=Paper=
{{Paper
|id=Vol-2391/paper38
|storemode=property
|title=Creation of digital elevation models for river floodplains
|pdfUrl=https://ceur-ws.org/Vol-2391/paper38.pdf
|volume=Vol-2391
|authors=Anna Klikunova,Alexander Khoperskov
}}
==Creation of digital elevation models for river floodplains ==
https://ceur-ws.org/Vol-2391/paper38.pdf
Creation of digital elevation models for river
floodplains
1 1
A Klikunova , A Khoperskov
1Volgograd State University, Volgograd, Russia, 400062
e-mail: klikunova@volsu.ru
Abstract. A procedure for constructing a digital elevation model (DEM) of the
northern part of the Volga-Akhtuba interfluve is described. The basis of our DEM is the
elevation matrix of Shuttle Radar Topography Mission (SRTM) for which we carried
out the refinement and updating of spatial data using satellite imagery, GPS data,
depth measurements of the River Volga and River Akhtuba stream beds. The most
important source of high-altitude data for the Volga-Akhtuba floodplain (VAF) can be
the results of observations of the coastlines dynamics of small reservoirs (lakes, eriks,
small channels) arising in the process of spring flooding and disappearing during low-
flow periods. A set of digitized coastlines at different times of flooding can significantly
improve the quality of the DEM. The method of constructing a digital elevation model
includes an iterative procedure that uses the results of morphostructural analysis of the
DEM and the numerical hydrodynamic simulations of the VAF flooding based on the
shallow water model.
1. Introduction
A high-resolution 3D topographic model for the large areas is essential to solving a variety
of applied problems in the geosciences that are associated with modeling and monitoring
the environment. The progress of computer technology and numerical methods gives us new
opportunities for modeling fluid dynamics in certain territories. Such problems include storm
surges, spring floods in river valleys, flooding due to heavy rainfall [1, 2]. Hydrodynamic models
allow technical and environmental expertise in the design of hydrological structures [3, 4]. The
important tasks are the determination of the watersheds’ boundaries [5], the creating tools to
help authorities respond to emergency situations [6].
One important research area is the creation of decision support systems (DSS) for solving
various hydrological problems, and the effectiveness of these DSS is determined by the quality
of the applied digital elevation models (DEM) [7, 8, 9]. Such DSS belong to the class of Spatial
Decision-Support System, which combine standard decision-making tools with geographic
information systems, providing new opportunities for water resources management [4, 10],
city and regional planning, real-time decision-making for land management [11], transportation
engineering [12], protecting the natural resources in conditions of increasing human pressures
on the ecosystem.
A quality DEM is a critical component for all these tasks [13]. The terrain is a major physical
factor that influences the dynamics of water. Unfortunately, the accuracy of best topographic
maps is not high enough for numerical simulations. In addition, new problems appear on small
spatial scales, and they are associated with changes in the surface of the relief caused by natural
V International Conference on "Information Technology and Nanotechnology" (ITNT-2019)
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Figure 1. The northern part of the Volga-Ahtuba floodplain.
and man-made factors [14, 15]. Changes in the profile of the bottom and adjacent areas are a
continuous process due to active sediment transfer and erosion processes, which require the use
of the self-consistent model of water and sediment dynamics and regular updating of the DEM
also [16].
In this paper, we describe the key stages of creating a DEM for river systems based on the
synthesis of various spatial data using the example of the northern part of the Volga-Akhtuba
floodplain (VAF). The Volga Hydroelectric Station controls the flow of water downstream of the
Volga River and the moisture reserves for the entire floodplain. The volume flow of water through
the dam is called discharge Q(t) (m3 ·sec−1 ) and it varies between Q(t) ' 4000 − 30000 m3 ·sec−1
during the year.
Important components of our methodology are the use of observational data on the dynamics
of the coastlines of numerous small reservoirs in the interfluve during the spring flood and the
verification of DTM using hydrodynamic modeling. Observations of the coastlines motions
for a large number of reservoirs during the spring flooding are a source of very accurate local
topography data. These water reservoirs are the results of the passage of spring water and they
usually disappear in early summer. Thus, the water surface area in the territory of VAF varies
strongly during a few weeks from 2-5% before flooding (low water) up to a maximum value
of 20-40%, which depends on the specific conditions in each year. In late summer, the water
basin area is smaller than in the early spring period before the flood, that connected with high
summer temperature and lack of rain. The coastline coincides with the contour line (isoline) of
the heights’ distribution with very high accuracy at each time point. Thus, the local DEM may
be the result of processing the monitoring data of the coastlines dynamics for a large number
of small reservoirs during the spring flood. These local DEMs are high-resolution data for the
most critical areas in terms of hydrology as a part of global DEM for the northern territory of
the Volga-Akhtuba floodplain (Fig. 1).
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Figure 2. Stages and sequence of DEM creation.
2. Iterative process of creating DEM
2.1. Main stages of creating DEM
Figure 2 shows the general scheme for constructing a digital elevation model and highlights
the most significant steps which we will discuss below. Our DEM is based on the height matrix
bij = b(xi , yj ) for nodes of the Pulkovo 95 coordinate system with step ∆x = ∆y: xi = x0 + i∆x,
[SRT M ]
yj = y0 + j∆y (i = 1, 2, ..., Nx , j = 1, 2, ..., Ny ). We take the SRTM3 SRTMGL1 data bij
as the initial height matrix. The professional GIS “Panorama” tools allow us to recalculate the
matrix by a smaller step (∆x = 15 m, 10 m, 5 m) using the weighted average interpolation in 16
[0]
directions. Such matrix bij will be called the basic digital elevation model.
[0]
The main stages of the transformation matrix bij are discussed below.
1) To clarify the model of the bottom of the Volga River and the Akhtuba River, we use
Sailing Directions (shipping charts) and water depth maps. To refine the bottom
model of the Volga River and the Akhtuba River, we use Sailing Directions (shipping charts)
[1]
and reservoir depth maps, and then we obtain the matrix bij after digitizing and embedding
[0]
this data into the basic DEM bij .
2) A unique feature of the VAF is a complex system of small channels in the interfluve (the
so-called eriks), which form a hierarchical system of channels between River Akhtuba and River
Volga (Fig. 3). We use the satellite images of the “RESURS-P” series and UK-DMC 2, the
DigitalGlobe’s satellite constellation (Google Earth services) to vectorize the linear objects of
[1]
this channel system for subsequent introduction into the DEM matrix of bij . UAV images and
geodesic data are an important source for clarifying the location of small channels (Fig. 5). As
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Figure 3. a — Typical dependence of discharge Q(t). b, c, d — The hierarchical structure of the
hydrological system in the VAF at different stages of flooding.
[2]
a result, we have the matrix bij , which contains the system of small channels.
3) To update the Volga River bottom model, we use the data of the last depth
measurements ranging from the Volga hydroelectric power station to the Svetly Yar settlement.
These data are very sparse and after approximation to all our grid nodes we have the matrix
[3]
bij with the height data of the river bed.
4) We use data on dynamics of coastlines of transient reservoirs, which are filled with
water at the stage of interfluve flooding (April – May) and dry out in the summer (Figure 4).
These measurements provide an additional set of lines with a constant level of relief with very
[3]
high accuracy. The refined matrix bij is the result of binding these isolines to heights. Our
studies have shown the effectiveness of the UAVs use to obtain data on the boundaries of water
bodies (Fig. 5). UAVs provide a more detailed sequence of isolines at the initial stage of flooding
rise, which is almost unattainable for satellite data. However, this approach is local and does
not allow to cover large areas.
[2]
Figure 4 shows vertical profiles along the AB and CD segments for the bij matrix, indicating
the positions of the corresponding intersections of coastlines with these segments. The points
for the same coastline on opposite slopes of the reservoir have different elevation levels, which
[2]
indicates the need to update the matrix bij . For example, the height difference is ∆b = 0.5 m
for a pair of points (1a, 1b) in the figure 4 b and ∆b = 1 m for (2a, 2d) in the figure 4 c.
5) Then we calculate the standard set of morphostructural analysis parameters [17]:
the profile curvature kt (xi , yj ), the tangential curvature ks (xi , yj ) and the tilt angles s(xi , yj )
(Figure 6):
360o q
s = arctan b2x + b2y , (1)
2π
bxx b2y − 2bxy bx by + byy b2x
kt = √ , (2)
p q
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a)
b)
c)
Figure 4. The position of coastlines at different points in time for small bodies of water near the
village Zonal’nyj.
bxx b2y + 2bxy bx by + byy b2x
ks = p , (3)
p q3
∂b ∂b ∂2b ∂2b ∂2b
bx = , by = , bxx = , byy = , bxy = , p = b2x + b2y , q = 1 + p.
∂x ∂y ∂x2 ∂y 2 ∂x∂y
We often encounter two types of artifacts:
[0]
a) Strong local errors of heights on the bij matrix are strongly highlighted against the background
of a rather flat territory. These errors are often caused by data processing problems for small
forests and small water reservoirs.
b) The second difficulty is related to the detection of small channels connectedness.
There are problems with the automatic selection of objects even in images for urbanized
areas, the morphology of which is simpler compared to the wooded marsh landscape of the
floodplain [18]. Analysis of the hyperspectral observational data for various platforms allows us
to improve the classification of objects [8], but this approach is algorithmically complex [19].
The spatial distributions of the parameters (1) – (3) help identify areas with artifacts, first of
all, areas with a violation of hydrological connectedness of watercourses on the digital elevation
model. The morphostructural analysis of the DEM allows simple means to detect possible errors
and promptly correct them, refining the hydrological network [20, 21].
6) Hydrodynamic modeling is carried out at the final stage (Fig. 3a, b), reproducing the
spring flooding of the interfluve territory in accordance with the procedure described in [1, 3, 22].
This allows you to check the channels connectedness of the hydrological system in addition to
the morphostructural analysis. Comparison of simulation results with observational data is a
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Figure 5. The vectorization of water bodies images with UAV. The colored lines show the
boundaries of the reservoirs.
Figure 6. a) The general structure of the VAF flooding is based on the results of our numerical
hydrodynamic modeling. b) The distribution of water for the specified area of the frame. c) The
distribution of the morphometric index ks for the same zone.
powerful tool for updating the DEM for the most important zones, which primarily provide for the
formation of vast reservoirs of the lake type due to the water outflow from small canals (eriks).
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Such verification based on hydrodynamic modeling is the most resource-intensive procedure. For
hydrodynamic simulations, we use the software for the numerical solution of the shallow water
equations described in [1, 22] and taking into account the parallel implementation for GPUs
[23].
2.2. Assimilation of local spatial data by the DEM matrix
One essential feature of building a digital model of river bed is the source data sparseness, which
include:
coast(~
r)
(i) There are two coastlines with water level mark Lcoast
1 (~r), L2 .
bed
(ii) There are several depth curves on topographic maps of Li (~r) (i = 1, ..., mB ). We have
only mB ∼ 3 − 4 even for the largest rivers.
(iii) Several soundings show, as a rule, only the deepest points on a topographic map.
(iv) Depth measurements using echo sounders require new field studies.
All these data form set of points P on the height matrix bij .
We used an iterative procedure to build a river bottom DEM:
h i h i
bp
n,m + α bpn+1,m − 2bpn,m + bpn−1,m + α bpn,m+1 − 2bpn,m + bpn,m−1 , Pn,m ∈
/P
bp+1
n,m = (exp)
bn,m , Pn,m ∈ P ,
(4)
(exp)
where bn,m is the depth at the points Pn,m , α is the parameter that determines the
convergence of the iterative procedure (4). The formula (4) is the finite-difference analog of the
diffusion equation. We obtain the solution to the Poisson’s equation in the case of converging
iterations (4). Figure 7 shows the results of the construction of the DEM of the Volga River
area, based on the approach described above.
a) b)
Figure 7. a) Vector map of the River Volga. b) Digital elevation model of riverbed of the Volga
downstream from the hydroelectric dam.
2.3. Coastlines dynamics as factor in improving DEM
Fig. 3 shows a schematic diagram of the hydrological regime in the VAF. Water flows from
the Volga River to the Akhtuba River in a low water period in the case Q ' 5 − 9 thousands
m3 ·sec−1 , but it is not enough to fill the channels and besides the moisture reserve is very small
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in the area between the rivers. All channels are quickly filled with the increase of Q up to
23–30 thousands m3 ·sec−1 and the water is poured onto the flat part of VAF. The water level
is maintained by the powerful moistening at the third stage with Q = 16000 − 20000 m3 ·sec−1 .
In late spring, there is a change to low-water and the total moisture content decreases in the
territory.
There is a large number of shallow lakes on the flat territory between the large and small
channels in spring and early summer. The coastlines of such reservoirs are moved on considerable
distances in a short time period (Fig. 8 and See Fig. 4). Measuring the position of coastline
at different points in time can help us determine an additional set of contour lines (isolines of
heights) of the terrain for critical zones.
Figure 8. Shallow lake near the Bulgakov Channel at various stagesof the flooding in 2014 is
shown: a) start the flooding (May 6), b) maximum the flooding (May 8), c) the dissipation of the
reservoir (May 18).
2.4. Verification based on the results of hydrodynamic simulations
Fig. 9 shows the results of hydrodynamic simulations in the floodplain of the small river at
various stages of the DEM refinement:
(i) We use the DEM after embedding the riverbed in the SRTM matrix and assignment the
coastlines, the fairway line and the river slope (Fig. 9a).
(ii) Panel b in the figure demonstrates the water distribution in the river channel after processing
the DEM in the “Construction of horizontals by elevation matrix” service in the GIS
Panorama.
(iii) The next iteration involves the DEM rebuilding taking into account the geodetic transverse
profiles of the river valley, which are obtained as a result of field measurements (Fig. 9c).
(iv) The final step involves updating the digital model on a small scale at the high water stage
(Fig. 9d ).
3. Conclusions
The object of our study is the valley between the River Volga and River Akhtuba, the ecosystem
of which is unique on Earth due to the special hydrological regime. We propose the iterative
procedure for creating the DEM for special floodplain areas with a large number of transient
reservoirs. The initial data are the SRTM matrix, the space images from the “Resource-P ”
and UK-DMC-2 satellites, the topographic maps, the geodetic measurements of the elevation
profiles, the depth measurements. The morphostructural analysis and the numerical simulations
of surface water dynamics on realistic topography can be powerful tools for verification of the
digital elevation model.
The observed dynamics of coastlines allows building elevation levels along the boundaries of
water bodies, and this approach is actively used to construct the DEM. However, this method
acquires special value in the case of periodically flooded areas, since the moving coastlines
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a) b)
c) d)
Figure 9. Results of local DEM refinement for the small river valley using hydrodynamic
simulations. By identifying the shortcomings of the DEM, we provide flooding in the model for the
nearest areas in accordance with the observations.
provide detailed sets of contour lines, being the basis for a very high-quality and relevant digital
elevation model.
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Acknowledgments
The work has been supported by the Ministry of Science and Higher Education (government task no.
2.852.2017/4.6). The research is carried out using the equipment of the shared research facilities of
HPC computing resources at Lomonosov Moscow State University. The authors are grateful to E.
Agafonnikova, S. Khrapov, A. Pisarev, K. Tertychny for their help and assistance in carrying out this
project. A. Klikunova thanks for the support of the Russian Federal Property Fund and the
Administration of the Volgograd region (grant 18-47-340003).
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