TOPOGRID Hydrologically Correct Digital Elevation Model 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 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., 1 st 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. 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 pre- processing 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. Networked catchments based on delineation and flow accumulation methods Mary Kneeland and David Theobald, Natural Resource Ecology Lab, Colorado State University ; ) NHD 1:100K Streams We compared the catchments delineated using the growing algorithm against a set of hand- delineated 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. 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 v2Enhanced River Reach File (National)EPA / USGS NHDNational Hydrography Data (Regional)EPA / USGS NEDNational Elevation Dataset(National)USGS PRISMParameter-Elevation Regressions on Independent Slopes Model (National)OSU Stream GaugeMonthly and Annual(National)USGS DamsBASINS(National)EPA STATSGOState Soils Geographic Database (National)USGS GeologyEMAP(Regional)EPA NLCDNational Land Cover Dataset(Regional)USGS SitesEPA Perennial/NonPerennial Sampling Sites(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 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. 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. This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR