I would like to ask you guys how would i burn streams in DEM the following images will help understand what I would like to do.
this is my DEM and the blue lines are my rivers. However, when I delineated flow accumulation from it, the following images resulted.
How would I resolve this? I tried DEM reconditioning in HEC-GeoHMS. I really believe it is stream burning, but, I don't think that it affects the DEM elevation since there has no effect on the high and low values in its layer. The high and the low values are still the same.
Okay, I've provided this answer to try and consolidate my comments above and to serve as a resource for others contending with the issue of stream burning. As I stated in my answer to this question Shapefile and DEM: check rivers behaviour, you would expect a mapped vector stream data set to deviate from a corresponding DEM-extracted stream network because you are comparing a model with a model, rather than a model with reality. This is particularly the case in the head waters of the channel network since that is where the mapped stream network is least accurate and the definition of what a stream is is somewhat ambiguous in these small first order streams. However, your stream networks differ just as much in the higher-order streams toward the basin outlets. This is a sign that your DEM is not of a sufficient quality to be able to accurately extract a stream network. Ultimately, to do what you want to do, you would need a more accurate and higher precision DEM. However, the main issue is this, with hydrological modelling the drainage patterns and basin delineation (represented by the flow accumulation raster derived from the DEM) is as important as the extracted stream network. Knowing whether a chicken-processing facility drains into a neighbouring field or directly into an adjacent stream makes all the difference and it's the DEM-derived flow network that tells you that. While you can burn your 'more accurate' mapped stream network into the DEM, doing so will not improve your flow accumulation (drainage pattern) map in the hillslope areas. It'll only improve the flow pattern along the mapped stream network and even then it'll likely result in parallel flow where a digital stream (in the DEM) and a mapped stream are not coincident.
Burning a stream network into a DEM is generally, in my opinion, a poor and much over-used practice. There are restricted cases where it makes sense. For example, it makes sense to burn streams through road embankments, effectively reinforcing flow along the buried culvert beneath the embankment. However people often have a false sense of high-accuracy when it comes to vector stream networks. The DEM-extracted stream networks derived from many modern DEM data sources, such as LiDAR, can often be more accurate than the blue-line network off of topographic maps, at least in lower-order basins. When the DEM is of low accuracy (as is the case with your DEM) a burnt stream network can give a false sense of accuracy when in reality it doesn't do anything to improve drainage patterns on hillslopes and can create strange artifacts along flood plains. These artifacts can sometimes be lessened when you apply an away-from-stream gradient as part of the stream burning, but most stream burning tools don't offer this option. A stream-burned DEM is unlikely to have more accurately defined basin divides than a non-stream burned DEM. Stream burning will also affect slopes measured from a DEM so it is strongly recommended that you use the original DEM for this purpose (although really that is the case for any hydrological pre-processing operation on DEMs). Often times, depression breaching algorithms can offer a better solution compared with stream burning, although depression filling, the more common DEM pre-processing method, will almost certainly offer a poorer solution.
In your case, your DEM has a very large flat area near the watershed bottom (lefthand side of the image). If that is the result of extensive DEM filling then my advice would be that if you are going to burn streams in a DEM be sure to fill depressions after the burn and not before. Of course, you'd likely be better off breaching those depressions anyhow. If the flat area is just the natural topography then I'd say that in such a low-relief area the whole basis of topographically driven flow path modelling breaks down and you really can't expect your drainage patterns to be anything near realistic in these areas. In such case, you'd need a much higher precision DEM than what you have. If you do need to burn streams, then you should use a decrement value that is sufficient to cut through artifact hills resulting from positive elevation errors. Therefore a decrement value of 3-10X the DEM error (RMSE) might be appropriate but this will depend on the data and topography. And remember, it would be a good idea to apply depression breaching or a hybrid breaching/filling algorithm afterwards.
Digital elevation models on accuracy validation and bias correction in vertical
Digital Elevation Model (DEM) is used to represent the terrain of the earth. A free provided DEMs are the 10 m DEM produced by the Geographical Survey Institute of Japan (GSI-DEM), Advanced Space Borne Thermal Emission and Reflection Radiometer-Global DEM, Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data 2010, Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales, and Global 30 Arc-Second Elevation that are actually used in scientific studies. DEMs have made a high accuracy to assess an error using an observation elevation point. The DEMs in this study at an original spatial resolution of the Shikoku Island, Japan were collected that were evaluated and corrected by using the referent elevation points observed by global position system. The evaluation and correction method of the DEMs were based on the statistical measures and linear transformation algorithm respectively. The results reveal that the GSI-DEM has higher accuracy than the five DEMs, and these DEMs have gotten more accuracy after corrected by the transform’s parameters. This approach will be used to recommend for a new DEM in a future, and it can be applied for making a high accuracy DEM to model the earth’s terrain.
Stream water consists of a multitude of dissolved and suspended materials including elements and dissolved organic carbon (DOC) which often reflect the hydrological and biogeochemical characteristics of the surrounding catchment [Andersson and Nyberg, 2009 Covino et al., 2012 ] as well as inputs, exchanges, and processing along the stream network. Some elements found in stream water are important nutrients for biota [Shine et al., 1995 ], while others can be toxic [Lydersen et al., 2002 Chan et al., 2003 ] depending on their concentrations and forms. It is therefore important to understand the sources and controls of these elements in the catchment so as to develop abatement strategies and sustainable catchment management plans.
The challenge to developing broadly applicable management strategies is that it is difficult to categorize elements since they all have unique chemical properties and behavior. However, one promising approach may be to leverage the differences between elements which move “conservatively” in soils and waters with the “nonconservative’ elements, which have a more complex biogeochemistry. Conservative elements are only weakly affected by various biological and chemical processes. An example of a more conservative element is Na, which is not taken up by biota to any higher degree, does not precipitate in stream water, or partake in redox reactions. Furthermore, Na does not bind strongly to organic matter, colloids, or minerals in general. Nonconservative elements are more strongly affected by various biological and/or chemical processes, e.g uptake by vegetation, precipitation, coprecipitation, reduction/oxidation, strong sorption onto organic matter and mineral surfaces, or formation of colloids. Examples of more nonconservative substances are Fe (because of its complicated redox chemistry), Th (because of its low solubility and high affinity for organic matter), Al (because its solubility is strongly pH dependent), and DOC (because of its biological role). Hence, by “nonconservative” we refer to elements, which for one reason or another do not passively follow the water flow. These definitions are also in accordance with previous work by Pokrovsky and Schott [ 2002 ] and Savenko et al. [ 2014 ], who defined conservative elements as those with little or no biological or chemical interactions, while Klaminder et al. [ 2011 ] highlighted that the most conservative elements such as many base cations and Si in stream water can be predicted from the water residence time in the catchment.
Despite recent advances in stream biogeochemistry, another challenge remains in understanding what controls the variability of nonconservative and conservative elements in running waters and how this can be simplified into a conceptual understanding. We know that landscape heterogeneity plays an important role in providing distinct chemical signatures to streams. These signals are integrated, transformed, and transmitted as streams flow from small headwaters to larger rivers downstream [McEachern et al., 2006 Jencso and McGlynn, 2011 Neubauer et al., 2013 ]. For instance, the mineral soil patches in the landscape, such as tills and sorted sediments, are sources of many elements which are derived from weathering processes (Figure 1). The organic soil patches in the landscape, such as riparian soils and peatlands, on the other hand are known to be sources of DOC and accumulation sites for elements with high affinity for organic matter (Figure 1). Both landscape patches (mineral soils and the organic soils) have distinct influences on stream chemistry, but Cooper et al. [ 2000 ] and Evans et al. [ 2006 ] showed that similar landscape patches often behave similarly since the variations within a specific landscape patch are smaller than the variation between them. Using this understanding of the landscape influence on stream chemistry, Lidman et al. [ 2014 ] showed that there is a direct linkage between the relative cover of these patches and stream chemistry which can be used to improve the prediction of elements across multiple scales and provide additional information about catchment structure and function.
However, the question remains about how to best relate the spatial structure of landscapes to the patterns of stream chemistry for diverse elements, especially as there are often large variations between the landscape patches in the areal coverage, the hydrological properties, as well as in the geomorphology [Jones et al., 1999 Harpold et al., 2010 Park et al., 2014 ]. For instance, if a landscape is viewed as a mosaic of different patches it is generally expected that the patch with the largest coverage will have a greater influence on stream chemistry [Tiwari et al., 2014 ]. This information has been widely used in creating landscape mixing models to infer and quantify the relative contributions of different landscape patches to streamflow by mixing headwater signals in proportion to their patch coverage [Laudon et al., 2011 ]. As such, it was successful at the headwater scale mainly because the magnitude of water input to streams was related to the proportion of catchment area dominated by a specific patch type. The areal coverage of different patches in a catchment can hence provide a simple technique that lumps all processes into an easily implemented model by assuming that the variability caused by the unique properties and processes of each patch type can be represented by in their coverage (Figure 1a).
In reality, the role of the landscape structure is likely to be different for different types of elements and not always directly related to the areal coverage of the different patches. The parts of the landscape patch where water is routed will be considerably wetter and be more biogeochemically active than the drier parts and hence have considerably greater influences on the more nonconservative elements [Zimmer et al., 2013 Golden et al., 2014 Weyer et al., 2014 ]. For example, if a catchment consists of twice as much till as peat soils, the areal coverage will give less importance to the peat landscape even though the majority of water is routed through the peat on its way to the stream due to its common location in the low-lying areas surrounding the stream initiation points [Grabs et al., 2012 ].
Thus, reclassifying catchments using wet area and flow path configuration as descriptors gives greater emphasis to patches and processes that should be disproportionately important for some elements. Patches in the landscapes that are naturally wetter than others (e.g., riparian peats and peatlands) may hence have greater importance for influencing stream chemistry since they represent the reactive areas of the catchment, i.e., “localized hotspots” [Kuglerová et al., 2014 ] (Figure 1c). Similarly, assessing how water is routed through different landscape patches using surface topography can show how water is channeled preferentially along flow paths to the stream [Ågren et al., 2014 ]. By tracing the groundwater flow paths from the catchment divide to the stream using catchment topography may hence help inform what parts of the landscape should be most important for the regulating the stream chemistry. Patch types located closer to the stream, measured along the flow paths of the water, could be hypothesized to have a greater influence on stream chemistry than those further away (Figure 1b). Both cases reflect that downstream processes can supersede upstream processes because they are more reactive, transport more water, and are more strongly connected to the receiving streams.
The difficulty in using landscape descriptors is defining them based on map information. While traditional approaches to define landscape patches have been based on remote sensing data where maps are created based on abrupt changes in vegetation, they often ignore spatial variability and the fuzzy boundaries due to small changes in the landscape soil properties [Murphy et al., 2011 ]. The use of new geographic information system (GIS) tools provides the possibility to create landscape descriptors through fine scale mapping of soil properties [Creed and Sass, 2011 Lang et al., 2012 Walker et al., 2012 ]. GIS can improve our hydrological understanding of catchment functioning at multiple spatial scales as it provides tools to represent soil properties in continuous pixel coverage across the catchment [Murphy et al., 2009 Pappas et al., 2015 Soulsby et al., 2016 ]. In this way, gradual changes in soil properties are represented rather than their surface cover only. The accuracy of such mapping is improved with high-resolution lidar data which can provide the potential to link catchment structure with functioning.
The overall objective of this study was to increase the understanding of the linkage between the hydrological and biogeochemical processes in the landscape and its relation to the transport and fate of a wide range of elements using high-resolution map information. A large selection of elements with contrasting biogeochemical properties was used to test the key characteristics of boreal catchments that regulate stream water chemistry. Given the fact that conservative and nonconservative elements differ in reactivity and mobility, we hypothesized that distinct models describing the relationship between catchment structure and stream chemistry would emerge for these different classes of elements. We predicted that the more sophisticated GIS approaches, taking into account factors such as flow pathways and soil wetness, mainly would improve the predictive power for elements with a more nonconservative biogeochemical behavior.
4.2 High-resolution DEM processing
Extracting an IDC supraglacial stream–river network from a DEM requires assignment of a prescribed location for the catchment outlet (sink). For this study, the topographic depression containing the known location of the terminal outlet moulin was used as the sink all other small depressions were filled as per Karlstrom and Yang (2016). This partially filled DEM was then used to calculate flow directions and a downstream flow-contributing area raster (Karlstrom and Yang, 2016). Finally, a global meltwater contribution area ( Ac ) threshold was used to simulate ice surface drainage networks. In practice, if Ac is set too large (small), modeled drainage networks will underestimate (overestimate) real-world channel travel distances and overestimate (underestimate) actual interfluve travel distances (Montgomery and Foufoula-Georgiou, 1993 Yang and Smith, 2016). Therefore, by deliberately varying this parameter we are able to simulate the seasonal evolution of the supraglacial stream–river network, which tends to have the lowest drainage density early and late in the melt season (Yang et al., 2015b, 2017 King et al., 2016). This study used a DEM stream burning technique to force the DEM to produce a reliable actively flowing river network (Lindsay, 2016). To burn the WV DEM, elevations of DEM raster pixels that are spatially coincident with our remotely sensed supraglacial map were lowered (“burned”) by 1.0 m, thereby forcing routed flow to pass through these accurately mapped supraglacial stream–river channels.
Adequacy of satellite derived rainfall data for stream flow modeling
Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated.
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DEM Stream Burning - Geographic Information Systems
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Coupled Hydrologic-Hydraulic Modeling of the Upper Paraguay River Basin
This paper presents a detailed modeling of rainfall-runoff processes and flow routing along a complex large-scale region, the Upper Paraguay River Basin (UPRB), encompassing a drainage area of approximately 6 00 , 000 km 2 , which extends over Brazil, Paraguay, and Bolivia. Within the UPRB lies the Pantanal, the world’s largest wetland, with extraordinary biodiversity and great ecologic value, but which currently is threatened by anthropogenic activities. A conceptual model was applied with two main components: (1) simulation of the basin and part of the Paraguay River tributaries by means of the distributed large-scale hydrological model MGB-IPH using simpler flow routing methods and (2) simulation of the main drainage network, approximately 4,800 km of river reaches, with a one-dimensional hydrodynamic model. Despite the data scarcity, complexity, and the intricate river drainage network of the region, the coupled model was able to represent the hydrological regime of the basin. Comparisons between observed and calculated hydrographs showed a good model skill in representing the flow regime of the upper Paraguay River and its tributaries, highlighting its value as a tool for understanding and predicting the system behavior. The proposed modeling of the hydrological processes of the UPRB, with a detail never presented before, provides a valuable tool for understanding ecosystem functioning and assessing its resilience to anthropogenic pressure, climate change, and climate variability.
There is currently an increasing effort for studying climate variability and climate change and its effects on biodiversity, water resources, food production, and other social, economic, and political aspects (Gedney et al. 2006 Heimann and Reichstein 2008 Barrios et al. 2008 Steele-Dunne et al. 2008 Jarvis et al. 2008). Inbetween the major effects predicted because of climate change (natural or human induced) are the changes in global and regional precipitation patterns and the consequent modification of flow regime in rivers (Betts et al. 2007 Gedney et al. 2006). Land-use changes caused by human activities have also significantly altered the hydrological processes related to the rainfall-runoff transformation in some parts of the world (Stohlgren et al. 1998 Galdino and Clarke 1997).
The investigation of these kind of effects over the water resources has required hydrologic studies encompassing large areas in which specific approaches must be employed (Overgaard et al. 2006 Xu 1999). In this sense, several distributed hydrologic models have been developed or adapted to deal with large-scale watersheds (Singh and Frevert 2002 Xu 1999 Wood et al. 1997 Croley II and He 2005 Croley II et al. 2005 DeMarchi et al. 2011). These models have been used to analyze the modifications in hydrological processes basically through sensibility analysis of its parameters or using predicted precipitation and other climatic variables provided by meteorological models in estimating future flows (Benoit et al. 2000 Habets et al. 2004 Collischonn et al. 2005 Yu et al. 1999).
In general, large-scale distributed hydrologic models are composed of modules for calculating the soil water budget, evapotranspiration, flow propagation inside each discretization element, and flow routing through the drainage network. Relative simplified schemes are often used for streamflow routing, such as linear reservoirs, Muskingum-Cunge, or kinematic wave methods. When dealing with large-scale rivers in relative flat areas, however, backwater effect and floodplain inundation may become governing factors for flood wave routing. The floodplains may be several times larger than the main channel and act as important storage areas during the floods, damping, and delaying peak flows. In this case, a one-dimensional (1D) hydrodynamic model may be coupled with the hydrologic model to provide a better representation of flow routing (Lian et al. 2007) as the application of more complex approaches such as two-dimensional (2D) and three-dimensional (3D) hydrodynamic modeling for large-scale sites may be infeasible because of data requirements, computational cost, and numeric instabilities (Bates and De Roo 2000 Verwey 2005 Werner 2004).
The main difficulties for combining hydrologic and hydrodynamic models for large-scale studies are related to numerical instabilities and data requirements and preparation. An off-line coupling of these models may weaken the first issue but not eliminate it as some care is still required when applying a full hydrodynamic model in terms of numerical instabilities (Cunge et al. 1980). The question of data requirements for hydrodynamic models, however, may be more critical, first because of scarcity of data availability for large areas, especially in developing countries (Patro et al. 2009), and secondly because it may involve data from different sources and formats. Data analysis and preparation may thus become an excessive time-consuming task.
In the Upper Paraguay River Basin (UPRB), located in three South American countries, Brazil, Bolivia, and Paraguay, in which data shortage and region complexity are a challenge, earlier hydrodynamic studies have focused on small portions of the basin or used a too simplified approach (Miguez 1994 Vila da Silva 1991 Hamilton et al. 1996 Pfafstetter 1993 Hamilton 1999 Tucci et al. 2005 Paz et al. 2007 Maathuis 2004 Kappel and Ververs 2004 Mascarenhas and Miguez 1994). The exception is the recent study of Paz et al. (2010), in which a full hydrodynamic model was applied using geographic information system (GIS)-based procedures for preparing input data and preserving spatial location of river hydraulics. In their study, a 4,800-km river drainage system was modeled, obtaining reasonable results. However, such a hydrodynamic modeling study was restricted to the Pantanal region only, which is the central portion of the UPRB with very low and flat relief and a complex drainage system and represents 25% of the basin area, using observed hydrographs as upstream boundary conditions.
As the study of Paz et al. (2010) represented only the flow routing along the river drainage system of the Pantanal region, it does not provide a way to assess land-use and climate change scenarios, for which a complete simulation of the rainfall-runoff processes in the entire UPRB is required. Modeling the rainfall-runoff transformation is extremely needed for predicting effects of several anthropogenic activities that currently threaten the region, such as agriculture, cattle raising, and dam building (Hamilton 2002 Da Silva and Girard 2004 Junk et al. 2006), beside improving the understanding of current system behavior. Nevertheless, the large dimensions of the UPRB, large diversity of biomes and topography, its peculiar hydrologic characteristics, and the lack of data to physically characterize the entire basin request a tremendous effort for developing such modeling studies.
This paper summarizes the methodology and results of the most complete and detailed coupled hydrologic-hydraulic modeling study applied for the whole UPRB. The entire drainage area of approximately 6 00 , 000 km 2 of the UPRB is modeled with a distributed hydrologic model coupled to the 1D-hydrodynamic model previously applied by Paz et al. (2010) for flow routing through the 4,800-km river drainage system. Results are evaluated by comparing observed and calculated hydrographs in 15 stream flow gauging stations. Analysis is also provided whether the inclusion of the rainfall-runoff processes simulation of the contributing areas of Pantanal compares with the modeling approach of Paz et al. (2010) in terms of reproducing river flow regime along Pantanal.
The Paraguay River is one of the main tributaries of the La Plata River, with a drainage area of 1 , 095 , 000 km 2 extending partially over four South American countries: Brazil (34% of the basin), Paraguay (32%), Bolivia (19%), and Argentina (15%). The Paraguay River basin may be divided into the upper and lower parts. The study site comprises UPRB, which is the contributing area upstream of the affluence of the Apa River into the Paraguay river, with a drainage area of approximately 6 00 , 000 km 2 . The UPRB may be further divided in three distinct regions (Fig. 1) according to topographic and hydrological characteristics: the Planalto ( 2 60 , 000 km 2 ), the Chaco ( 2 00 , 000 km 2 ) and the Pantanal ( 1 40 , 000 km 2 ) regions.
Fig. 1. Location of Upper Paraguay River Basin and its division in the Planalto, Chaco, and Pantanal regions
The Planalto region consists of land lying above the 200-m elevation contour, primarily situated in the east and north parts of the basin, and presents a relatively rapid drainage. The annual rainfall exceeds 1,400 mm, with distinct seasonal distribution, and the land is used primarily for cattle raising and agriculture. In this region, the major part of the runoff of the entire UPRB originates approximately 80% of the streamflow at the UPRB outlet comes from the Planalto.
The west portion of the UPRB belongs to the Chaco biome, which is characterized by very low rainfall and an endorreic drainage network, thus without significant contributions to the Paraguay River (MMA 1997).
The Pantanal region is located in the central portion of the UPRB and receives contributions from the basins draining the Planalto. The Pantanal, the world’s largest wetland, is classified as a sandy wetland on the basis of the grainsize of its sediments (Iriondo 2004). Because of the gentle slope and low margins of the rivers in the Pantanal, the drainage system of this region is not able to convey the flood waters flowing from the Planalto, and extensive areas are flooded. Also, the tributaries of the Paraguay River exhibit a distributary drainage pattern responsible for reduction of channel capacity and diversion to the floodplains (Assine and Soares 2004). The flow regime of the Paraguay River tributaries is primarily governed by this flooding process, which reduces peak discharges to more than a half (Bravo et al. 2005) and strongly modifies the shape of hydrographs from upstream to downstream along each river. This “loss to floodplain” behavior is also presented by the Paraguay River, as discussed by Assine and Silva (2009) for the reach of this river along the north border of the Pantanal.
The water that spills over the main channels may remain stored in large floodplains and shallow lakes during months or spreads over a divergent drainage system formed over alluvial fans (Bordas 1996 Assine 2005). This flood pulse process is seasonally marked, with an average flooded area of 50 , 000 km 2 each year (Hamilton et al. 1996), and strongly regulates the entire ecosystem integrity and conservation (Junk et al. 2006 Hamilton 2002).
As a result of the complex hydrologic regime of the UPRB, which governs much more complex sediment dynamics, there is an intricate drainage system in the Pantanal, including vast shallow lakes and divergent and endorreic drainage networks. In summary, as the annual rainfall is less than potential evaporation and drainage is very slow because of shallow gradients (Tucci et al. 1999 Bordas 1996), the Pantanal functions as a large natural hydrologic controller of the Paraguay River and its tributaries (Tucci et al. 2005 Bravo et al. 2005 Paz et al. 2010).
According to the Köppen climate classification, the region’s predominant climate is of the type tropical savanna, with rainfall concentrated in the summer. The rainy season begins in October and ends in April. Over most of the region, rainfall in the six wettest months accounts for more than 80% of the annual total. The wettest 3-month period is from November to January, whereas the dry season extends from May to September. In most of the basin, average temperatures range from 18 to 22°C. September and October are the hottest months, with temperatures averaging 24 to 26°C. July is the coldest month, with mean temperatures ranging from 16 to 18°C.
An important climatic characteristic of the UPRB is the spatial variability of the annual rainfall, with a very strong east-west gradient, more than 1,500 mm to the east of the basin, less than 700 mm in the central region, and higher rainfall rates in a small region to the west. The Paraguay River itself runs from north to south following a path more or less aligned to the meridian 58°W. This line also approximately follows the 1,000-mm isohyets, with more rainfall to the east of the river and less than 1,000 mm west of it (Fig. 2). This rainfall distribution has a strong influence on regional hydrology. Because rainfall is higher to the east, tributaries of the east margin of the Paraguay River commonly contribute with more runoff than those to the west.
Fig. 2. Isohyets map for the Upper Paraguay River Basin
The conceptual model presented in this study has two main components: (1) simulation of the basin and part of the Paraguay River tributaries by means of a rainfall-runoff model with a simplified flow routing method and (2) simulation of the main drainage network by a full 1D hydrodynamic model.
The large-scale, distributed hydrological model MGB-IPH was used as the first component of the conceptual model. It was developed for use in large South American basins with scarce data, and is fully described in Collischonn et al. (2007a). The drainage basin is represented by square-grid elements connected by channels, each one of these elements contains a limited number of distinct grouped response units (GRUs), i.e., areas with similar combination of soil and land cover (Kouwen et al. 1993), similarly to the hydrological response unit presented by Beven (2001). Each element has dimensions of 0.1 × 0.1 ° (approximately 10 × 10 km ) in general however, it depends on the basin size and model spatial discretization adopted. The MGB-IPH model consists of modules for calculating soil water budget, evapotranspiration, flow propagation inside an element, and flow routing through the drainage network. A soil water budget is calculated for each GRU, and runoff generated from the different GRUs in the element are then summed and propagated to the stream network using three linear reservoirs: base flow, subsurface flow, and surface flow. Streamflow is routed through the river network using the Muskingum-Cunge method, considering different length and slope for each river reach representing the connection between two given elements, which are automatically derived from digital elevation model (DEM) processing (Paz and Collischonn 2007 Paz et al. 2006).
The MGB-IPH model has been typically applied with a daily time step as this is commonly the time discretization of available rainfall data, although smaller time steps could also be used. Precipitation data are interpolated to the center of each model grid element at each time step using the inverse-distance-squared method, whereas a nearest neighbor method is applied for other meteorological variables. The calibration of the MGB-IPH model is achieved by changing parameters values while maintaining relations between them and landuse. The multiobjective evolutionary algorithm MOCOM-UA (Yapo et al. 1998) is employed for MGB-IPH automatic calibration considering three objective functions: volume bias ( Δ V ) Nash-Sutcliffe efficiency index for streamflow (NSS) and Nash-Sutcliffe efficiency index for the logarithms of streamflow (NSSLOG). Several applications of the MGB-IPH model for hydrological modeling and streamflow forecasting at large basins have been carried out with acceptable results (Allasia et al. 2006 Bravo et al. 2009 Collischonn et al. 2007b Collischonn et al. 2005 Tucci et al. 2003).
The second component of the conceptual model is a 1D hydraulic-hydrodynamic model, which was used to perform river flow routing along the main drainage network of the Pantanal region. The well-known Hydrologic Engineering Center–River Analysis System (HEC-RAS) hydraulic model (USACE 2004) was used. It solves the full Saint Venant equations using an implicit Preismann four point scheme of finite differences (Cunge et al. 1980), and Manning roughness coefficients are used to represent the resistance to flow.
The MGB-IPH model was applied to the entire Upper Paraguay River Basin with a spatial discretization of square-grid elements of 0.1° resolution and a total of 5,195 grid elements, with surface area ranging from 114.5 to 120.2 km 2 . A daily time step was used as precipitation, streamflow, and meteorological data are available in this time interval.
The river drainage network represented in the hydrologic model was automatically derived from the SRTM-90m DEM (Fig. 3), including flow direction and accumulated drainage area for each grid element and length and slope of river reaches connecting model elements. However, even using a stream burning procedure for preprocessing the DEM (Saunders 1999 Turcotte et al. 2001), manual corrections were necessary in the drainage network owing to the extreme flatness of the Pantanal.
Fig. 3. Hydrological and meteorological gauge stations and digital elevation model from SRTM of the Upper Paraguay River Basin
The scarce precipitation and meteorological data (Fig. 3) were interpolated to the center of each grid element of the hydrological model at each time step. Soils were characterized using data available from the RADAMBrasil survey (Ministério das Minas e Energia 1983), PCBAP project (MMA 1997), and soil map published by the Food and Agriculture Organization of the United Nations (FAO) (1974, 1988). The soil types of the UPRB were grouped into eight classes according to their occurrence area and water storage capacity. Land-use classification was obtained by analyzing several Landsat7 ETM+ images, aiming basically at identifying areas occupied by crops, pasture, or native vegetation and seasonally flooded areas. According to available soil and land cover maps, soil types and land-use classes were combined. This resulted in a number of combinations, which were then grouped in 10 GRUs this restriction in the number of GRUs aims at reducing model parameters and also to avoid representation of irrelevant or nonrepresentative physical characteristics for the scale of this study.
Table 1 summarizes the inputs and outputs of the hydrologic model for its application to the UPRB.
Table 1. Description of Input and Output Data for Applying the MGB-IPH Model to the UPRB
|Data type||Description||Digital format|
|Input basin physical characteristics||Soil type and land use, which are combined into GRUs topography parameters related to land use: leaf area index, surface resistance, albedo, and vegetation height||Raster raster ASCII|
|Input discretization and drainage network||Basin division into elements drainage network connecting model elements length and slope of river reaches connecting model elements surface area and contributing drainage area for each element||Raster raster raster raster|
|Input hydro-meteorological data||Precipitation time series meteorological information: air temperature, atmospheric pressure, solar radiation, wind speed, and relative humidity||ASCII ASCII|
|Output||Streamflow at the boundary conditions of the hydraulic model||ASCII|
|Output||Lateral inflow to reaches of the hydraulic model||ASCII|
The hydrodynamic model was applied for flow routing along the Paraguay River and its main tributaries, representing its vast shallow lakes and divergent and endorreic drainage networks through a combination of reaches and storage areas approach.
As described in Paz et al. (2010), input data for the hydraulic model was prepared in a consistent and georeferenced database through GIS-based automatic procedures developed for dealing with the large amount of data provided by several different sources, with distinct formats and horizontal and vertical datum. Detailed cross-section profiles of the main channel were combined with elevation values extracted from SRTM-90m for characterizing the floodplains, maintaining the link between hydraulic data and spatial location. Following these procedures, all the geometric data relative to the 24 river reaches, 12 junctions, 1,124 cross sections, and 11 storage areas were entered into the hydraulic model in a coherent and not time-expensive way.
Model calibration consisted in the setup of Manning’s coefficients ( n ). In this study, the coefficients calibrated by Paz et al. (2010) were used. These authors defined distinct values of n for the main channel and floodplain for each river flow segment between two streamflow gauge stations by comparing observed and calculated hydrographs. This procedure was performed from upstream to downstream along the modeled drainage network manually varying n , but restricting them to the range reported in literature for these kinds of channels and floodplains. In total, there were 27 distinct river flow segments to have their corresponding n coefficients calibrated. They run the HEC-RAS hydraulic model with a 12 hour-to-hour time step for the period of January 1, 1996 to December 31, 2000. Manning coefficients ranging from 0.02 to 0.035 were obtained for the main channel, and from 0.04 to 0.2 for the floodplain. Table 2 summarizes the inputs and outputs of the hydraulic model for its application to the UPRB.
Table 2. Description of Input and Output Data for Applying the HEC-RAS Model to the UPRB
|Input geometry||River reaches and system network composed (channel-floodplain) cross-section profiles distance between cross sections characteristics of storage areas (location and elevation-volume curves)|
|Input parameter||Manning’s roughness coefficient in channel and floodplain for each river flow segment between two streamflow gauge stations|
|Input boundary conditions||Streamflow at the boundary condition estimated by the MGB-IPH model lateral inflow into several river reaches estimated by the MGB-IPH model|
|Output||Streamflow at the several control points inside the Pantanal region|
A one-way and off-line coupling between the hydrologic and hydraulic models was adopted. The HEC-RAS hydraulic model fitted by Paz et al. (2010) was run with a 12-h time step for the same time period adopted by those authors (January 1, 1996 to December 31, 2000), but now using as input at the upstream boundary conditions the daily flows calculated by MGB-IPH model.
The entrance of each river into the Pantanal area was adopted as the upstream boundary condition in the hydraulic model (Fig. 4) as downstream from this point the flood wave is not correctly routed by the Muskingum-Cunge methodology used in the hydrologic model. Thus, the hydrographs calculated by the MGB-IPH model at these upstream boundary conditions were considered as the contribution of the Planalto region for the downstream river network modeled by the HEC-RAS model. In addition, streamflow generated in the Pantanal and Chaco areas through the rainfall-runoff transformation represented by the MGB-IPH model was considered as lateral inflows to the hydraulic model.
Fig. 4. Discretization of the modeled system in the hydrologic model and river network represented in the hydraulic model
Model parameters were primarily set according with each GRU's characteristics, but some of them were adjusted by the automatic calibration process available for the MGB-IPH, restricting the parameter search between realistic physical limits. In spite of being a physical distributed model, as the number of distinct GRUs was set to a maximum of 10, because of computational limitations, some regions of the basin were not represented with enough details to correctly characterize the local hydrological processes. Consequently, slight differences among subbasins were allowed for varying the MGB-IPH model parameters related to GRU characteristics.
Different time periods were considered for calibrating each of the subbasins at the Planalto according to data availability. A total of 15 parameters of the MGB-IPH model was calibrated. The description of each parameter and its influence on distinct aspects of hydrological processes representation are described by Collischonn et al. (2007a). As a result of the calibration process, several Pareto optimal solutions were found for each gauging station, and a single solution was chosen among them, aiming at providing an acceptable tradeoff in fitting of the different parts of the hydrograph (Bastidas et al. 2002).
In general, the model fitted well as the NSS and NSSLOG coefficients were approximately 0.80 in most catchments in both calibration and validation periods (Table 3). The errors in volume between observed and calculated hydrographs were also acceptable, being close to 2–4% in most cases. Examples of the fit between calculated and observed hydrographs are presented in Fig. 5 for eight control points in the Planalto region, in which six of them represent the outlet sections of rivers flowing from Planalto to Pantanal.
Fig. 5. Calculated (black line) and observed (gray line) hydrographs at the outlet sections of each river in the Pantanal region and other control points in the Planalto during January 1997 to January 2000
Table 3. Statistics Showing Goodness of Hydrologic Model Fit in the Planalto Region of the Upper Paraguay River Basin
|Reference||River||Station name||Time period||Drainage area ( km 2 )||NSS||Volume error (%)||NSSLOG|
|(g)||Taquari||Perto Pedro Gomes||1978–1984||9,300||0.49||2.40||0.65|
|(o)||Upper Paraguay||Barra do Bugres||1993–1999||10,120||0.80||0.18||0.80|
|(q)||Upper Paraguay||São José Sepotuba||1993–1999||8,640||0.73||- 0.20||0.76|
|(r)||Upper Paraguay||Cáceres||1993–1999||33,890||0.88||- 0.45||0.91|
|(j)||Aquidauana||Ponte do Grego||1992–1997||6,830||0.74||- 3.00||0.74|
The statistics presented previously should be understood in the context of data scarcity. The study used data from 86 streamflow and 92 rainfall gauges, which indicates a density of one discharge gauge every 2 , 953 km 2 and one rainfall information every 2 , 760 km 2 in the best and rare situation of having available data in a given day at all the stations simultaneously. These densities are too far from the World Meteorological Organization’s recommended ones for regions characterized by difficult data acquisition. Consequently, worst (best) results were achieved in the least (best) monitored basins. For example, it was not uncommon to have only one working rainfall station in the Aquidauana basin during most of the period, resulting in a density close to one rainfall information every 10 , 000 km 2 . In light of this, results obtained were considered acceptable.
The first interesting analysis of the results at Pantanal is the comparison to the results obtained in the former study of Paz et al. (2010), which used observed streamflow as an upstream boundary condition instead of coupling a distributed hydrological model. The statistics obtained after comparing observed and calculated hydrographs along the Paraguay River and tributaries were similar in both studies in terms of NSS and root-mean-square error (RMSE) (Fig. 6). Overall, the better the fit obtained by Paz et al. (2010), the smaller the difference between the statistics of those authors and this paper's. In other words, the difficulties in reproducing the observed flow regime at some streamflow gauge stations found by the discussed study, in which the Manning coefficients of the 1D hydrodynamic model were adjusted, were enlarged when the modeling of the rainfall-runoff processes over their contributing areas was included. This was expected because the uncertainties in precipitation estimates and in the rainfall-runoff modeling are now incorporated in the modeling. However, the similarity between the results achieved by this study relative to that obtained by the simplified approach of using observed discharges as upstream boundary conditions highlights the very reasonable results of the effort to model the entire UPRB.
Fig. 6. Comparison between the performance measures NSS and RMSE obtained in this study, coupling hydrologic-hydrodynamic models, and those reported by Paz et al. (2010), which used observed streamflow as an upstream boundary condition to the hydrodynamic model circles refer to stations along Paraguay River triangles refer to tributaries
The coupled model satisfactorily reproduced the observed flow regime at the tributaries, as illustrated by visual inspection of the hydrographs (Fig. 7) and according to the statistics obtained and showed in Table 4, in which MAE means mean absolute error and MRE means mean relative error. The NSS coefficients ranged between 0.7 and 0.9 for half of the available gauge stations and were less than 0.5 for only three of them (São João, São Jerônimo, and Porto Ciríaco). Along the tributaries, the MRE ranged between 12 and 22%, except at the Barão de Melgaço, São João, and Miranda stations, in which this measure was greater than 30%.
Fig. 7. Calculated (black line) and observed (gray line) hydrographs at six control points along tributaries of the Paraguay River
Table 4. Performance Measures Using the Coupled Model at Control Points for the Period January 1, 1996–December 31, 2000
|Reference||Control point||River||RMSE ( m 3 · s - 1 )||MAE ( m 3 · s - 1 )||MRE (%)||NSS|
|(14)||Porto da Manga||Paraguay||238.88||194.60||10.70||0.77|
|(1)||Barão de Melgaço||Cuiabá||178.55||118.99||33.04||0.63|
|(3)||A. Corrego Grande||São Lourenço||82.93||59.54||16.42||0.84|
|(4)||São José Borireu||São Lourenço||41.12||32.07||12.39||0.79|
|(6)||São José Piquirí||Piquiri||110.40||78.92||21.73||0.69|
|(7)||Porto do Alegre||Cuiabá||102.40||80.71||12.76||0.73|
The major difficulty in reproducing flow regime along the Paraguay River tributaries is mostly because of uncertainties related to scarcity of available cross-section profiles. However, the proposed coupled hydrologic-hydraulic model was capable of reproducing the distinct observed flow regimes at the tributaries of the Paraguay River. At Barão de Melgaço, the Cuiabá River presents a marked seasonal flow regime, with the flood period occurring between October and May and peak flows reaching 1 , 600 m 3 · s , whereas the recession flows are approximately 100 m 3 · s . The São Lourenço River at the São José station also presents a marked seasonal flow regime, but this is characterized by smoother raising and falling limbs in comparison to the Cuiabá River at Barão de Melgaço, which presents more nervous oscillations in response to precipitation events. At São José, peak and recession flows are typically approximately 400 and 160 m 3 · s , respectively, which mean flood peaks of just 1.5 times greater than recession flows. This ratio is 10 times greater for the Cuiabá River at Barão de Melgaço. However, downstream of this station, at Porto do Alegre, the flow regime of the Cuiabá River becomes very similar to that of the São Lourenço River at São José: smooth raising and falling limbs of flood hydrographs with relative small variation between flood peaks and recession flows. At São José Piquiri in the Piquiri River, a behavior slightly similar to this is observed but with more pronounced flood peaks. In turn, the flow regime at Miranda station in the Miranda River presents weak seasonality but with rapid and very pronounced response of flows to precipitation events. Lastly, at the Porto Ciríaco station in the Aquidauana River, the flow regime is very distinct to all those discussed previously, presenting a marked maximum value of 150 m 3 · s and slight seasonality. The observed hydrograph at this station presents only small peak flows, as during the major floods a huge volume of water spills over the main channel and inundates the floodplains, resulting in a slash of the raising limb at the value of 150 m 3 · s .
All the complex flow regimes described were satisfactorily reproduced by the coupled hydrologic-hydraulic model, as indicated by visual comparison of observed and calculated hydrographs and by statistics shown in Table 4. Additionally, the remarkable changes in flow regime along each river flowing from Planalto to Pantanal were also reproduced by the proposed model. These changes are a consequence of the low conveyance capacity of river channels at Pantanal and the resultant floodplain inundation. For instance, compare the hydrographs between Rondonópolis (Fig. 5) and São José (Fig. 7) at the former, which represents the São Lourenço River outlet section from Planalto, seasonal peak flows range between 900 and 1 , 200 m 3 · s , whereas at São José, the hydrograph is much smoother and peak flows are less than 450 m 3 · s . Another notable example is along the 230-km reach of the Aquidauana River between the Aquidauana (Planalto) and Porto Ciríaco (Pantanal) stations, in which peak flows reduce from 700 m 3 · s to less than 150 m 3 · s .
As obtained in the study of Paz et al. (2010), the worst results were found for the São João station, located in the Cuiabá River upstream the confluence of the São Lourenço and Piquiri Rivers. Along this river reach, the main channel capacity is reduced, and floods are diverted to the floodplain even at the smaller discharges more than 50% of the annual volume is lost to the floodplain, and the peak discharge decreases from 2 , 000 m 3 · s - 1 to less than 400 m 3 · s - 1 . Water in the floodplain may remain up to 3–5 months stored and evaporating before return in a diffused and not very well understood way through the complex system of secondary channels, shallow lakes, and ponds. However, the good model fit obtained at the Porto de Alegre station, for instance, MRE = 18 % and NSS = 0.73 , located 50 km downstream of São João suggests that the bias found at São João becomes less important after the inflow of the other tributaries of the Cuiabá River.
The results obtained at the Paraguay River show that the applied model was able to reproduce the marked seasonal flow regime and the typical relatively smoothed shape of hydrographs, as illustrated by Fig. 8. In Fig. 8, calculated and observed hydrographs at six stations along the Paraguay River are shown, depicting the changes in hydrographs according to flood routing from Cáceres up to Porto Murtinho, a reach of approximately 1,300 km. Along this flowpath, the Paraguay River receives significant contributions along its left margin, mostly from drainage basins of Cuiabá and Miranda Rivers. More importantly, these contributions occur by means of waters drained by both the main channel and floodplains, i.e., water flowing along floodplains of tributaries may contribute to channel flow along some reaches of the Paraguay River. On the contrary, along other reaches of this river, huge volumes of water spill over the main channel and inundate the floodplain. These multifaceted lateral water exchanges between channel and floodplain strongly make the hydrodynamic modeling of the Paraguay River difficult. Thus, the results obtained are very satisfactory as the applied model reproduced the variations on time and magnitude of flood peaks and recession flows along the Paraguay River, as indicated by statistics summarizing the comparison between observed and calculated hydrographs. At the Cáceres, Descalvados, Porto Conceição, Amolar, and Porto da Manga stations, NSS was greater than 0.74. The worst results were obtained at the São Francisco ( NSS = 0.50 ) and Porto Murtinho ( NSS = 0.48 ) stations. Along the Paraguay River, RMSE ranged between 52 and 510 m 3 · s , and MAE ranged from 44 to 402 m 3 · s . These values are apparently too large but correspond to MREs varying from 10 to 12%, except at São Francisco (21%) and Porto Murtinho (16%). The difficulty in reproducing observed flow regime three of them ( Francisco may be because of occurrence of contributions drained by Taquari River floodplains, which was not well-represented in the proposed model. At Porto Murtinho, the major reason for not achieving better results is the data scarcity for characterizing the contributions of the Bolivian part of the basin along the right margin of the Paraguay River. This was already suggested in the study of Paz et al. (2010), which indicated the secondary flood peaks in the observed hydrograph as being probably generated in the Bolivian part of Pantanal. On the contrary, an earlier study had concluded that the contribution of the Bolivian side of the basin was insignificant and could be disregarded (MMA 1997), considering the loss of water because of floodplain inundation and evaporation process and using a simplified hydrological balance procedure on the basis of available discharge data, i.e., data from streamflow gauging stations located in the Brazilian part of the basin. The study indicates, however, that for better fitting hydrograph volumes and timing along the Paraguay River downstream of Amolar, the contribution from the Bolivian part of the basin must be better quantified and represented in the model.
Fig. 8. Calculated (black line) and observed (gray line) hydrographs at six control points along the Paraguay River
This paper showed the application and results of coupled hydrologic-hydraulic modeling of the entire Upper Paraguay River Basin, encompassing a drainage area of approximately 6 00 , 000 km 2 . This is the most comprehensive study on the hydrologic simulation of the whole Upper Paraguay River Basin and flow regime of the Paraguay River and its tributaries. This study greatly amplifies the previous one presented by Paz et al. (2010), which focused on fitting the 1D hydrodynamic model for the Paraguay River and its tributaries flowing along Pantanal. In the study, the transformation of rainfall into runoff was incorporated in the modeling, consequently bringing together the uncertainties in estimates of precipitation and of other meteorological variables and the difficulties in physically characterizing such a large area in terms of soil type and vegetation cover and representing them into the distributed hydrological model. Overall, the similarity of the results achieved by this study relative to that obtained by the simplified approach of using observed discharges as upstream boundary conditions highlights the capability of the model presented in this paper.
Despite the data scarcity, complexity, and the intricate river drainage network of the region, the coupled modeling was able to satisfactorily represent the rainfall-runoff transformation and flow routing along the basin. The distinct and complex flow regimes along each of the tributaries of Paraguay River were well-represented, including the changes in hydrograph shape as a result of the differences in slope and cross-section area between river reaches at Planalto and at Pantanal. Flood flow routing along the 1,300-km reach of the Paraguay River was also reasonable reproduced by the proposed model, both in terms of magnitude and timing of peak and recession flows. Some difficulties were encountered for reproducing flow regime downstream of the Amolar station and largely at Porto Murtinho because of data scarcity (discharges, precipitation, and other meteorological variables) to properly estimate contribution from the Bolivian part of the basin at the right margin of the Paraguay River.
The effort on modeling the hydrological processes of the entire UPRB provides a valuable tool for understanding the ecosystem’s functioning and for assessing its resilience to anthropogenic pressure, climate change, and climate variability. For instance, the applied coupled model will be able to predict how landuse, precipitation, and temperature change scenarios will affect the streamflow at major river reaches. However, the achievement of these goals is dependent on gathering more data, which, because of the characteristics of the basin, should be relied in remote sensing techniques or meteorological reanalysis.
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- BLM OR Wild and Scenic Rivers Line (wsr_arc) (Search Result/Metadata)
- BLM OR Designated Wilderness Line (wld_arc) (Search Result/Metadata)
- BLM OR Designated Wilderness Polygon (wld_poly) (Search Result/Metadata)
- BLM OR Wilderness Characteristics Polygon (wild_char_poly) (Search Result/Metadata)
- BLM OR Wilderness Characteristics Road Line (wild_char_road_arc) (Search Result/Metadata)
Wilderness Study Areas
- BLM OR Wilderness Study Area Line (wsa_arc) (Search Result/Metadata)
- BLM OR Wilderness Study Area Polygon (wsa_poly) (Search Result/Metadata) (Web Services)
This category includes themes having to do with planning and land management data including land use planning boundaries and office or area specific planning activities.
Areas of Critical Environmental Concern
Oregon Statewide Comprehensive Outdoor Recreation Plan
Plan Area Boundaries
- BLM OR Activity Plan Boundary Polygon (AVY_PLAN_POLY) (Search Result/Metadata) (Web Services)
- BLM OR LUP Current Boundary Polygon (LUP_CRNT_POLY) (Search Result/Metadata) (Web Services)
- BLM OR LUP Historic Boundary Polygon (LUP_HIST_POLY) (Search Result/Metadata) (Web Services)
- BLM OR LUP In Progress Boundary Polygon (LUP_PRGS_POLY) (Search Result/Metadata) (Web Services)
- BLM OR Visual Resource Management Publication Polygon (vri_pub_poly) (Search Result/Metadata) (Web Services)
- BLM OR Visual Resource Management Publication Polygon (vrm_pub_poly) (Search Result/Metadata) (Web Services)
- BLM OR Grazing Allotments Polygon (GRA_ALLOTMENT_POLY) (Search Result/Metadata) (Web Services)
- BLM OR Grazing Allotments and Pastures Line (gra_arc) (Search Result/Metadata) (Web Services)
- BLM OR Grazing Allotments and Pastures Polygon (gra_poly) (Search Result/Metadata) (Web Services)
- BLM OR Rangeland Administration System Authorization 2020 Table (RAS_AUTH_20200327) (Search Result/Metadata)
- BLM OR Rangeland Administration System Pasture 2020 Table (RAS_PASTURE_20200327) (Search Result/Metadata)
Wild Horse and Burro
- BLM OR Wild Horse and Burro Herd Area Polygons (whb_ha_poly) (Search Result/Metadata)
- BLM OR Wild Horse and Burro Herd Management Area Polygons (whb_hma_poly) (Search Result/Metadata)
This category includes themes such as campgrounds and trailheads.
- BLM OR Recreation Site Point (recsite_point) (Search Result/Metadata)
- BLM OR Recreation Site Polygon (recsite_poly) (Search Result/Metadata)
- BLM OR Recreation Site Polygon Centroid Point (recsite_pub_poly_point) (Search Result/Metadata)
This category contains themes having to do with transportation including travel management and off highway vehicle (OHV) use.
- BLM OR GTRN Back Country Byways Line (back_country_byways_arc) (Search Result/Metadata) (Web Services)
- BLM OR GTRN Publication Roads Line (gtrn_pub_roads_arc) (Search Result/Metadata) (Web Services)
- BLM OR GTRN Publication Segment Point (gtrn_segment_pt) (Search Result/Metadata) (Web Services)
- BLM OR GTRN Publication Trails Line (gtrn_pub_trails_arc) (Search Result/Metadata) (Web Services)
- BLM OR Oregon National Historic Trail Line (oregon_historic_trail_arc) (Search Result/Metadata) (Web Services)
- BLM OR Oregon and Washington Highways Line (highways_arc) (Search Result/Metadata) (Web Services)
This category includes themes such as vegetation treatments, forestry, botany, and weeds.
Current Vegetation Survey
- BLM OR CVS Accuracy Table (CVS_ACCURACY_TBL) (Search Result/Metadata)
- BLM OR CVS Coarse Downed Woody Material Table (CVS_DWMCOARSE_TBL) (Search Result/Metadata)
- BLM OR CVS Condition Class Boundaries Table (CVS_CCBOUNDARIES_TBL) (Search Result/Metadata)
- BLM OR CVS Condition Class Definitions Table (CVS_CCDEFINITIONS_TBL) (Search Result/Metadata)
- BLM OR CVS Condition Class Proportions Table (CVS_CCPROPORTIONS_TBL) (Search Result/Metadata)
- BLM OR CVS Data Errors Table (CVS_DATAERRORS_TBL) (Search Result/Metadata)
- BLM OR CVS Downed Wodd Transect Line (CVS_SMP_DWTRAN_ARC) (Search Result/Metadata)
- BLM OR CVS Downed Woody Material Fines Table (CVS_DWMFINES_TBL) (Search Result/Metadata)
- BLM OR CVS Error History Table (CVS_ERRORHISTORY_TBL) (Search Result/Metadata)
- BLM OR CVS Ground Cover Data Table (CVS_GNDCOVER_TBL) (Search Result/Metadata)
- BLM OR CVS Hard Wood Clumps Data Table (CVS_HWCLUMPS_TBL) (Search Result/Metadata)
- BLM OR CVS Non-Tally Site Tree Data Table (CVS_NTSITEDATA_TBL) (Search Result/Metadata)
- BLM OR CVS Non-Tally Sub Plot Reference Trees Point (CVS_RES_NT_SPREF_PT) (Search Result/Metadata)
- BLM OR CVS Non-Tally Subplot Reference Data Table (CVS_NTSPREFS_TBL) (Search Result/Metadata)
- BLM OR CVS Primary Sample Unit Administration Table (CVS_PSUADMIN_TBL) (Search Result/Metadata)
- BLM OR CVS Primary Sample Unit Center Point (CVS_SMP_PSUPLT_PT) (Search Result/Metadata)
- BLM OR CVS Primary Sample Unit Data Table (CVS_PSUDATA_TBL) (Search Result/Metadata)
- BLM OR CVS Primary Sample Unit History Table (CVS_PSUHISTORY_TBL) (Search Result/Metadata)
- BLM OR CVS Primary Sample Unit Subplot Centers Point (CVS_SMP_PSUSUBPLT_PT) (Search Result/Metadata)
- BLM OR CVS Resource Downed Wood Pieces Point (CVS_RES_DW_PT) (Search Result/Metadata)
- BLM OR CVS Resource Hard Wood Clumps Point (CVS_RES_HWCL_PT) (Search Result/Metadata)
- BLM OR CVS Resource Non-Tally Site Tree Point (CVS_RES_NT_ST_PT) (Search Result/Metadata)
- BLM OR CVS Stumps Data Table (CVS_STUMPS_TBL) (Search Result/Metadata)
- BLM OR CVS Subplot Data Table (CVS_SUBPLOTDATA_TBL) (Search Result/Metadata)
- BLM OR CVS Survey Condition Class Polygon (CVS_SRV_CC_POLY) (Search Result/Metadata)
- BLM OR CVS Survey Subplots Polygon (CVS_SRV_SUBPLT_POLY) (Search Result/Metadata)
- BLM OR CVS Tree Data Table (CVS_TREEDATA_TBL) (Search Result/Metadata)
- BLM OR CVS Trees Point (CVS_RES_TREE_PT) (Search Result/Metadata)
- BLM OR CVS Understory Vegetation Data Table (CVS_UNDERVEGDATA_TBL) (Search Result/Metadata)
- CVS Sample Panel Point (CVS_SMP_PANEL_PT) (Search Result/Metadata)
Forest Breeding Units
- BLM OR Co-Op and Consolidated Forest Breeding Units Polygon (FBU_COOPCONSOL_POLY) (Search Result/Metadata) (Web Services)
- BLM OR Douglas Fir Breeding Units Polygon (FBU_DFIR_POLY) (Search Result/Metadata) (Web Services)
Forest Operations Inventory
- BLM OR FOI Down Log Decay Table (MS_DL_DECAY_PUB) (Search Result/Metadata)
- BLM OR FOI Downed Logs Publication Table (MS_DOWNLOG_PUB) (Search Result/Metadata)
- BLM OR FOI Layers Publication Table (MS_LAYERS_PUB) (Search Result/Metadata)
- BLM OR FOI Layers Species Percent Publication Table (MS_LYR_SPP_PUB) (Search Result/Metadata)
- BLM OR FOI Snag Decay Table (MS_SG_DECAY_PUB) (Search Result/Metadata)
- BLM OR FOI Snag Publication Table (MS_SNAG_PUB) (Search Result/Metadata)
- BLM OR FOI Stand Publication Table (MS_STAND_PUB) (Search Result/Metadata)
- BLM OR FOI Stand Species Percent Publication Table (MS_STD_SPP_PUB) (Search Result/Metadata)
- BLM OR Forest Operations Inventory Vegetation Publication Polygon (FOIVEG_PUB_POLY) (Search Result/Metadata) (Web Services)
Site Potential Tree Height
- Site Potential Tree Height Line (SPTH_ARC) (Search Result/Metadata)
- Site Potential Tree Height Polygon (SPTH_POLY) (Search Result/Metadata) (Web Services)
Timber Production Capability Class
- BLM OR Biological Treatments Polygon (bio_poly) (Search Result/Metadata) (Web Services)
- BLM OR Chemical Treatments Polygon (chem_poly) (Search Result/Metadata) (Web Services)
- BLM OR Exclosure Protection Treatments Polygon (prot_poly) (Search Result/Metadata) (Web Services)
- BLM OR Harvest Treatments Polygon (harv_poly) (Search Result/Metadata) (Web Services)
- BLM OR Mechanical Treatments Polygon (mech_poly) (Search Result/Metadata) (Web Services)
- BLM OR Prescribed Fire Treatments Polygon (burn_poly) (Search Result/Metadata) (Web Services)
- BLM OR Revegetation Treatments Polygon (reveg_poly) (Search Result/Metadata) (Web Services)
This category houses wildlife data that was created or managed by the BLM.
- BLM OR Anadromous Fish Arc (FISH_ANADROMOUS_ARC) (Search Result/Metadata)
- BLM OR Anadromous Fish Polygon (FISH_ANADROMOUS_POLY) (Search Result/Metadata)
- BLM OR Fish Resident Line (FISH_RESIDENT_ARC) (Search Result/Metadata)
- BLM OR Fish Resident Poly (FISH_RESIDENT_POLY) (Search Result/Metadata)
- BLM OR Non-Native Fish Line (FISH_NON_NATIVE_ARC) (Search Result/Metadata)
- BLM OR Non-Native Fish Polygon (FISH_NON_NATIVE_POLY) (Search Result/Metadata)
Fish Passage Barrier
Greater Sage-grouse PHMA/GHMA
Marbled Murrelet Zone
- BLM OR Known Spotted Owl Activity Centers Oregon Polygon (koac_poly) (Search Result/Metadata) (Web Services)
- BLM OR Northern Spotted Owl Site Summary Publication Point (nso_sitesum_public_point) (Search Result/Metadata) (Web Services)
- BLM OR Northern Spotted Owl Sites Publication Point (nso_site_public_point) (Search Result/Metadata) (Web Services)
Greater Sage-grouse Record of Decision
Greater Sage-grouse Record of Decision 2015
- BLM OR GSGROD Greater Sage-grouse Priority Habitat Management Area General Habitat Management Area clipped to BLM Ownership Polygon R6 Albers (wld_ROD_PHMAGHMA_BLMOwn_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Right of Way Designations Wind and Solar Polygon R6 Albers (lnds_ROD_ROWDSG_windSolar_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Grazing Allotments clipped to BLM Ownership Polygon R6 Albers (rng_ROD_GraAllotment_BLMOwn_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Greater Sage-grouse Priority Habitat Management Area General Habitat Management Area Polygon R6 Albers (wld_ROD_PHMAGHMA_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Key Areas of Critical Environmental Concern (ACEC) R6 Albers (sma_ROD_KeyACECs_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Key Research Natural Areas (RNA) Polygon R6 Albers (sma_ROD_KeyRNA_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Mineral Stipulations Polygon R6 Albers (min_ROD_Mineral Stipulations_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Non Energy Leaseables Polygon R6 Albers (min_ROD_nonEnergyLSE_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Off-Highway Vehicle Designations Polygon R6 Albers (sma_ROD_OHV_r6alb_20150706) (Search Result/Metadata)
- BLM OR GSGROD Planning Area Polygon R6 Albers (adm_ROD_PlanningArea_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Planning Area clipped to BLM Ownership Polygon R6 Albers (adm_ROD_BLMOwn_PlanArea_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Right of Way Designations Major Polygon R6 Albers (lnds_ROD_ROWDSG_major_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Right of Way Designations Minor Polygon R6 Albers (lnds_ROD_ROWDSG_minor_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Sagebrush Focal Areas (SFA) Polygon R6 Albers (adm_ROD_SFA_Final_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Sagebrush Focal Areas (SFA) clipped to BLM Ownership Polygon R6 Albers (adm_ROD_SFA_BLMOwn_r6alb) (Search Result/Metadata)
- BLM OR GSGROD Land Tenure Polygon R6 Albers (lnds_ROD_LandTenure_r6alb) (Search Result/Metadata)
RMP for Western Oregon
This category includes data from the Resource Management Plans of Western Oregon.
RMP for Western Oregon Records of Decision Northwestern and Coastal Oregon
- BLM OR ROD NCO Areas Closed to Salable Mineral Entry Polygon (RWO_ROD_NCO_SALABLE_CLOSED_poly) (Search Result/Metadata)
- BLM OR ROD NCO Areas of Critical Environmental Concern Polygon (RWO_ROD_NCO_ACEC_poly) (Search Result/Metadata)
- BLM OR ROD NCO Designated Wild & Scenic River Corridors Polygon (RWO_ROD_NCO_Designated_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD NCO Designated Wild & Scenic Rivers Line (RWO_ROD_NCO_Designated_WSR_arc) (Search Result/Metadata)
- BLM OR ROD NCO Land Tenure Zones Polygon (RWO_ROD_NCO_LTZ_poly) (Search Result/Metadata)
- BLM OR ROD NCO Land Use Allocations Polygon (RWO_ROD_NCO_LUA_poly) (Search Result/Metadata)
- BLM OR ROD NCO Land Use Planning Boundary Polygon (RWO_ROD_NCO_LUP_Boundary_poly) (Search Result/Metadata)
- BLM OR ROD NCO Lands Wilderness Characteristics Polygon (RWO_ROD_NCO_LWC_poly) (Search Result/Metadata)
- BLM OR ROD NCO Public Motorized Access Designations Polygon (RWO_ROD_NCO_MOTORIZED_ACCESS_DESIGNATION_poly) (Search Result/Metadata)
- BLM OR ROD NCO Recreation Management Areas Polygon (RWO_ROD_NCO_RMA_poly) (Search Result/Metadata)
- BLM OR ROD NCO Right of Way Avoidance and Exclusion Areas Polygon (RWO_ROD_NCO_ROW_poly) (Search Result/Metadata)
- BLM OR ROD NCO Riparian Reserves Polygon (RWO_ROD_NCO_Riparian_Reserves_poly) (Search Result/Metadata)
- BLM OR ROD NCO Suitable Wild and Scenic River Corridors Polygon (RWO_ROD_NCO_Suitable_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD NCO Suitable Wild and Scenic Rivers Line (RWO_ROD_NCO_Suitable_WSR_arc) (Search Result/Metadata)
- BLM OR ROD NCO Visual Resource Management Areas Polygon (RWO_ROD_NCO_VRM_poly) (Search Result/Metadata)
RMP for Western Oregon Records of Decision Southwestern Oregon
- BLM OR ROD SWO Areas Closed to Salable Mineral Entry Polygon (RWO_ROD_SWO_SALABLE_CLOSED_poly) (Search Result/Metadata)
- BLM OR ROD SWO Areas of Critical Environmental Concern Polygon (RWO_ROD_SWO_ACEC_poly) (Search Result/Metadata)
- BLM OR ROD SWO Designated Wild & Scenic River Corridors Polygon (RWO_ROD_SWO_Designated_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD SWO Designated Wild & Scenic Rivers Line (RWO_ROD_SWO_Designated_WSR_arc) (Search Result/Metadata)
- BLM OR ROD SWO Grazing Allotments Polygon (RWO_ROD_SWO_Grazing_Allotments_poly) (Search Result/Metadata)
- BLM OR ROD SWO Land Use Allocations Polygon (RWO_ROD_SWO_LUA_poly) (Search Result/Metadata)
- BLM OR ROD SWO Land Use Planning Boundary Polygon (RWO_ROD_SWO_LUP_Boundary_poly) (Search Result/Metadata)
- BLM OR ROD SWO Lands Managed for Their Wilderness Characteristics Polygon (RWO_ROD_SWO_LWC_poly) (Search Result/Metadata)
- BLM OR ROD SWO Public Motorized Access Designations Polygon (RWO_ROD_SWO_MOTORIZED_ACCESS_DESIGNATION_poly) (Search Result/Metadata)
- BLM OR ROD SWO Recreation Management Areas Polygon (RWO_ROD_SWO_RMA_poly) (Search Result/Metadata)
- BLM OR ROD SWO Right of Way Avoidance and Exclusion Areas Polygon (RWO_ROD_SWO_ROW_poly) (Search Result/Metadata)
- BLM OR ROD SWO Riparian Reserves Polygon (RWO_ROD_SWO_Riparian_Reserves_poly) (Search Result/Metadata)
- BLM OR ROD SWO Suitable Wild and Scenic River Corridors Polygon (RWO_ROD_SWO_Suitable_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD SWO Suitable Wild and Scenic Rivers Line (RWO_ROD_SWO_Suitable_WSR_arc) (Search Result/Metadata)
- BLM OR ROD SWO Visual Resource Management Areas Polygon (RWO_ROD_SWO_VRM_poly) (Search Result/Metadata)
- BLM OR ROW ROD SWO Land Tenure Zones Polygon (RWO_ROD_SWO_LTZ_poly) (Search Result/Metadata)
RWO ROD External Services
- BLM OR ROD PSDV Suitable Wild and Scenic Rivers Line (PSDV_RWO_ROD_Suitable_WSR_arc) (Search Result/Metadata)
- BLM OR ROD PSDV Areas of Critical Environmental Concern Polygon (PSDV_RWO_ROD_ACEC_poly) (Search Result/Metadata)
- BLM OR ROD PSDV BLM District Harvest Model Metrics Polygon (PSDV_ecn_MMBFbyDOB_c_v1_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Designated Wild & Scenic River Corridors Polygon (PSDV_RWO_ROD_Designated_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Designated Wild & Scenic Rivers Line (PSDV_RWO_ROD_Designated_WSR_arc) (Search Result/Metadata)
- BLM OR ROD PSDV Eastside Management Area Land Use Allocations Raster (PSDV_RWO_ROD_LUA_EMA_rst) (Search Result/Metadata)
- BLM OR ROD PSDV Forest Site Moisture Condition Class Raster (PSDV_RWO_ROD_FSMCC_30m_rst) (Search Result/Metadata)
- BLM OR ROD PSDV Grazing Allotments Polygon (PSDV_RWO_ROD_Grazing_Allotments_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Harvest Land Base Land Use Allocations Raster (PSDV_RWO_ROD_LUA_HLB_rst) (Search Result/Metadata)
- BLM OR ROD PSDV Land Use Allocations Raster (PSDV_RWO_ROD_LUA_rst) (Search Result/Metadata) (Web Services)
- BLM OR ROD PSDV Land Use Planning Boundaries Polygon (PSDV_RWO_ROD_LUP_Boundaries_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Lands Managed for Their Wilderness Characteristics Polygon (PSDV_RWO_ROD_LWC_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Ownership Raster (PSDV_pol_ownership_c_v1_rst) (Search Result/Metadata)
- BLM OR ROD PSDV Population Change by County Polygon (PSDV_ecn_PopByCounty_c_v1_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Recreation Management Areas Polygon (PSDV_RWO_ROD_RMA_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Reserves and National Conservation Lands Land Use Allocations Raster (PSDV_RWO_ROD_LUA_Reserves_NLCS_rst) (Search Result/Metadata)
- BLM OR ROD PSDV Suitable Wild and Scenic River Corridors Polygon (PSDV_RWO_ROD_Suitable_WSR_Corridors_poly) (Search Result/Metadata)
- BLM OR ROD PSDV Visual Resource Management Areas Polygon (PSDV_RWO_ROD_VRM_poly) (Search Result/Metadata)
- BLM OR RWO Harvest Metrics By District Boundaries Polygon (RMPWO_MMBF_By_DOB) (Search Result/Metadata)
- BLM OR RWO Ownership 10 Meter Raster (RMPWO_Ownership_10m_rst) (Search Result/Metadata)
- BLM OR RWO Population Change By COB Polygon (RMPWO_Population_By_COB) (Search Result/Metadata)
- BLM OR RWO ROD PSDV Riparian Reserves Raster (PSDV_RWO_ROD_Riparian_Reserves_rst) (Search Result/Metadata)
San Juan Islands National Monument RMP
This category includes data from the San Juan Islands National Monument Resource Management Plan.
- BLM WA SJINMRMP BLM Ownership Draft Polygon (SJIRMP_BLM_Ownership_poly) (Search Result/Metadata)
- BLM WA SJINMRMP BLM Shoreline 50 Meter Buffer Draft Polygon (SJIRMP_BLM_Shoreline_50M_buffer_poly) (Search Result/Metadata)
- BLM WA SJINMRMP ERMA Intersected with Veg Alternative B Poly (SJIRMP_ERMA_Veg_Intersect_ALTB_poly) (Search Result/Metadata)
- BLM WA SJINMRMP ERMA Intersected with Veg Alternative C Poly (SJIRMP_ERMA_Veg_Intersect_ALTC_poly) (Search Result/Metadata)
- BLM WA SJINMRMP ERMA Intersected with Veg Alternative D Polygon (SJIRMP_ERMA_Veg_Intersect_ALTD_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Extensive Recreation Management Area Alternative B Polygon (SJIRMP_ERMA_AltB_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Extensive Recreation Management Area Alternative C Poly (SJIRMP_ERMA_AltC_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Extensive Recreation Management Area Alternative D Polygon (SJIRMP_ERMA_AltD_poly) (Search Result/Metadata)
- BLM WA SJINMRMP GeoBOB Publication Flora Sites Polygon (SJIRMP_Flora_Sites_poly) (Search Result/Metadata)
- BLM WA SJINMRMP NOAA Shoreline Buffered 200 feet Poly (SJIRMP_Shoreline_200ftBuffer_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Ownership Polygon (SJIRMP_Ownership_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Succession Clipped to Vegetation DRAFT Line (SJIRMP_Succession_Clipped_to_VEG_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Travel and Transportation Managemen Plan Alternative D Line (SJIRMP_TTMP_AltD_line) (Search Result/Metadata)
- BLM WA SJINMRMP Travel and Transportation Management Plan Alternative A Line (SJIRMP_TTMP_AltA_line) (Search Result/Metadata)
- BLM WA SJINMRMP Travel and Transportation Management Plan Current Line (SJIRMP_TTMP_Current_line) (Search Result/Metadata)
- BLM WA SJINMRMP Travel and Transportation Management Plan Alternative C Line (SJIRMP_TTMP_AltC_line) (Search Result/Metadata)
- BLM WA SJINMRMP Visual Resource Management - VRM (Polygon) Alternative A (SJIRMP_VRM_altA_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Visual Resource Management - VRM (Polygon) Alternative B (SJIRMP_VRM_altB_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Visual Resource Management - VRM (Polygon) Alternative C (SJIRMP_VRM_altC_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Visual Resource Management - VRM (Polygon) Alternative D (SJIRMP_VRM_altD_poly) (Search Result/Metadata)
- BLM WA SJINMRMP Wilderness Characteristics Poly DRAFT (SJIRMP_WLD_Characteristics_Poly) (Search Result/Metadata)
- BLM WA SJINMRMPTravel and Transportation Management Plan Alternative B Line (SJIRMP_TTMP_AltB_line) (Search Result/Metadata)
- BLM WA SJIRMP Dispersed Camping No Action Alternative Polygon (SJIRMP_BLM_Dispersed_Camping_NoAction_poly) (Search Result/Metadata)
- BLM WA SJIRMP Maritime Heritage Management Areas Polygon (SJINM_Martime_Heritage_Management_Areas_poly) (Search Result/Metadata)
- BLM WA SJIRMP Ocean Areas 20 meters Polygon (SJIRMP_BLM_OceanAreas_To_depth20m_poly) (Search Result/Metadata)
Southeastern Oregon RMP Amendment
This category includes data from the Southeastern Oregon RMP Amendment Resource Management Plan.
Detailed Data Description
The RAMP DEM was developed by integrating a broad variety of available topographic source data in a GIS environment. By combining the comparative advantages of all available sources, the developers were able to fully exploit the most detailed and accurate topographic information in each data set. Error checking procedures included global statistical analysis, cross-validation methods, and creation of a synthetic stereo image for visualizing and detecting gross errors in the elevation data (Liu 1999). A new data integration technique allowed the developers to produce a DEM that is both seamless and geomorphologically consistent with ice-covered and ice-free terrain. The DEM captures details of geomorphology, ranging from small-scale mountain valleys to extensive ice sheet drainage basins.
Version 2 of the RAMP DEM incorporates diverse improvements over the original version, with effects in numerous regions of Antarctica, as summarized below (table adapted from Jezek et al. 1999):
|Increased accuracy and resolution using newly available data||Coats Land, Theron Mountains, Berkner Island, Henry Ice Rise, and Korff Ice Rise|
|Extended DEM over islands and surrounding ocean surface in support of sea ice and oceanography studies||South Shetland Islands, Latady Island, Weddell Sea, Amundsen Sea, Davis Sea, ocean around Queen Maud Land, and Shackleton Ice Shelf|
|Achieved better data selection and surface constraints using updated coastlines and grounding lines derived from the SAR mosaic||coastline for the entire continent, particularly the ice margins of Ross Ice Shelf, Amery Ice Shelf, and Filchner-Ronne Ice Shelf Filchner Ice Shelf and Roosevelt Island grounding lines|
|Removed artifacts in the DEM by adjusting interpolation parameters and densifying contours||Crary Mountains, Sør Rondane Mountains|
|Corrected planimetric errors using SAR simulation and warping techniques||Ellsworth Mountains|
The data originate from various sources. The developers and data contributors compiled a comprehensive collection of digital topographic source data. The data used can be grouped into the following three categories:
Cartographic data include contours, spot height points, and surface structure lines digitized from paper topographic map sheets. Remotely sensed data consist of ERS-1 satellite radar altimetry data and airborne radar echo-sounding data. Survey data include ground-based survey data and satellite-based GPS measurements.
The following investigators contributed data:
- Jay Zwally of the NASA Goddard Space Flight Center, USA
- Anita Brenner and John DiMarzio of Raytheon Corporation, USA
- Jonathan Bamber of the Center of Remote Sensing, University of Bristol, UK
- Paul Cooper, David Vaughan, and Phil Homes of the British Antarctic Survey, UK
- Ted Scambos of the National Snow and Ice Data Center, USA
- Craig Lingle of the University of Alaska, USA
- Lee Belbin, Ursula Ryan, and Mike Craven of the Australian Antarctic Division, Australia
- Cheryl Hallam and Jerry Mullins of the USGS, USA
- Johannes Ihde of the Institut fur Angewandte Geodasie, Germany
- Ian Whillans, Paul Berkman, and Terry Wilson of the Ohio State University, USA
All DEM data are provided in ARC/INFO and binary grid formats, and the 1 km and 400 m DEMs are also available in ASCII format. Following is a chart that summarizes characteristics of the binary grid.
|1 km||400 m||200 m|
|Byte Order||Big Endian||Big Endian||Big Endian|
|Bytes per Cell||2||2||2|
|Cell Size||1000 m||400 m||200 m|
ASCII grids contain fields for latitude, longitude, elevation relative to the WGS84 ellipsoid, and elevation relative to the OSU91A geoid. Data are represented in decimal degrees, from -180 degrees (west) to 180 degrees (east) longitude and -90 to -60 (south) degrees latitude.
ARC/INFO coverages of RAMP DEMs are organized into individual directories for each resolution (1 km, 400 m, and 200 m) and geoid/ellipsoid.
Unit of Measurement
Elevations for points in this data set are measured in meters [m] above both the WGS84 ellipsoid and the OSU91A geoid. (See the section titled Data Granularity for a list of files referencing these two models.)
While the WGS84 ellipsoid is based on an approximation of the Earth's shape using only an equatorial radius and a polar radius (or a radius and an eccentricity), the OSU91A geoid is a more complex surface representing mean sea level. The OSU91A geoid is reported as a height above or below the WGS84 ellipsoid. The relationship between the two for the RAMP DEM can be described algebraically as follows:
W are the WGS84 elevations
G are the OSU91A elevations
S are mean sea level elevations of the OSU91A geoid, relative to the WGS84 ellipsoid
Values are in meters for each grid.
|OSU91A 200 m||0||5022|
|WGS84 200 m||-67||5008|
|OSU91A 400 m||0||5012|
|WGS84 400 m||-67||4997|
|OSU91A 1 km||0||4982|
|WGS84 1 km||-67||4968|
Note: Maximum values decrease with increasing grid spacing because a larger region is averaged for each grid cell. Maximum elevation values are found in the Ellsworth Mountains near Vinson Massif. Zero values are at the coast there are no points in the interior of Antarctica that are at or below sea level (i.e. with a geoid elevation of zero or less).
The following is sample output from a 1 km ASCII DEM file:
A granule of RAMP DEM data (i.e. the smallest aggregation of data that is independently retrievable) includes coverage of the entire continent at a given resolution and geoid/ellipsoid model. Compressed and uncompressed file sizes are summarized below.
Derivation Techniques and Algorithms
Interpolation of Satellite Radar Altimeter Data
The radar altimetry data sets for the RAMP Antarctic DEM had already been corrected for tracking and slope errors and preprocessed into evenly distributed points with a spacing of about 5 km, prior to being combined with the many additional data sets. Refer to NSIDC's Radar Altimeter document for more information, (Davis and Zwally 1993, Zwally et al. 1983, and Brenner et al. 1983.) The RAMP DEM development team used the Triangulated Irregular Network (TIN) Quintic interpolation method to further interpolate the satellite radar altimeter data (Liu, Jezek, and Li 1999).
Interpolation of Traverse Airborne Radar Data
Airborne radar data are densely sampled along flight lines but widely separated between flight transects. Most interpolation algorithms have difficulty resolving such a pattern. The development team for the RAMP Antarctic DEM used a procedure that combines the quadrant neighborhood-based Inverse Distance Weight (IDW) method to stabilize the interpolation result, with the TIN method to retain the topographic details present in the source data (Liu, Jezek, and Li 1999).
Interpolation of Contour-based Cartographic Data
Contour data are characterized by oversampling of information along contour lines and undersampling between contour lines, especially in low relief areas with widely spaced contours. It is the most difficult data type to interpolate with general-purpose interpolation techniques. The development team chose to use the TOPOGRID-based method (Hutchinson 1988 Hutchinson 1989 ESRI 1991 Gesch and Larson 1996) to interpolate the cartographic data in the RAMP Antarctic DEM. The team modified the TOPOGRID method slightly (Liu, Jezek, and Li 1999) to compensate for spurious sinks that occur in contour sparse areas corresponding to low slope areas like glacial valley floors (Bliss and Olsen 1996).
Determination of DEM Grid Spacing
The horizontal grid spacing of DEMs is an important parameter that needs to be specified during interpolation. In general, a small grid spacing is required to obtain an accurate representation of the surface details for a rugged and mountainous terrain, while a large grid spacing is sufficient for a low-relief terrain. For the satellite radar altimeter data and the airborne radar data, a post spacing of 1 km was used. For the contour data, 200 m grid spacing was used for rugged mountainous areas where the contour density is very high, while 400 m grid spacing was used for the sloped coastal area where the contours are relatively smooth and regularly, broadly spaced (Liu, Jezek, and Li 1999).
For mountainous and sloped coastal margins, the development team integrated the contour data, the spot elevation points, coastlines, grounding lines, and limited GPS data during the interpolation process. To avoid the edge effects, all the source data layers are merged into a number of overlapping blocks, and the interpolation extent at each time is set much smaller than that of input data. Individual DEM data sets are merged by using GIS logical "clipping" and "inserting" operations along coastlines and grounding lines, and by using a cubic Hermite blending function (S-shaped) along irregular buffer zones (Liu, Jezek, and Li 1999).
Horizontal (Spatial) Resolution
The real horizontal resolution of the DEM varies from place to place according to the density and scale of the original source data. The developers of the data set estimate that the horizontal resolution of the DEM is about 200 m in the Transantarctic Mountains and Antarctic Peninsula, and about 400 m in the sloped coastal regions. For ice shelves and the inland ice sheet covered by satellite radar altimeter data, the horizontal resolution is about 5 km, but where the airborne radar sounding data were used, the horizontal resolution is about 1 km. For the plateau inside 81.5 degrees south latitude, horizontal resolution is estimated at about 10 km (Liu, Jezek, and Li 1999).
The accuracy of geolocation (i.e., the accuracy of the position of a given feature on the DEM) is governed by the accuracy of topographic data sources, and is generally better than the horizontal resolution of the DEM.
Vertical accuracy of the RAMP Antarctic DEM is ± 100 m over rugged mountainous areas, ± 15 m for steeply sloped coastal regions, ± 1 m on the ice shelves, ± 7.5 m for the gently sloping interior ice sheet, and ± 17.5 m for the relatively rough and steeply sloped portions of the ice sheet perimeter. For latitudes south of 81.5 degrees south, within the interior East Antarctic ice sheet and away from the mountain ranges, vertical accuracy is estimated to be ± 50 m (Liu, Jezek, and Li 1999).
Potential errors in the RAMP DEM include imperfections in the measuring instrument, faulty readings or recordings, calculation and execution faults, and digitizing errors. Errors were noted in the ARC/INFO contour coverages, with mislabeled contours and intersections of contours. In some cases, digitized contour lines deviated from their original position on the source map and often intersected one other, resulting in some positions having two or more conflicting values. Also, poor ground control and inaccurate navigation techniques used to acquire the original topographic data were noted. Some ground control points were assigned erroneously large values, due to data entry errors. The RAMP DEM development team employed a variety of techniques to detect and correct these errors (Liu 1999).
Global Statistical Analysis
Summary statistics were calculated from the ARC/INFO attribute tables where elevation data reside. These global statistics were used to identify extreme erroneous values, namely the elevation range, from prior knowledge about a specific region or from the frequency distribution of elevation measurements. Data points with elevation values outside the reasonable range were flagged, and erroneously large values and negative values for elevation were removed. Contour lines with irregular elevation values were detected and corrected according to the values of neighboring contours.
Cross-validation methods were used for multiple data sets that overlap in the same area. Spot height points were checked against corresponding contour coverages by first predicting the elevation values at the positions of spot points by interpolating between contours, then computing the differences between the interpolated values and spot height values. Points that had an absolute difference greater than one contour interval in the flat area and two times greater than the contour interval in highly variable areas were removed. Cross-checking was similarly conducted between contour data and satellite radar alimeter data.
Errors in elevation data were detected with a variety of interactive methods including perspective views, color sequence in contour lines, hill shading, and synthetic stereo display. In areas of question, contour lines were overlain with the source data to reveal errors in elevation values. When the DEM grid was rendered as a hill shaded image or synthetic stereo image, errors would appear as anomalous valleys or scars, especially when vertical exaggeration was increased or the illumination angle was adjusted.
This method integrated a digital synthesis of a satellite image according to a DEM grid, with information about the satellite illumination angle and image geometry. In an area with homogenous land cover like that of Antarctica, a comparison and correlation analysis between the simulated image and real image can often reveal errors in the DEM.
Subtle errors in elevation values were detected using rigorous statistical methods, and by tracing errors back to the original source data after locating areas of spatial discontinuity. In checking the consistency and continuity of each data point relative to nearby points, erroneous data points are flagged as local outliers if they are inconsistent with neighboring points.