The United States Geological Survey (USGS) National Hydrography Dataset (NHD) 1:24,000-scale flow lines (Kelmelis, 2003; Kelmelis et al., 2003; Simley, 2006) need to be improved to reflect current topographic conditions (Colson et al., 2006; Sheng et al., 2007; Kloiber and Hinz, 2008; Kaiser et al., 2010; Ducey et al., 2012; Quinn and López-Torrijos, 2012). These mapped hydrography updates are needed because of temporal changes in surface channels. The USGS NHD 1:24,000-scale dataset, also known as high-resolution NHD, is a digital vector dataset containing hydrographic features and is the surface water component of The National Map (Kelmelis et al., 2003). Although vector NHD flow lines are frequently used in geographic information systems (GIS), the tools used for collaborative maintenance of the dataset are quite complex (Kloiber and Hinz, 2008) and require upfront labor cost estimates associated with maintenance edits (Kaiser et al., 2010). Thus, updating a nationwide dataset as complex as the NHD is not a trivial task.
Historically, hydrography data were derived by digitizing surface water features from aerial photography and paper maps (Guptill, 1979, 1983; Stephens et al., 1980; Marks et al., 1984; Usery, 2012). Duplicating those laborious efforts to obtain nationwide time-relevant hydrography is inefficient and cost-prohibitive (Marks et al., 1984; Colson et al., 2006). With the increasing availability of computing power, digital methods for updating mapped hydrography can employ change detection analysis using bare earth digital elevation models (DEMs). Because the shape of the land exerts strong control over the collection and flow of surface water, changes to the topography can have a significant effect on local drainage conditions (Hjerdt et al., 2004; Gesch, 2006). Therefore, terrain analysis of elevation data, including light detection and ranging (LiDAR) DEMs, is frequently used to extract surface water information (O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Jenson, 1991; Moore et al., 1991; Tarboton et al., 1991; Garbrecht and Martz, 1997; Tarboton, 1997; Maidment, 2002; Liu et al., 2005; Colson et al., 2006; Jones et al., 2008; Stoker et al., 2008; Höfle et al., 2009; Jenkins and Frazier, 2010; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; Quinn and López-Torrijos, 2012). The extracted LiDAR-derived features are useful for updating NHD-mapped hydrography flow lines.
In this article, we illustrate methods for improving NHD mapped hydrography that employ change detection analysis of LiDAR-derived surface channels and NHD hydrography flow lines to identify anomalies that exceed 12.2 m, a National Map Accuracy Standard (NMAS) guideline for 1:24,000-scale maps (USGS, 1999; National Digital Elevation Program, 2004; Maune et al., 2007b). Anomalies that exceed that informational metric are spatially validated by sampling and quantifying elevation values of both the LiDAR-derived surface channel and mapped hydrography flow lines. In other words, LiDAR surface channels that deviate in excess of 12.2 m horizontally from mapped hydrography flow lines are locations of potential surface channel changes. These locations are validated by measuring the vertical elevation differences between the LiDAR surface channels and mapped hydrography flow lines. These methods are beneficial for updating NHD because only the locations suspected of hydrography change will need to be reviewed for spatial accuracy and currency, rather than an entire hydrography dataset. Even though surface flow alteration studies have been well documented in the scientific literature (King and Tennyson, 1984; Brown and Bauer, 2009; Arrigoni et al., 2010; Carlisle et al., 2010, 2011), the spatial accuracy, or the spatial component, of elevation-derived surface channels has not been emphasized in terrain analysis methods.
The accuracy of surface water information is essential for many applications (Höfle et al., 2009; Petroselli, 2012), including habitat descriptions and river restoration (Jones, 2006), water land boundary delineations (Mandlburger and Brockmann, 2001), monitoring of river corridors for natural hazard management (French, 2003; Brügelmann and Bollweg, 2004; Hollaus et al., 2005), sediment transport modeling (Carrivick et al., 2010), distribution of landslide activity (Passalacqua et al., 2010), wetland dynamics (Jenkins and Frazier, 2010), estimation of gully erosion and depth of depressions (Perroy et al., 2010; Zandbergen, 2010), geomorphological change of floodplains (Thoma et al., 2005; Jones et al., 2007; Höfle et al., 2009), and surface water mapping (Heine et al., 2004; Liu et al., 2005; Höfle et al., 2009; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; López-Torrijos et al., 2012). Therefore, terrain analysis using LiDAR DEMs has become increasingly important for surface water applications because of the spatial resolution and vertical accuracy that is essential for time-relevant mapped hydrography.
Several hydrologic and hydrographic models, created over a decade ago, employ terrain analysis methods to coarser (30 m) DEMs to automate the delineation of surface channels (O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Jenson, 1991; Tarboton et al., 1991; Garbrecht and Martz, 1997; Tarboton, 1997; Maidment, 2002). Because coarser (30 m) DEMs contain less detailed elevation information than LiDAR DEMs, surface channel networks can be derived that consistently flow downstream. However, applying the same hydrologic analyses to the fine spatial resolution of LiDAR DEMs does not generate the same results. For example, geographic features such as bridges, roads, or other elevated surfaces over conduits are more prominent in LiDAR DEMs. Thus, elevations above conduits will function like dams, so a surface channel network from terrain modeling would erroneously indicate that the water cannot pass through the conduits (Maune et al., 2007a; Poppenga et al., 2010, 2012). This is problematic for commonly used ESRI GIS hydrology tools that fill depressions to derive overland flow direction. Many standard GIS hydrology tools do not analyze the next steepest downslope neighbor as an underground conduit, so hydro-enforcement is not implemented (Poppenga et al., 2010, 2012).
Arc Hydro® tools (Maidment, 2002) contain options to burn in stream networks to raster elevation data by lowering the elevation of the stream network cells (Maidment, 1996). This process changes elevation values in burned locations and alters the integrity of the DEM. Furthermore, using NHD flow lines that need to be updated to burn stream networks into newly acquired LiDAR DEMs may cause integration problems between LiDAR DEMs and mapped hydrography. Considering that LiDAR DEMs contain current topographic and hydrographic information, burning stream networks into LiDAR DEMs does not solve the issue of updating the NHD and does not solve the problem of elevations above conduits functioning like dams.
Tarboton’s (1997) method, termed D-∞ (D-infinity), uses triangular facets to identify an infinite number of possible single-direction flow paths, and the threshold is determined by multiple flow direction of one or two downslope cells. The procedure is based on representing flow direction as a single angle taken as the steepest downwards slope on the eight triangular facets centered at each pixel (Tarboton, 1997; Seibert and McGlynn, 2007; Nardi et al., 2008; Li and Wong, 2010). According to Tarboton, this procedure offers improvements over prior procedures that have restricted flow to eight possible directions (introducing grid bias) or proportioned flow according to slope (introducing unrealistic dispersion). It is questionable whether one or two cells in a 1-m LiDAR DEM are sufficient for identifying the next steepest downward slope pixel beyond elevations over conduits to hydrologically enforce surface channels, leaving the hydrologic barrier issue unresolved. Infinite flow direction methods are valuable for defining downslope direction in complex braided streams, but for updating 1:24,000-scale NHD flow lines, more readily available and commonly used tools are needed.
There are several common issues with hydrology models when hydrologic analyses are applied to DEMs. Most methods have been tested in applications of coarser (30 m) DEMs (Mark, 1983; Marks et al., 1984; O’Callaghan and Mark, 1984; Jenson and Domingue, 1988; Tarboton et al., 1988; Jenson, 1991; Martz and Garbrecht, 1992; Band, 1993; Nardi et al., 2008; Pan et al., 2012), but few have been tested on hydro-enforcement of high-resolution LiDAR DEMs. The topographic complexities of detailed LiDAR DEMs cause problems for most hydrologic and hydrographic models because hydro-enforcement is needed to generate fully connected surface channels. For example, in coarser (30 m) DEMs, the elevation data over conduits are usually not as problematic to defining a downstream path as they are in LiDAR DEMs (Maune et al., 2007a; Poppenga et al., 2010, 2012; Ducey et al., 2012). Thus, if currently available hydrologic analyses are applied to LiDAR DEMs, additional techniques are needed to generate consistently connected surface channels. Some models that attempt to solve this problem contain computationally complex mathematical processes that are time-consuming and not readily available in standard GIS software. The models are not easily applicable to LiDAR DEMs and not economically efficient for updating NHD. Additionally, if LiDAR DEMs are to be useful for updating NHD, methods are needed that can be applied to larger geographic areas. The D8 method (Jenson and Domingue, 1988) was implemented at a nationwide scale to generate a hydrologically conditioned version of the 30-m National Elevation Dataset (NED) (Gesch et al., 2002; Gesch, 2007). The resulting Elevation Derivatives for National Applications (EDNA) database activity was proposed to improve the density of 1:100,000-scale information in NHD (Kost and Kelly, 2001; Kelmelis, 2003; Franken, 2004). Considering that the nationwide EDNA database was generated with D8 hydrology tools that were readily available in standard GIS software, the D8 method is a probable framework to efficiently and cost-effectively extract LiDAR-derived surface channels for updating the NHD.
The D8 method implemented by Jenson and Domingue (1988) and Jenson (1991) is a contributing area threshold model that assigns a flow direction value to each cell that describes the direction of its steepest downslope neighbor. This is accomplished by first generating a depressionless DEM to route overland flow. Additional selective drainage methods, as proposed by Poppenga et al. (2010, 2012), can be incorporated into the contributing area threshold model to hydrologically enforce depressions located upstream of conduits and extract vector surface channels from LiDAR DEMs. By differencing a LiDAR DEM from a depressionless, or filled, LiDAR DEM, depressions that need hydro-enforcement are detected with assigned parameters. Using a least accumulative cost path analysis, the elevation values over conduits are adjusted to the lowest elevation cell value within the depression allowing a continuous downstream path. D8 hydrologically conditioning processes (Jenson and Domingue, 1988) are then applied to the hydrologically enforced DEM to derive a hydrologically enforced flow direction grid that is converted to vector surface channels. The original LiDAR DEM (unfilled), the hydrologically conditioned LiDAR DEM (filled), and the hydrologically enforced LiDAR DEM are preserved thereby minimally modifying the elevation data. The selective drainage methods, written in Python scripts and executed in an ESRI ArcGIS geoprocessing environment, have been the catalyst for several LiDAR-based research projects, including those attempting to update NHD (Kaiser et al., 2010; Ducey et al., 2012; López-Torrijos et al., 2012; Quinn and López-Torrijos, 2012).
It is well documented in the scientific literature that the high-vertical accuracy and spatial resolution of LiDAR DEMs provide the topographic detail needed for more accurate and current surface water information (Casas et al., 2006; Colson et al., 2006; Jones et al., 2008; Murphy et al., 2008; Stoker et al., 2008; Höfle et al., 2009; Li and Wong, 2010; Poppenga et al., 2010, 2012; Ducey et al., 2012; López-Torrijos et al., 2012; Petroselli, 2012; Quinn and López-Torrijos, 2012). Li and Wong (2010) state that high-resolution LiDAR DEMs offer superior results in extracting river networks. Murphy et al. (2008) indicate that LiDAR-derived DEM networks were the most accurate representation of field-mapped networks, even more accurate than photo-derived networks. Poppenga et al. (2010) conclude that LiDAR-derived surface channels more closely reflect what is currently occurring on the landscape. Jones et al. (2007) reported that the use of airborne LiDAR data and GIS technology allows the rapid production of detailed geomorphological maps of river valley environments. Liu et al. (2005) demonstrate that LiDAR-derived DEMs with high-accuracy and high-resolution offer the capability of improving the quality of hydrological features extracted from DEMs. According to the National Research Council (2009), high-quality digital mapping is essential to communicating flood hazards to those at risk. Their report, Mapping the Zone, Improving Flood Map Accuracy, concludes that even the most expensive aspect of making more accurate maps — collecting high-accuracy, high-resolution topographic data — yields more benefits than costs.