into ArcMap, select the appropriate layer from the dropdown boxes for each input.  Then, set the smoothing factor to 40.  This factor controls how much the water table resembles the topography and can be estimated from well measurements. It is generally in the range 20 to 80 for the USGS DEM, but may be larger for LiDAR DEM so that the water table can be viewed as a subdued replica of topography.  Leave the other parameters as default.  Select the output file locations by clicking the folder icons next to the appropriate boxes.  To execute the flow model, click on the Execute menu and select the ‘Groundwater’ entry.  After completion, you will get two rasters, one representing flow velocity magnitude and the other flow velocity direction. You can turn off these raster layers as they are of little use on their own.

 

2.2  Generate Flow Lines

Flow lines serve two purposes. The first is to calculate the flow from each septic tank and the second is to visualize the flow field.  In the Particle Tracking tab of ArcNLET, select the source locations input layer to be the septic tank locations.  For the velocity magnitude and direction layers, select the rasters you just calculated in the previous step.  The remaining inputs (porosity and water bodies) are the same ones used for the Groundwater Flow module. Select the location of the output shapefile by clicking the folder icon. Leave the parameters at their default values.  Execute the module by selecting the Particle Tracking entry from the Execute menu.

After execution is complete, you will see the flow lines for each septic tank.  The lines can even be color-coded using the data in the attribute table to indicate the flow velocity along the path. In the figure below, the lines have been color-coded where green and red colors indicate low and high flow velocity, respectively.

 

2.3  Calculating Nitrate Plumes

ArcNLET can calculate nitrate plumes from one or more septic tanks.  In the Transport tab, you can see that there are more options and parameters for this module compared to the others.  These options and parameters are related to various physical characteristics of the study area (e.g., dispersivity and decay constant), characteristics of the septic tanks (e.g., nitrate concentration at the drain field and width of the drain field), or are simulation parameters (e.g., plume cell size and volume conversion factor).  Some parameters such as the nitrate concentration at the drain field (C0) can be set on a septic tank by septic tank basis using the attribute table of the source locations input file. In this example dataset, C0 has been set via this method to 40 mg/l (a “-1” in the C0 box in ArcNLET indicates that the value of C0 will be read from the attribute table for each septic tank).
The default values of all the parameters are appropriate for the Lakeshore dataset therefore it is not necessary to change them right now.  For the input layers select the flow lines which were calculated in the previous step, the septic tank locations and the water bodies.  Select the location of the output plumes raster by clicking the folder icon.

Once the process completes, you will get a black and white raster file showing the concentration distribution of nitrate. This can then be colorized with a color ramp of your choice.  Additionally, you will get a point shapefile showing the locations of the calculated septic tanks, the attribute table of which contains much information (e.g., input load from each tank to groundwater).

 

 

2.4  Nitrate Load Estimation

The final step is to estimate the amount of nitrate entering the river.  This is done via the Nitrate Load Estimation module which can be accessed via the ‘Denitrification’ tab.  As input, select the point shapefile generated by the Transport module. The Risk Factor box is a multiplicative safety margin applied to the estimated load and will be left at “1” for this example. Run the module using the Execute menu.

The output appears in the box and shows the load to the river, the rate of nitrate removal due to denitrification, and the input load of nitrate to groundwater from all septic tanks.  If there were more water bodies, they would each have their own load estimates.  In this example, units in the output are milligrams because the input concentration, C0, in the Transport module was specified in milligrams.  The time unit is days because the hydraulic conductivity in the Flow module was specified in days. The estimated load to the river is 46,997 mg/day (0.1 lb/day).  

We thank Rick Hicks at the Florida Department of Environmental Protection for his help on this project. Download the ArcNLET installation package for ArcGIS 9.3.x or ArcGIS 10 at http://people.sc.fsu.edu/~mye/ArcNLET/

Thanks to Fernando Rios, Ming Ye, and Paul Z. Lee for providing this post. Questions: jfernand@buffalo.edu, mye@fsu.edu, paul.lee@dep.state.fl.us

The Australian Hydrological Geospatial Fabric (GeoFabric) is the foundation of the Australian Water Resources Information System (AWRIS), developed by Australia’s Bureau of Meteorology as a spatial framework for discovery, query, reporting and modeling of water information. AWRIS is an effort mandated by the Australian Government to help enhance the understanding  and management of Australia’s water resources.  The GeoFabric provides a modular conceptual model that connects input datasets and products for analysis based on:

1. combinations of data products at different spatial and temporal resolutions,
   
2. different feature representations from a single maintenance environment

which allows you to model how water is stored, transported and used through the landscape.

The key to the GeoFabric that makes it unique, versatile and robust, is

The underlying infrastructure of the Geofabric is a set of points, also called 'contracted nodes', that represent known features in the landscape such as the confluence of two major rivers, river outlets, inland sinks and water body inflow and outflow points.

…Each contracted node feature has an associated 'contracted catchment'. Contracted nodes are linked together into a node-link network (Australian Hydrologic Geospatial Fabric Product Guide, 2011).

These points have a permanent identifier present in all versions of the Geofabric, thus providing a framework through space and time.

It’s the node-links that connect the system together, and relate the system components: Surface Cartography, Surface Flow Network, Surface Catchments, Groundwater Cartography, Hydrology Reporting Catchments, and Hydrology Reporting Regions. Elevation data, the derived digital terrain models with their derivative, and the surface water features are connected in an integrated system that allow analysis and modeling, as well as cartographic quality display of the results.




The GeoFabric is an exemplary hydro GIS, which continues to be enhanced, and Version 2.0 was released in November.

The new component in Version 2.0 is Groundwater Cartography. Groundwater Cartography is a geodatabase that contains Feature Classes that include information about aquifer boundaries, types, and contours, as well as information about the groundwater like hydraulic conductivity and salinity.

 

The Bureau has a wealth of information about the GeoFabric on their website, including documentation for each GeoFabric product. Their  product tutorials are going to be updated as well (coming in the next few months).

Check back to the Hydro Blog for additional updates.

Thanks to Caitlin Scopel and Erika Boghici for providing this post. Questions: cscopel@esri.com.

WaterML stands for Water Markup Language. It is a standardized dialect of XML designed for transmitting hydrological data over the internet via web services.  The schema is defined by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), which also built WaterOneFlow, a family of web services optimized for querying hydrologic data.  With CUAHSI’s help, a number of federal agencies have implemented this system, including USGS NWIS. The Canadian and Australian governments are planning to follow suit. By standardizing the way that hydrological data is queried, it becomes easy to access data from all across the world. And, because the data returned is encoded in a standardized format, a single web map can easily ingest the data from any number of sources.

 

WaterML 2.0

The Open Geospatial Consortium (OGC) and World Meteorological Organization (WMO) have created a Hydrology Domain Working Group to help steward the process of designing WaterML 2.0, which will be the official OGC standard for encoding hydrological data. A draft of WaterML 2.0 was released last July, and another version will be complete at the end of this month, which means we should have a new WaterML 2.0 OGC Standard in 2012!

WaterML 2.0 will carry on the legacy of WaterML, which is the sharing of hydrologic observations data for the betterment of the global community. As it continues to gain international acceptance, including OGC certification, more governments will begin using it to publish their data. One day, we hope that the entire world’s hydro data will be accessible in a standard language. Check back to the Hydro Blog in January for news about the WaterML 2.0 OGC Standard.

Thanks to Daniel Siegel and Caitlin Scopel for providing this post. Questions: dsiegel@esri.com

 

 

Zichuan Ye and Lori Arstrong presented "HydroViewer: Utilizing Web-Based Hydrologic Data And Analytical Services" as a poster presentation at the American Geophysical Union Fall Meeting. You can now view this poster online.

Groundwater simulation models are mathematical models that help us understand and predict how water behaves in subsurface aquifers. Groundwater models, such as MODFLOW, are commonly used in the industry to predict different aspects of groundwater management including water availability, effects of pumping, contaminant transport, and more.

One of the challenges facing organizations is the communication of information regarding groundwater systems from professional modelers to others within the organization or to the public. Usually, visualization of groundwater models is achieved with specialized software packages. The model input and output files are a set of text or binary files that are difficult to interpret and map without specialized tools.

The workflow presented enables organizations to quickly create maps and GIS layers of MODFLOW models and to publish those on ArcGIS Online. By posting on ArcGIS Online you can share MODFLOW inputs and outputs with anyone within your organization, or share the model results with the public.

Step 1 – Use MODFLOW Analyst tools to import your model into a geodatabase and create maps of your model inputs and outputs

MODFLOW Analyst tools were used to import a MODFLOW model of the Cache valley in northern Utah into a geodatabase. After importing the model a set of map layers were prepared representing model inputs and outputs such as simulated heads, recharge, bottom elevation of layers, and starting heads.

Map of model inputs and outputs from a MODFLOW model of the Cache valley in northern Utah. The model was imported into ArcGIS using the MODFLOW Analyst import tools.

 

Step 2 – Create map and layer packages and share them on ArcGIS Online

In ArcMap, Map and Layer Packages were prepared. The Map Package enables the whole map content to be packed as a single file and the Layer Package enables exporting a set of selected layers as single file. The exported layers contain the data and layer symbology.

Map and Layer Packages can be automatically uploaded to your account in ArcGIS Online. The process includes entering a description of the map/layers, selecting appropriate tags, and specifying who you want to share the packages with. In this case the packages are shared with everyone.

Adding Map and Layer Packages to ArcGIS Online.

 

Once the packages are uploaded anyone can search for the data on ArcGIS Online and can open the packages in ArcGIS applications (ArcMap, ArcGlobe, ArcScene) or using the free ArcGIS Explorer viewer. The following figure shows the search results when searching for “MODFLOW” in ArcGIS Online.

Search results in ArcGIS Online when searching for MODFLOW.

 

The map and layer packages can be downloaded to your local computer or can be opened directly in ArcMap, ArcScene, and ArcGlobe.

 

Step 3 – Visualizing in ArcGIS Explorer

One of the interesting options is to open the packaged layers in ArcGIS Explorer. This allows anyone that doesn’t have ArcGIS for Desktop to visualize the results. Using ArcGIS Explorer increases the potential for sharing model information with a large number of users and clients that do not have professional GIS software.

Location of the Cache valley MODFLOW model shown in ArcGIS Explorer.

In addition, ArcGIS Explorer enables you to query the data, change base maps, and add notes and drawings on top of the model data. For example, one can use the Query Builder to display simulated heads for different layers of the model.

ArcGIS Explorer Query Builder is used to display simulated heads for layer 5 of the MODFLOW model.

 

The resulting layer from this query is a set of cells displaying only the simulated heads for layer 5. Notice, that the base map has been changed to show the world topographic map with a transparency on top of the world imagery layer. You can be creative and design your own base maps to display your data on.

View of simulated heads for layer 5 in the Cache MODFLOW model. The background base map was customized to show the world topographic map with transparency on top of the world imagery layer.

 

If you are interested in inputs/results at specific locations you can zoom and select individual cells and view their values. The following figure shows the bottom elevation value at a selected cell in layer 5 of the model.

Bottom elevation value at a selected cell in layer 5 of the MODFLOW model.

 

One of the interesting features of ArcGIS explorer is the ability to create presentations. With this feature you can capture different snapshots of your maps and add text. It is an easy way to communicate your model results. To access the Cache model presentation see the details at the end of the blog.

Presentation created in ArcGIS Explorer to show different inputs/outputs of the Cache valley MODFLOW model.

 

Finally, the ArcGIS Explorer project can be posted on ArcGIS Online. By simply selecting the Share command you have the option to share the map via email or to upload it to your ArcGIS Online account. Once uploaded, the ArcGIS Explorer project can be downloaded and opened by anyone with access to ArcGIS Online.

Sharing ArcGIS Explorer maps. You can automatically load the ArcGIS Explorer project to ArcGIS Online for sharing.

 

Summary

The workflow presented is an easy and cost effective way to publish and share information from groundwater simulation models with a wide range of users. Map and Layer Packages can be posted on ArcGIS online and these can be viewed in any of the ArcGIS for Desktop applications. Another option is to view the data in ArcGIS Explorer, so even users and clients that don’t have the professional GIS software can view and explore your models. With ArcGIS Explorer you also have the capability to create presentations that highlight different views of the model.

To view model described in this post you can find the files on ArcGIS Online by searching for “MODFLOW”. Or just follow this link:
http://www.arcgis.com/home/search.html?q=MODFLOW&t=content.

To view the model in ArcMap, just select to open the Map Package or Layer Package using ArcGIS Desktop.

To view the ArcGIS Explorer project:

• Make sure to first download the Layers Package (.lpk file) and the Explorer Project (.nmf file) to the same folder on your desktop and only then open the project.
• You also need to have ArcGIS Explorer build 1750 or later.Thanks to Gil Strassberg for providing this post.

Thanks to Gil Strassberg for providing the post. For more information contact Gil (gstrassberg@aquaveo.com). For more information on the Arc Hydro Groundwater tools visit the Aquaveo website (www.aquaveo.com/archydro-groundwater).

More information on using ArcGIS Online can be found at: http://www.arcgis.com/home/ and on the dedicated blog: http://blogs.esri.com/Support/blogs/arcgisonline/

 

 

 

Aquatic biologists and other researchers often need to make and share maps with simple point locations. ArcGIS Online and the Hydro Reference Overlay together make a great platform for mapping and sharing museum records for aquatic species.

Information on species localities is available from a variety of sources. One particularly useful site, VertNet, provides open access to over 84 million museum records from 72 vertebrate collections. The data are shared through 4 distributed database systems - MaNIS (mammals), HerpNET (reptiles and amphibians), ORNIS (birds), and FishNET (fish).

To produce a map of records for one of my favorite fish, the speckled dace (Rhinichthys osculus), I searched for it by scientific name on FishNet and downloaded the records as a csv file. I opened a new map in ArcGIS.com and dragged the csv file onto the map.

I switched to the terrain base map and added the hydro reference layer to provide an easy to understand reference map with features relevant to aquatic species (see below for details on how to add these layers). I then configured the pop up window and marker symbols and saved the map. The entire process took less than 2 minutes.  Early in my career it took weeks to make a simple static map of museum records. The advent of web access to georeferenced collections information makes producing web maps rapid and easy.

http://www.arcgis.com/home/webmap/viewer.html?webmap=7b00561e90fb4799a3b30a235ec26f9f

 

Once a web map is created in ArcGIS.com and saved it can be shared in a variety of ways. Click the “Share” button at the top of the web map to access these options. You can share the map with everyone or only with members of ArcGIS.com groups. The share window provides a short link you can email, Facebook and Twitter links, and code that you can embed in your own website. Additionally you can select “Make a Web Application” to build your own application using one of the hosted templates.

To make an application:
Click “Share”, check “Everyone”, and then click “Make a Web Application”

Select a template and click “Publish”. Give the application a title in the next window. Then go to the details page for the application from “My Content” and share and configure the application.

 

The details section of the application is populated from the details field of the webmap. Here I’ve included appropriate references to the data sources.

 

http://apps.arcgis.com/hosted/OnePane/basicviewer/index.html?appid=0c4a4a3f7ca04f4b8ae89745524622a1

 

Additional Information

To add the terrain base map and hydro reference layer:

1. Click the “Add” button and then select “Add Layer from Web”
2. Paste the link to the terrain base map into the “URL” box
3. Repeat the process with the hydro overlay url

 

URL for terrain basemap:

http://server.arcgisonline.com/ArcGIS/rest/services/World_Terrain_Base/MapServer

URL for hydro reference layer:

http://hydro_bm.esri.com/ArcGIS/rest/services/Hydro/HydroReferenceOverlay_MXD/MapServer

 

Useful links for getting started:

http://blogs.esri.com/Dev/blogs/arcgis/archive/2011/08/26/Drag-and-Drop-Files-on-Your-Web-Map.aspx

http://www.esri.com/software/arcgis/arcgisonline/videos.html

 

Special thanks to:

FishNET for providing data access and to the following museums for making their data available:

US National Museum of Natural History
North Carolina Museum of Natural Sciences
University of Kansas Natural History Museum
Canadian Museum of Nature
Harvard University Museum of Comparative Zoology
University of Washington Fish Collection

For more information on using biodiversity data in ArcGIS.com web maps and applications contact Richard Nauman (rnauman@esri.com).

Hydro scientists like us are commonly asked questions by policy makers and stakeholders like: “how do I know if this new well or new canal will affect the surrounding area?” or “Are my wells going to dry out because my neighbor built a new one?” One of the major challenges in GIScience to address these questions is to develop a tool that can translate the scientific understanding into easily understood, evidence-based policy or decision. We present plug-ins that work with ArcGIS Explorer Desktop (AGX): the ubiquitous WebGIS Analysis Toolkit for Extensive Resources (uWATER), and its extension: uWATER-Pumping Assessment (uWATER-PA) to accomplish these tasks. The uWATER package provides quick answers by visualizing the alternative developments for natural resource management in intuitive and simple approaches.  We follow with a few examples of how policy makers can implement the uWATER package.

 

Using uWATER to identify the most vulnerable wetlands due to groundwater pumping

To find the vulnerable wetlands using uWATER, first, load the wetlands and modeled groundwater drawdown shapefiles into AGX. Then click the Refresh button on the uWATER interface, to display in the listbox all loaded shapefiles. Move the cursor to highlight the wetland shapefile, and then click the Select button to display some basic information about the selected shapefile in the uWATER interface. Use the radio buttons on the uWATER interface to select the spatial relationship for the query. Then select and employ for query criteria a second layer (the groundwater drawdown shapefile) from the dropdown list in order to enable the selection of alternative drawdown conditions (in this case, S_2050B representing drawdown at 2050 under a normal pumping scenario) for policy making. Finally, specify minimum and maximum attribute values (the range of drawdown) to employ in the query. Click the Identify Features button to show the final results.

The cells outlined in green denote areas having predicted drawdown between 5 and 150 feet by a numerical model. uWATER identifies in red the wetlands intersecting the green-outlined cells; these wetlands are the most vulnerable wetlands to the predicted groundwater drawdown in 2050.

 

Using uWATER-PA to evaluate the effect of a new pumping well on existing wells

The second example we’ll discuss shows how uWATER-PA can help a stakeholder evaluate the impact of a new pumping well. The figure below shows the “Data Input” tab on uWATER-PA interface. First, specify the computing unit, then, click on the “Add a well on the map using the cursor” button and add a new well with one click on the map area. Next, choose the desired impact radius and then import the preloaded hydrogeological parameters (i.e. transmissivity and storativity) and existing well shapefile from AGX.

 

Next, use the “Impact Assessment” tab to input some simple configurations of the new well: maximum and actual pumping rate and duration. After clicking the compute impact buttons, the uWATER-PA will evaluate the maximal and/or actual impact of the new well and the impacted area and existed wells using color coding adapted from the Homeland Security Advisory System.

 

An example result of uWATER-PA is shown in the following figure. The stakeholder can see the impact range on the map and by clicking the resulted cells or wells, the actual impact (approximate additional drawdown) will show in the pop-up window.

 

The uWATER packages (programs, example files, and users’ manuals) are free to download at http://www.isws.illinois.edu/gws/sware/.

Special thanks to Drs. Yu-Feng Forrest Lin and Yi-Chen Ethan Yang for contributing this post. Questions for Dr. Lin: yflin@illinois.edu.  Questions for Dr. Yang: yceyang@engin.umass.edu

Water Hackathon 2011 was a success! There were over 800 registrants and nearly 100 problems brought to the table for the group to solve. Esri collaborated with great Hackathon minds, including IDModeling, to produce a web, smart phone, and text message based VGI solution to create a Water Access Map of the World.

We’re fast approaching 2012, and there are still a devastating number of people on this earth without access to clean water. The World Bank has made tremendous progress, but we can move even faster and more efficiently with a well organized GIS of the world’s water availability.

The Water Access Map of the World is a volunteered geographic information system of accessible water sources, with attributes like suitability of water for drinking, reliability of supply, and accessibility. The mapping application’s interface and web functionality make it easy for anyone to collect data about accessible water and submit that data to a central GIS database.

 

 

The data from the GIS is published as a Web Map Service and Web Feature Service, so it’s available to view, edit, and analyze. The application is written in open source code, so it’s easily extendable. Future plans for the application include a routing engine which will show the path from the current location to the nearest water source based on attributes like distance and ease of access.

For more information about the Water Access Map of the World project see the Random Hacks of Kindness website.

Download the BETA application

View the Water Access Map Web Feature Service on ArcGIS Online.

We added a new tool for summarizing classified data to our SSURGO toolbox. The SSURGO Component Classified tool takes a categorical field in the SSURGO component table and produces an output table summarizing the proportion of each category in the field for each map unit.

The SSURGO Tools version 2.0 toolbox is available in the Esri Hydro Resource Center and the Soils Atlas Group on ArcGIS.com.

In SSURGO Soils Tools Part 1, we discussed how values contained in the SSURGO component table could be mapped by flattening the many-to-one relationship between records in the component table and the map unit polygons using a weighted average approach. However, many useful fields in the component table contain categorical data. For example, the component table field hydricrating has values of yes, no, unranked, and not prime. Since each map unit polygon has one or more records in the component table, the components must be aggregated to produce a map. The Component Classified Tool aggregates the data by summing the proportion of each class for all the components in each map unit. To produce a map, the output table from this tool is joined to the map unit polygons using the mukey field and symbolized.

Like the weighted average tool, the SSURGO component classified tool takes three inputs:

1. A list of component table fields to process separated by semicolons
2. A path to a geodatabase containing two tables – “component” and “mapunit”
3. A path to an output folder or geodatabase

For each field in the input field list, the script creates an output table using the field’s name as the output table name and adds a field to the table to store the mukey which is later used to join to the map unit polygons and a no data field to store the proportion of the map unit with no data in the selected field. The script reads through the field and creates a list of unique values and adds a field in the output table for each unique value.

Note that if more than 20 unique values are detected the script will abort the current field and begin processing the next field in the list. This value can be changed by modifying the maxLen value in line 127 of the script.

Once the output table has been set up, the script cycles through each record in the map unit table, selects all the component table records associated with that map unit, and sums the proportion of each value in the component table field for the map unit. The summed proportion of each value is then written to the appropriate field in the output table.

Figure 1: Example of output table.

 

The output from this tool can be mapped in a number of ways. In this example we can join the output table to the map unit polygons and symbolize areas by the proportion of prime farmland with hydric soils (hydricrating = “yes”).

Figure 2: Hydric soils in the Sacramento, California area.

 

The Classifed and weighted average tools can be downloaded from the Hydro Resource Center Script Gallery or the ArcGIS.com Soil Atlas Group.

For more information, comments, and to share ideas contact Richard Nauman (RNauman@esri.com) or Michael Dangermond (MDangermond@esri.com).

A new release of the Hydrography Event Management (HEM) Tool is available for ArcGIS 10. The HEM Tool provides full functionality for adding and editing events in the National Hydrography Dataset (NHD). Events are informational data that are linked to the NHD using a linear referencing on NHDFlowlines. The HEM tool handles all the linear referencing mechanics to make working with events simple. Hydrography events support many applications such as resource management, environmental protection, emergency response, fisheries and much more.

 

 

 

 

 

Enhancements to Version 2.5 include:

• Added a Pan button to candidate form.

• Candidate form can list an additional attribute per event layer. Values from each event layer’s ‘Display  Expression’ field will be listed in the candidate form.

• Import to Events – before starting batch import the tool will now verify the spatial reference of the data frame and the NHDFlowline feature class are exactly the same.

• Import to Events will warn the user if they are about to apply approved imported events to a base table with the same name. Users have the option to stop or continue the process.

• Import to Events can now transfer no fields, just the link field, or all fields. All fields only works for standard field types (string, date, and numeric). Blobs, OIDs, GUIDs and shape type fields will not transfer.

• Create Multiple Events Downstream – Added new functionality (button) to create multiple downstream events from a point layer in the map. Not default on HEM toolbar, found under Customize > Commands > Categories > HEM > Batch Downstream from Points. Option to create overlapping or non-overlapping events from input points.

• Import to Events QC and Batch Sync QC will now allow users to select multiple rows at once and approve them all.

• SourceFeatureID now has an option to enforce unique IDs in event feature classes.

Find out more information and download the tool at: http://nhd.usgs.gov/tools.html.

An online demo of new features will be scheduled for the week of Oct 31st, email hem@usgs.gov to attend.

Special thanks to Ariel Doumbouya for providing this post. Questions for Ariel: hem@usgs.gov.