As a result of the high soil fertility, more than 75% of the catchment area are used by croplands. Typical crops are winter wheat, rape, sugar beet, and corn. Grassland is limited to narrow stripes on the valley bottoms. Settlements cover 5%, whereas forest is limited to small isles (Figure 3).

**Figure 3: Relief, soil types, and land use of the Baderitz Reservoir catchment. Figure 3.** Relief, soil types, and land use of the Baderitz Reservoir catchment.

#### **2.2 The soil loss simulation model EROSION 3D. 2.2. The soil loss simulation model EROSION 3D**

**.**

EROSION 3D [9‐11]is a field‐tested, physically‐based soil erosion model to estimate the spatio‐ temporal distribution of erosion and deposition, as well as the delivery of suspended soil material into surface water courses [10]. Since more than 20 years, the model was continuously improved and EROSION 3D [9-11] is a field-tested, physically-based soil erosion model to estimate the spatiotemporal distribution of erosion and deposition, as well as the delivery of suspended soil material into surface water courses [10]. Since more than 20 years, the model was continuously improved and extensively validated using plot- and catchment-based soil erosion data [12-14].

extensively validated using plot‐ and catchment‐based soil erosion data [12‐14]. The theoretical concept of the model is based on the momentum flux approach. The erosive impact of overland flow and rainfall droplets is proportional to the momentum flux exerted by the flow and falling droplets, respectively[9‐10, 15]. In detail, the model covers rainfall infiltration, excess rainfall and subsequent generation of surface runoff, detachment of soil particles by raindrop impact and The theoretical concept of the model is based on the momentum flux approach. The erosive impact of overland flow and rainfall droplets is proportional to the momentum flux exerted by the flow and falling droplets, respectively [9-10, 15]. In detail, the model covers rainfall infiltration, excess rainfall and subsequent generation of surface runoff, detachment of soil particles by raindrop impact and surface runoff, transport of detached soil particles by surface runoff, routing of surface runoff, and sediment transport through the catchment.

surface runoff, transport of detached soil particles by surface runoff, routing of surface runoff, and sediment transport through the catchment. Since the model is raster-based, EROSION 3D demands a grid-cell parameter representation of the catchment. Using internal parameter transfer functions, EROSION 3D only needs few

Since the model is raster‐based, EROSION 3D demands a grid‐cell parameter representation of the catchment. Using internal parameter transfer functions, EROSION 3D only needs few input soil parameters compared to other process‐based erosion models asEUROSEM [16], WEPP [17] and

Overland flow routing is generated by applying amultiple flow algorithm FD8[19‐20]. After reaching a

equivalent to channel flow. Once the runoff and transported sediments are entering the channel network,sediments are routed outside the catchment, disregarding deposition processes (Figure 4).

], multiple flow is converged to single flow (D8) [21]

LISEM [18], which allows application for research as well as planning purposes.

critical source area of runoff generation [m2

input soil parameters compared to other process-based erosion models as EUROSEM [16], WEPP [17] and LISEM [18], which allows application for research as well as planning purposes.

Overland flow routing is generated by applying a multiple flow algorithm FD8 [19-20]. After reaching a critical source area of runoff generation [m2 ], multiple flow is converged to single flow (D8) [21] equivalent to channel flow. Once the runoff and transported sediments are entering the channel network, sediments are routed outside the catchment, disregarding deposition processes (Figure 4).

**Figure 4.** Multi-direction flow (FD8, left) and single direction flow (D8, right).

Jahna Creek is draining the catchment with a mean discharge of 0.15 m3

**Figure 3: Relief, soil types, and land use of the Baderitz Reservoir catchment.**

**Figure 3.** Relief, soil types, and land use of the Baderitz Reservoir catchment.

**Soil types (WRB) Luvisol**

> **eroded Cambic Luvisols Cambic-Podsolic Luvisol Stagnic Luvisol Colluvisol**

extensively validated using plot‐ and catchment‐based soil erosion data [12‐14].

runoff, routing of surface runoff, and sediment transport through the catchment.

LISEM [18], which allows application for research as well as planning purposes.

**2.2 The soil loss simulation model EROSION 3D.**

**2.2. The soil loss simulation model EROSION 3D**

0 1000 2000 4000 meters

**reservoir streams altitude a.s.l. [m]** high: 290 low: 160

sediment transport through the catchment.

critical source area of runoff generation [m2

As a result of the high soil fertility, more than 75% of the catchment area are used by croplands. Typical crops are winter wheat, rape, sugar beet, and corn. Grassland is limited to narrow stripes on the valley bottoms. Settlements cover 5%, whereas forest is limited to small isles

EROSION 3D [9‐11]is a field‐tested, physically‐based soil erosion model to estimate the spatio‐ temporal distribution of erosion and deposition, as well as the delivery of suspended soil material into surface water courses [10]. Since more than 20 years, the model was continuously improved and

EROSION 3D [9-11] is a field-tested, physically-based soil erosion model to estimate the spatiotemporal distribution of erosion and deposition, as well as the delivery of suspended soil material into surface water courses [10]. Since more than 20 years, the model was continuously improved and extensively validated using plot- and catchment-based soil erosion data [12-14].

The theoretical concept of the model is based on the momentum flux approach. The erosive impact of overland flow and rainfall droplets is proportional to the momentum flux exerted by the flow and falling droplets, respectively[9‐10, 15]. In detail, the model covers rainfall infiltration, excess rainfall and subsequent generation of surface runoff, detachment of soil particles by raindrop impact and surface runoff, transport of detached soil particles by surface runoff, routing of surface runoff, and

The theoretical concept of the model is based on the momentum flux approach. The erosive impact of overland flow and rainfall droplets is proportional to the momentum flux exerted by the flow and falling droplets, respectively [9-10, 15]. In detail, the model covers rainfall infiltration, excess rainfall and subsequent generation of surface runoff, detachment of soil particles by raindrop impact and surface runoff, transport of detached soil particles by surface

Since the model is raster‐based, EROSION 3D demands a grid‐cell parameter representation of the catchment. Using internal parameter transfer functions, EROSION 3D only needs few input soil parameters compared to other process‐based erosion models asEUROSEM [16], WEPP [17] and

Since the model is raster-based, EROSION 3D demands a grid-cell parameter representation of the catchment. Using internal parameter transfer functions, EROSION 3D only needs few

Overland flow routing is generated by applying amultiple flow algorithm FD8[19‐20]. After reaching a

equivalent to channel flow. Once the runoff and transported sediments are entering the channel network,sediments are routed outside the catchment, disregarding deposition processes (Figure 4).

], multiple flow is converged to single flow (D8) [21]

**Land use cropland forest garden grassland**

**Colluvic Gleysol Fluvic Gleysols Luvic Stagnosol Regosol Anthrosol**

**pond settlement dump road**

mostly related to heavy rainfall events.

44 Effects of Sediment Transport on Hydraulic Structures

(Figure 3).

**.**

/s. Peak flows are

Regarding parameter identification, the interactive software tool DPROC was developed to enable the semiautomatic generation of model input data by using available relief, land use, and soil data sets. Model parameters (Table 1) are derived as functions of land use, soil characteristics, and simulations date, using an extensive database deducted from numerous rainfall experiment simulations on test plots in the mid-nineties [22-24] Experiments cover a broad range of different soil, crop, and tillage types, as well as special soil characteristics and crop phenology stages.

**Table 1.** Input parameters of the EROSION 3D simulation model.

#### **2.3. Data preprocessing and parameterization**

GIS data preprocessing of land use, soil type, relief, and rainfall information was accomplished according to [24]. Land use maps were applied from available sources in a scale of 1:25,000, soil maps in a 1:50,000 scale. Since soil erosion is a complex process, highly influenced by local morphology, the DEM of 20 m cell size was interpolated to a 5 m grid according to [25].

#### *2.3.1. Digital elevation model*

Considering the morphological shape of the reservoir bottom, no digital information was available from official sources. Consequently, the resulting shape of the reservoir bottom is a more or less flat surface. Isolines of a historical topographic map [26] were digitized, interpo‐ lated, and merged with the existing DEM. This map of 1935 represents the land surface prior to the construction of the reservoir and so the assumed underwater relief. Finally, the dam of 9 m height was attached (Figure 5).

**Figure 5.** Original DEM in a 20 m cell size (left), digitized contour lines of a historical map of 1935 (center), and modi‐ fied DEM of 5 m cell size with the elevated dam.

#### *2.3.2. Representation of water bodies in the EROSION 3D model*

Since the EROSION 3D model was originally developed for runoff and deposition processes on arable land, complex hydrodynamic 2D or 3D flow algorithms for water bodies are not included. Considering a critical source area of concentrated flow in the study region of approximately 0.08 km2 , the reservoir is among the channel network, only permitting single direction flow. Since hydrodynamic conditions in stand water are different to those in a turbulent stream, single flow calculations need to be disabled within the reservoir body. Additionally, hydraulic roughness needs to be increased on that area to enable sedimentation processes as they take place in water bodies.

#### *2.3.3. Parameterization of the worst-case scenario*

**2.3. Data preprocessing and parameterization**

46 Effects of Sediment Transport on Hydraulic Structures

*2.3.1. Digital elevation model*

9 m height was attached (Figure 5).

fied DEM of 5 m cell size with the elevated dam.

processes as they take place in water bodies.

approximately 0.08 km2

*2.3.2. Representation of water bodies in the EROSION 3D model*

GIS data preprocessing of land use, soil type, relief, and rainfall information was accomplished according to [24]. Land use maps were applied from available sources in a scale of 1:25,000, soil maps in a 1:50,000 scale. Since soil erosion is a complex process, highly influenced by local morphology, the DEM of 20 m cell size was interpolated to a 5 m grid according to [25].

Considering the morphological shape of the reservoir bottom, no digital information was available from official sources. Consequently, the resulting shape of the reservoir bottom is a more or less flat surface. Isolines of a historical topographic map [26] were digitized, interpo‐ lated, and merged with the existing DEM. This map of 1935 represents the land surface prior to the construction of the reservoir and so the assumed underwater relief. Finally, the dam of

**Figure 5.** Original DEM in a 20 m cell size (left), digitized contour lines of a historical map of 1935 (center), and modi‐

Since the EROSION 3D model was originally developed for runoff and deposition processes on arable land, complex hydrodynamic 2D or 3D flow algorithms for water bodies are not included. Considering a critical source area of concentrated flow in the study region of

direction flow. Since hydrodynamic conditions in stand water are different to those in a turbulent stream, single flow calculations need to be disabled within the reservoir body. Additionally, hydraulic roughness needs to be increased on that area to enable sedimentation

, the reservoir is among the channel network, only permitting single

As a first step of model application, a worst-case scenario is simulated in order to identify the most important sediment delivery areas, as well as the sediment pass over points into surface waters. In this regard, all croplands are assumed to be under seedbed conditions related to conventional tillage with the moldboard plough. This implies no soil cover of plants or plant residues, low hydraulic roughness due to the fine soil management, and high soil moisture contents (field capacity).

#### *2.3.4. Parameterization of normal heavy rainfall years*

Since the model is capable to process single storms as well as storm sequences, annual soil losses and sediment input into the stream network can be simulated. For this purpose, the model demands single events of a normal heavy rainfall year (Figure 6), which was generated by the [27].

**Figure 6.** Normal heavy rainfall year of the study region.

**Figure 7.** Five-year rainfall sequence with normal heavy rains and statistical extreme events.

. **Figure 8: Crop type distribution in five crop years (start 2004). Figure 8.** Crop type distribution in five crop years (start 2004).

In order to simulate a ten‐year rainfall sequence, rainfall events of the normal years have to be In order to simulate a ten-year rainfall sequence, rainfall events of the normal years have to be combined with statistical extremes as it is compiled in Figure 7.

combined with statistical extremes as it is compiled in Figure 7. By applying land use and crop type maps from 2004 to 2009 (Figure 8),initial conditions of all rainfall occurrence dates were parameterized with the software DPROC. Considering dynamic moisture conditions, all events with previous rainfall events, less than 24 h ago, were set to high soil moisture contents (field capacity, Figure 6). Land use and soil parameters were derived from the database of DPROC. After the harvest of grains and rape in August and September, a stubble cover was assumed By applying land use and crop type maps from 2004 to 2009 (Figure 8), initial conditions of all rainfall occurrence dates were parameterized with the software DPROC. Considering dynamic moisture conditions, all events with previous rainfall events, less than 24 h ago, were set to high soil moisture contents (field capacity, Figure 6). Land use and soil parameters were derived from the database of DPROC. After the harvest of grains and rape in August and September, a stubble cover was assumed and the planting of rape was supposed to early September.

#### **3. Results**

#### **3. Results. 3.1. Soil loss and sediment flux for the worst-case scenario**

and the planting of rape was supposed to early September.

**3.1 Soil loss and sediment flux for the worst‐case scenario.** The Baderitz Reservoir catchment is highly endangered by soil losses. All croplands tend to deliver considerable amounts of sediments. Since all soil types are characterized by high silt contents, soil The Baderitz Reservoir catchment is highly endangered by soil losses. All croplands tend to deliver considerable amounts of sediments. Since all soil types are characterized by high silt contents, soil erosion is mainly controlled by slope length and slope steepness. Highest soil

erosion is mainly controlled by slope length and slope steepness. Highest soil loss rates are reached

loss rates are reached in thalwegs (Figure 9), where surface runoff concentration allows highest transport capacities and related sediment concentrations.

**Figure 9.** Sediment budget, sediment flux, and sediment input into surface waters for the worst-case scenario.

.

**3. Results.**

**3. Results**

September.

**Figure 8: Crop type distribution in five crop years (start 2004).**

48 Effects of Sediment Transport on Hydraulic Structures

**Figure 8.** Crop type distribution in five crop years (start 2004).

combined with statistical extremes as it is compiled in Figure 7.

combined with statistical extremes as it is compiled in Figure 7.

and the planting of rape was supposed to early September.

**3.1 Soil loss and sediment flux for the worst‐case scenario.**

**3.1. Soil loss and sediment flux for the worst-case scenario**

In order to simulate a ten‐year rainfall sequence, rainfall events of the normal years have to be

In order to simulate a ten-year rainfall sequence, rainfall events of the normal years have to be

**Land use**

**Cropland Forest Garden Grassland Pond Settlement Dump Road**

**Crop pea corn rape sugar beet summer barley potatoes winter barley winter wheat**

0 1000 2000 4000 meters

By applying land use and crop type maps from 2004 to 2009 (Figure 8),initial conditions of all rainfall occurrence dates were parameterized with the software DPROC. Considering dynamic moisture conditions, all events with previous rainfall events, less than 24 h ago, were set to high soil moisture contents (field capacity, Figure 6). Land use and soil parameters were derived from the database of DPROC. After the harvest of grains and rape in August and September, a stubble cover was assumed

By applying land use and crop type maps from 2004 to 2009 (Figure 8), initial conditions of all rainfall occurrence dates were parameterized with the software DPROC. Considering dynamic moisture conditions, all events with previous rainfall events, less than 24 h ago, were set to high soil moisture contents (field capacity, Figure 6). Land use and soil parameters were derived from the database of DPROC. After the harvest of grains and rape in August and September, a stubble cover was assumed and the planting of rape was supposed to early

The Baderitz Reservoir catchment is highly endangered by soil losses. All croplands tend to deliver considerable amounts of sediments. Since all soil types are characterized by high silt contents, soil erosion is mainly controlled by slope length and slope steepness. Highest soil loss rates are reached

The Baderitz Reservoir catchment is highly endangered by soil losses. All croplands tend to deliver considerable amounts of sediments. Since all soil types are characterized by high silt contents, soil erosion is mainly controlled by slope length and slope steepness. Highest soil

Mean soil loss amounts to 9.8 t/ha. All croplands are endangered by soil erosion, whereas grasslands, hedges, and woods act as buffers, where sediments are deposited (Figure 9). In this regard, all streams with insufficient buffers are affected by high sediment inputs.

#### **3.2. Soil loss and sediment flux in the catchment during long-term simulations**

The overall soil loss for the first reference year is 12.3 t/ha in the reservoir catchment.

Comparable to the worst-case scenario, all croplands are endangered by erosion. A distinct relation to the applied crop is visible (Figure 10).

Compared to prior simulations, croplands reveal high soil loss values, buffer stripes act as important retention areas. As overall trapping efficiency of this buffer is insufficient, effect of crop rotation becomes more important with the 10-year estimation compared to single events or single years (Figure 11). The mean soil loss after the 10-year simulation is about 193 t/ha. A distinct pattern of high endangered croplands of different categories is visible. Nearly comprehensive values of red pixels (<−25 kg/m2 ), mixed of orange and red values (−2.5–25 and <−25), and a third class with red pixels only in areas of runoff concentration (Figure 11) are simulated.

**Figure 10.** Sediment budget and sediment flux for the first reference year.

#### **3.3. Sediment inputs into Baderitz Reservoir during long-term simulation.**

During all simulation steps (1 year, 5 years, and 10 years) maximum sediment deposition can be identified at the water entrance area of the reservoir (Figure 12), which can be explained by the abrupt change in runoff velocity when water enters the reservoir. Additionally, sediments are deposited along the natural channel as shown in Figure 12.

Consequently, the bottom level is increasing due to sediment deposition after each erosive event. In a long-term simulation run, the EROSION 3D model adjusts the surface relief according to the amount of erosion and deposition. The changes are compiled in Figure 13, with an increasing reservoir bottom level and a resulting decreasing expanse of the water surface.

Simulation of Reservoir Siltation with a Process-based Soil Loss and Deposition Model http://dx.doi.org/10.5772/61576 51

**Figure 11.** Sediment budget and sediment flux for ten reference years.

comprehensive values of red pixels (<−25 kg/m2

50 Effects of Sediment Transport on Hydraulic Structures

simulated.

**Erosion/Deposition [kg/m²]** <-25 -25 - -2.5 -2.5 - -0.25 -0.25 - -0.001

surface.

0 1000 2000 4000 meters

**Figure 10.** Sediment budget and sediment flux for the first reference year.

are deposited along the natural channel as shown in Figure 12.

**3.3. Sediment inputs into Baderitz Reservoir during long-term simulation.**

During all simulation steps (1 year, 5 years, and 10 years) maximum sediment deposition can be identified at the water entrance area of the reservoir (Figure 12), which can be explained by the abrupt change in runoff velocity when water enters the reservoir. Additionally, sediments

Consequently, the bottom level is increasing due to sediment deposition after each erosive event. In a long-term simulation run, the EROSION 3D model adjusts the surface relief according to the amount of erosion and deposition. The changes are compiled in Figure 13, with an increasing reservoir bottom level and a resulting decreasing expanse of the water

<−25), and a third class with red pixels only in areas of runoff concentration (Figure 11) are

), mixed of orange and red values (−2.5–25 and

**Sediment mass flux [kg/m]**

**Figure 12.** Siltation of the Baderitz Reservoir. Growing deposition areas after 1, 5, and 10 years.

**Figure 13.** Siltation of the Baderitz Reservoir.

After the first year, inputs of 7943 t sediment or 5673 m3 (estimated bulk density of 1.4 g/cm3 ) were computed. This means, approximately 1% of retention reduction during the first year after installing the dam. After 10 years long-term simulation, inputs in the reservoir amounted to 124,470 t, which corresponds to 88,910 m3 and a loss of 13% retention volume and a total elimination of the uncommon flood retention (Figure 14). On average, the reservoir bottom is heightened by about 0.9 m, with an increase of 16,900 m2 in land surface.

**Figure 14.** Siltation of the Baderitz Reservoir.

#### **4. Discussion**

Erosion and sedimentation processes are driven by temporal dynamic characteristics as land use, crop types and crop rotation, and relative constant catchment characteristics such as slope morphology and soil type.

Since most endangered rainfalls and "high" moisture conditions occur in late spring, crops with low plant cover reveal high losses of <−2.5 kg/ha after the first simulation year. Although winter wheat has a comparable high plant cover in May, high soil bulk densities decreasein‐ filtration rates and water storage capacities of the soil. As most of the precipitation is runoff, protection value of plant cover is obsolete.

Croplands with phenologically dependent high plant covers in the most endangered spring season (e.g., rape, Figure 8) are less affected.

 As the two extreme events statistically occur during July, crops with low cover in this month reveal high soil losses.

Compared to the worst-case scenario, sediment delivery ratio (SDR), described as throughflow in Figure 15 for one-year or ten-year long-term simulation, is much smaller. This means that the retention efficiency of sediment buffers is increasing with decreasing sediment fluxes. During high fluxes as simulated with the worst-case run, two-third of the sediments are routed into the water network and only 6% are remaining within the reservoir.

After the first year, inputs of 7943 t sediment or 5673 m3

heightened by about 0.9 m, with an increase of 16,900 m2

to 124,470 t, which corresponds to 88,910 m3

**Figure 13.** Siltation of the Baderitz Reservoir.

52 Effects of Sediment Transport on Hydraulic Structures

**Figure 14.** Siltation of the Baderitz Reservoir.

**4. Discussion**

morphology and soil type.

were computed. This means, approximately 1% of retention reduction during the first year after installing the dam. After 10 years long-term simulation, inputs in the reservoir amounted

elimination of the uncommon flood retention (Figure 14). On average, the reservoir bottom is

Erosion and sedimentation processes are driven by temporal dynamic characteristics as land use, crop types and crop rotation, and relative constant catchment characteristics such as slope

(estimated bulk density of 1.4 g/cm3

and a loss of 13% retention volume and a total

in land surface.

)

Decreasing SDR with increasing simulation year is caused by a cumulative bank up and resulting trapping efficiency of deposition areas. Between 70 and 75% of the sediments are deposited in the morphology of the catchment and between 12 and 15% are remaining in the reservoir (Figure 15).

**Figure 15.** Sediment budgets for worst-case and long-term simulation, changed after [3].

Since bathymetric data is lacking so far, sediment inputs only can be plausibly tested by comparisons with ortho-photos (Figure 16).

The picture was taken in 2009 after the reservoir was drained and reflooded. The water level is 3 m below the original level. Simulation results for a 20 year long-term run reveal an almost identical extent and shape. As this project was simulated with standard pre-calibrated database values, the results reveal high model quality, which makes further application for reservoir siltation even on larger scales possible.

**Figure 16.** Comparison of observed and simulated sediment fan.

#### **5. Conclusion**

The process-based EROSION 3D model was successfully applied to simulate reservoir siltation in a meso-scaled German loess catchment. The simulation results in a progressive siltation of the reservoir, which is seriously impairing the primary purposes of the reservoir. Reservoir live span as flood retention is halved after 20 years, which immediately makes a sediment disposal essential.

To avoid excessive siltation, soil conservation measures should be implemented within the catchment. The EROSION 3D-based soil loss prediction maps could help to identify the most erosion-sensitive areas within the catchment, as well as the points of sediment transfer into surface water bodies. Further studies should focus on modelling and implementing of soil loss mitigation in the catchment. Both, local authorities and farmers will be made aware to take appropriate and effective actions to reduce soil loss.

#### **Author details**

The picture was taken in 2009 after the reservoir was drained and reflooded. The water level is 3 m below the original level. Simulation results for a 20 year long-term run reveal an almost identical extent and shape. As this project was simulated with standard pre-calibrated database values, the results reveal high model quality, which makes further application for reservoir

The process-based EROSION 3D model was successfully applied to simulate reservoir siltation in a meso-scaled German loess catchment. The simulation results in a progressive siltation of the reservoir, which is seriously impairing the primary purposes of the reservoir. Reservoir live span as flood retention is halved after 20 years, which immediately makes a sediment

To avoid excessive siltation, soil conservation measures should be implemented within the catchment. The EROSION 3D-based soil loss prediction maps could help to identify the most erosion-sensitive areas within the catchment, as well as the points of sediment transfer into surface water bodies. Further studies should focus on modelling and implementing of soil loss mitigation in the catchment. Both, local authorities and farmers will be made aware to take

siltation even on larger scales possible.

54 Effects of Sediment Transport on Hydraulic Structures

**Figure 16.** Comparison of observed and simulated sediment fan.

appropriate and effective actions to reduce soil loss.

**5. Conclusion**

disposal essential.

Marcus Schindewolf\* , Constanze Bornkampf, Michael von Werner and Jürgen Schmidt

\*Address all correspondence to<EMAIL_ADDRESS>
Soil and Water Conservation Unit, Technical University of Freiberg, Freiberg, Germany

#### **References**

*tion*. Freiberg: Fakultät für Geowissenschaften, Geotechnik und Bergbau der TU Ber‐ gakademie Freiberg. 2000

[10] Schmidt J: Entwicklung und Anwendung eines physikalisch begründeten Simula‐ tionsmodells für die Erosion geneigter landwirtschaftlicher Nutzflächen. *Berliner Geo‐*

[11] von Werner MGIS-orientierte Methoden der digitalen Reliefanalyse zur Modellier‐ ung von Bodenerosion in kleinen Einzugsgebieten. *Dissertation*. Berlin, Freie Univer‐

[12] Jetten V, de Roo APJ, Favis-Mortlock D :Evaluation of field-scale and catchment-scale

[13] Defersha MB, Melesse AM:Field-scale investigation of the effect of land use on sedi‐ ment yield and runoff using runoff plot data and models in the Mara River basin,

[14] Starkloff T, Stolte J: Applied comparison of the erosion risk models EROSION 3D and LISEM for a small catchment in Norway. *CATENA. 2014;*118: 154–167. DOI:

[15] Schmidt J : Modelling long term soil loss and landform change. In: A. Abrahams and A. Parson (eds.): Overland Flow—Hydraulics and Erosion Mechanics, University

[16] Morgan R, Quinton J, Smith R, Govers G, Poesen J, Auerswald K, Chisci C, Torri D, Styczen ME: The European soil erosion model (EUROSEM)—a process-based ap‐ proach for predicting sediment transport from fields and small catchments. *Earth*

[17] Laflen JM, Elliot WJ, Flanagan DC, Meyer CR, Nearing MA: WEPP-predicting water erosion using a process-based model. *Journal of Soil and Water Conservation. 1997;*52:

[18] De Roo APJ, Wesseling CG, Ritsema CJ): LISEM: A single event physically based hy‐ drological and soil erosion model for drainage basins. I: Theory, input and output.

[19] Freeman GT Calculating catchment area with divergent flow based on a regular grid.

[20] Quinn PF, Beven KJ, Chevallier P, Planchon O: The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. *Hydrological*

[21] O'Callaghan JF, Mark DM: The extraction of drainage networks from digital eleva‐ tion data. Computer Vision. *Graphics and Image Processing. 1984;*28: 323–344.

[22] Michael A: Anwendung des physikalisch begründeten Erosionsprognosemodells EROSION 2D/3D - Empirische Ansätze zur Ableitung der Modellparameter. *Disserta‐*

Kenya. *CATENA. 2012;*89 (1): 54–64. DOI: 10.1016/j.catena.2011.07.010

*grafische Abhandlungen 61.* Berlin: Freie Universität. 1996

soil erosion models. *CATENA.1999;*37 (3–4): 521–541

College London Press, London, 409–433. 1992

*Surface Processes and Landforms. 1998;* 23: 527–544

*Hydrological Processes. 1996;*10: 1107–1117

*Computers and Geosciences. 1991;*17: 413–22

*Processes. 1991;*5: 59–79

sität. Berlin. 1995

56 Effects of Sediment Transport on Hydraulic Structures

10.1016/j.catena.2014.02.004

96–102

[27] German Weather Service: *Expertise.* Zentralstelle für Hydrometeorologische Entwick‐ lungen und Anwendungen, Abteilung Klimatologie. Offenbach: DWD, 1994

## **Assessment of Reservoir Sedimentation Effect on Coastal Erosion and Evaluation of Sediment Removal Techniques for Its Reduction — The Case of Nestos River, Greece**

Manolia Andredaki, Anastasios Georgoulas, Vlassios Hrissanthou and Nikolaos Kotsovinos

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61459

#### **Abstract**

Nestos is one of the most important transboundary rivers flowing through Bulgaria and Greece. In the Greek part of the river, two reservoirs, the Thisavros Reservoir and the Platanovrysi Reservoir, have already been constructed and started operating in 1997 and 1999, respectively. In the first part of the chapter, the reservoir sedimentation effect on the coastal erosion is investigated, for the case of the Nestos River delta and the adjacent shorelines, through a combination of mathematical modeling, modern remote sensing techniques, and field surveying, while in the second part, the mechanical removal as well as the flushing of sediment from the reservoir of Platanovrysi and its disposal in the subbasin downstream of the Platanovrysi Dam up to the Nestos River delta are investigated as potential treatment methods of reducing coastal erosion, using a modification of the same mathematical model that is utilized in the first part of the chapter. The overall findings and conclusions arising from the work presented and discussed in the present chapter contribute to the overall need to thoroughly understand the direct effect of dam construction on coastal erosion, as well as to examine the effectiveness of potential sediment management treatments.

**Keywords:** Sediment transport/management, reservoir, mathematical modeling, coastal erosion, shoreline change monitoring, Nestos River

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1. Introduction**

Coastal erosion constitutes a major environmental problem in many parts of the world. Especially in deltaic regions, the construction of dams in the river basin acts as an artificial barrier to the sediment supply to the river mouths and therefore the rates of shoreline retreat and sea level rise may exceed the corresponding rates of vertical shoreline accretion, resulting in the increase and in many cases the predomination of deltaic and coastal erosion. Over the last decades, many investigations have been directly or indirectly focused on the assessment of reservoir sedimentation and its effect on sediment yield reduction and coastal erosion, in the wider coastal regions of various rivers worldwide, using different investigation method‐ ologies and techniques (e.g., [1]-[8]).

Nestos constitutes an important transboundary river, characterized by its great biodiversity. It flows through two European countries, Bulgaria and Greece, discharging into the Aegean Sea. It originates from Mount Rila (2716 m) in South Bulgaria, where Nestos River is known as Mesta. Its total length reaches 234 km and the river basin covers an area of 5749 km2 , 130 km (<56%) and 2280 km2 (<40%) of which lies in Greek territory [9].

In the Greek part of the river, two hydroelectric dams, the Thisavros Dam and the Platanovrysi Dam, have already been constructed and started operating in 1997 and 1999, respectively. This implies a reduction of sediment yield at the outlet of the Nestos River basin and a correspond‐ ing disturbance of the sediment balance in the basin in general, which can result in coastal erosion. However, the reduction of the sediment yield at the outlet of the considered river due to the construction of these two reservoirs as well as the increase in the coastal erosion of the deltaic and the adjacent coastal regions have never been evaluated and correlated previously.

According to the authors' best knowledge, the work that is presented in the first part of this chapter constitutes one of the first assessments of reservoir sedimentation effect on the coastal erosion for the case of the Nestos River delta and the adjacent shorelines, utilizing mathemat‐ ical modeling, remote sensing techniques, and field surveying [10]. The main objectives are to evaluate the overall reduction of the sediment yield at the outlet of the river due to the construction of the two dams and to examine the resulting erosion/accretion response of the deltaic as well as the adjacent shorelines. For this purpose, the sediment yield at the outlet of the Nestos River basin before and after the construction of the two dams is calculated, through the application of a mathematical simulation model (RUNERSET – RUNoff ERosion SEdiment Transport). The model is initially tested against appropriate field measurements that are available in the literature. Moreover, a shoreline change monitoring methodology for the coastal region of the Nestos River delta and the adjacent shorelines is proposed, tested, and applied for the digital and detailed extraction of the shoreline position with respect to time, aiming to determine the erosion/accretion shoreline balance for two time periods that corre‐ spond to the periods before and after the construction of the dams. Finally, the mathematically calculated reduction of the sediment yield at the outlet of the river is correlated with the results from the application of the shoreline change monitoring methodology and some valuable conclusions and recommendations are drawn. The considered mathematical simulation model (RUNERSET) calculates the mean annual value of sediment yield, due to rainfall and runoff. The proposed model consists of three submodels: a rainfall-runoff submodel, a soil erosion submodel and a sediment transport submodel for streams. The coastal erosion/accretion monitoring data that were used in order to determine the shoreline evolution consist of remote sensing data from high-resolution satellite images (year 2002, considered period of construc‐ tion/operation of the dams) and aerial photographs (year 1945, period before the construction/ operation of the dams/reservoirs) as well as from high-resolution DGPS (Differential Global Positioning System) field measurements (year 2007, period after the construction/operation of the dams/reservoirs). It is calculated that the construction and operation of the considered dams have caused a dramatic decrease (about 83%) in the sediments supplied directly to the basin outlet and indirectly to the neighboring coast, and that this fact has almost inversed the erosion/accretion balance in the deltaic as well as the adjacent shorelines. Before the construc‐ tion of the reservoirs, in the entire pilot study region, accretion predominated erosion by 25.36%, while just within 5 years of the construction/operation of the reservoirs, erosion predominates accretion by 21.26% [10]. Moreover, in the same part of this chapter, aiming to quantitatively investigate the dynamic evolution of erosion in the considered coastal region, a more recent shoreline (2013) was also extracted. For this purpose, additional DGPS field measurements were performed, following the same methodology and time period (spring) with the corresponding field measurements of the year 2007. Recording this more recent state of the coastline (2013) and comparing with the previous shorelines up to the perceived period of the construction and operation of the dams (2002) showed that erosion, in relation to the deposition/accretion that has been observed in the period 2002-2007, shows an increasing trend.

**1. Introduction**

ologies and techniques (e.g., [1]-[8]).

60 Effects of Sediment Transport on Hydraulic Structures

km (<56%) and 2280 km2

Coastal erosion constitutes a major environmental problem in many parts of the world. Especially in deltaic regions, the construction of dams in the river basin acts as an artificial barrier to the sediment supply to the river mouths and therefore the rates of shoreline retreat and sea level rise may exceed the corresponding rates of vertical shoreline accretion, resulting in the increase and in many cases the predomination of deltaic and coastal erosion. Over the last decades, many investigations have been directly or indirectly focused on the assessment of reservoir sedimentation and its effect on sediment yield reduction and coastal erosion, in the wider coastal regions of various rivers worldwide, using different investigation method‐

Nestos constitutes an important transboundary river, characterized by its great biodiversity. It flows through two European countries, Bulgaria and Greece, discharging into the Aegean Sea. It originates from Mount Rila (2716 m) in South Bulgaria, where Nestos River is known as Mesta. Its total length reaches 234 km and the river basin covers an area of 5749 km2

 (<40%) of which lies in Greek territory [9]. In the Greek part of the river, two hydroelectric dams, the Thisavros Dam and the Platanovrysi Dam, have already been constructed and started operating in 1997 and 1999, respectively. This implies a reduction of sediment yield at the outlet of the Nestos River basin and a correspond‐ ing disturbance of the sediment balance in the basin in general, which can result in coastal erosion. However, the reduction of the sediment yield at the outlet of the considered river due to the construction of these two reservoirs as well as the increase in the coastal erosion of the deltaic and the adjacent coastal regions have never been evaluated and correlated previously. According to the authors' best knowledge, the work that is presented in the first part of this chapter constitutes one of the first assessments of reservoir sedimentation effect on the coastal erosion for the case of the Nestos River delta and the adjacent shorelines, utilizing mathemat‐ ical modeling, remote sensing techniques, and field surveying [10]. The main objectives are to evaluate the overall reduction of the sediment yield at the outlet of the river due to the construction of the two dams and to examine the resulting erosion/accretion response of the deltaic as well as the adjacent shorelines. For this purpose, the sediment yield at the outlet of the Nestos River basin before and after the construction of the two dams is calculated, through the application of a mathematical simulation model (RUNERSET – RUNoff ERosion SEdiment Transport). The model is initially tested against appropriate field measurements that are available in the literature. Moreover, a shoreline change monitoring methodology for the coastal region of the Nestos River delta and the adjacent shorelines is proposed, tested, and applied for the digital and detailed extraction of the shoreline position with respect to time, aiming to determine the erosion/accretion shoreline balance for two time periods that corre‐ spond to the periods before and after the construction of the dams. Finally, the mathematically calculated reduction of the sediment yield at the outlet of the river is correlated with the results from the application of the shoreline change monitoring methodology and some valuable conclusions and recommendations are drawn. The considered mathematical simulation model (RUNERSET) calculates the mean annual value of sediment yield, due to rainfall and runoff.

, 130

It has been estimated that every year almost 2% of the effective volume of reservoirs worldwide is lost due to sedimentation. This is an unavoidable fact, which however can be reduced, quantified, and incorporated into the design and operation of a reservoir [11]. The sediment management methods in reservoirs can be classified into three main categories: a) methods that minimize the inflow of sediment into the reservoirs, b) methods that minimize the accumulation of sediment in the upstream part of the reservoir and c) methods that maximize the overall sediment discharge that passes downstream of the reservoirs. The application of this last class of methods could serve as a possible treatment for the reduction of shoreline erosion, which has been mainly caused by the construction of the above-mentioned dams in the case of Nestos River. Two of the most widely applied methods of sediment removal from reservoirs are the processes of mechanical removal or dredging [11] and flushing [12]. In the work that is presented in the second part of this chapter, the dredging as well as the flushing of sediment from the reservoir of Platanovrysi, and its disposal in the subbasin downstream of the Platanovrysi Dam up to the Nestos River delta, are investigated as potential treatment methods of reducing coastal erosion ([13], [14]). For this purpose, the considered mathematical model (RUNERSET) was accordingly modified in order to incorporate these effects, and various scenarios of sediment management in the Platanovrysi Reservoir were simulated, while an assessment of the most effective and consistent month of the year for sediment flushing or dredging, as well as of the maximum transport quantity of the removed material was conducted. Finally, a comparison of the two methods takes place, concerning the maxi‐ mum sediment quantities that reach the basin outlet, at the optimal for each method month of the year.

The overall findings and conclusions, arising from the work presented and discussed in this chapter, contribute to the need to thoroughly understand the direct effect of dam construction on coastal erosion. More specifically, the presented work constitutes one of the first attempts to quantify the disruption of the sediment balance in the delta of Nestos River due to the construction and operation of the Thisavros and Platanovrysi Reservoirs, considering simul‐ taneously the impact of this disturbance on the erosion/accretion balance both at the river delta and the adjacent shorelines. Moreover, for the first time, an effort is made for the evaluation of the optimum periods and sediment management methods, aiming to increase the annual sediment that reaches the outlet of the Nestos River basin, and consequently to reduce erosion in the delta of the river and the adjacent shorelines. Finally, it is evident that the overall research methodology applied may constitute a quite useful tool for the further investigation of the effect of dam construction on the coastal erosion for other pilot application areas, throughout the world.

#### **2. Assessment of reservoir sedimentation effect on coastal erosion**

#### **2.1. Description of the mathematical simulation model**

As mentioned previously in the introduction section of this chapter, the considered mathe‐ matical simulation model (RUNERSET) calculates the mean annual value of sediment yield, due to rainfall and runoff, and it consists of three submodels: a rainfall-runoff submodel, a soil erosion submodel, and a sediment transport submodel for streams.

By means of the rainfall-runoff submodel, the runoff depth for a certain rainfall depth is computed. It is a simplified water balance model [15], in which the variation of soil moisture due to rainfall, evapotranspiration, deep percolation, and runoff is considered. The basic balancing equation is:

$$\mathcal{S}\_{n}^{'} = \mathcal{S}\_{n-1} + \mathcal{N}\_{n} - E\_{pn} \tag{1}$$

where *Sn*−1 is the available soil moisture for the time step *n* −1 (mm); *Nn*is the rainfall depth for the time step *n* (mm); *Epn*is the potential evapotranspiration for the time step *n* (mm); and *Sn* ′ is an auxiliary variable (mm).

The direct runoff depth *hon* (mm) and the deep percolation *I Nn* (mm) for the time step *n* can be evaluated by comparing *Sn* ′ with the maximum available soil moisture *Smax* (mm), which is estimated by the following relationship of the US Soil Conservation Service [16]:

$$S\_{\text{max}} = 25.4 \text{[(1000 / CN)} - 10] \tag{2}$$

where *CN* is the curve number depending on the soil cover, the hydrologic soil group, and the antecedent soil moisture conditions (0<*CN* <100).

In the present study, two different methods were used for the estimation of the potential evapotranspiration *Ep* : the radiation method improved by Doorenbos and Pruitt [17] was used for the Greek part of the Nestos River basin, while the Thornthwaite method [18] was used for the Bulgarian part of the Nestos River basin.

The overall findings and conclusions, arising from the work presented and discussed in this chapter, contribute to the need to thoroughly understand the direct effect of dam construction on coastal erosion. More specifically, the presented work constitutes one of the first attempts to quantify the disruption of the sediment balance in the delta of Nestos River due to the construction and operation of the Thisavros and Platanovrysi Reservoirs, considering simul‐ taneously the impact of this disturbance on the erosion/accretion balance both at the river delta and the adjacent shorelines. Moreover, for the first time, an effort is made for the evaluation of the optimum periods and sediment management methods, aiming to increase the annual sediment that reaches the outlet of the Nestos River basin, and consequently to reduce erosion in the delta of the river and the adjacent shorelines. Finally, it is evident that the overall research methodology applied may constitute a quite useful tool for the further investigation of the effect of dam construction on the coastal erosion for other pilot application areas, throughout

**2. Assessment of reservoir sedimentation effect on coastal erosion**

As mentioned previously in the introduction section of this chapter, the considered mathe‐ matical simulation model (RUNERSET) calculates the mean annual value of sediment yield, due to rainfall and runoff, and it consists of three submodels: a rainfall-runoff submodel, a soil

By means of the rainfall-runoff submodel, the runoff depth for a certain rainfall depth is computed. It is a simplified water balance model [15], in which the variation of soil moisture due to rainfall, evapotranspiration, deep percolation, and runoff is considered. The basic

where *Sn*−1 is the available soil moisture for the time step *n* −1 (mm); *Nn*is the rainfall depth for the time step *n* (mm); *Epn*is the potential evapotranspiration for the time step *n* (mm); and *Sn*

The direct runoff depth *hon* (mm) and the deep percolation *I Nn* (mm) for the time step *n* can

where *CN* is the curve number depending on the soil cover, the hydrologic soil group, and the

¢ = +- *n n n pn* <sup>1</sup> *S S NE* (1)

′ with the maximum available soil moisture *Smax* (mm), which is

= - max *S* 25.4[(1000 / ) 10] *CN* (2)

′

estimated by the following relationship of the US Soil Conservation Service [16]:

**2.1. Description of the mathematical simulation model**

62 Effects of Sediment Transport on Hydraulic Structures

erosion submodel, and a sediment transport submodel for streams.

the world.

balancing equation is:

is an auxiliary variable (mm).

be evaluated by comparing *Sn*

antecedent soil moisture conditions (0<*CN* <100).

The following meteorological data are required for the application of the radiation method: mean daily temperature (o C), sunlight hours per day (hr/day), mean daily relative humidity (%), and mean daily wind velocity (m/s). These data were available in the Greek part of Nestos River basin. For the application of the Thornthwaite method, only mean daily temperature data (o C) are required, which were available in the Bulgarian part of the Nestos River basin.

According to the equations given above, apart from the meteorological data, the input data for the rainfall-runoff submodel are: monthly rainfall depth, altitude, latitude, soil cover – land use, and hydrologic soil group.

The soil erosion submodel is based on the assumption that the impact of droplets on the soil surface and the surface runoff are proportional to the momentum flux contained in the droplets and the runoff, respectively [19].

The momentum flux exerted by the falling droplets, *ϕr*(kg m/s2 ), is given by:

$$
\phi\_r = \text{Cr}\,\rho A u\_r \sin \sigma \tag{3}
$$

where *C* is the soil cover factor; *r*is the rainfall intensity (m/s); *ρ*is the water density (kg/m3 ); *A*is the subbasin area (m2 ); *ur* is the mean fall velocity of the droplets (m/s); and *α* is the mean slope angle of the soil surface (o ).

The original relationship of Schmidt for the momentum flux exerted by the droplets is valid for bare soils. Therefore, an additional factor is necessary to express the decrease of the momentum flux because of the vegetation. It is believed that the dimensionless crop and management factor *C* of the USLE (Universal Soil Loss Equation) is appropriate to express the vegetation influence.

The momentum flux exerted by the runoff, *ϕ<sup>f</sup>* (kg m/s2 ), is given by:

$$
\phi\_f = q \,\rho b u \tag{4}
$$

where *q* is the direct runoff rate per unit width [m3 /(s m)]; *b*is the width of the subbasin area (m); and *u* is the mean flow velocity (m/s).

The available sediment discharge per unit width, *qrf* [(kg/(m s)], due to rainfall and runoff, in the subbasin considered is given by [19]:

$$q\_{rf} = (1.7E - 1.7)10^{-4} \tag{5}$$

where

$$E = (\phi\_r + \phi\_f) / \phi\_{cr} \tag{6}$$

and *ϕcr* is the critical momentum flux (kg m/s2 ).

The critical momentum flux *ϕcr*, which designates the soil erodibility, can be calculated from:

$$
\phi\_{cr} = q\_{cr} \rho b u
\tag{7}
$$

where *qcr* [m3 /(s m)] is the direct runoff rate per unit width at initial erosion.

The critical runoff rate *qcr* is determined from the critical erosion velocity depending on soil roughness.

Equation (6) suggests the concept of critical situation characterizing the initiation of sediment motion on the soil surface.

The sediment supply *ES* [(kg/(s m)] to the main stream of the subbasin considered is estimated by means of a comparison between the available sediment discharge *qrf* in the subbasin and the sediment transport capacity by overland flow per unit width, *qt*[kg/(s m)], which is computed as follows [19]:

$$
\sigma\_t = \mathcal{c}\_{\text{max}} \rho\_s q \tag{8}
$$

where *cmax* is the concentration of suspended particles at transport capacity (m3 /m3 ); *ρs*is the sediment density (kg/m3 ).

The additional input data for the soil erosion submodel, with reference to the rainfall-runoff submodel, are: mean slope angle of soil surface, subbasin area, soil cover factor, length of the main stream of the subbasins, roughness coefficient of soil surface, critical erosion velocity, water, and sediment density.

The sediment yield at the outlet of the main stream of the subbasin considered can be computed by the concept of sediment transport capacity by the stream flow. The following relationships are used to compute sediment transport capacity by the stream flow [20]:

$$\begin{aligned} \log c\_{\ast} &= 5.435 - 0.286 \log \frac{wD\_{y0}}{\nu} - 0.457 \log \frac{u\_{\ast}}{w} + \\ &+ (1.799 - 0.409 \log \frac{wD\_{y0}}{\nu} - 0.314 \log \frac{u\_{\ast}}{w}) \log(\frac{us}{w} - \frac{u\_{\alpha^{\ast}}s}{w}) \end{aligned} \tag{9}$$

$$\frac{u\_{cr}}{w} = \frac{2.5}{\log(u\_\* D\_{g\_0}/\nu) - 0.66}, \text{ if } 1.2 < u\_\* D\_{g\_0}/\nu < 70 \tag{10}$$

$$\frac{\mu\_{cr}}{w} = 2.05 \text{, if } \frac{\mu\_\* D\_{\frac{\nu\_0}{\nu}}}{\nu} \ge 70 \tag{11}$$

where *ct* is the total sediment concentration by weight (ppm); *w*is the terminal fall velocity of suspended particles (m/s); *D*50is the median grain diameter of the bed material (m); *ν*is the kinematic viscosity of the water (m2 /s); *u*\* is the shear velocity (m/s); *u*is the mean flow velocity (m/s); *ucr*is the critical mean flow velocity (m/s); and *s* is the energy slope.

Equation (9) was determined from the concept of unit stream power (rate of potential energy expenditure per unit weight of water,*us*) and dimensional analysis. The variable *ucr* in Equation (9) suggests that a critical situation is considered at the beginning of sediment particle motion, as in most sediment transport equations.

The sediment yield *FLO* [kg/(s m)] at the outlet of the main stream of the subbasin considered can be estimated by comparing the available sediment in the stream, *ESI*[kg/(s m)], with the transport capacity by the stream flow, *qts*[kg/(s m)], resulting from the total sediment concen‐ tration*ct*.

It is implied from the above relationships that only the main stream of each subbasin is considered, because numerous unavailable data for the geometry and hydraulics of the entire stream system would otherwise be required. Therefore, the additional input data for the stream sediment transport submodel, with reference to the foregoing submodels, concern the main stream of the subbasins: base flow, bottom slope, bottom width, bed roughness, diameter of suspended particles, grain diameter of bed material, and kinematic viscosity of water.

Finally, a sediment routing plan is necessary in order to specify the sediment motion from subbasin to subbasin.

#### **2.2. Application of the simulation model**

where

where *qcr* [m3

roughness.

motion on the soil surface.

computed as follows [19]:

sediment density (kg/m3

water, and sediment density.

).

*t*

*c*

=+ > ( ) / ( 1)

 f

).

The critical momentum flux *ϕcr*, which designates the soil erodibility, can be calculated from:

 r

/(s m)] is the direct runoff rate per unit width at initial erosion.

The critical runoff rate *qcr* is determined from the critical erosion velocity depending on soil

Equation (6) suggests the concept of critical situation characterizing the initiation of sediment

The sediment supply *ES* [(kg/(s m)] to the main stream of the subbasin considered is estimated by means of a comparison between the available sediment discharge *qrf* in the subbasin and the sediment transport capacity by overland flow per unit width, *qt*[kg/(s m)], which is

r

The additional input data for the soil erosion submodel, with reference to the rainfall-runoff submodel, are: mean slope angle of soil surface, subbasin area, soil cover factor, length of the main stream of the subbasins, roughness coefficient of soil surface, critical erosion velocity,

The sediment yield at the outlet of the main stream of the subbasin considered can be computed by the concept of sediment transport capacity by the stream flow. The following relationships

50 \*

*wD u us u s*

*w*

*w ww*

*wD u*

n

(1.799 0.409log 0.314log )log( )

50 \*

=- - +

=

where *cmax* is the concentration of suspended particles at transport capacity (m3

are used to compute sediment transport capacity by the stream flow [20]:

log 5.435 0.286log 0.457 log

n

+- - -

*r f cr E E* (6)

= *cr cr q bu* (7)

*t s* max *qc q* (8)

*cr*

/m3

); *ρs*is the

(9)

ff

f

and *ϕcr* is the critical momentum flux (kg m/s2

64 Effects of Sediment Transport on Hydraulic Structures

#### *2.2.1. Available data and maps for Nestos River basin*

For more precise calculations, the Nestos River basin was divided into 60 subbasins. In more detail, the basin of the Thisavros Reservoir (Bulgarian and Greek parts) was divided into 31 subbasins, the basin of the Platanovrysi Reservoir (Greece) into nine subbasins and the basin downstream of the Platanovrysi Reservoir into 20 subbasins. The outlet of the last basin is known as Toxotes outlet.

Available meteorological data (monthly rainfall data and mean monthly temperature data) from 22 meteorological stations in Greece and Bulgaria were used as input data for the

**Figure 1.** Subbasins/main streams map and soil cover map of the Nestos River basin

simulation model. Various digital thematic maps were constructed from georeferenced background maps, for the accurate computation of the required input parameters. Indicatively, the subbasins and main streams map as well as the soil cover map are depicted in Figure 1.

However, other kind of thematic maps were also constructed and used such as a Thiessen polygons map and a geological map. The calculations were performed on a monthly time basis for each subbasin.

#### *2.2.2. Model testing*

In order to validate the mathematical model predictions, sediment measurements (suspended load) for 53 years (1937-1989) that were available for the location "Momina Koula" [21] in the Bulgarian part of Nestos River (Figure 1), were utilized. According to the measurements, the mean annual suspended sediment yield for the considered time period is 202 t/km2 [21]. Bed load measurements were not available and therefore, the following assumption [10] was made: the ratio of bed load to suspended load at the outlet of a basin on an annual basis amounts approximately to 0.25. According to this assumption, the measured mean annual sediment yield at "Momina Koula" is 252.5 t/km2 .

The mathematical model described in the previous section was applied to the basin corre‐ sponding to this location for the same time period [10]. The basin area is 1511 km2 , which is about 30% of the entire basin area of the Nestos River. The results of the model application for the different years are given in Table 1.

The mean annual value of sediment yield at the basin outlet, according to Table 1, is 315 000 t or 207.9 t/km2 . This means that the mathematical model underestimates the measured mean annual sediment yield by about 18%. However, taking into account the overall assumptions

**Table 1.** Computational results of sediment yield for the location "Momina Koula"

simulation model. Various digital thematic maps were constructed from georeferenced background maps, for the accurate computation of the required input parameters. Indicatively, the subbasins and main streams map as well as the soil cover map are depicted in Figure 1. However, other kind of thematic maps were also constructed and used such as a Thiessen polygons map and a geological map. The calculations were performed on a monthly time basis

**Figure 1.** Subbasins/main streams map and soil cover map of the Nestos River basin

In order to validate the mathematical model predictions, sediment measurements (suspended load) for 53 years (1937-1989) that were available for the location "Momina Koula" [21] in the Bulgarian part of Nestos River (Figure 1), were utilized. According to the measurements, the

load measurements were not available and therefore, the following assumption [10] was made: the ratio of bed load to suspended load at the outlet of a basin on an annual basis amounts approximately to 0.25. According to this assumption, the measured mean annual sediment

The mathematical model described in the previous section was applied to the basin corre‐

about 30% of the entire basin area of the Nestos River. The results of the model application for

The mean annual value of sediment yield at the basin outlet, according to Table 1, is 315 000 t

annual sediment yield by about 18%. However, taking into account the overall assumptions

. This means that the mathematical model underestimates the measured mean

[21]. Bed

, which is

mean annual suspended sediment yield for the considered time period is 202 t/km2

.

sponding to this location for the same time period [10]. The basin area is 1511 km2

for each subbasin.

*2.2.2. Model testing*

or 207.9 t/km2

yield at "Momina Koula" is 252.5 t/km2

66 Effects of Sediment Transport on Hydraulic Structures

the different years are given in Table 1.

of the mathematical model as well as the complexity of the simulated process, these arithmetic predictions can be considered to be acceptable.

The relatively low deviation between computation and measurement results for the mean annual sediment yield at the location "Momina Koula" was an encouraging indication for the further application of the simulation model to other parts of the Nestos River basin. The following calculations were therefore performed for a time period of 11 years (1980-1990), which corresponds to the period before the construction and operation of the considered reservoirs:

In a previous study [24], the mean annual value of sediment yield at the outlet of the Nestos River basin (Toxotes) was calculated. Due to the construction and operation of the considered reservoirs, the sediment yield originates mainly from the part of the Nestos River basin which lies downstream of the Platanovrysi Reservoir (840 km2 , Greece).

The calculated values of the annual sediment yield for different years at certain locations of the Nestos River basin (Thisavros Reservoir, Platanovrysi Reservoir, and Toxotes) are sum‐ marized in Table 2.

**Table 2.** Computational results of sediment yield at various locations of the Nestos River basin

#### *2.2.3. Main computations of sediment yield for the Nestos River Basin*

For these calculations, the major assumption is that all sediments inflowing into the reservoirs are deposited in the reservoirs, which represents the most unfavorable case regarding coastal erosion. According to Table 2, the mean annual value of sediment yield at the outlet of the Nestos River basin before the construction of the dams (mean annual value of sediment yield at the outlet of the entire Nestos River basin) is about 1.9x106 t, while after the construction of the dams (mean annual value of sediment yield at the outlet of basin downstream of the Platanovrysi Reservoir) this amounts to 0.33x106 t. Therefore, it is evident that the construction and operation of the considered reservoirs has caused a dramatic decrease, of about 83%, in the sediments supplied directly to the basin outlet and indirectly to the neighboring coast. Since Nestos River constitutes one of the main sediment supply sources for the west and east parts of the coastal region in the vicinity of its delta, it is expected that the calculated reduction in the sediment yield that reaches the Nestos River mouth will influence the seashore sediment balance, and it may result in a considerable increase in the erosion rates of the Nestos River mouth and the adjacent shorelines.

#### **2.3. Description and testing of adopted methodology for shoreline change monitoring**

#### *2.3.1. Overview*

The calculated values of the annual sediment yield for different years at certain locations of the Nestos River basin (Thisavros Reservoir, Platanovrysi Reservoir, and Toxotes) are sum‐

 1 084 000 184 000 154 500 366 000 278 000 2 066 500 968 000 128 000 128 500 344 000 588 000 2 156 500 850 000 312 500 112 500 409 000 426 000 2 110 000 309 500 114 000 32 500 99 500 73 000 628 500 678 500 360 500 107 500 277 000 494 000 1 917 500 991 000 94 500 127 500 54 500 131 000 1 398 500 1 495 500 613 000 162 000 303 500 198 000 2 772 500 1 021 000 875 500 131 500 761 500 673 000 3 462 500 884 000 357 500 130 000 241 000 383 000 1 995 500 73 500 121 000 xx 192 500 207 000 594 000 545 500 552 500 46 500 289 500 64 000 1 498 000

809 000 337 500 113 000 314 500 331 000 1 873 000

For these calculations, the major assumption is that all sediments inflowing into the reservoirs are deposited in the reservoirs, which represents the most unfavorable case regarding coastal erosion. According to Table 2, the mean annual value of sediment yield at the outlet of the Nestos River basin before the construction of the dams (mean annual value of sediment yield at the outlet of the entire Nestos River basin) is about 1.9x106 t, while after the construction of the dams (mean annual value of sediment yield at the outlet of basin downstream of the

and operation of the considered reservoirs has caused a dramatic decrease, of about 83%, in the sediments supplied directly to the basin outlet and indirectly to the neighboring coast. Since Nestos River constitutes one of the main sediment supply sources for the west and east parts of the coastal region in the vicinity of its delta, it is expected that the calculated reduction in the sediment yield that reaches the Nestos River mouth will influence the seashore sediment balance, and it may result in a considerable increase in the erosion rates of the Nestos River

**Table 2.** Computational results of sediment yield at various locations of the Nestos River basin

*2.2.3. Main computations of sediment yield for the Nestos River Basin*

Platanovrysi Reservoir) this amounts to 0.33x106

mouth and the adjacent shorelines.

**Basin of Platanovrysi Reservoir (t)**

**Basin downstream of Platanovrysi Reservoir (t)**

t. Therefore, it is evident that the construction

**Entire basin of Nestos River (t)**

**Basin downstream of Dospat Reservoir (t)**

marized in Table 2.

**Basin of Thisavros Reservoir (Bulgarian part) (t)**

68 Effects of Sediment Transport on Hydraulic Structures

**Basin of Thisavros Reservoir (Greek part) (t)**

**Year**

Mean value The adopted shoreline change monitoring methodology for the Nestos River delta and the adjacent shorelines was the use of available high-resolution satellite images from the Quick‐ Bird (QB) satellite archive and aerial photographs from the Hellenic Military Geographical Service (HMGS), in conjunction with high-resolution GPS field measurements of the region [10]. In more detail:

In order to test the accuracy of the adopted shoreline change monitoring methodology, this was first applied to a small part of the total pilot study region (Figure 2). The proposed test part is composed by the mainland shoreline at the west of Nestos River delta extending from the Akroneri Cape until the Keramoti Bay.

The main steps of the utilized methodology are outlined in the following subsections.

#### *2.3.2. Creation of common working background*

In order to use a common working subbase, an ortho-rectified background of the wider geographical region was created using various available topographic maps from the HMGS. The merging and ortho-rectification of the proposed maps was conducted in ArcMap software from the ArcGIS 9 package, using as Ground Control Points (GCPs) a wide number of known, benchmark trigonometric points of the region that were available by the HMGS.

**Figure 2.** Pilot study region for shoreline change monitoring and selected region for testing the accuracy and effective‐ ness of the adopted shoreline change monitoring methodology

#### *2.3.3. Ortho-rectification of aerial photographs and extraction of digital shoreline*

The ortho-rectification of the available old aerial photographs of the region and the digital extraction of the corresponding shoreline was conducted with the ArcMap software of the ArcGIS 9 package. The basic stages of the proposed procedure are summarized below:

Figure 3 (a) illustrates the digitally extracted shoreline for the testing region (year 1945) superimposed on the corresponding aerial photograph where the adequate accuracy and the validity of the extraction method described above can be clearly seen.

#### *2.3.4. Step 3 – Ortho-rectification of high-resolution satellite images and extraction of digital shoreline*

The digital shoreline extraction from the ortho-rectified, high-resolution, satellite images of the region was also conducted using the ArcMap software of the ArcGIS 9 package. The basic stages are summarized below:

*2.3.3. Ortho-rectification of aerial photographs and extraction of digital shoreline*

ness of the adopted shoreline change monitoring methodology

70 Effects of Sediment Transport on Hydraulic Structures

total number of 6-8 GCPs were used for each aerial photograph.

validity of the extraction method described above can be clearly seen.

were generated.

the year 1945.

The ortho-rectification of the available old aerial photographs of the region and the digital extraction of the corresponding shoreline was conducted with the ArcMap software of the ArcGIS 9 package. The basic stages of the proposed procedure are summarized below:

**Figure 2.** Pilot study region for shoreline change monitoring and selected region for testing the accuracy and effective‐

**•** The aerial photographs were scanned and digitally imported into the ArcMap database in order to be ortho-rectified in the EGSA 87 coordinate system, using as GCPs points that were both visible in the aerial photographs as well as in the ortho-rectified map background. A

**•** A surface analysis was then performed in the aerial photographs and automatic contours

**•** The contour that corresponded to the dividing line between land and sea pixels was manually selected by visual inspection and digitally extracted as the shoreline position in

Figure 3 (a) illustrates the digitally extracted shoreline for the testing region (year 1945) superimposed on the corresponding aerial photograph where the adequate accuracy and the

**Figure 3.** (a) Digitally extracted shoreline from the aerial photograph that corresponds to the test region (year 1945), superimposed on the aerial photograph. (b) Digitally extracted shoreline from the high-resolution satellite images that correspond to the test region (year 2002), superimposed on the satellite image

#### *2.3.5. Step 4 – DGPS field measurements and extraction of digital shoreline*

The DGPS field measurements were conducted using a high-resolution DGPS system that consists of two GPS receivers. A static receiver that was installed at various, known, benchmark trigonometric points of the region that were preinstalled by the HMGS and a rover (mobile) receiver which was used for the surveying of the shoreline in the field. The recorded data points from the rover receiver along the shoreline were then postprocessed and extracted in AutoCAD files in the EGSA 87 coordinate system and finally imported superimposed at the ArcMap database. The accuracy of the DGPS measurements according to the specifications of the proposed equipment was of the order of few centimetres. However, this was double-checked and verified using GCPs with known coordinates.

#### **2.4. Quantitative and qualitative results from the application of the selected shoreline change monitoring methodology at the entire pilot study area**

#### *2.4.1. General*

In the present subsection, quantitative and qualitative results from the application of the proposed methodology to the entire pilot study region are presented and discussed in detail. Figure 4 illustrates indicatively some stages of the application for the entire pilot study area.

**Figure 4.** (a) Digitally extracted shoreline from aerial photographs (year 1945) for the entire pilot study region, super‐ imposed on the aerial photographs. (b) Digitally extracted shoreline from satellite images (year 2002) for the entire pi‐ lot study region, superimposed on the satellite images. (c) Digitally extracted shoreline from DGPS measurements (year 2007) for the entire pilot study region, superimposed on the common working background

*2.4.2. Calculation of erosion/accretion balance for the region of the Nestos River delta and the adjacent shorelines, before and after the construction of the dams*

*2.3.5. Step 4 – DGPS field measurements and extraction of digital shoreline*

and verified using GCPs with known coordinates.

72 Effects of Sediment Transport on Hydraulic Structures

*2.4.1. General*

**change monitoring methodology at the entire pilot study area**

The DGPS field measurements were conducted using a high-resolution DGPS system that consists of two GPS receivers. A static receiver that was installed at various, known, benchmark trigonometric points of the region that were preinstalled by the HMGS and a rover (mobile) receiver which was used for the surveying of the shoreline in the field. The recorded data points from the rover receiver along the shoreline were then postprocessed and extracted in AutoCAD files in the EGSA 87 coordinate system and finally imported superimposed at the ArcMap database. The accuracy of the DGPS measurements according to the specifications of the proposed equipment was of the order of few centimetres. However, this was double-checked

**2.4. Quantitative and qualitative results from the application of the selected shoreline**

In the present subsection, quantitative and qualitative results from the application of the proposed methodology to the entire pilot study region are presented and discussed in detail. Figure 4 illustrates indicatively some stages of the application for the entire pilot study area.

**Figure 4.** (a) Digitally extracted shoreline from aerial photographs (year 1945) for the entire pilot study region, super‐ imposed on the aerial photographs. (b) Digitally extracted shoreline from satellite images (year 2002) for the entire pi‐ lot study region, superimposed on the satellite images. (c) Digitally extracted shoreline from DGPS measurements

(year 2007) for the entire pilot study region, superimposed on the common working background

In order to investigate the erosion/accretion balance between the years 1945 and 2002 (time period before the construction of the dams) and the years 2002 to 2007 (time period after the construction of the dams), polygons that represent eroded and accreted areas were extracted from the ArcMap database. These are illustrated in Figure 5 for the two different time periods, respectively. The boundaries of these polygons are defined by the nonintersecting parts of the two digitally extracted shorelines in each of the examined time periods.

**Figure 5.** Eroded/accreted areas for the time periods 1945-2002 and 2002-2007

The resulting erosion/accretion balance expressed in m2 as well as in percentages relative to the total area, is summarized in Table 3.

**Table 3.** Areas of erosion and accretion in the pilot study region, for the time periods 1945-2002 and 2002-2007

As it can be observed, from the year 1945 up to the year 2002 and for the considered shoreline (total length of 25 km), the erosion was about 1335028 m2 (23421 m2 per year) and the accretion 2242208 m2 (39337 m2 per year), i.e., the overall balance of the eroded/ accreted areas from 1945 to 2002, indicates that accretion is the dominant mechanism covering a total area almost 1.7 times bigger than the corresponding area of the eroded parts. In other words, accretion dominates erosion by 25.36%. On the other hand, examin‐ ing the same shoreline region from 2002 to 2007, the erosion was about 374893 m2 (74979 m2 per year) and the accretion 243453 m2 (48691 m2 per year), i.e., the erosion mechanism dominates accretion by approximately 21.26%. Therefore, it can be concluded that the dramatic decrease in the sediments supplied directly to the Nestos River basin outlet and indirectly to the neighboring coast, due to the construction and operation of the consid‐ ered reservoirs, has almost inversed the previous state regarding the erosion/accretion balance in the considered region, just within 5 years of the construction of the dams. This finding evaluates the direct effect of the construction of the Thisavros and Platanovrysi Dams, to the erosion increase in the coastal region of the Nestos River delta and the adjacent shorelines.

#### **2.5. New shoreline**

As mentioned previously, for the purposes of the present investigation it was deemed appropriate to also extract a more recent shoreline (year 2013), in order to perform a comparison with the shorelines of the years 2002 and 2007, aiming at the quantitative investigation of the dynamic evolution of the examined coastal region, from the shoreline erosion point of view [14].

For this purpose, additional DGPS field measurements were conducted, following the same methodology and time period (spring) with the corresponding field measurements that were conducted in the year 2007. This more recent shoreline (2013) consists of three subregions: the Akroneri Cape region, the Keramoti Bay region and the region in the vicinity of the Nestos River delta.

The erosion/accretion balance between the shorelines of 2002-2007, 2007-2013, and 2002-2013 is illustrated in detail for these three subregions (Akroneri Cape in Figure 6, Keramoti Bay in Figure 7, and Nestos River delta in Figure 8).

As it can be macroscopically observed from Figure 6, in Akroneri Cape region, the eroded areas (red color) dominate the accreted areas (green color) in each one of the examined time periods. Examining both the subsequent periods 2002-2007 and 2007-2013 as well as in total the period from 2002 (considered period for reservoir construction and operation) to 2013 (most recent situation), in comparison with the initially examined period (2002-2007), it is characteristic that as years are passing, areas that in the first period were under relatively severe accretion are suffering in the subsequent periods from quite noticeable erosion.

Almost the same trend is also observed in the region of Keramoti Bay, as it can be concluded by comparing the same time periods as previously (Figure 7). In more detail, while in the initial Assessment of Reservoir Sedimentation Effect on Coastal Erosion and... http://dx.doi.org/10.5772/61459 75

**Figure 6.** Erosion (red color) /Accretion (green color) balance at Akroneri Cape region

As it can be observed, from the year 1945 up to the year 2002 and for the considered shoreline (total length of 25 km), the erosion was about 1335028 m2 (23421 m2 per year) and the accretion 2242208 m2 (39337 m2 per year), i.e., the overall balance of the eroded/ accreted areas from 1945 to 2002, indicates that accretion is the dominant mechanism covering a total area almost 1.7 times bigger than the corresponding area of the eroded parts. In other words, accretion dominates erosion by 25.36%. On the other hand, examin‐ ing the same shoreline region from 2002 to 2007, the erosion was about 374893 m2 (74979 m2 per year) and the accretion 243453 m2 (48691 m2 per year), i.e., the erosion mechanism dominates accretion by approximately 21.26%. Therefore, it can be concluded that the dramatic decrease in the sediments supplied directly to the Nestos River basin outlet and indirectly to the neighboring coast, due to the construction and operation of the consid‐ ered reservoirs, has almost inversed the previous state regarding the erosion/accretion balance in the considered region, just within 5 years of the construction of the dams. This finding evaluates the direct effect of the construction of the Thisavros and Platanovrysi Dams, to the erosion increase in the coastal region of the Nestos River delta and the adjacent

As mentioned previously, for the purposes of the present investigation it was deemed appropriate to also extract a more recent shoreline (year 2013), in order to perform a comparison with the shorelines of the years 2002 and 2007, aiming at the quantitative investigation of the dynamic evolution of the examined coastal region, from the shoreline

For this purpose, additional DGPS field measurements were conducted, following the same methodology and time period (spring) with the corresponding field measurements that were conducted in the year 2007. This more recent shoreline (2013) consists of three subregions: the Akroneri Cape region, the Keramoti Bay region and the region in the vicinity of the Nestos

The erosion/accretion balance between the shorelines of 2002-2007, 2007-2013, and 2002-2013 is illustrated in detail for these three subregions (Akroneri Cape in Figure 6, Keramoti Bay in

As it can be macroscopically observed from Figure 6, in Akroneri Cape region, the eroded areas (red color) dominate the accreted areas (green color) in each one of the examined time periods. Examining both the subsequent periods 2002-2007 and 2007-2013 as well as in total the period from 2002 (considered period for reservoir construction and operation) to 2013 (most recent situation), in comparison with the initially examined period (2002-2007), it is characteristic that as years are passing, areas that in the first period were under relatively severe accretion are

Almost the same trend is also observed in the region of Keramoti Bay, as it can be concluded by comparing the same time periods as previously (Figure 7). In more detail, while in the initial

shorelines.

River delta.

**2.5. New shoreline**

erosion point of view [14].

74 Effects of Sediment Transport on Hydraulic Structures

Figure 7, and Nestos River delta in Figure 8).

suffering in the subsequent periods from quite noticeable erosion.

**Figure 7.** Erosion (red color) /accretion (green color) balance at Keramoti Bay region

time period quite big areas of erosion as well as accretion can be traced, in the subsequent time period the areas that were initially under severe accretion, are now subjected to light accretion or even under considerable erosion. Accordingly, the areas that were initially subjected to considerable erosion, present now a more intense shoreline retreat.

Regarding the region in the vicinity of the Nestos River delta, despite the fact that the shoreline presents a more intense and dynamic evolution, also in this case the initially presented

**Figure 8.** Erosion/accretion balance in the region of the Nestos River delta

(2002-2007) erosion/accretion balance in the subsequent time period (2007-2013) has been disturbed, indicating an increasing trend in the areas that are subjected to erosion with respect to the areas that are subjected to accretion.

All the above macroscopic observations are quantitatively summarized in Figure 9, where the percentages of erosion and accretion for the two subsequent time periods as well as for the entire examination period are depicted for each one of the examined regions.

**Figure 9.** Erosion (red color) /accretion (green color) percentages for the considered time periods, at Akroneri Cape region (a), at Keramoti Bay region (b) and the Nestos River delta region (c)

As it can be observed in all three considered regions, the erosion percentage in the time period 2007-2013 shows a significant increase in comparison with the previous time period (2002-2007). At Akroneri Cape region, an increase in the areas subjected to erosion by just 4% is observed. On the other hand, in the regions of Keramoti Bay and Nestos River delta, the corresponding increase reaches 23% and 24%, respectively, within just 6 years. It is also characteristic that in the case of the Nestos River delta, while in the time period 2002-2007 an erosion/accretion balance is observed, in the subsequent time period erosion predominates accretion by a considerable percentage. All the above quantitative observations can lead to the conclusion that the predomination of erosion with respect to accretion, after the construction and operation of the two reservoirs (year 2002), shows a continuously increasing trend.

### **3. Evaluation of sediment management techniques for coastal erosion reduction**

#### **3.1. Overview**

(2002-2007) erosion/accretion balance in the subsequent time period (2007-2013) has been disturbed, indicating an increasing trend in the areas that are subjected to erosion with respect

All the above macroscopic observations are quantitatively summarized in Figure 9, where the percentages of erosion and accretion for the two subsequent time periods as well as for the

**Figure 9.** Erosion (red color) /accretion (green color) percentages for the considered time periods, at Akroneri Cape

region (a), at Keramoti Bay region (b) and the Nestos River delta region (c)

entire examination period are depicted for each one of the examined regions.

to the areas that are subjected to accretion.

76 Effects of Sediment Transport on Hydraulic Structures

**Figure 8.** Erosion/accretion balance in the region of the Nestos River delta

In this third part of the present chapter, the effect of sediment removal by dredging and flushing from the Platanovrysi Reservoir downstream is investigated, in order to evaluate the increase in the sediment budget that reaches the outlet basin, as this increase can contribute to the reduction of shoreline erosion. For this purpose, the previously validated and applied mathematical model (RUNERSET - RUNoff ERosion SEdiment Transport) is modified accordingly in order to take into account sediment dredging and flushing applications. The proposed modifications involve the addition of different amounts of eroded material (sedi‐ ment) in the subbasin that lies directly downstream of the Platanovrysi Reservoir for dredging processes as well as the inclusion of a flushing discharge. In more detail, a parametric inves‐ tigation is conducted using a wide series of simulated scenarios, aiming to identify the optimum periods that dredging and flushing can be applied in order to maximize the increase of the sediment that is transported and reach the basin outlet [13].

#### **3.2. Application of sediment dredging technique to Platanovrysi Reservoir**

In order to investigate the possible contribution of the mechanical removal (dredging) of sediment from the Platanovrysi Reservoir, and its deposition in the subbasin downstream of the Platanovrysi Dam, the mathematical model RUNERSET [10] was accordingly modified. In more detail, the proposed modification involves the addition of different, eroded sediment amounts in Subbasin 7 (Figure 1) for specific months of each year, for the time period 1980-1990.

A total number of 888 simulations were conducted for the years 1980-1990 assuming the following scenarios: application of dredging in each month of each year and for various amounts of sediment removal. Initially, diagrams of the total annual amount of sediment that reaches the basin outlet, versus the amount of sediment that was removed by dredging from the Platanovrysi Reservoir, were constructed. Figure 10 illustrates indicatively these diagrams for two of the considered years.

**Figure 10.** Indicative results from simulated dredging scenarios for the years 1981 (a) and 1986 (b)

From these diagrams it is obvious that for dredging scenarios in specific months of the year, the total annual sediment amount that reaches the basin outlet is higher than the corresponding amount in the rest of the months. Therefore, the months of the year can be clearly classified as "effective" and "ineffective" for a dredging application. In more detail, from the overall analysis of the simulation results, it can be concluded that January, February, March, April, November, and December can be, in general, characterized as "effective" months, while for the time period from May to October (ineffective months), a potential dredging application would not alter significantly the annual sediment amount that reaches the outlet of the river. The month classification in "effective" and "ineffective" is summarized schematically in Figure 11 (a), where the additional annual amount of dredged sediment that reaches Toxotes outlet of the river (Figure 1), is plotted against the amount of sediment that was removed in the specific month of the year in each case, for the overall simulated scenarios.

It has also to be mentioned that for amounts of sediment removal up to 35000 tn and for dredging scenarios in the effective months of the year, the ratio of the additional sediment amount that reaches Toxotes outlet, to the removed sediment amount, is equal to unity. This indicates that, generally, up to a limiting value of 35000 tn of dredged sediment from the Platanovrysi Reservoir, in an effective month, all of this removed amount of sediment will reach Toxotes outlet. Moreover, from a certain amount of removed sediment and above, there is not any change in the amount of sediment that reaches the basin outlet. This indicates that there is a maximum transport quantity of dredged material that the Nestos River can transport

**Figure 11.** (a) Effective and ineffective months for dredging application, (b) Result of dredging application during the effective months of the year (the data points represent the overall simulated scenarios)

**Figure 10.** Indicative results from simulated dredging scenarios for the years 1981 (a) and 1986 (b)

78 Effects of Sediment Transport on Hydraulic Structures

specific month of the year in each case, for the overall simulated scenarios.

From these diagrams it is obvious that for dredging scenarios in specific months of the year, the total annual sediment amount that reaches the basin outlet is higher than the corresponding amount in the rest of the months. Therefore, the months of the year can be clearly classified as "effective" and "ineffective" for a dredging application. In more detail, from the overall analysis of the simulation results, it can be concluded that January, February, March, April, November, and December can be, in general, characterized as "effective" months, while for the time period from May to October (ineffective months), a potential dredging application would not alter significantly the annual sediment amount that reaches the outlet of the river. The month classification in "effective" and "ineffective" is summarized schematically in Figure 11 (a), where the additional annual amount of dredged sediment that reaches Toxotes outlet of the river (Figure 1), is plotted against the amount of sediment that was removed in the

It has also to be mentioned that for amounts of sediment removal up to 35000 tn and for dredging scenarios in the effective months of the year, the ratio of the additional sediment amount that reaches Toxotes outlet, to the removed sediment amount, is equal to unity. This indicates that, generally, up to a limiting value of 35000 tn of dredged sediment from the Platanovrysi Reservoir, in an effective month, all of this removed amount of sediment will reach Toxotes outlet. Moreover, from a certain amount of removed sediment and above, there is not any change in the amount of sediment that reaches the basin outlet. This indicates that there is a maximum transport quantity of dredged material that the Nestos River can transport

up to the basin outlet. This limiting amount varies accordingly for each month. Therefore, it is vital to define the month of the year in which this sediment transport quantity is maximized. Figure 11 (b) indicates that the maximum value of sediment surplus that reaches the basin outlet, takes place if dredging is applied in November.

However, for November there are also a lot of simulations that resulted in a zero sediment surplus at the basin outlet (in some of the overall simulated years). This means that while the Nestos River can transport the maximum amount of sediment that reaches the basin outlet, if the dredging application is performed during November for a certain year, it is also possible in a different year that no sediment at all reaches the outlet. Therefore, an additional classifi‐ cation of the "effective" months to "consistent" and "inconsistent" must be conducted. According to the diagram of Figure 11 (b), the months January and April can be characterized as "consistent," while February, March, November, and December can be characterized as "inconsistent."

In order now to investigate the most "effective" and "consistent" month of the year for applying sediment dredging, between January and April, a direct comparison of these two months was conducted. For this purpose, the overall results from all the simulated years (1980-1990) and for all the amounts of dredged sediment were collected, for January and April. The quantitative analysis of the proposed simulation results indicated that the overall sediment dredging performance of these two months could be classified into three distinct parts. The first part refers to dredging scenarios with sediment removal up to 34400 tn, in which the performance of each month with respect to the amount of sediment surplus that reaches the basin outlet is the same, having a value of 100%. The second and third parts consist of dredging scenarios with sediment removal from 34400 tn up to 36150 tn and greater than 36150 tn, respectively. In these last two parts of the collected data, the performance of each month is different, having values also lower than 100%. Taking into account the fact that the Nestos River cannot in general transport to the basin outlet dredged sediment amounts greater than 35000 tn, the third part of the resulting data was neglected from the comparison process. For the proposed comparison, the mean value of the dredged sediment amount that did not reach the basin outlet was taken as the effectiveness criterion, while the corresponding standard deviation was used as the criterion for consistency. The overall analysis from the comparison between January and April is summarized in Table 4. It is obvious that April can be assumed as the most suitable month of the year for a dredging application to the Platanovrysi Reservoir. It is also worth mentioning that the sediment amount of 35000 tn, that is, the maximum amount of dredged sediment that can reach the outlet of the Nestos River, is equal to 2.46% of the mean annual sediment yield of the river at its outlet, before the construction of the considered dams, and the 10.95% of the mean annual yield of the river after the construction and operation of the reservoirs. Finally, this sediment amount also constitutes the 11.53% of the mean annual sediment yield that reaches the Platanovrysi Reservoir.

**Table 4.** Comparison of effectiveness and consistency between January and March for dredging application

#### **3.3. Application of sediment flushing to Platanovrysi Reservoir**

The quantitative analysis of the proposed simulation results indicated that the overall sediment dredging performance of these two months could be classified into three distinct parts. The first part refers to dredging scenarios with sediment removal up to 34400 tn, in which the performance of each month with respect to the amount of sediment surplus that reaches the basin outlet is the same, having a value of 100%. The second and third parts consist of dredging scenarios with sediment removal from 34400 tn up to 36150 tn and greater than 36150 tn, respectively. In these last two parts of the collected data, the performance of each month is different, having values also lower than 100%. Taking into account the fact that the Nestos River cannot in general transport to the basin outlet dredged sediment amounts greater than 35000 tn, the third part of the resulting data was neglected from the comparison process. For the proposed comparison, the mean value of the dredged sediment amount that did not reach the basin outlet was taken as the effectiveness criterion, while the corresponding standard deviation was used as the criterion for consistency. The overall analysis from the comparison between January and April is summarized in Table 4. It is obvious that April can be assumed as the most suitable month of the year for a dredging application to the Platanovrysi Reservoir. It is also worth mentioning that the sediment amount of 35000 tn, that is, the maximum amount of dredged sediment that can reach the outlet of the Nestos River, is equal to 2.46% of the mean annual sediment yield of the river at its outlet, before the construction of the considered dams, and the 10.95% of the mean annual yield of the river after the construction and operation of the reservoirs. Finally, this sediment amount also constitutes the 11.53% of the mean annual

**Additional annual sediment yield at Toxotes outlet [tn]**

Mean value 2407.33 Mean value 254.17

3396.38

**APRIL**

Standard deviation **Nondelivered sediment quantity [tn]**

394.55

**Nondelivered sediment quantity [tn]**

sediment yield that reaches the Platanovrysi Reservoir.

**JANUARY**

 27806 6594 34400 0 34626 -1 34625 0 27806 6938 34744 0 34825 0 34102 723 34902 1 34101 802 35238 912 36150 0 Mean value 34941.17 32533.83 34687

attribution [%] – 93.11 99.27

Standard deviation

**Table 4.** Comparison of effectiveness and consistency between January and March for dredging application

**Quantity of removed material [tn]**

80 Effects of Sediment Transport on Hydraulic Structures

Mean

In order to investigate the possibility of minimizing shoreline erosion due to the construction of the proposed dams, with the application of flushing, the mathematical model RUNERSET was accordingly modified, in order to take into account the corresponding flushing discharge. Also in these series of simulations, the model was applied for the years 1980–1990. For the calculation of the flushing discharge, Equation (12) was used assuming that the sediment flushing discharge is known [11]:

$$\mathbf{Q}\_{i} = \left(\frac{\mathbf{Q}\_{\ast}}{3.5 \cdot 10^{-3} \cdot \left(\mathbf{S} \cdot 10^{4}\right)^{1.8}}\right)^{\mathbf{X}\_{1,2}} \mathbf{Q}\_{i} = \left(\frac{\mathbf{Q}\_{\ast}}{3.5 \cdot 10^{-3} \cdot \left(\mathbf{S} \cdot 10^{4}\right)^{1.8}}\right)^{\mathbf{X}\_{1,2}} \mathbf{Q}\_{i} = \left(\frac{\mathbf{Q}\_{\ast}}{3.5 \cdot 10^{-3} \cdot \left(\mathbf{S} \cdot 10^{4}\right)^{1.8}}\right)^{\mathbf{X}\_{1,2}} \tag{12}$$

where Qs is the removed sediment discharge from the reservoir during a flushing event (tn/s); S is the bottom slope along the flushing channel, assuming that the flow is uniform and therefore the slope of the energy line and the flow line coincide; and Qf : is the flushing discharge (m3 /s).

In order to determine the optimum month of the year for the application of flushing in the Platanovrysi Reservoir, a series of simulations were performed with a sediment flushing discharge of 0.0135 tn/s (that corresponds to the value of 35000 tn) and for flushing events occurring in different months. The results are summarized in Table 5.

**Table 5.** Results from simulations of flushing scenarios for different months of the year

It is obvious that the optimum month for the application of a flushing event, in order to maximize the overall flushed sediment amount that reaches the basin outlet, and therefore decreases the indirect shoreline erosion, is January and not April (as in the corresponding investigation for the application of dredging). However, April is found to be the second most effective month. In the literature it is stated that in order to maximize the amount of sediment that is removed from a reservoir and therefore restore its storing capacity, a flushing event will be more effective if it occurs at the beginning of the ice melting period [11]. Therefore, from this point of view, April could be characterized as the optimum month of the year for the application of a flushing event.

In order to calculate the river's maximum transport quantity of flushed sediment at the outlet of its basin, additional scenarios of continuous through each year flushing events were simulated. For this purpose, the mathematical model RUNERSET was further modified in order to take into account sediment flushing discharge equal to 0.1 tn/s, a value much greater than the previously identified critical value of 0.0135 tn/s. From the analysis of the results it is found that the Nestos River could transport to the basin outlet approximately 200000 tn of additional sediment, if the flushing of sediment was applied continuously during the whole year. It is worth mentioning that this amount constitutes the 62% of the mean annual sediment yield today.

Taking into account that the application of flushing could be more effective, at least from the point of view of limiting shoreline erosion, during certain months of the year (time period from November to April), additional continuous flushing scenarios were simulated for the period of the six more effective months of the year for each of the considered years (1980-1990). The overall results are summarized in Figure 12.

**Figure 12.** Results from simulated flushing scenarios

Examining Figure 12, it can be concluded that in the case that the continuous flushing event happens during the six of the most effective months of the year, the amount of sediment that reaches the basin outlet of the Nestos River reaches the 76.5% of the corresponding amount in the case of continuous flushing throughout the whole year. Moreover, in the case that the sediment flushing event is applied only during January the amount of sediment that reaches the outlet constitutes the 17.35% of the maximum possible amount (continuous flushing throughout the year). This amount is also equal to the 73.5% of the amount that would reach Toxotes outlet, in the case of a continuous flushing event during the noneffective months of the year (period from May to October).

#### **3.4. Comparison of the considered sediment removal methods**

It is obvious that the optimum month for the application of a flushing event, in order to maximize the overall flushed sediment amount that reaches the basin outlet, and therefore decreases the indirect shoreline erosion, is January and not April (as in the corresponding investigation for the application of dredging). However, April is found to be the second most effective month. In the literature it is stated that in order to maximize the amount of sediment that is removed from a reservoir and therefore restore its storing capacity, a flushing event will be more effective if it occurs at the beginning of the ice melting period [11]. Therefore, from this point of view, April could be characterized as the optimum month of the year for the

In order to calculate the river's maximum transport quantity of flushed sediment at the outlet of its basin, additional scenarios of continuous through each year flushing events were simulated. For this purpose, the mathematical model RUNERSET was further modified in order to take into account sediment flushing discharge equal to 0.1 tn/s, a value much greater than the previously identified critical value of 0.0135 tn/s. From the analysis of the results it is found that the Nestos River could transport to the basin outlet approximately 200000 tn of additional sediment, if the flushing of sediment was applied continuously during the whole year. It is worth mentioning that this amount constitutes the 62% of the mean annual sediment

Taking into account that the application of flushing could be more effective, at least from the point of view of limiting shoreline erosion, during certain months of the year (time period from November to April), additional continuous flushing scenarios were simulated for the period of the six more effective months of the year for each of the considered years (1980-1990). The

Examining Figure 12, it can be concluded that in the case that the continuous flushing event happens during the six of the most effective months of the year, the amount of sediment that reaches the basin outlet of the Nestos River reaches the 76.5% of the corresponding amount in

application of a flushing event.

82 Effects of Sediment Transport on Hydraulic Structures

overall results are summarized in Figure 12.

**Figure 12.** Results from simulated flushing scenarios

yield today.

In order to select the optimum of the two examined methods, a comparison was conducted by collecting the maximum amount of sediment that reaches the basin outlet of the Nestos River, that are attributed, if each method is applied in its optimum identified month of the year; April for the application of mechanical sediment removal, and January for the application of sediment flushing.

In order to calculate these quantities, a sediment discharge (dredging/flushing) greater than the previously used values of 35000 tn for the case of dredging and 0.0135 tn/s for the case of flushing was assumed. In more detail, the mathematical model was further modified in order to take into account a sediment discharge of Qs=289200 tn and qsf=0,1 tn/s, since it was proven that for values generally above 35000 tn (approximately 0.0135 tn/s for the case of a flushing event with a duration of 30 days), the results of the simulations did not change. The results of the proposed comparison are summarized diagrammatically in Figure 13.

**Figure 13.** Comparison between dredging and flushing

It can be seen that flushing seems to perform slightly better than dredging but in a degree that cannot be chosen as the optimum method. Therefore, in order to choose the optimal method for the aim of the present investigation, other criteria such as technical difficulties in their application as well as cost of application should be taken into consideration.

#### **4. Conclusions**

Coastal erosion that is generated by the reduction of the annual sediment yield at river outlets due to the construction of dams constitutes one of the main environmental problems in many parts of the world. Nestos is one of the most important transboundary rivers, characterized by its great biodiversity. The Nestos River flows through two European countries, Bulgaria and Greece, and discharges its water into the Aegean Sea. In the Greek part of the river, two dams, the Thisavros Dam and the Platanovrysi Dam, have already been constructed and started operating in 1997 and 1999, respectively. The construction of the dams implies a reduction of sediment yield at the outlet of the Nestos River basin and the alteration of the sediment balance of the basin in general, which results in coastal erosion.

The present chapter deals with the assessment of reservoir sedimentation effect on the coastal erosion for the case of the Nestos River delta and the adjacent shorelines, through mathematical modeling, remote sensing techniques, and field surveying. The mathematical model is applied for the estimation of the sediment yield reduction at the outlet of the river due to the subsequent sediment accumulation within the reservoirs, while a shoreline change monitoring method‐ ology is applied for the estimation of the alteration of the erosion/accretion balance in the wider coastal region of the Nestos River delta, examining the proposed balance in two different time periods, before and after the construction of the dams.

The mathematical model results indicate that the construction of the considered dams has caused a dramatic decrease (about 83%) in the sediments supplied directly to the basin outlet (delta) and indirectly to the neighboring coast. Comparing the overall balance of the eroded and accreted areas in the region, before and after the construction and operation of the reservoirs, it can be concluded that the decrease in the sediments supplied directly to the Nestos River basin outlet and indirectly to the neighboring coast, has almost inversed the previous situation (where accretion predomin;ated erosion by 25.36%), just within 5 years of the construction of the reservoirs, with erosion now predominating accretion by 21.26%. The extraction of a more recent shoreline and the corresponding comparison with the previously identified shoreline states clearly indicates a continuously increasing trend in the predomina‐ tion of erosion in relation to accretion.

Based on the above, it emerges that the construction and operation of the Thisavros and Platanovrysi Reservoirs have significantly increased coastal erosion in the Nestos River delta and the adjacent shorelines. This fact, together with the anticipated rise of sea level, may pose a great problem to the coastal area resources, threatening the local communities and ecosys‐ tems of the considered region. Therefore, it is necessary to further investigate the sediment budget within the estuarine and the adjacent coastal systems and to further monitor erosion/ accretion trends of the region for the coming decades.

In the present chapter also, a previously validated and applied mathematical model is modified accordingly in order to take into account the application of sediment dredging and flushing in a reservoir of river hydrological basin. The modified model is applied for the case of the Nestos River (Greece) in order to identify the optimum sediment removal scenario in a certain reservoir (Platanovrysi Reservoir) upstream of the considered river outlet (Toxotes outlet), in order to maximize the amount of sediment that reaches the delta of the river. In more detail, a wide series of parametric simulation scenarios is performed, aiming to identify the optimum periods that the dredging and flushing can be applied. From the overall analysis of the simulation results, a definite classification of the months into "effective" and "ineffective" as well as into "consistent" and "inconsistent" with respect to the total amount of removed sediment that reaches the basin outlet is made. It is also found that the considered river has a maximum transport capacity of the removed material, regardless of the further increase of the dredged and/or flushed sediment capacity from the identified limiting value. A comparison between the two considered methods identified flushing to be slightly more effective than dredging.

Finally, the overall results of the present investigation indicate that the proposed modified mathematical model can serve as a quite effective and useful tool that can be applied for similar investigations at various river basins worldwide and it could also be incorporated into the early design stages of new reservoirs that are going to be constructed, estimating the amount of sediment discharge that needs to pass through the reservoir in the downstream part of the river basin in order to minimize the unavoidable deltaic and adjacent shoreline erosion.

#### **Acknowledgements**

**4. Conclusions**

84 Effects of Sediment Transport on Hydraulic Structures

Coastal erosion that is generated by the reduction of the annual sediment yield at river outlets due to the construction of dams constitutes one of the main environmental problems in many parts of the world. Nestos is one of the most important transboundary rivers, characterized by its great biodiversity. The Nestos River flows through two European countries, Bulgaria and Greece, and discharges its water into the Aegean Sea. In the Greek part of the river, two dams, the Thisavros Dam and the Platanovrysi Dam, have already been constructed and started operating in 1997 and 1999, respectively. The construction of the dams implies a reduction of sediment yield at the outlet of the Nestos River basin and the alteration of the sediment balance

The present chapter deals with the assessment of reservoir sedimentation effect on the coastal erosion for the case of the Nestos River delta and the adjacent shorelines, through mathematical modeling, remote sensing techniques, and field surveying. The mathematical model is applied for the estimation of the sediment yield reduction at the outlet of the river due to the subsequent sediment accumulation within the reservoirs, while a shoreline change monitoring method‐ ology is applied for the estimation of the alteration of the erosion/accretion balance in the wider coastal region of the Nestos River delta, examining the proposed balance in two different time

The mathematical model results indicate that the construction of the considered dams has caused a dramatic decrease (about 83%) in the sediments supplied directly to the basin outlet (delta) and indirectly to the neighboring coast. Comparing the overall balance of the eroded and accreted areas in the region, before and after the construction and operation of the reservoirs, it can be concluded that the decrease in the sediments supplied directly to the Nestos River basin outlet and indirectly to the neighboring coast, has almost inversed the previous situation (where accretion predomin;ated erosion by 25.36%), just within 5 years of the construction of the reservoirs, with erosion now predominating accretion by 21.26%. The extraction of a more recent shoreline and the corresponding comparison with the previously identified shoreline states clearly indicates a continuously increasing trend in the predomina‐

Based on the above, it emerges that the construction and operation of the Thisavros and Platanovrysi Reservoirs have significantly increased coastal erosion in the Nestos River delta and the adjacent shorelines. This fact, together with the anticipated rise of sea level, may pose a great problem to the coastal area resources, threatening the local communities and ecosys‐ tems of the considered region. Therefore, it is necessary to further investigate the sediment budget within the estuarine and the adjacent coastal systems and to further monitor erosion/

In the present chapter also, a previously validated and applied mathematical model is modified accordingly in order to take into account the application of sediment dredging and flushing in a reservoir of river hydrological basin. The modified model is applied for the case of the Nestos River (Greece) in order to identify the optimum sediment removal scenario in a

of the basin in general, which results in coastal erosion.

periods, before and after the construction of the dams.

accretion trends of the region for the coming decades.

tion of erosion in relation to accretion.

The authors would like to acknowledge the financial support from the Research Project, INTEREG IIIC BEACHMED-e, "Strategic management of beach protection for sustainable development of Mediterranean coastal zones."

#### **Author details**

Manolia Andredaki1 , Anastasios Georgoulas2\*, Vlassios Hrissanthou1 and Nikolaos Kotsovinos1

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

2 Department of Engineering, University of Bergamo, Dalmine (BG), Italy

#### **References**

[1] Ly C.K. 1980, The role of the Akosombo Reservoir on the Volta river in causing coast‐ al erosion in central and eastern Ghana (West Africa), *Mar Geol*, Vol. 37, pp. 323-332.

[15] Giakoumakis S., Tsakiris G. and Efremides D. 1991, On the rainfall-runoff modeling in a Mediterranean island environment. In: *Advances in Water Resources Technology*, ed. by G. Tsakiris, Balkema, Rotterdam, pp. 137-148.

[2] Chen X. and Zong Y. 1998, Coastal erosion along the Changjiang deltaic shoreline,

[3] El-Raey M., Sharaf El-Din S.H, Khafagy A.A. and Abo Zed A.I. 1999, Remote sensing of beach erosion/accretion patterns along Reservoirietta-Port Said shoreline, Egypt,

[4] Malini B.M. and Rao K.N. 2004, Coastal erosion and habitat loss along the Godavari delta front – a fallout of reservoir construction (?), *Curr Sci*, Vol. 87, No. 9, pp.

[5] Chen X., Yan Y., Fu R., Dou X. and Zhang E. 2008, Sediment transport from the Yang‐ tze River, China, into the sea over the Post-Three Gorge Reservoir Period: A discus‐

[6] Liu C., Sui J. and Wang Z. 2008, Sediment load reduction in Chinese rivers, *Int J Sed*

[7] Huang G. 2011, Time lag between reduction of sediment supply and coastal erosion,

[8] Zhang W., Mu S., Zhang Y. and Chen K. 2011, Temporal variation of suspended sedi‐ ment load in the Pearl River due to human activities, *Int J Sed Res*, Vol. 26, pp.

[9] Samaras A.G. and Koutitas C.G. 2008, Modelling the impact on coastal morphology of the water management in transboundary river basins: The case of River Nestos,

[10] Andredaki M., Georgoulas A., Hrissanthou V. and Kotsovinos N. 2014, Assessment of reservoir sedimentation effect on coastal erosion in the case of Nestos River,

[11] Morris G.L. and Fan J. 1998, *Reservoir Sedimentation Handbook*. New York: McGraw-

[12] Basson G. and Rooseboom A. 1997, Dealing with reservoir sedimentation. South Af‐

[13] Andredaki M., Georgoulas A. and Hrissanthou V. 2014, Sediment management in reservoirs in order to reduce shoreline erosion: The case of Nestos River, Greece, 4th International Symposium on Sediment Management, 14-16 September, Ferrara, Italy

[14] Andredaki M. 2014, Effect of reservoir sedimentation on shoreline erosion – The ex‐ ample of Nestos River, PhD Thesis, Sector of Hydraulic Structures, Department of

Civil Engineering, Democritus University of Thrace, Xanthi, Greece.

China: history and prospective, *Estuar Coast Self Sci*, Vol. 46, pp. 733-742.

*Int J Remote Sensing*, Vol. 20, No. 6, pp. 1087-1106.

*Manag Environ Qual: Int J*, Vol. 10, No. 4, pp. 455-466.

Greece, *Int J Sed Res*, Vol. 29, pp. 34-48.

rican Water Research Commission.

sion, *Quater Int*, Vol. 186, pp. 55-64.

*Int J Sed Res*, Vol. 26, pp. 27-35.

*Res*, Vol. 23, pp. 44-55.

86 Effects of Sediment Transport on Hydraulic Structures

1232-1236.

487-497.

Hill Book Co.

(Paper ref. no. 112).

**Section 3**

## **Local Scour**

## **The Impact of Ice Cover and Sediment Nonuniformity on Erosion Around Hydraulic Structures**

Peng Wu, Jueyi Sui and Ram Balachandar

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61468

#### **Abstract**

Based on two case studies, the impact of ice cover on local scour around bridge piers is presented in this chapter. Bed material with different grain sizes is used and ice covers with different roughness is used to study the scour characteristics. The impact of nonuni‐ formity of sediment is also investigated. Results show that with the increase in densimet‐ ric Froude number, there is a corresponding increase in the dimensionless scour depth. For nonuniform sediment, due to the formation of an armor layer, less maximum scour depth was noted around bridge foundation structures compared to uniformly distributed sediment. The increase in ice cover roughness results in a larger scour depth and geome‐ try. The results indicate that it is imperative to pay attention to the impact of ice cover on the scour around hydraulic structures.

**Keywords:** ice cover, local scour, non-uniform sand, bridge abutment

#### **1. Introduction**

#### **1.1. Sediment transport around hydraulic structures**

Sediment transport embodies the processes of erosion, entrainment, movement, and deposi‐ tion. In nature, these processes are always present. Although there are many types of sediment, fluvial sediment is one of the most common. It comprises sediment accumulated by the erosion of rock and mineral particles that are transported by flowing water. The process of erosion is complex and plays a vital role in the formation of different landscapes of the world we live in.

Statistics show that thirteen of the large rivers in the world carry sediment loads in excess of 5.8 billion tons annually [5]. In addition to producing large quantities of sediment, erosion also causes other problems such as the pollution of water body and altering the runoff conditions.

Additionally, from the perspective of hydraulic engineering, sediment transport can result in serious on-site damage to hydraulic structures such as bridge abutments, bridge piers, spur dikes, etc.

The presence of a hydraulic structure in a river introduces the phenomenon of local scour. The interaction between flow and structures has been an actively researched topic in the past decade. Reynolds stress is an important parameter to quantify the suspended load of sediment transport, while the bed shear stress is pertinent to determine the bed load of sediment transport. Kuhnle et al. [14] examined the three-dimensional flow field around a submerged spur dike. An amplification factor of bed shear stress 3 was found. Duan et al. [7] measured the flow and turbulence around an experimental spur dike in a flat and scour bed. Differences of mean velocity, turbulent intensity, and Reynolds stresses between those two flow fields were analyzed. The bed shear stress calculated from the Reynolds stresses was 2-3 times that of the incoming flow. Since the abutment and spur dike have similar contraction impact on the flow, all the three studies showed similar amplification factor of bed shear stress in open channel flow.

#### **1.2. Impact of ice cover**

Extensive studies have been conducted to study the local scour around structures in the past few decades. However, most of the previous studies were focused on flow in open channels. The impact of ice cover on local scour has not been well understood. For many regions in the northern hemisphere, winter may last up to six months. Figure 1 shows the ice cover around Ambassador Bridge, Windsor, Canada, which is located on the Detroit River. It can be seen from Figure 1 that the region around the pier is completely covered by ice. The presence of the ice cover affects the flow characteristics of the river. Hence, further research on the impact of ice cover on the local scour around bridge piers becomes important.

As pointed out by Morse and Hicks [21], fundamental problems of ice engineering are primarily related to ice jams, floods, and transportation over ice. River ice hydrology has been an implicit part in ice engineering. Some studies on ice engineering research can be found in the references [1,12,22,9,21,23,26,30,32]. To date, the mechanism of local scour around bridge abutments and piers under ice cover is still not well understood.

Lau and Krishnappan [15] used the *k-ε* turbulence model to numerically determine the velocity distribution and suspended load transport under ice cover. The assumption was that the ice covered flow can be treated as two-layer flow which is divided at the location of maximum velocity. Based on the assumption that the mechanics of bed-form formation is the same for open and ice covered channels, Smith and Ettema [27] developed a method from flume data for estimating the flow resistance in ice covered channels. Ettema et al. [8] proposed a different approach to estimate the sediment transport under ice cover by using the procedures from open channel flow. The variation of ice cover roughness and flow cross sections under ice cover can limit the accuracy of this method. Ettema and Daly [9] conducted a series of flume experiments on the sediment transport under ice cover. Since the flow distribution is substan‐ tially modified by ice cover, the sediment transport under ice cover is hard to estimate. Sui et al. [26] conducted a series of flume experiments for the incipient motion under different flow and boundary conditions. The influence of ice cover roughness has been assessed. They found that the slope of ice cover has a great impact on the critical dimensionless shear stress for the river sediment motion.

found that the slope of ice cover has a great impact on the critical dimensionless shear stress for

Figure 1. The ice cover around bridge piers (Ambassador Bridge, Windsor, ON, Canada, 2015)

**Figure 1.** The ice cover around bridge piers (Ambassador Bridge, Windsor, ON, Canada, 2015)

#### **1.3. Impact of nonuniformity of sediment**

*1.3 Impact of nonuniformity of sediment*

abutment is *α*.

*D R W F F*

sin

*L R*

cos

satisfied:

the river sediment motion.

Additionally, from the perspective of hydraulic engineering, sediment transport can result in serious on-site damage to hydraulic structures such as bridge abutments, bridge piers, spur

The presence of a hydraulic structure in a river introduces the phenomenon of local scour. The interaction between flow and structures has been an actively researched topic in the past decade. Reynolds stress is an important parameter to quantify the suspended load of sediment transport, while the bed shear stress is pertinent to determine the bed load of sediment transport. Kuhnle et al. [14] examined the three-dimensional flow field around a submerged spur dike. An amplification factor of bed shear stress 3 was found. Duan et al. [7] measured the flow and turbulence around an experimental spur dike in a flat and scour bed. Differences of mean velocity, turbulent intensity, and Reynolds stresses between those two flow fields were analyzed. The bed shear stress calculated from the Reynolds stresses was 2-3 times that of the incoming flow. Since the abutment and spur dike have similar contraction impact on the flow, all the three studies showed similar amplification factor of bed shear stress in open