Watershed Data Processing

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Raster-network regionalization for watershed dataprocessing

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Research Article

Raster-network regionalization for watershed data processing

T. L. WHITEAKER*{, D. R. MAIDMENT{, H. GOPALAN{, C. PATINO{and

D. C. MCKINNEY{

{Pickle Research Campus, Bldg 119, MC R8000, University of Texas, Austin, TX 78712,

USA

{Florida Department of Environmental Protection, 3900 Commonwealth Boulevard MS

49, Tallahassee, FL 32399, USA

(Received 11 February 2005; in final form 3 August 2006)

Difficulties exist in calculating watershed parameters from raster datasets overlarge regions because of the excessive computation time involved. A technique is

presented which divides a large region into hydrologically distinct subregions, in

each of which raster analyses are performed, in order to efficiently process large

raster datasets. The results of raster analyses are stored as attributes on the

resulting vector data. The vector data are then merged, and appropriate values

accumulated to obtain regional parameter values for points of interest along the

stream network. The technique, called Raster-Network Regionalization, uses the

vector stream network as the backbone for the integration of subregions into a

single region. A case study is presented which utilizes the technique to prepare

geospatial inputs for the Water Rights Analysis Package simulation model.

Keywords: Regionalization; ArcGIS Hydro data model (Arc Hydro); WRAP

Hydro; Water Supply Modelling

1. Introduction

The State of Texas manages the supply and demand of surface water through a

priority-based system of water rights. Water users must apply for permits and

adhere to rules in the Texas Water Code regarding surface-water usage. In 1997, the

Texas legislature passed Senate Bill 1, mandating improved management and

modelling capabilities for surface-water resources in Texas. As part of the bill, the

Texas Commission on Environmental Quality was given the task of developingwater availability models for the major river basins in Texas. These models help

water-resources planners understand how much water is in the system, how that

water is being allocated, and the impacts of additions or changes in water-rights

permits (Hudgens and Maidment 1999). However, previous methods used to

support water-availability modelling with Geographic Information Systems (GIS)

were cumbersome, requiring the processing of large raster datasets. The motivation

for the research presented in this paper is to overcome the limitations of raster

processing in support of water-availability modelling.

This paper discusses the concept of Raster-Network Regionalization, which refers

to the integration of subregional datasets into regional datasets for analysis

purposes. First, problems with raster-based analysis of large datasets are outlined,

*Corresponding author. Email: [email protected]

International Journal of Geographical Information Science

Vol. 21, No. 3, March 2007, 341353

International Journal of Geographical Information ScienceISSN 1365-8816 print/ISSN 1362-3087 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/13658810600965255


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followed by a discussion of how network-based accumulation improves upon raster

techniques. With this background, the Raster-Network Regionalization Technique

is then described. Finally, a case study utilizing Raster-Network Regionalization to

prepare GIS data for use in the Water Rights Analysis Package (WRAP) simulation

model is presented.

2. Previous work

Raster-based analysis of GIS data provides useful parameters for hydrologic

modelling, water-quality assessments, and other water-resources applications

(Mason 2000, Osborne 2000). Distributed parameters can be summarized and

accumulated using a variety of raster tools available through the GIS. However,

problems arise as the size of the raster datasets increase, as when working with very

large areas or with very-high-resolution data. These problems include:

1. The time required to process rasters becomes unreasonable. Figurski (2001)

reported processing times on the order of 10 days for calculating watershedparameters for the Trinity Basin at 30-m raster resolution (over 100 million

cells).

2. Data storage becomes cumbersome. The calculation of multiple grids covering

a vast number of cells leads to a large amount of data that must be stored on

disk and makes it difficult to transfer data to other parties.

3. The required grid size exceeds limits. In ArcGIS version 8, grids are limited to

a maximum size of 2 GB. If a raster analysis required a grid with a resolution

sufficient to exceed the 2 GB limit, then that raster analysis cannot be

performed. Note that this limitation has been removed for ArcGIS version 9.

Despite these drawbacks, the distributed analysis capabilities of raster processingare too valuable to discard. Figurski (2001) developed a technique of cascading

parameters, so that rasters could be split into parts. However, the technique still

relies on rasters for accumulation routines and is prone to error due to the large

number of hand manipulations of data involved.

Streit and Kleeberg (1996) encountered the problem of transferring hydrologic

information from small catchment scales to basin scales and used generalization to

address the issue. However, Streit and Kleeberg also recognized that generalization

may introduce errors into the data. Schumann and Funke (1996) addressed this

problem by creating components for rainfall-runoff modelling which utilize new

scale-independent parameters. However, those applications were limited to amaximum basin size of roughly 10 000 km2.

Abrahart et al(1996) developed an application which divided a study basin into

sub-basins for use in MEDRUSH, a GIS combined with distributed process model

designed to simulate hydrologic and vegetative processes important in studying

desertification. Each sub-basin contains a set of flow-strips, in which hydrologic

simulations occur. The total output from each flow-strip in a basin is converted to

water and sediment output for that basin. Each basin feeds its total output to the

stream network, where information is accumulated in the downstream direction.

The application may operate on basins up to 5000 km2 in area.

The research presented in this paper introduces a Raster-NetworkRegionalization Technique, in which large basins are subdivided into subregions

suitable for the calculation of distributed watershed parameters. The subregional

data are then merged to provide an integrated regional dataset attributed with the

342 T. L. Whiteakeret al.


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necessary hydrologic parameters. The technique has been successfully applied to the

Rio Grande basin in Texas, which has a contributing area of over 550 000 km2.

3. ArcGIS Hydro data model

The Raster-Network Regionalization technique utilizes the data structure and toolsprovided by Arc Hydro. The ArcGIS Hydro data model (Arc Hydro) is a water-

resources data model that uses GIS to capture the essence of surface water features.

The goal of Arc Hydro is to represent data in a manner that supports hydrologic

simulation modelling (Davis 1999). Arc Hydro defines a set of water-resources

feature classes, such as watersheds, cross-sections, and monitoring points. In

addition to prescribing an attribute structure and organization, Arc Hydro also

defines relationships between features, so that a watershed may know which point

represents its outlet (Maidment 2002). An Arc Hydro toolset populates the

attributes of Arc Hydro and performs operations using the Arc Hydro data

structure. Although Arc Hydro and the Arc Hydro tools were designed to work inthe ArcGIS environment, the data structure of Arc Hydro could in principle be

applied to any GIS environment.

4. Methodology

The Raster-Network Regionalization Technique computes watershed parameters

from raster sources by (1) using zonal statistics to summarize raster values for a set

of watersheds and (2) using Consolidation and Accumulation techniques to pass

values from the watersheds to the stream network, and subsequently downstream

through the stream network. The technique is valid for hydrologic parameters that

may be summed or spatially averaged for watersheds such as drainage area, averageprecipitation, and average curve number. Before proceeding further, a definition of

terms is useful:

N Consolidation: the process of summing values from a set of features onto a

related feature participating in the stream network;

N Accumulation: the process of summing values downstream through the stream

network.

Whiteaker (2001) developed Accumulation and Consolidation GIS tools that use

relationships between features to accumulate information from one feature to

another. The Consolidation tool transfers values from features in the landscape torelated features along the stream network. The relationships between features are

specified in attributes. For example, drainage area from watersheds can be

consolidated to the junction feature on the stream network that serves as the outlet

for each watershed (figure 1), if each watershed contains an attribute storing the

identifier for its outlet junction.

The Accumulation tool accumulates attributes downstream among junction

features in a stream network. Downstream navigation is performed by reading a

next downstream ID attribute on a given junction, which stores the identifier of the

next downstream junction in the network. Each junctions value (e.g. drainage area)

is added to the next downstream junctions value, so that the final value for a givenjunction contains the sum of values from all upstream junctions plus its own value

(figure 1). The Accumulation tool is more complicated than the Consolidation tool,

because the Accumulation tool must navigate through a series of attribute

Raster-network regionalization for watershed data processing 343


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associations until all upstream junctions have been accounted for, for a given

junction, whereas the Consolidation tool only navigates a single attribute

association for each feature. Also, the Accumulation tool is designed to work

within the same feature class (e.g. stream junctions), while the Consolidation tool is

designed to work with two different feature classes.

An important caveat for the Accumulation tool is that the tool is designed to

work with a dendritic stream network. If branching occurs, each side of the branch

may not be accounted for correctly, as only one value for the next downstream IDcan be stored for a given junction, even if that junction is upstream of two other

junctions on either side of a branch. Therefore, when branching occurs, the user

must decide to which branch the values should be accumulated, and assign the next

downstream ID accordingly.

The Accumulation and Consolidation tools allow for rapid summation of

attributes in the vector domain. Not only is vector accumulation typically faster

than computing large raster datasets, but the size on disk of vector data required to

describe the entire basin is typically much less than the amount of space required to

store flow-accumulated rasters covering the study area.

For example, consider the Guadalupe River Basin in Texas, which covers15462km2, or roughly 17 million raster cells at a 30 m630 m cell size. As a test case,

this basin was divided into 2035 watersheds, with an outlet junction on the stream

network for each watershed. The accumulation and consolidation tools were used to

Figure 1. (a) Area from Upstream Watershed Consolidated in Outlet Junction. (b)

Accumulated value for the outlet junction: the sum of all upstream values plus the value atthe outlet.



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calculate total upstream drainage area for each junction. On a 2-GHz computer with

512 MB of RAM, the process takes 22 min and requires an additional 0.04 MB of

disk space to compute the result. Computing the same values using traditional raster

methods such as those found in Figurski (2001) requires 1 h 43 min and 281 MB of

additional disk space.

Yet, despite the efficiency in using network-based analysis techniques, some rasteranalyses must still be performed. So, how can the Accumulation and Consolidation

routines be applied to geospatial processing for water-resources applications?

The key is in the watersheds. Often, hydrologic parameters are defined and

calculated for watersheds. For example, in the traditional method of calculating

watershed parameters in a GIS, control points (points of interest) are overlain on a

flow-accumulated raster. A grid cell underneath a given control point is read to

determine the value for that control point. The cell represents a weighted flow

accumulation for a certain value, such as average curve number, which includes the

influence of all grid cells which flow (hydrologically) to that grid cell. Together, all

of those cells form the watershed for that point of interest. Therefore, by reading thevalue of the cell underneath a given control point, the average value for the

watershed draining to that control point is determined.

The Raster-Network Regionalization Technique uses a different approach. A

summarization of raster values over watersheds is determined by using the

watersheds as distinct zones which define the area of analysis for a zonal statistics

tool in GIS. This tool calculates statistics such as mean, sum, max, and min for each

zone by reading the values of cells within each zone and performing the necessary

statistical operations. Thus, with this approach, accumulated grids whose cell values

are influenced by all upstream cells are no longer needed. The only cells that a

watershed is interested in are the cells that lie directly over that watershed.Once attribute values have been determined for watersheds, these values are

consolidated to outlet junctions and then accumulated throughout the stream

network in the vector domain. The watersheds become the basic processing unit

with basin-wide coverage, while raster coverage is reduced to each individual

watersheds extent. Thus, watersheds effectively replace grid cells as the units of

analysis. (Note that if a vector watershed dataset is not available, the vector

watersheds can be determined from elevation data using standard GIS tools.

Techniques for watershed delineation are beyond the scope of this paper.)

This allows a basin or region to be divided into hydrologically distinct subregions,

in which the necessary raster analyses take place. The smaller size of the subregions

permits faster raster processing, with results from raster analyses stored on vector

watersheds. The Consolidation and Accumulation techniques described above are

then used to accumulate watershed parameters across the entire basin.

The Raster-Network Regionalization Technique involves the following steps

(figure 2):

1. Clip raster and vector data to hydrologically distinct subregions. Subregions

may be defined by using established watershed boundaries such as USGS

HUC boundaries.

2. Perform raster processing on each subregion, to obtain necessary values

summarized onto vector watersheds.3. Merge the subregional vector data to a regional vector dataset, and update

connectivity for the outlet junction of each subregion to point to the nearest

junction in the next downstream subregion.



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4. Use the Accumulation and Consolidation routines to compute final values for

each point of interest on the stream network.

The benefits of the Raster-Network Regionalization approach are (1) processing

time is reduced, since much of the processing occurs in the vector domain rather

than in the raster domain; (2) data storage requirements are reduced, since

accumulation grids no longer need to be created; and (3) the remaining grids can be

split into hydrologically distinct regions defined by one or more watersheds,

resulting in faster raster processing, more modular data storage, and less of a raster

reprocessing effort if data in a given watershed change (since only those cells within

that watershed matter to that watershed). With Raster-Network Regionalization,

the weight of processing is shifted from the raster side to the vector side. Even the

largest basins can now be processed with high-resolution raster data.

5. Case study: WRAP Hydro

WRAP Hydro is a preprocessing data model created to provide the Water Rights

Analysis Package (WRAP) with geospatial inputs regarding watershed parameters

and water-rights connectivity. In addition to the data model, WRAP Hydro alsoconsists of a toolset called the WRAP Hydro tools, which calculates those geospatial

parameters for WRAP. WRAP Hydro utilizes the Raster-Network Regionalization

Technique to handle large river basins.

Figure 2. Steps in the Raster Network Regionalization Technique: (a) defining subregions,(b) performing subregional raster analyses, (c) merging vector subregions, and (d)

accumulating vector attributes using the stream network.



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5.1 Water Rights Analysis Package

The Water Rights Analysis Package was chosen by the Texas Commission on

Environmental Quality (TCEQ) as the model to simulate water-allocation in

support of water-availability modelling. WRAP uses a record of monthly

naturalized streamflows (naturalized streamflows are those that would existnaturally without the influence of man or man-made structures and diversions),

reservoir evaporation rates, basin characteristics (such as curve number and

drainage area), and mean annual precipitation rates to simulate the hydrology for a

basin. WRAP uses water-right priorities, rules, and target relationships to allocate

water during the simulation period using a monthly time step. Simulation results

include such information as reservoir storage levels and reliability indices for

meeting water-use requirements (Wurbs 2001).

To facilitate the calculation of control point and watershed parameters (basin

characteristics) for WRAP, Hudgens and Maidment (1999) created a set of tools

encapsulated in an ArcView 3 project file called WRAP 1117, which computesaverage curve number, mean annual precipitation, drainage area, and connectivity

for the watershed draining to each control point on the stream network. The first

three parameters are calculated using raster analyses. The procedure involves

burning streams (reducing the elevation values along the stream so that once water

enters the stream, it does not jump back onto the floodplain) into a DEM, filling

sinks, calculating a flow direction grid, and then calculating flow accumulation

grids. The drainage area value over a given point is the number of cells accumulated

to that point, times the area of each cell. A weighted flow accumulation is used to

determine the average curve number and mean annual precipitation over a given

point. Thus, those three parameters are calculated for every single cell over the

extent of the DEM, and the value for a given control point is found by picking up

the grid cell value for the cell underneath that control point. The connectivity of

control points is determined by identifying the next downstream control point in the

stream network. Once the parameters are calculated for all control points, the results

are then input to a WRAP model (Hudgens and Maidment 1999).

WRAP 1117 encounters scalability problems when applied to large river basins.

In basins large enough to cover more than 100 million cells in a raster dataset,

processing demands may exceed a high-end desktop computers processing

capabilities (Figurski 2001). The Raster-Network Regionalization technique

presented in this paper overcomes the problems encountered by WRAP 1117 when

processing large regions.

5.2 WRAP Hydro Data model

WRAP Hydro utilizes a GIS to store geospatial inputs required to calculate

parameters for WRAP. WRAP Hydro, developed primarily by Gopalan (2003),

divides data into separate geodatabases and folder locations for each region in the

analysis. Raster data for each region are processed separately, with resulting

watershed parameters attributed to vector watersheds. The vector data for each

region are then merged to produce so that the Consolidation and Accumulation

tools can be run over the entire region.The actual processing of WRAP Hydro data is facilitated by the Arc Hydro and

WRAP Hydro tools. The resulting data contain all of the geospatial inputs required

by the WRAP simulation model.



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5.3 WRAP Hydro tools

The WRAP Hydro tools consist of a set of public domain utilities developed on top

of the Arc Hydro data model. These tools operate in the ArcGIS ArcMap

environment, with some of the functions requiring the Spatial Analyst extension.

The tools provide functionality in addition to the Arc Hydro tools in order tocalculate drainage area, average curve number, average precipitation, and the next

downstream identifier for controls points in WRAP. The WRAP Hydro tools use

Raster-Network Regionalization, with additional refinements to better insure

proper parameter calculation, such as the delineation of drainage areas to the stream

network lines rather than the junctions. The general process flow utilized by the

WRAP Hydro tools is shown in figure 3.

This section describes some of the core functionality of the WRAP Hydro tools

and how the tools are used to calculate WRAP parameters. Full help documentation

for the tools, as well as the tools themselves, may be downloaded from http://

www.ce.utexas.edu/prof/maidment/grad/whiteaker/hydrotools.html.The WRAP Hydro tools include utilities for delineating watersheds, which allow

the user to choose a feature class of point, line, or polygon geometry to serve as the

outlet zones for the watersheds. Polygon and line geometries are more secure than

points for delineating watersheds, because if a point is not over a cell within a

natural channel in the digital elevation model, then the watershed that is delineated

for that point will not define the intended drainage boundary. With points or lines

Figure 3. WRAP Hydro processing flow chart.



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serving as the watershed outlets, there is a much greater chance that the geometries

will intersect a cell in the channel of the DEM. This approach becomes particularly

useful in flat areas, where the flow direction is more ambiguous (figure 4).

Once watersheds have been delineated, the WRAP parameters of average curve

number, average precipitation, and drainage area may be calculated using the

WRAP Hydro tools. The drainage area is simply read from the shape area of the

watershed feature, which is calculated automatically by ArcGIS, and then converted

to desired units of analysis. The average curve number for a given watershed is

found by laying a curve number grid over a watershed and then using zonal statisticsto computer the average curve number value for all cells over that watershed

(figure 5). The same process is used to calculate average precipitation for watersheds.

Once parameter values have been calculated for each watershed, they can be

accumulated onto control points using the consolidation and accumulation

techniques described above. The WRAP Hydro tools use those routines without

modification to accumulate drainage area values. For average curve number, each

watersheds curve number value is multiplied by that watersheds area. This

weighted curve number is then accumulated for the control points. The final

accumulated value is divided by the total combined area of upstream watersheds to

provide an average curve number for the entire drainage area of a given control

Figure 4. Use of different features as outlet zones for watershed delineation yieldingdifferent results (only a portion of the watershed formed from using edges as outlets isshown).

Figure 5. Zonal statistics providing an average curve number for each watershed.



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point, as shown in equation (1).

CNj~

Pn

i~1

AiCNi

Pn

i~1

Ai

, 1

where: CNj5average curve number for control point j;Ai5drainage area for the ith

watershed draining to control point j; CNi5curve number for the ith watershed

draining to control point j; n5number of watersheds draining to control point j.

The average precipitation for each control point is found in the same manner.

Once these attributes have been calculated, the results may be exported for use in the

WRAP simulation model.

With its well-organized data model and custom toolset, WRAP Hydro provides a

useful mechanism for managing Water Availability Modelling (WAM) data, and

calculating parameters required to run WRAP simulations. The Texas Commission

on Environmental Quality has adopted WRAP Hydro for its WAM projects. The

Raster-Network Regionalization Technique is vital to that effort, as the Rio Grande

comprises a contributing area over 550 000 km2, or well over 500 million grid cells at

30-m resolution (figure 6).

This case study has illustrated an example of an Arc Hydro-based Preprocessing

Data Model and application of the Raster-Network Regionalization Technique.

Both of these methods of geospatial integration facilitate the use of a GIS to prepare

input parameters for simulation models. Full details of WRAP Hydro, including the

role of each feature class in the data model and further elaboration on the use of the

WRAP Hydro tools, may be found in Gopalan (2003).

6. Conclusions

This paper has illustrated a Raster-Network Regionalization Technique which

utilizes raster-based analysis at the subregional scale and network-based attribute

accumulation at the regional scale in order to process large regions in an efficient

manner. The benefits of raster-based analysis are preserved at the subregional level,

so long as the analyses are independent of other subregions, such as with zonal

statistics operations. These types of operations are useful in obtaining hydrologic

information pertaining to each watershed.

Subregions are integrated into regions through the vector datasets and relation-ships. The stream network provides the backbone for connectivity between

subregions, with watersheds being related to the network through their outlet

junction on the network. The Raster-Network Regionalization Technique has been

applied to the analysis of large river basins in Texas. The technique could also be

applied at a local level when high-resolution data, such as LIDAR data, are available.

An implication of the Raster-Network Regionalization Technique is the

categorization of datasets into one of three scales: Local, Subregional, and

Regional. Local datasets contain the most detailed information about an area,

such as detailed channel geometry or LIDAR data, and are useful for supplying

information to models with a local scope, such as a hydraulic model. These datasetsmay be maintained by local entities and tend to vary greatly between locations.

Certain information from several Local datasets may be generalized and used to

create a Subregional dataset, which covers a much larger area than a single local



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Figure 6. Rio Grande Basin.



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dataset. These datasets may be maintained by state or federal agencies and provide

the most commonly used types of subregional information, such as the stream

network or digital elevation models at a 30-m resolution. Subregional datasets may

be integrated using the Raster-Network Regionalization Technique to form

Regional datasets, which allow analysis over very large areas.

A powerful conclusion from this research is that next downstream ID assignmentis critical to the success of regionalization. The ID enables the connection between

features in the landscape, including:

N the connection of watersheds to outlet junctions;

N the connection of junctions with next downstream junctions;

N the integration of subregions into regions, through the update of the next

downstream ID in the most downstream junction in each region

The limits of network-based analysis were not reached in this research as far as

computing time and processing power are concerned, even when analysing river

basins such as the Brazos River basin, with an eastwest expanse larger than thestate of Texas. Thus, with Raster-Network Regionalization and Arc Hydro, the

same types of hydrologic analyses may be performed in the GIS, independent of

scale.

Acknowledgements

This research was financially supported by the GIS in Water Resources Consortium,

the National Council of Science and Technology (CONACYT), the Texas

Commission on Environmental Quality, the US Army Corps of Engineers, and

NAD Bank.

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