Tag: Texas

Use ZIP Codes to discover how Americans think, feel, and live.

Tapestry Segmentation

In a new Learn ArcGIS lesson, ZIP Codes are your access into learning how Americans think, feel, and live. Get Started with Tapestry reveals demographic and socioeconomic data for any of the estimated 43,000 ZIP Codes throughout the United States. In the … Continue reading

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Hydrology is online at the University of Texas – Austin

AGOL landscape services

Students at the University of Texas at Austin have found a reengineered GIS in Water Resources class this fall. The GIS class, which was first offered by Dr. David Maidment in 1996, has changed the way that the information is … Continue reading

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Stormwater Utility Mapping of Impervious Area

Stormwater Fee

by Amit Sinha, Esri, Inc. Stormwater is the water that runs off property when it rains. When stormwater flows across driveways, parking lots and other surfaces, it picks up dirt and pollutants along the way. When this polluted water reaches … Continue reading

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Optimized Tool for DEM Pit Removal now available

LiDAR scene

by Stephen Jackson, Graduate Research Assistant, Center for Research in Water Resources, The University of Texas at Austin, srj9@utexas.edu Extracting hydrologic features such as stream centerlines and watershed extents from a Digital Elevation Model (DEM) typically requires first hydrologically conditioning … Continue reading

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Texas Reservoir Sedimentation Study: ArcGIS Map Package now available!

Watershed Protection for Texas Reservoirs - Addressing sedimentation risk

by Ernest To1*, Carissa Belsky3, David Harkins1, Jim Patek2, Steve Stecher3, Mel Vargas2, Jennifer Walker4 1RPS Espey 2Parsons Corporation 3Crespo Consulting Services, Inc. 4Watearth, Inc. *corresponding author From 2011 to 2012, the Texas Water Development Board funded a study (TWDB … Continue reading

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Soil Moisture Mapping of Drought in Texas

Soil Moisture Mapping of Drought in Texas

by Johnny Sullivan, Graduate Research Assistant, Center for Research in Water Resources, The University of Texas at Austin, johnrsull@utexas.edu It is unclear at present exactly how climate change will affect global precipitation patterns on a long-term time scale. The Intergovernnmental … Continue reading

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Using NHDPlus to address sedimentation risks to water quality and availability in Texas reservoirs

by Ernest To* – RPS Espey, Carissa Belsky – Crespo Consulting Services, Inc., David Harkins – RPS Espey,

Jim Patek – Parsons Corporation, Steve Stecher – Crespo Consulting Services, Inc., Mel Vargas – Parsons Corporation,

Jennifer Walker – Watearth, Inc.

* =corresponding author

Sedimentation in Texas reservoirs is a significant problem that affects both water availability and quality in Texas.  The Texas Water Development Board estimates that Texas’ major reservoirs are losing 90,000 acre-feet per year due to sedimentation. This is equivalent to a loss of 4.5 million acre-feet by 2060 and exceeds the projected increase of 3.4 million acre-feet from new reservoirs.   The reduction in storage volume not only impacts water supply but also necessitates the alteration of supply infrastructure to handle deteriorating water quality.  Uncertainty regarding rainfall supply due to climate change further burdens the already stressed reservoirs.

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Automated Data Acquisition for Integrating Multiple Datasets Using GIS Applications

by Alison Wood, Graduate Student, The University of Texas at Austin

New Jersey Application



As GIS users, we often have to collect data from many sources and compile them into a single map.  For just a few sources and a single map, this might be feasible.  But what if you have to make a new map with updated data every day?  Or every hour?  Automation can save you the enormous time it would take to do that by hand, and also help to avoid the errors that can happen in repetitive tasks done by hand.  In this blog entry, I’ll describe an example of automating a process to retrieve data, execute file format conversions, and update an online map; I’ll also talk a little bit about some of the tools and strategies I used that will be useful for someone else automating a similar process.

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Heightening Situational Awareness During Floods Events

Prepared by: Fernando Salas, Graduate Research Assistant, University of Texas at Austin
Special thanks to Dr. David Maidment (CRWR), Dr. Stefan Fuest and Matt Ables (KISTERS), and Dan Siegel (Esri) for their individual contributions to the design and implementation of the Central Texas HUB.

The central Texas corridor, better known as “Flash Flood Alley,” is one of the most flood prone regions in the United States. In fact, Texas regularly leads the nation in flood fatalities and flood related property damage each year. During a flash flood, rapidly changing water levels can trap both emergency responders and citizens with little to no warning. In order to mitigate risk to residents and infrastructure, citizens and emergency responders need to exhibit “real-time” situational awareness to respond proactively instead of reactively. With the emergence of the internet, mobile communication networks and social media, it is now possible to quickly disseminate information to a vast audience in “real-time.” Furthermore, the emergence of GIS technology and web services has facilitated the creation of easily understandable map applications that readily convert data into actionable intelligence.

Flooding in Central Texas

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TMDL Balance: Simulating Coastal Water Quality in GIS


Bacterial contamination is the leading cause of water quality impairment in Texas waters.  Many of these bacterial impairments are along the Texas Gulf Coast because coastal waters often are regulated for oyster harvesting, which requires strict water quality standards.  Per the US Environmental Protection Agency’s Clean Water Act, each of these impaired waterbodies requires a Total Maximum Daily Load (TMDL) study to be performed.  TMDL Balance is a steady state, mass balance, GIS-based model for simulating pollutant loads and concentrations in coastal systems.  Through use of the schematic processor, the model passes pollutant loads through the system, accumulating and decaying the loads as they move along through a series of processing ops.

The TMDL Balance model was developed and applied in the Copano Bay watershed of southeast Texas.  The model is used to simulate bacterial loading, decaying these loads as they move via overland flow and through three distinct types of waterbodies: freshwater non-tidal rivers; tidal rivers; and bays/estuaries.  Though the model was developed in the context of modeling bacteria TMDLs along the Texas Gulf Coast, it is applicable to a variety of pollutants and geographic areas.

Figure 1


Defining the Flow Network

The schematic network is the framework upon which the TMDL Balance model (through the schematic processor) performs its calculations.  Representing hydrologic features on the landscape through a series of links and nodes, schematic networks are built from river and catchment features using the Arc Hydro Tools.  Nodes represent catchments, river headwater locations and river junctions.  Links are created to connect the river headwater locations and junctions to create the river network and to connect catchment nodes to the river network.

Figure 2


To model bacterial loading in coastal systems, additional nodes and links are needed.  The nodes represent the bay/estuary, itself, and point sources that add bacteria into the system.  In the Copano Bay watershed, point sources include wastewater treatment plants, colonies of birds nesting adjacent to the Bay and failing septic systems adjacent to the Bay.  After manually adding the nodes, they are connected to the network through the use of links.  One last manipulation to the schematic network is the differentiation of tidal from non-tidal rivers.  Water quality processes in the two river types are simulated differently; therefore, they must be differentiated in the schematic network.  In the Copano Bay example, the location of tidal rivers was defined by regulation.


Figure 3


Computing Loads

Bacterial load is added to the system at each catchment and point source node.  Loading from the catchments represents non-point source loading and is computed as a function of the various non-point sources in that geographic area (determined as a function of land use/land cover and a number of other resources) and the computed mean annual runoff.  Point source loads are computed as the product of bacterial concentrations (measured or estimated) in the point source flow and the mean annual flow amount.  In the Copano Bay watershed non-point sources of bacteria include: agricultural animals, wildlife, natural background and failing septic systems away from the Bay.


Simulating Water Quality

TMDL Balance uses a series of processing ops to simulate water quality, including tidal interactions and bacterial decay.  These ops include: first-order decay (overland flow and non-tidal rivers); continuous stirred-tank reactor (tidal rivers); and the tidal prism approach (bays/estuaries).  The ops are implemented throughout the network using the schematic processor.  The ops are applied to the nodes and links, depending on the features that they represent.  Any feature not specifically assigned a processing op is simulated using the schematic processor’s default operation of simple accumulation.


Figure 4


Computing the TMDL

Once the bacterial loads are assigned to the schematic network and the model has been calibrated to observed values, the model can be used to compute the maximum load that can enter the system’s waters while still meeting water quality standards (i.e., the TMDL).  Simulating reductions within the watershed can then inform implementation activities.


Special thanks to Dr. Stephanie Johnson for contributing this blog. The TMDL Balance model was developed by Dr. Johnson, and for information on the model or its use contact Stephanie at sjohnson@houstoneng.com.


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