Networked catchments based on delineation and flow accumulation methods
Mary Kneeland and David Theobald, Natural Resource Ecology Lab, Colorado State University (mkneel@nrel.colostate.edu ; davet@nrel.colostate.edu )
A central goal of our research is to develop landscape-level indicators that can be used to predict
associated aquatic response variables derived from EPA EMAP data. Because the target end-users of
this work are states, tribes, and other water resource agencies, we aim to produce methods and tools that
are robust and use widely-available hydrologic and topographic data to model specific aspects of
watersheds at both regional and national levels. To that end, we have identified two primary research
objectives:
Objective 1: Delineate catchment boundaries to link watersheds to
stream reaches
Objective 2: Develop GIS tools to assist computation of landscape
metrics for use with aquatic responses
The initial development of the methodologies to meet our objectives were conducted using USGS/EPA
National Hydrography Data (NHD) within the Mid Atlantic Highlands Project (MAHA) study area,
Colorado, and the Northern Coast of Oregon. Once our methods were refined, we ran the model for the
entire US using ERF1 v2 (Enhanced River Reach File version 2.0 USGS/EPA) in order to test its
robustness and efficiency.
Network-based metrics
Delineating reach catchments
There are a number of different methods to automate the delineation of watershed boundaries using
GIS, and most methods involve computing the cell-to-cell direction of flow. By back-tracing the paths from
a single cell, the catchment area of any given point can be found. In most methods, tracing the catchment of
a stream reach is usually found by identifying the location at the end of the reach (the “to” node) and
identifying the catchment from that point. However, in practice this method requires extensive preprocessing of Digital Elevation Model (DEM) data to fill in artificial “pits” that cause artificial “striping”
(long, linear catchments) and artificial ridge lines. The USGS NHD program has developed a watershed tool
that relies on this method, and is beginning to produce fine-scale (using 1:24K hydrology) watershed
boundaries that will become available over time.
We sought to develop a method that was more robust to possible artifacts in the DEM. Moreover,
because we want to delineate the catchment area for each stream reach, not basins per se, we were able to
develop a method that uses a hyrdologically correct DEM and the reach (stream line) to identify its
catchment. Our method “grows” catchments around each unique stream reach. We found that during the
watershed “growing” process, artificial ridges caused an incorrect watershed boundary to form. Our method
corrects this problem by finding the artificial ridge areas and buffering the portion of the watershed
boundary, thereby ‘bumping’ it past the artificial ridges.
Our method works as follows:
1. Convert stream reaches to GRID using reach Ids  S
2. Compute Hydrologically correct DEM using TOPOGRID (ArcInfo)  T
3. Compute the flow direction from the DEM (T)  D
4. Find the catchment using WATERSHED (D)  C
5. Locate artificial ridges along watershed boundaries  A
6. Buffer A by 1 cell to “bump” it out of artificial boundaries  A’
7. C = A’, then Go To 4 until catchments are space-filled
8. End.
NHD 1:100K Streams
TOPOGRID
Hydrologically Correct Digital Elevation Model
The hydrologically correct DEM was created by the ArcInfo module: TOPOGRID. TOPOGRID is
an interpolation method which uses a spatially varying RMS error to interpolate an elevation grid based on
input datasets of streams, waterbodies, and elevation. This method creates accurate representation of areas
with flat valleys adjacent to steep mountains.
We developed an extensive database for the development of the catchment delineation model and for
the calculation of metrics associated with a hydrologic network. This data include:
ERF1 v2
NHD
NED
PRISM
Enhanced River Reach File
National Hydrography Data
National Elevation Dataset
Parameter-Elevation Regressions on
Independent Slopes Model
Stream Gauge Monthly and Annual
Dams
BASINS
STATSGO
State Soils Geographic Database
Geology
EMAP
NLCD
National Land Cover Dataset
Sites
EPA Perennial/NonPerennial
Sampling Sites
(National)
(Regional)
(National)
EPA / USGS
EPA / USGS
USGS
(National)
(National)
(National)
(National)
(Regional)
(Regional)
OSU
USGS
EPA
USGS
EPA
USGS
(Regional)
EPA
We are developing these models and tools using ArcGIS as a platform and will make the extension
available for widespread use.
Funding/Disclaimer:
The work reported here was developed under the STAR
Research Assistance Agreement CR-829095 awarded by
the U.S. Environmental Protection Agency (EPA) to
Colorado State University. This presentation has not
been formally reviewed by EPA. The views expressed
here are solely those of the presenter and STARMAP, the
Program (s)he represents. EPA does not endorse any
products or commercial services mentioned in this
presentation.
This research is funded by
U.S.EPA – Science To Achieve
Results (STAR) Program
Cooperative
Agreement
# CR - 829095
We compared the catchments delineated using the growing algorithm against a set of handdelineated watersheds in Colorado. The delineated basins closely approximated the hand-delineated basins,
resulting in an average percent difference of 6 percent. These results are highly encouraging given that we
used 1:100K hydrology and 30 m DEM, and thus can be improved considerably when 1:24K hydrology
becomes available. We also have tested this method using RF1 (ERF1v2) reach data and 1 km DEM for the
coterminous US, creating nearly 64,000 catchments.
Most measures that link watersheds to streams rely on surrogate variables, such as Strahler
stream order. These are widely-used and have been useful in distilling complex variables into a
reasonable metric. However, with the combination of widely-available spatially-explicit data, network
data structures, and fast computer processing, a new opportunity has emerged to develop indicators
that directly measure various aspects of hydrology. A primary benefit is that these metrics will be
more robust to possible artifacts introduced by scaling (e.g., 1st order streams change with map scale)
and field-survey method (e.g., hydrology networks change at boundaries of quadrangles).
A first step was to use the length streams (e.g., upstream from a location, or between two
locations on a stream or network) as a surrogate for the catchment area. Given that we can estimate
the catchment area fairly well, we felt we could combine the idea of catchment area with the
hydrologic network. For each stream reach, we are to associate the measured catchment area directly
to the stream reach. Moreover, the catchment GRID has been used to associate ancillary information
that will assist direct measurement of flow. To date, we have computed the volume of precipitation (in
acre feet) input into each reach catchment based on the average annual precipitation (from PRISM)
and the catchment area. We have also computed the downstream affect of dams based on the normal
volume capacity of dams identified in the BASINS data.
We have developed ArcGIS scripts (using Visual Basic) to build an ArcGIS geodatabase,
display, and analyze the stream data as a hydrologic network (using the available Geometric Network
Modules). The Geometric Network data structure and associated tools allows the network structure to
be created from geometric coincidence of features (e.g., a reach connecting to its upstream reaches).
We developed a script to trace upstream from
each reach, calculate total precipitation, and
record the value in each reach’s attributes.
This allows metrics to be accumulated down
the hydrologic network, such as watershed
area or predicted discharge. It also allows
metrics to take into account other features
within the watershed, such as dams. Flow
modification can be computed to depict the
proportion of discharge at any reach that is
modified by dams upstream (below). This
provides an initial picture of the “shadow of a
dam”, and could be used in an analysis to
understand the relative impacts of remove or
add a particular structure or structures.