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338
Sediment yield estimation from a hydrographic survey:
A case study for the Kremasta reservoir basin, Greece.
D. ZARRIS(1), E. LYKOUDI(2) AND D. KOUTSOYIANNIS(1)
(1) Department of Water Resources, Faculty of Civil Engineering, National Technical University
of Athens, Heroon Polytechneiou 5, Zographou, Greece.
(2) Department of Geological Sciences, Faculty of Mining Engineering and Metallurgy,
National Technical University of Athens, Heroon Polytechneiou 5, Zographou, Greece.
Abstract: Sediment discharge measurements in streams are quite rare even in
technologically advanced countries, whilst comprehensive physically based
models are generally unable to reliably estimate sediment yield of large-scale
hydrological basins. A more realistic and reliable alternative method for
sediment yield estimation, suitable for watersheds with a dam at the outlet, is
the hydrographic surveying of the reservoir’s invert and comparison with the
one prior to the dam construction resulting to the computation of sediment
deposits’ volume and mass. This method has been applied to the Acheloos
River basin with the hydrographic surveying of Kremasta, a large reservoir
with net storage capacity exceeding 3 cubic kilometers. The sediment yield has
been estimated not only for the total watershed but also for each of the three
tributaries (Acheloos R., Agrafiotis R. and Megdobas R.). Besides, the soil
erosion of the watershed has been estimated using an implementation of the
Universal Soil Loss Equation on a geographical information system. The
sediment delivery ratios have been finally computed combining the sediment
yield and soil erosion estimates.
Key words: delivery ratio, sediment yield, hydrographic survey, Digital Elevation Models,
source erosion, Kremasta reservoir, Greece
1. INTRODUCTION
The deposition of sediment in reservoirs can variously impact their
performance through storage capacity losses, damage to valves and conduits,
reduced flood attenuation and changes in water quality. Though generally
not recognised as a widespread water resource problem in Greece, growing
evidence points to areas with locally severe sediment discharge problems,
particularly in the upland areas of western Greece (Zarris et al., 2001). The
production, transportation and deposition of sediment are extremely variable
both in space and time. There is variation within and between catchments,
such that Campell (1992) reported that 70% of the sediment load for a river
Sediment yield estimation from hydrographic survey 339
in Alberta, Canada was contributed by only 2% of its area. He suggested that
drainage basins are “fuzzy systems”, with internal basins constantly
changing and this may cause major difficulties in estimates of catchment
sediment yields. Additionally, simple statistical models (such as the
sediment rating curves) and advanced physically-based models fail to
produce an accurate estimation of sediment yields in large scale water
systems. The difficulties of recording such variations have led researchers
such as Heinmann (1984), Duck and McManus (1994), Rowan et al. (1995)
to prefer the use of reservoir studies for establishing catchment sediment
yields. Foster and Walling (1994) have suggested that, given the absence of
long-term fluvial sediment monitoring programmes in global terms, the
sediment records stored in lakes and reservoirs offer very considerable
potentional for reconstructing the history of sediment mobilisation and
transport over the past 100 years.
2. RESEARCH APPROACH
2.1 Description of the Kremasta reservoir
The Kremasta reservoir was constructed in 1964 and is located in North-
Western Greece. The reservoir area at the spillway crest is 80.6 km2 and the
total storage volume is 4495 hm3. The reservoir watershed has an area of
3292 km2, elevation ranging from +284 m to +2433 m and the mean annual
inflow to the reservoir equals 117.1 m3/s. This inflow is largely provided by
Acheloos River and to a lesser extent by Agrafiotis River and Megdovas
River (see Figure 1). Mean annual areal precipitation equals 1433 mm. The
geology of the catchment is largely dominated by limestone and flysch.
340 D. Zarris, E. Lykoudi and D. Koutsoyiannis
Figure 1: Kremasta reservoir watershed.
2.2 Description of research method
A key element of the proposed method is to construct the Digital
Elevation Models (DEM) for two periods of interest, one prior to the dam
construction (1964) and the other during the hydrographic survey (1998-99).
The hydrographic survey has been carried out using a differential Global
Positioning System (GPS) technique and a typical fathometer operating at
the frequency of 130 kHz for depth determination. Therefore the method is
subject to the usual errors e.g. GPS limited availability and the definition of
the water-mud interface. The DEM at the time prior to the dam completion
was constructed from digitising the original survey maps (scale 1:5000). The
corresponding DEM from the hydrographic survey resulted from an irregular
network of points in three dimensions (position and elevation). The
associated grids were interpolated from triangulation with linear
interpolation procedures available in the SURFER mapping package. The
difference in elevation results in the volume of deposited sediments.
The spatial distribution of accumulated sediment in the reservoir shows
profoundly that the total incoming sediment remains in the reservoir and
particularly at the uppermost parts (deltaic deposits) (see Figure 2).
Sediment yield estimation from hydrographic survey 341
Figure 2: Spatial distribution of accumulated sediments in the Kremasta reservoir.
The total sediment deposits volume was calculated equal to 66.6 hm3. To
convert volumetric changes to sediment yield in mass units the material
properties of the deposited sediment were also investigated by collecting two
core samples from the reservoir invert using appropriate instrumentation
(i.e. LONGYEAR 36 hydraulic corer). Direct measurement of deposits
density was not possible mainly because it was impossible to collect
undisturbed samples. However, density was estimated from the proportion of
sand, silt and clay in the samples using the Lane and Koelzer (1943)
formula. The total sediment mass accumulated in the reservoir for the whole
period of dam operation was estimated at 112.5 Mt. Therefore the mean
annual sediment yield is estimated equal to 1005 t/km2 and the
corresponding mean annual sediment discharge equal to 106.4 kg/s. The
final results in terms of accumulated volume, accumulated mass, mean
annual sediment yield and discharge for the whole catchment as well as the
three tributary sub basins are shown in Table 1.
Agrafiotis River basin, which is the smallest one, contributes the most
considerable sediment load per unit catchment area. The corresponding
value is one of the highest mean annual sediment yield found in international
literature and is a result of rainfall intensity, geology, morphology and the
small extent of its area.
342 D. Zarris, E. Lykoudi and D. Koutsoyiannis
Table 1: Characteristic variables for each sub-catchment.
Basin Area
(km2)
Accumulated
volume
(hm3)
Accumulated
mass (Mt)
Mean annual
sediment
yield (t/km2)
Mean annual
sediment
discharge
(kg/s)
Acheloos R. 1733 41.3 69.8 1184.6 66.0
Agrafiotis R. 320 13.1 22.1 2034.8 20.9
Megdovas R. 1239 12.2 20.6 489.4 19.5
Total 3292 66.6 112.5 1005.6 106.4
In contrast to sediment yield, mean annual sediment discharge is less
significant due to the smaller extent of its watershed, but is still higher than
the adjacent Megdovas River catchment.
2.3 Source erosion estimation
Source erosion may be computed using the well known Universal Soil
Loss Equation (USLE) (Wischmeier and Smith, 1965, 1978). The numerical
values of the different factors of the equation have been computed after
processing data collected in small catchments in the United States. This
obviously suggests a weakness of the method in case of applying it
elsewhere from the US with different climatic and topographic conditions.
Additionally, USLE does not account for sediment transport in hillslopes and
streams and does not perform well in large scale catchments. However, in
terms of computing only the catchment soil erosion, USLE is a quite
satisfactory preliminary approximation. The value of the rainfall erosivity
factor R is computed from the mean annual rainfall using the relation given
by Schwertmann et al. (1990).
In the present study soil erosion was computed using a GIS
implementation of the USLE. The graphical interface is called SEAGIS
(after Soil Erosion Assessment using GIS) and was originally developed at
the Danish Hydraulic Institute (DHI, 2000).
Mean annual erosion rates (Ye) and sediment delivery ratios (D) (i.e. the
ratio of sediment yield to source erosion) for each catchment are shown in
Table 2. Delivery ratios follow the well-established trend of decreasing
values with increasing catchment surface.
Table 2: Source erosion and delivery ratios.
Basin Area (km2) Mean annual
source erosion
(t/km2)
Delivery
ratio
Acheloos R. 1733 7077 0.17
Agrafiotis R. 320 4847 0.42
Megdovas R. 1239 2251 0.22
Total 3292 5040 0.20
Sediment yield estimation from hydrographic survey 343
3. SEDIMENT DELIVERY PROCESSES
The value of sediment volume for 50 years of the dam operation was
determined in the original dam design study equal to 394 hm3 (ECI, 1974).
This value is profoundly higher than the actual one resulted from the
hydrographic survey. The reason of the over-dimension of the reservoir’s
dead volume lies in the sediment discharge measurements taken at that time.
A total of 29 suspended sediment measurements had been accomplished in
two months time during a winter period and a sediment rating curve had
been evaluated. Besides the purely statistical considerations related to
serially correlated error terms (Weber et al., 1976, Lemke, 1991), it is
obvious that the two month period is too short to lead to a reliable estimation
of the overyear sediment yield.
Additionally, the spatial distribution of the sediment deposits in the
reservoir illustrates that at least for large reservoirs, the concept of designing
the dead volume near the dam (i.e., below a certain constant reservoir level)
is under serious doubt. Specifically, for the reservoir under study, the
deposits tend to occupy a significant (in absolute terms) part of the
reservoir’s useful volume whilst the nominal dead volume is almost empty
of sediments.
Sediment delivery ratios resulted from the above methodology shows a
generally compatible behavior with existing data. For example, delivery
ratios are in very close agreement with the relation given by Laurence
(1996), who correlated data from various catchments of the world and
concluded that delivery ratio D is expressed according to catchment area
A (km2) with the power law D = A-0.2. Renfro (1972) on the other side using
different source of data concluded in the relationship logD = 1.877 −
0.1419 log(25.9A), where D is expressed as a percentage and A in km2. The
associated values are 0.2 and 0.15 respectively. However, mean annual
sediment yield values are considerably higher than corresponding values
given in the literature. For instance, Dendy and Bolton (1976) used data from
the US which suggested that there is a statistically significant relationship
between mean annual sediment yield (t/km2/y) and drainage area (km2)
expressed as Yi = 674A-0.2. In our case this leads to a seriously
underestimated value of 133.4 t/km2. Furthermore, Parker and Osterkamp
(1995) compiled mean annual suspended sediment discharges from 24
gauged rivers in the US. Drainage areas ranged from 1.6×103 to
1.8×106 km2. Mean annual suspended sediment yields ranged from less than
5 to over 1480 t/km2. A possible explanation for the considerable higher
sediment yields in Greece lies on the fact that morphological factors (e.g
tectonic activity) coupled with the dominant geological layers (e.g. flysch)
act as additional forces to sediment availability within the catchment.
344 D. Zarris, E. Lykoudi and D. Koutsoyiannis
4. CONCLUSION AND DISCUSSION
The hydrographic survey of a reservoir is a quite satisfactory procedure
for reconstructing sediment yield records of a drainage basin. An apparent
weakness of the method is that it gives only an overyear average of the
sediment yield and not its temporal evolution. However, if frequent
hydrographic surveying of the reservoir is permitted (e.g. every 5 years) then
sediment yield can be computed in finer time scales. Alternatively, this
method can be combined with hydrological models as well as sediment
discharge measurements in upstream locations to reconstruct the temporal
evolution of reservoir sedimentation.
Its strongest merit, however, remains the illustration of the spatial
distribution of accumulated sediments within the reservoir. Dead storage
remains almost free of deposited sediments whilst parts of the nominal
useful storage are occupied from accumulated sediments. This obviously
means that the total loss of stored water is significantly greater than it was
originally assumed and it certainly becomes a waste of a valuable natural
resource. In this specific case, the depositional pattern inside the reservoir
reveals the apparent necessity of reconsidering the dead volume principle, in
terms of a thorough investigation and modelling of sediment yield in the
water resources management context.
AKNOWLEDGMENTS
This work is part of a research project funded from the General
Secretariat of Research and Technology (GSRT) and the Public Power
Corporation (PPC). The authors would like to thank D. Paradeisis,
T. Zissopoulos and J. Kotsis for their valuable assistance during the
hydrographic survey of the Kremasta reservoir and also N. Mamassis and
A. Koukouvinos for their contributions in GIS issues. The assistance of the
PPC supervisor of the project H. Makrygiorgos is very much appreciated.
The authors are very much indebted in the Danish Hydraulic Institute (DHI)
for offering the SEAGIS software for the source erosion analysis. Last but
not least the authors would like to show their gratitude to the staff of the PPC
responsible for the digitisation of the initial topographic maps.
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