**HydroSHEDS technical documentation, Version 1.0**

**by Bernhard Lehner**

**World Wildlife Fund US, Washington, DC**

**December 2005**

1\. Overview

2\. Data sources

3\. Data set development

4\. Quality assessment

5\. Available HydroSHEDS data layers and specifications

6\. Data formats and data distribution

7\. Notes for HydroSHEDS users

8\. References

9\. Disclaimer

**1. Overview**

HydroSHEDS (**[Hydro]{.underline}**logical data and maps based on
**[SH]{.underline}**uttle **[E]{.underline}**levation
**[D]{.underline}**erivatives at multiple **[S]{.underline}**cales)
provides hydrographic information in a comprehensive and consistent
format for both local and global-scale applications. The goal of
developing this database was to generate key data layers in support of
regional and global watershed analyses, hydrological modeling, and
freshwater conservation planning at previously inaccessible quality,
resolution, and extent. HydroSHEDS offers a suite of geo-referenced data
sets (vector and raster), including stream networks, watershed
boundaries, drainage directions, and ancillary data layers such as flow
accumulations, distances, and river topology information. Available
resolutions range from 3 arc-second (\~ 90 meter) to 5 minute (\~ 10 km)
at seamless near-global extent.

HydroSHEDS is based on elevation data of the Shuttle Radar Topography
Mission (SRTM) at 3 arc-second (\~ 90 meter) resolution. To generate
HydroSHEDS, the original SRTM elevation data have been hydrologically
conditioned in a sequence of automated procedures. Both standard methods
of data improvement and newly developed algorithms have been applied,
including customized gap filling, filtering, stream burning, and
upscaling techniques. Manual corrections were added where necessary.
Preliminary quality assessments indicate that the accuracy of HydroSHEDS
significantly exceeds that of existing global watershed and river maps.

HydroSHEDS has been developed at the Conservation Science Program of the
World Wildlife Fund US (WWF-US), Washington DC, in collaboration with
the U.S. Geological Survey (USGS), the International Centre for Tropical
Agriculture (CIAT), The Nature Conservancy (TNC), and the Center for
Environmental Systems Research (CESR) of the University of Kassel,
Germany. Additional funding was provided by the SC Johnson Fund.
HydroSHEDS data is free for scientific, educational and environmental
use. Detailed descriptions of the processing steps involved in the
development of HydroSHEDS can be found in the HydroSHEDS technical
documentation file.

Constructive comments from users of HydroSHEDS are welcomed. Please send
your comments to <hydrosheds@wwfus.org>. Please be aware that we may not
be able to meet individual requests due to limited capacities. We will
regularly update the technical documentation to address key questions
and topics.

**2. Data sources**

This section briefly describes the main data sources that have been
applied in the generation of HydroSHEDS. The actual processing steps are
addressed in section 3.

2.1 Shuttle Radar Topography Mission

The primary data source of HydroSHEDS is the digital elevation model
(DEM) of the Shuttle Radar Topography Mission (SRTM). SRTM elevation
data has been obtained by a specially modified radar system that flew
onboard the Space Shuttle Endeavour during an 11-day mission in February
of 2000. The SRTM project is a collaborative effort by the National
Aeronautics and Space Administration (NASA), the National
Geospatial-Intelligence Agency of the U.S. Department of Defense (NGA),
as well as the German Aerospace Center (DLR), and the Italian Space
Agency (ASI). The mission has been managed by NASA\'s Jet Propulsion
Laboratory (JPL), and the Earth Resources Observation and Science Data
Center of the U.S. Geological Survey (USGS EROS Data Center) is
responsible for hosting, distributing and archiving the final SRTM data
products. A general description of the SRTM mission can be found in Farr
and Kobrick (2000).

2.2 SRTM elevation data, Version 1 (SRTM-1 and SRTM-3 "unfinished" data)

To derive an initial research-quality DEM, the raw SRTM data has been
processed at JPL. No further editing has been performed, and the
resulting elevation data may contain numerous voids and other spurious
points such as anomalously high (spike) or low (well) values. Water
bodies are generally not well-defined and, since water surfaces produce
very low radar backscatter, appear quite \"noisy\". Also, coastlines are
not defined. The original 1 arc-second (30 meter; SRTM-1) data has been
aggregated into 3 arc-second (90 meter; SRTM-3) data by averaging, i.e.
each 3 arc-second data point is generated by averaging the corresponding
3x3 kernel of the 1 arc-second data. For more details see NASA/JPL
(2005).

2.3 SRTM elevation data, Version 2 (DTED-2 and DTED-1 "finished" data)

After JPL completed the raw data processing, NGA performed quality
assurance checks and then carried out several additional finishing steps
to comply with the required data standards of the Digital Terrain
Elevation Data (DTED®) format (NASA, 2003). Spikes and wells in the data
were detected and voided out. Small voids were filled by interpolation
of surrounding elevations. Large voids, however, were left in the data.
The ocean elevation was set to 0 meters. Lakes of 600 meters or more in
length were flattened and set to a constant height. Rivers that exceeded
183 meters in width were delineated and monotonically stepped down in
height. Islands were depicted if they had a major axis exceeding 300
meters or the relief was greater than 15 meters. All "finishing" steps
were performed at the original 1 arc-second (30 meter) resolution,
resulting in DTED Level 2 data products. DTED-2 has then been aggregated
into 3 arc-second (90 meter) DTED-1 data. Unlike SRTM-3 data, DTED-1
data has been generated by "subsampling", i.e. each 3 arc-second data
point is generated by assigning the value of the center pixel of the
corresponding 3x3 kernel of the 1 arc-second data. For more details see
NASA/JPL (2005).

2.4 SRTM tiling format and data availability

SRTM elevation data have been processed in a systematic fashion and were
mosaicked into approximately 15,000 one degree by one degree tiles. The
names of individual data tiles refer to the longitude and latitude of
the lower-left (southwest) corner of the tile (following the DTED
convention). For example, the coordinates of the lower-left corner of
tile N40W118 are 40 degrees north latitude and 118 degrees west
longitude. To be more exact, these coordinates refer to the geometric
center of the lower left pixel. In the case of DTED-1 and SRTM-3 (3
arc-second) data, a single tile consists of 1201 data rows and 1201 data
columns. Due to the definition via pixel centers, the four edges of a
tile each exceed the assigned coordinates by half a pixel (\~ 45 meters)
and the outermost rows and columns of adjacent tiles are overlapping.
For more details see NASA/JPL (2005).

Global SRTM-1 and DTED-2 (1 arc-second) data are only available upon
request for scientific purposes. SRTM-3 and DTED-1 data are public
domain and may be obtained from NASA via anonymous ftp at
ftp://e0srp01u.ecs.nasa.gov/srtm/ or from the USGS EROS Data Center via
their Seamless Data Distribution System at http://seamless.usgs.gov/.

2.5 SRTM Water Body Data (SWBD)

SRTM Water Body Data (SWBD) files are a by-product of the data editing
performed by NGA to produce the finished SRTM DTED-2 data. Ocean, lake
and river shorelines were identified and delineated from the 1
arc-second (30 meter) DTED-2 data (for details see NASA 2003) and were
saved as vectors in ESRI 3-D Shapefile format. There are approximately
12,000 SWBD files (only those SRTM tiles that contain water have a
corresponding SWBD shapefile).

The guiding principle for SWBD was that water must be depicted as it was
in February 2000 at the time of the shuttle flight. In most cases, two
orthorectified SRTM image mosaics were used as the primary source for
water body editing. A landcover water layer and medium-scale maps and
charts were used as supplemental data sources. Since the landcover water
layer was derived mostly from Landsat 5 data collected a decade earlier
than the Shuttle mission and the map sources had similar currency
problems, there were significant seasonal and temporal differences
between the depiction of water in the SRTM data and the ancillary
sources in many instances. For more details see NASA/NGA (2003, 2005).

2.6 DCW 1:1 Mio. global vectorized river network

The Digital Chart of the World (DCW; ESRI 1993) is a global vector map
at a resolution of 1:1 million that includes a layer on hydrographic
features such as rivers and lakes. DCW (also known as VMAP-0) is
generally considered to provide the most comprehensive and consistent
global river network data. It is based on the US DMA (later NIMA, now
NGA) Operational Navigation Charts (ONC) whose information dates from
between the 1970s to the 1990s (Birkett and Mason, 1995). The locational
accuracy of DCW varies considerably between regions, and there is no
distinction between natural rivers and artificial canals.

2.7 ArcWorld 1:3 Mio. global vectorized river network

The ArcWorld data set (ESRI 1992) includes a global vector map of
surface water bodies at a resolution of 1:3 million. Among others, it
classifies linear rivers into being natural (perennial and intermittent)
or artificial (canals) and provides approximately 7000 polygons of large
open water bodies (including rivers and lakes). Although digitized at a
coarser scale, ArcWorld seems to include some corrections as compared to
DCW and provides a consistent focus on major rivers and lakes of the
world.

2.8 Global Lakes and Wetlands Database (GLWD)

The Global Lakes and Wetlands Database (GLWD; Lehner and Döll 2004)
combines a variety of existing global lake and wetland maps (1:1 to 1:3
million resolution) into one consistent coverage. It provides shoreline
polygons of approximately 250,000 lakes and reservoirs worldwide,
including their surface areas and other attributes. As for lakes and
reservoirs, GLWD is largely based on DCW and ArcWorld but also includes
various updates and data corrections.

**3. Data set development**

With all digital geospatial data sets, users must be aware of certain
characteristics of the data (resolution, accuracy, method of production
and any resulting artifacts, etc.) in order to better judge its
suitability for a specific application. A characteristic of the data
that renders it unsuitable for one application may have no relevance as
a limiting factor for its use in a different application (NASA/JPL
2005).

This section provides an overview of the applied processing steps for
the generation of HydroSHEDS and discusses some key technical
specifications in order to allow a user to better estimate the
suitability for a specific application. Further aspects of data
validation are addressed in section 4.

3.1 Combination of "unfinished" SRTM-3 and "finished" DTED-1 data

*3.1.1 Combining SRTM-3 and DTED-1 original data*

For the generation of HydroSHEDS, the performance of the publicly
available SRTM-3 and DTED-1 versions of SRTM at 3 arc-second (90 meter)
resolution have been tested. Due to their specific characteristics, each
data set showed both advantages and disadvantages for hydrological
applications.

SRTM-3 has been derived through averaging of 1 arc-second (30 meter)
SRTM data, as opposed to the subsampling method of DTED-1. As averaging
reduces the high frequency \"noise\" that is characteristic of
radar-derived elevation data, it is the method generally preferred by
the research community (NASA/JPL 2005).

On the other hand, SRTM-3 data does not represent open water surfaces
and shorelines well. DTED-1 has been particularly corrected for these
objects, yet the correction protocol introduced some critical artifacts
for hydrological applications. For example, when larger rivers were
identified and monotonically stepped down in height towards the ocean,
it was assured that the surface of each river pixel was lower than that
of the directly adjacent land pixels. As this processing was performed
at 1 arc-second resolution, a slightly elevated shoreline (e.g. a levee,
a natural river bank, or riparian vegetation that is picked up in the
radar signal) may allow for river elevations higher than their generally
surrounding floodplain. If the shoreline is thin (only one pixel wide)
it can disappear in the aggregated 3 arc-second version (due to
subsampling), resulting in a possible break-out of the river course into
the floodplain.

After conducting a series of local tests, it has been decided to apply
both SRTM-3 and DTED-1 data in combination. For each pixel the minimum
value found on either SRTM-3 or DTED-1 was used to generate a new,
initial HydroSHEDS elevation model. The minimum requirement may
reintroduce wells from SRTM-3 (but not spikes) and some noise in lake
surfaces (but consistently equal or below the lake surface of DTED-1).
In general, the combined elevation data has an emphasis on low surfaces,
which is considered desirable for the later identification of drainage
directions.

*3.1.2 Ocean shoreline*

At the ocean surface, the combined data initially shows elevation values
of 0 (from DTED-1) or negative (from SRTM-3). This elevation criteria
alone does not allow for a clean identification of the ocean shoreline,
as land close to the shoreline can also be 0 or even negative (e.g.
behind dunes). For this reason, SWBD has been used as ancillary data:
where SWBD indicates "ocean", the values of the HydroSHEDS elevation
model have been replaced by no-data. In order to remove small artefacts,
the resulting shoreline has been slightly generalized, i.e. extended by
a one-pixel rim and then slightly smoothed through a local cell filter.
All detached ocean surfaces (e.g. small estuaries entirely surrounded by
land cells) were treated as land and their elevation values were
retained rather than set to no-data. Some larger estuaries are defined
in SWBD to extend relatively far into the ocean. In these cases, the
shoreline has been modified based on the shoreline of DCW. Finally, some
small errors were detected in SWBD in visual inspections (e.g. some
incomplete island boundaries) and were individually corrected.

*3.1.3 Data shift*

Both SRTM-3 and DTED-1 original one degree by 1 degree data tiles are
defined through the coordinates of their lower-left pixel; to be
precise, the center of their lower-left pixel (see 2.4). This
characteristic leads to overlapping edges of adjacent tiles and to some
artifacts when aggregating (upscaling) the data to coarser resolutions:
either the overlapping edges have to be eliminated in the result, or the
adjacent tiles have to be included in the aggregation process. As the
processing steps for the generation of HydroSHEDS are rather complex and
scaling plays a dominant role, it has been decided to shift the original
SRTM data by 1.5 arc-seconds (\~ 45 meters) to the north and east, and
to remove each tile's overlapping right column and top row. This shift
leads to a 3 arc-second (90 meter) HydroSHEDS tile having 1200 rows and
1200 columns at an extent of exactly one degree by one degree without
overlaps to adjacent tiles. All other HydroSHEDS resolutions are based
on the 3 arc-second data and thus include this shift. With respect to
deriving river networks, the effect of this plain shift (no projection
change) on the accuracy of the data is considered negligible,
particularly when compared to the subsequently applied data
manipulations (see below). Note, however, that the shift may lead to
significant anomalies when directly comparing HydroSHEDS elevation data
and original SRTM elevation data.

3.2 Gap filling

In its original release, SRTM data contains regions of no-data gaps
(voids), specifically over large water bodies (lakes and rivers) and in
areas where radar-specific problems prevented the production of reliable
elevation data. These problematic areas include mountainous regions
("radar shadow" effect; e.g. Himalayas and Andes) as well as certain
land surfaces (e.g. bare sand/rock in the Sahara desert). The existence
of no-data regions in the DEM causes significant problems for deriving
hydrological products, which generally require continuous flow surfaces.
Therefore, a gap-filling procedure has been applied to provide a
continuous DEM for HydroSHEDS.

Numerous methods have been developed for gap-filling in SRTM data (see
e.g., Gamache 2004a), but they rarely focus on specific hydrological
requirements. For HydroSHEDS, two different gap-filling algorithms have
been applied in combination. The first has been developed by the
International Centre for Tropical Agriculture (CIAT; see Jarvis 2004; in
collaboration with R. Hijmans and A. Nelson). The second has been
specifically developed for HydroSHEDS. While the CIAT algorithm delivers
smooth interpolation surfaces, the HydroSHEDS algorithm focuses on low
and flat water surfaces. Both methods and their combination are
summarized below.

*3.2.1 CIAT algorithm*

The CIAT algorithm fills the no-data gaps through an interpolative
technique within an Arc/Info AML model. The original SRTM elevation data
is used to produce contours at an interval of 10 vertical meters. The
contours are interpolated using the TOPOGRID algorithm in Arc/Info.
TOPOGRID is based upon the established algorithms of Hutchinson (1988;
1989), designed to use contour data (and stream and point data if
available) to produce hydrologically sound DEMs. This method produces a
smooth elevation surface within the no-data regions. While micro-scale
topographic variation is likely to be underrepresented, most macro-scale
features are captured well in small to intermediate sized gaps. Jarvis
et al. (2004) performed a detailed analysis of the accuracy of the
interpolated elevation data for a region in Colombia and found little
difference when compared to a cartographic DEM, particularly for
hydrological applications. Gamache (2004b and personal communication)
also analyzed the CIAT results and concluded that the gap-filling
algorithm is quite successful in representing broad scale patterns in
topography. The gap-filled elevation data (CIAT 2004) is available from
the CGIAR-CSI SRTM 90m Database at http://srtm.csi.cgiar.org.

*3.2.2 HydroSHEDS algorithm*

The HydroSHEDS algorithm fills the no-data gaps through an iterative
neighborhood analysis. The first step fills the outermost pixel-rim of a
no-data gap through a combination of a 3x3 minimum and a 5x5 mean filter
(the minimum filter dominates the mean filter by a factor of 3:1). Then
the next pixel-rim is filled until the entire no-data gap is processed.
The no-data area is finally smoothed through a 9x9 mean filter.
Particularly in the case of lakes and large river surfaces, this method
leads to rather low elevation values inside the gaps (due to the
emphasis of the minimum filter) and a relatively flat relief (as small
peaks are successively filtered out).

*3.2.3 Combination of gap-filling algorithms*

The lowering effect of the HydroSHEDS algorithm for open water surfaces
seems desirable for hydrological applications as it tends to force the
flow course to stay within river channels and lakes. In mountainous
regions, however, the CIAT results are expected to better represent the
general topography. To optimize results, both algorithms were combined.
For each pixel the minimum value of either the CIAT or HydroSHEDS
algorithm was used. Only if the HydroSHEDS algorithm computed values
more than 30 meters lower than CIAT, CIAT values minus 30 meters were
used.

In some large no-data gaps entire mountains are lost using any of the
two filling methods. Therefore, starting at a distance of 0.03 degrees
(\~ 3 km) from the rim of large gaps, elevation values were inserted
from GTOPO30, a global DEM at 30 arc-second (1 km) resolution (USGS
1996). To avoid cliff effects, the inserted values were smoothed in a
0.03 degree (\~ 3 km) wide transition zone ("feathering").

The filled gaps were then merged into the combined HydroSHEDS elevation
data to provide a continuous elevation surface without no-data regions.
The entire process was performed for each one degree by one degree tile
with a 0.25 degree overlap to the eight adjacent tiles, thus ensuring
seamless transitions of topography even in areas with large gaps.

The final result of steps 3.1 and 3.2 is the HydroSHEDS *gap-filled
elevation model*, termed **HydroSHEDS_NoGap**, at 3 arc-second (90
meter) resolution.

3.3 Sink identification

Typically, an original DEM will show an often large number of "sinks"
(or depressions), i.e. single or multiple pixels which are entirely
surrounded by higher elevation cells. Some of these sinks are occurring
in nature, representing endorheic (or inland) basins which have no
outlet to the ocean. In most cases, however, the sinks are caused by
random and mostly small deviations in the elevation surface. These
anomalies occur even in high quality DEMs and high resolutions due to
inherent uncertainties and errors. The random sinks are critical
problems in hydrological applications as they terminate an otherwise
continuous flow. Therefore, sinks are typically removed from the DEM
before deriving a river network. Standard GIS procedures have been
developed to remove sinks, and a common approach is to lift the sinks
until they overflow. Unfortunately, this automated process also removes
natural sinks. Natural sinks can be forced to remain through "seeding",
e.g. by putting a no-data cell into their center.

As for HydroSHEDS, the definition of natural vs. random sinks has been
accomplished through a GIS-assisted manual process. All sinks of the
gap-filled elevation model were identified in a standard GIS procedure,
and their maximum depth and extent were calculated. Sinks both deeper
than 10 meters and larger than 10 km^2^ were highlighted as "potential"
natural sinks. All regions of potential natural sinks were then
inspected visually and have either been seeded or rejected. The decision
has been based on information derived from DCW, ArcWorld, GLWD, or
additional atlases and maps. For example, a salt lake with no obvious
river draining from it is considered a strong indication for an
endorheic basin. Note, however, that this manual process is subjective
to the executing person. The visual inspections were performed at a zoom
to one degree by one degree windows, and sinks were identified at
several thousand locations globally.

In many cases, the definition of natural sinks may be difficult and
ambiguous. Some depressions overflow following seasonal flooding cycles,
others spill only occasionally. Some large, relatively dry areas may
show numerous small depressions within a generally sloped surface while
flow paths are poorly developed, if at all (e.g. the Argentinean Pampas
or many desert areas). These depressions may or may not overflow in a
rain event. In some areas of no obvious drainage only some "structural"
sinks have been placed at strategic locations. They do not terminate the
flow at single depressions but indicate the endorheic character of the
region. In karstic areas, rivers may disappear in surface depressions,
yet they can be closely connected to a larger basin via underground
pathways. In cases of large karstic depressions, sinks were introduced
as it seemed easier for a user to later remove the sinks and restore
connectivity than to introduce them from scratch. Artificial sinks,
however, like large pits in surface mining areas, were rejected.

3.4 Hydrologic conditioning

Besides sinks, original DEMs show a series of other characteristics,
artifacts and anomalies that can cause significant problems or errors in
hydrological applications. Some types of problems that are typical for
the SRTM elevation model are discussed in section 4. The most
significant characteristic is likely the fact that the elevation values
of SRTM, being a radar-derived product, are influenced by the vegetation
cover. In areas of low relief, these small deviations from the true
surface elevation can cause significant errors in the derived river
courses and flow directions.

In order to improve the performance of a DEM for hydrological
applications, a series of GIS processes and procedures exist and are
routinely applied. Yet, due to the individual characteristics of
different DEMs and, on a global scale, due to the regional variations in
the type of errors, no one method exists that addresses all possible
problems. For HydroSHEDS, a sequence of hydrologic conditioning
procedures has been implemented, either adapted from standard GIS
functionality, newly developed, programmed, or customized. The general
focus is to strike a compromise between forcing the DEM to deliver
correct river network topology, particularly for the largest of rivers,
while preserving as much original SRTM information as possible. Note
that in any case the conditioning process alters the original elevation
data and may render it incorrect for other applications.

The following hydrologic conditioning procedures have been applied for
the HydroSHEDS elevation data:

*3.4.1 Deepening of open water surfaces*

All rivers and lakes as identified in SWBD were deepened by 10 meters in
order to force the derived flow to stay within these objects. As no-data
gaps in the original SRTM elevation data may also indicate open water
surfaces (see 3.2), all gap areas were lowered by 10 meters as well. The
10 meter threshold has been chosen as it forces a strong enough effect
in flat areas (where the identification of river channels and lakes is
particularly difficult), while producing only insignificant changes in
areas with steeper slopes (where no-data gaps are probably caused by
radar shadow rather than open water).

*3.4.2 Weeding of coastal zone*

The coastal zone, i.e. a 0.02 degree (\~ 2 km) wide buffer along the
ocean shoreline, has been "weeded" by reducing every random third cell
by 5 meters. The effect of this subtle change, in combination with the
following filters, is that an occasional break-through is forced into
slightly elevated coastal embankments. These embankments may represent
mangrove zones which otherwise can cause significant backwater effects
(in the radar-derived elevation model mangrove or coastal vegetation
belts may be interpreted as a low but continuous barrier blocking any
direct outflow to the ocean).

*3.4.3 Stream burning*

The most extensive conditioning process that has been applied in the
generation of HydroSHEDS is the so-called "stream burning" procedure.
Stream burning is a standard process to enforce known river courses into
an elevation surface. The elevation values along the rivers, e.g.
depicted from an existing vector layer, are lowered by a certain value,
thus "burning" deep gorges into the elevation surface. The burning can
be extended to include a buffer around the river lines in order to shape
a smoother transition between original surface and gorge. For
HydroSHEDS, only large rivers (and lakes) were burnt into the elevation
surface in order to avoid excessive alterations of the SRTM surface
based on potentially erroneous global river maps. All perennial and
intermittent rivers and lakes of ArcWorld, as well as all rivers and
lakes of GLWD were used. After multiple tests, the burning depth for
rivers was set to 12 meters, with a buffer of 0.005 degrees (\~ 500
meters) around the river courses in which the burning depth was reduced
to 0 in a stepwise manner. Lakes were treated with parameters of 14
meters and 0.0025 degrees (\~ 250 meters), respectively. The parameter
setting aims for a noticeable forcing of the main rivers in flat areas,
where otherwise the correct delineation of rivers is difficult. In steep
regions, the small burning depth results in rather insignificant changes
of the elevation surface, so that the SRTM data remains the dominant
information for deriving drainage directions.

*3.4.4 Filtering*

The entire elevation surface has then been filtered by applying a
directional 3x3 neighborhood analysis. The elevation values of all
possible straight and obtuse-angle flow paths in a 3x3 kernel have been
averaged and the minimum value has been assigned to the center cell.
This filter aims to remove remaining spikes and wells while preserving
and enforcing linear river courses and valley bottoms. In particular,
single pixels that can block a continuous flow path are removed.

*3.4.5 Molding of valley courses*

Valley courses have been depicted through a neighborhood terrain
analysis and have been deepened by 3 meters. Valleys were identified
through a 5x5 kernel median analysis combined with a grid thinning
algorithm to detect linear features. This procedure of valley "molding"
has specifically been developed to improve river delineations in
tropical lowland areas by removing small obstacles in shallow valleys.
Due to the small deepening of 3 meters, no significant changes occur in
areas with stronger relief.

*3.4.6 Sink filling*

In a standard process, all sinks in the elevation surface have been
filled. Natural sinks have been seeded and are thus excluded from
removal (see 3.3).

*3.4.7 Carving through barriers*

From the conditioned elevation surface as processed so far, a draft
river map has been produced and all main river courses, defined as all
rivers with an upstream catchment area of more than 1000 cells (\~ 8
km^2^), have been depicted. The rivers were then projected onto the
initial HydroSHEDS elevation model. All elevation rises, when stepping
downstream along the river courses, were identified. These rising
reaches in the original data, which have obviously been removed through
filtering or sink-filling in the conditioning process, may represent
dams, bridges, embankments of any kind, or narrow gorges that block the
flow path. In many of these cases, the sink-filling effect, i.e. the
lifting and implicit flattening of the dammed area, may not be desirable
as any existing relief information within the filled area is lost. To
minimize this effect, a second conditioning iteration was performed:
first, all rises in the initial elevation data along the draft river
courses were leveled by appropriately deepening the respective heights,
thus effectively "carving" through the barriers. After this process, all
other conditioning steps (3.4.1 to 3.4.6) were repeated.

During the entire conditioning process, hard- and software limitations
were reached due to the very large data sizes at 3 arc-second
resolution. All steps have therefore been performed on a tile by tile
basis, with tiles sizes between one degree by one degree and five degree
by five degree. In order to avoid edge effects, appropriate overlaps to
the adjacent tiles were added. In particular the sink-filling algorithm
proved highly susceptible to data sizes and edge effects and had to be
implemented in an iterative approach, and an overlap of up to 5 degrees
(\~ 500 km) with adjacent tiles was required to ensure seamless results
without edge effects.

3.5 Manual corrections

The result of section 3.4 is a hydrologically conditioned elevation
surface at 3 arc-second (90 meter) resolution. From this elevation
surface, an initial river linear network can be derived and used for
error checking. Because computation of the river network at 3 arc-second
resolution is very time intensive, the data has first been upscaled to
15 arc-second (\~ 500 meter) resolution (see 3.6 below). The derived
river network has then been compared visually to the rivers of DCW,
ArcWorld, and various atlases and paper maps.

Errors occurred particularly in flat areas with varying vegetation cover
(see section 4), like floodplains and coastal zones. If the actual
rivers could be visually detected in the raw elevation data, their
courses were digitized or adopted from the existing DCW river layer.
These rivers were then added in the river burning procedure of 3.4. In
some areas, the given elevation values significantly misrepresented the
actual flow conditions (e.g. blocked pathways due to narrow gorges, or
due to inadequate filling of no-data gaps). In these cases the burning
depth was individually adjusted. Some other topological problems (caused
e.g. by canals or multiple spillways of reservoirs) were treated in a
similar manner through introduction and adjustment of a main pathway.
Actual flow channels of braided rivers and large river deltas could not
be topologically resolved due to the permission of only one drainage
direction per cell which does not allow for river bifurcations. These
zones have only been "cleaned" to represent the main channel properly.

After detecting the errors and preparing the according correction data,
all steps of 3.4 were repeated. In some areas, several iterations of
manual corrections were performed. Note that this manual correction
process is subjective to the executing person. The visual inspections
were performed at a zoom to one degree by one degree windows, and
corrections were applied for several thousand locations globally.

The final results of steps 3.4 and 3.5 are the HydroSHEDS
*hydrologically conditioned elevation model*, termed
**HydroSHEDS_HyCon**, and the derived HydroSHEDS *drainage direction
map*, termed **HydroSHEDS_DDM** at 3 arc-second (90 meter) resolution.

3.6 Upscaling

All procedures described in sections 3.1 to 3.5 have been performed at 3
arc-second (90 meter) resolution. Yet for many applications, in
particular continental or global assessments, coarser resolutions are
desirable as they may significantly reduce calculation times while
providing acceptable accuracy. HydroSHEDS therefore delivers various
resolutions, from 3 arc-second (\~ 90 meter) to 5 minute (\~ 10 km). The
coarser resolutions are all derived through upscaling of the 3
arc-second data.

Upscaling drainage directions is not a straight forward process, as
typical aggregation methods (like averaging of neighborhood kernels) is
not appropriate for directional values. A frequently applied upscaling
method is to first upscale the elevation data, and then derive a new
drainage direction map from this coarser DEM. This method is generally
fast and easy to perform, but it often delivers low-quality results with
respect to river network topology, due to the loss of significant
information in the aggregation process. An alternative option is to
first derive the river network at high resolution, and then to upscale
this network. This option preserves the network information, which is
most important for hydrological applications, but it requires complex
procedures which are difficult to realize at a global scale and for the
desired high resolutions. As a compromise, a combined method has been
applied for generating HydroSHEDS. The main steps in the upscaling
process are as follows:

1.  The gap-filled DEM is upscaled from the original 3 arc-second (90
    meter) to the desired resolution. For this process, an algorithm has
    been applied that calculates both the mean and minimum value found
    within the aggregation kernel and then takes the average (the
    minimum value emphasizes valleys). Natural sinks were preserved in
    the upscaling process.

2.  The river network of main rivers is calculated at 3 arc-second (90
    meter) resolution. Main rivers have been defined as having an
    upstream catchment area of more than 1000 cells (\~ 8 km^2^). The
    river network was derived for five degree by five degree tiles with
    a one degree overlap to adjacent tiles to avoid edge effects.

3.  The main rivers are burnt into the upscaled elevation surface. The
    burning depth has been defined as the sum of a constant (500 meters)
    and a size dependent value (0-400 meters, proportional to logarithm
    of upstream cells). The relatively large burning depth assures that
    the river channels are preserved in the new elevation surface. No
    buffering has been applied.

4.  New drainage directions are calculated from the upscaled and burnt
    elevation surface. Note that due to the strong burning, the
    elevation surface does not resemble natural conditions any more, yet
    it is appropriate for deriving drainage directions (the artificial
    elevation surface is not offered as a standard HydroSHEDS product).

The result of the upscaling process delivers a new drainage direction
map (DDM) from which a new river network can be derived. Due to the
applied stream burning, the main rivers (as defined in the upscaling
process) are in very good alignment with the original river network.
Only if two close-by rivers drain through the same or directly adjacent
upscaled cells, they may be incorrectly merged into one flow channel.
Smaller rivers are defined through the upscaled elevation surface. Their
quality may thus differ from the river network at original resolution.

The final results of step 3.6 are HydroSHEDS *drainage direction maps*,
termed **HydroSHEDS_DDM**, at resolutions of 15 arc-second (500 meter)
and 30 arc-second (1 km). Also, a 5 minute (10 km) product is in
preparation.

3.7 Derived products

Ancillary HydroSHEDS products can be derived from the individual DDMs at
their respective resolutions. These products include flow accumulations,
flow distances, river networks, and watershed boundaries. A list of
available data sets is provided in section 5.

**4. Quality assessment**

At this stage of developing HydroSHEDS, the final data quality has not
been evaluated systematically. Yet preliminary comparisons with other
global hydrographic data sets indicate the following:

-   Generally, HydroSHEDS shows significantly better accuracy than
    HYDRO1k, a global hydrographic data set at 1 km resolution (USGS
    2000), due to HydroSHEDS being based on a superior digital elevation
    model.

-   Generally, HydroSHEDS shows significantly better accuracy than than
    the river layer of ArcWorld (even in difficult areas) as ArcWorld
    has been incorporated in the conditioning process of HydroSHEDS.

-   Generally, HydroSHEDS shows better accuracy than DCW, but
    comparisons depend on location. In some regions where HydroSHEDS is
    particularly susceptible to errors (e.g. vegetated floodplains, see
    below) the quality of DCW can be significantly superior to
    HydroSHEDS.

-   Generally, HydroSHEDS does not reach the accuracy of high resolution
    local river networks as derived from existing maps or remote sensing
    imagery (if available). The user is thus encouraged to further
    improve HydroSHEDS through incorporating local information.

Typically, river network products derived from a digital elevation
surfaces are susceptible to various errors, foremost in flat regions
without well-defined relief. Additionally, the quality of HydroSHEDS
depends on the characteristics of the applied elevation model, i.e.
SRTM. Being a radar product, SRTM elevation values are influenced by
vegetation and other surface effects (e.g. roughness, wetness, low
backscatter signal at open water surfaces, radar shadow; for further
details see e.g. Freeman 1996). At this point, known sources of errors
in HydroSHEDS include:

-   Areas of low or not well-defined relief (including lake surfaces)

-   Varying vegetation cover, particularly in areas of low-relief, e.g.
    large river floodplains (the radar signal is, at least partly,
    reflected from atop and within the vegetation cover; the returned
    signal is thus a complex mix of land surface elevation and
    vegetation height)

-   Low-relief coastal areas (barrier effect from mangroves)

-   Large-scale roads or clearings in vegetation of low-relief areas
    (the lack of vegetation causes artificial depressions in the
    elevation surface)

-   Rivers of less than 90 m width enclosed by riparian vegetation (the
    vegetation effect can cause the river channel to appear slightly
    elevated)

-   Braided rivers and deltas (drainage directions in ESRI format cannot
    reflect river bifurcations)

-   Narrow gorges ( if they are less than 90 m wide, they can appear
    closed on the elevation surface at 90 m resolution)

-   Inland sinks and depressions (often ambiguous or temporary in
    nature; in karst areas, flow paths are not necessarily terminated at
    sinks due to possible underground connectivity; artificial
    depressions like large-scale mining may have flow bypasses)

-   Artificial barriers in the elevation surface (e.g. bridges,
    high-density housing areas)

-   Large no-data gaps (voids) in the SRTM data due to various reasons
    (see 3.2)

**5. Available HydroSHEDS data layers**

The HydroSHEDS data are being developed for all landmasses of the globe
north of 56° south (i.e. excluding Antarctica). The HydroSHEDS package
provides a suite of various raster and vector data sets, covering many
of the common derivative products used in hydrological analyses. The
data is prepared in seamless mode (no edge effects) and is provided as
global, continental, or five degree by five degree tile coverage. All
data is produced in geographic (lat/long) projection and datum WGS84.
All elevations are in meters referenced to the WGS84 EGM96 geoid. For
technical reasons, HydroSHEDS products show a consistent shift of 1.5
arc-seconds (\~ 45 meters) to the north and east as compared to the
original SRTM data (see 3.1.3).

At this time, HydroSHEDS provides only selected core data sets at 3
arc-second (\~ 90 meter) and 15 arc-second (\~ 500 meter) resolutions,
including drainage directions and elevation surfaces. A vectorized
stream network is available at 15 arc-second (500 meter) resolution
only. Future data developments are scheduled to include basins; flow
accumulations; slope; aspect; wetness index; stream orders; flow
distances up- and downstream; as well as seamless global products at 30
arc-second (\~ 1 km) and 5 minute (\~ 10 km) resolution. Currently, the
following data layers and resolutions are available.

5.1 Elevation with filled no-data gaps

Name signature: **NoGap**

Data in raster format

Values are elevation in meters (referenced to WGS84 EGM96 geoid)

Available resolutions: 3 arc-second (\~ 90 m), 15 arc-second (\~ 500 m)

The elevation layers distributed with the HydroSHEDS data set are based
on a combination of the original SRTM-3 and DTED-1 elevation models of
SRTM (for further specifications see 2.1 to 2.4 and 3.1). No-data gaps
have been filled through interpolation algorithms, and the data has been
clipped at the ocean shoreline. Resolutions other than 3 arc-second are
derived through aggregation (averaging): each upscaled data pixel is
generated by averaging the corresponding neighborhood kernel of the 3
arc-second data. Note that for technical reasons HydroSHEDS elevation
data shows a consistent shift of 1.5 arc-seconds (\~ 45 meters) to the
north and east as compared to original SRTM data (see 3.1.3).

5.2 Hydrologically conditioned elevation

Name signature: **HyCon**

Data in raster format

Values are elevation in meters (referenced to WGS84 EGM96 geoid)

Available resolution: 3 arc-second (\~ 90 m)

The hydrologically conditioned elevation layers distributed with the
HydroSHEDS data set are the result of an iterative conditioning and
correction process described in detail in section 3. Note that the
conditioning process alters the original DEM and may render it incorrect
for applications other than deriving drainage directions. Inland sinks
have been set to no-data (seeding).

5.3 Drainage direction map

Name signature: **DDM**

Data in raster format

Values are drainage directions in ESRI format (see below)

Available resolutions: 3 arc-second (\~ 90 m), 15 arc-second (\~ 500 m)

The drainage direction map (DDM) defines the direction of flow from each
cell in the DEM to its steepest down-slope neighbor. Values of flow
direction vary from 1 to 128. All final outlets to the ocean are flagged
with a value of 0. All cells which represent an inland sink are flagged
with a value of -1. Defined flow directions follow the convention
adopted by ESRI\'s flow direction implementation:

  -------------- -------------- -------------- -------------- --------------
  *NW*                          *N*                           *NE*

                 **32**         **64**         **128**        

  *W*            **16**         **0**          **1**          *E*

                 **8**          **4**          **2**          

  *SW*                          *S*                           *SE*
  -------------- -------------- -------------- -------------- --------------

5.4 Flow accumulation map

Name signature: **FAM**

Data in raster format

Values are flow accumulations in number of cells (see below)

Available resolution: 15 arc-second (\~ 500 m)

The flow accumulation map (FAM) defines the amount of upstream area (in
number of cells) draining into each cell. The DDM layer is used to
define which cells flow into the target cell. The number of accumulated
cells is essentially a measure of the upstream catchment area. But since
the cell size of the HydroSHEDS data set depends on latitude, the cell
accumulation value cannot directly be translated into drainage areas in
square kilometers (a FAM reflecting true catchment areas is in
preparation). Values range from 1 at topographic highs (river sources)
to very large numbers (on the order of millions of cells) at the mouths
of large rivers.

5.5 River network (stream lines)

Name signature: **RIV**

Data in vector format

Available resolution: 15 arc-second (\~ 500 m)

The river network (RIV) layer distributed with the HydroSHEDS data set
is directly derived from the DDM layer. The FAM layer has been used for
selection and attribution. Only rivers with upstream drainage areas
exceeding a certain threshold are selected. The vectorized river reaches
are currently attributed with the maximum flow accumulation (in number
of cells) occurring within each reach (it is planned to provide more
attributes in future versions):

Column headings:

ID = unique identifier

Up_cells = maximum flow accumulation value (number of cells) of the
stream reach

5.6 Drainage basins (watershed boundaries)

Name signature: **BAS**

Data in vector format

Available resolution: 15 arc-second (\~ 500 m)

The drainage basin (BAS) layer of the HydroSHEDS data set is not yet
finalized and will be distributed at a later stage.

**[6. Data formats and data distribution]{.mark}**

[to be added]{.mark}

**7. Notes for HydroSHEDS users**

This section will be updated with useful notes and hints for users of
HydroSHEDS.

FAQs:

**Q 01:** I have additional local information available in form of
high-quality river maps. How can I incorporate this information in
HydroSHEDS?

**A 01:** First, compare your local maps with the HydroSHEDS river
network. If there are only a few differences, you can depict the
respective river lines from the local map and burn them into the
*hydrologically conditioned* DEM of HydroSHEDS. Then run a sink-fill
process and derive a new DDM and river network from the corrected and
conditioned DEM. If the local river map shows generally better quality
at many locations, or if the conditioning process of HydroSHEDS
introduced an error that is not apparent in the raw SRTM data, you can
use the local river map and burn it into the *gap-filled*
(un-conditioned) DEM of HydroSHEDS. However, you then have to apply your
own individual conditioning processes (at a minimum you have to apply a
sink-fill procedure) in order to produce a new hydrologically
conditioned DEM. You can finally derive a new DDM and river network from
the corrected and conditioned DEM.

**Q 02:** How can I remove an incorrect sink from HydroSHEDS and restore
continuous flow?

**A 02:** Use the *hydrologically conditioned* DEM of HydroSHEDS.
Reclassify the no-data cell that represents the incorrect sink with an
appropriate elevation value. Then run a sink-fill process and derive a
new DDM and river network from the corrected and conditioned DEM.

**8. References**

Birkett, C.M., Mason, I.M. (1995): A new global lakes database for a
remote sensing program studying climatically sensitive large lakes.
Journal of Great Lakes Research 21(3): 307-318.

CIAT -- International Centre for Tropical Agriculture (2004):
Void-filled seamless SRTM data V2. Data available from the CGIAR-CSI
SRTM 90m Database at http://srtm.csi.cgiar.org

ESRI -- Environmental Systems Research Institute (1992): ArcWorld 1:3
Mio. Continental Coverage. ESRI, Redlands, CA. Data available on CD.

ESRI -- Environmental Systems Research Institute (1993): Digital Chart
of the World 1:1 Mio. ESRI, Redlands, CA. Data available on 4 CDs or at
http://www.maproom.psu.edu/dcw/

Farr, T.G., M. Kobrick (2000): Shuttle Radar Topography Mission produces
a wealth of data. American Geophysical Union, Eos 81: 583-585.

Freeman, T. (1996): What is Imaging Radar? Jet Propulsion Laboratory.
Document available at
http://southport.jpl.nasa.gov/desc/imagingradarv3.html

Gamache, M. (2004a): Free and low cost datasets for international
mountain cartography. Documentation for the Alpine Mapping Guild, 42 pp.
Available at http://www.icc.es/workshop/abstracts/ica_paper_web3.pdf

Gamache, M. (2004b): Various documents including evaluations of CIAT's
gap-filling methodology, available at:
http://www.icc.es/workshop/presentacions/s5/1_martin/presentation.pdf
and http://www.alpinemapguild.com/ICA_CMC2004/Gamache final web.pdf

Hutchinson, M. (1988): Calculation of hydrologically sound digital
elevation models. Third International Symposium on Spatial Data
Handling, Columbus, Ohio, International Geographical Union.

Hutchinson, M. (1989): A new procedure for gridding elevation and stream
line data with automatic removal of spurious pits. Journal of Hydrology
106: 211-232.

Jarvis, A. (2004): SRTM Data Processing Methodology. Available at
http://srtm.csi.cgiar.org/SRTMdataProcessingMethodology.asp

Jarvis, A., J. Rubiano, A. Nelson, A. Farrow, Mulligan M. (2004):
Practical use of SRTM data in the tropics: comparisons with digital
elevation models generated from cartographic data. Working Document no.
198. Cali, International Centre for Tropical Agriculture (CIAT): 32 pp.

Lehner, B., Döll, P. (2004): Development and validation of a global
database of lakes, reservoirs and wetlands. Journal of Hydrology
296(1-4): 1-22. Data available at
http://www.wwfus.org/science/data/globallakes.cfm

NASA (2003): SRTM Data Editing Rules, Version 2.0. 23 pp. Available at
ftp://e0srp01u.ecs.nasa.gov/srtm/version2/Documentation/SRTM_edit_rules.pdf

NASA/JPL (2005): SRTM Topography (SRTM documentation). 8pp. Available at
ftp://e0srp01u.ecs.nasa.gov/srtm/version2/Documentation/SRTM_Topo.pdf

NASA/NGA (2003): SRTM Water Body Data Product Specific Guidance, Version
2.0. 4pp. Available at
ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SWBD_Documentation/SWDB_Product_Specific_Guidance.pdf

NASA/NGA (2005): Documentation for the Shuttle Radar Topography Mission
(SRTM) Water Body Data Files. 2pp. Available at
ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SWBD_Documentation/Readme_SRTM_Water_Body_Data.pdf

USGS -- U.S. Geological Survey (1996): GTOPO30. USGS EROS Data Center,
Sioux Falls, SD. Available at
http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html

USGS -- U.S. Geological Survey (2000): HYDRO1k Elevation Derivative
Database. USGS EROS Data Center, Sioux Falls, SD. Available at
http://edc.usgs.gov/products/elevation/gtopo30/hydro/

**9. Disclaimer**

Any use of trade, product, or firm names is for descriptive purposes
only and does not imply endorsement by the U.S. Government. Please note
that some information contained in this data set and documentation may
be preliminary in nature and presented prior to final review and
approval by the Director of the USGS. This information is provided with
the understanding that it is not guaranteed to be correct or complete
and conclusions drawn from such information are the sole responsibility
of the user.

NO WARRANTY OR LIABILITY

WWF, in collaboration with USGS provides the HydroSHEDS data without any
warranty of any kind whatsoever, either express or implied, including
warranties of merchantability and fitness for a particular purpose. WWF
and/or USGS shall not be liable for incidental, consequential, or
special damages arising out of the use of any HydroSHEDS data.

ACKNOWLEDGMENT AND CITATION

We kindly ask users to cite HydroSHEDS data in any published material
produced using this data. If possible, please link web pages to the
HydroSHEDS website ([to be determined]{.mark}).

Citations should be made as follows:

Lehner, B. (2005): HydroSHEDS technical documentation, Version 1.0.
World Wildlife Fund US, Washington, DC. Available at ([to be
determined]{.mark}).