Vlassios Hrissanthou

Comparative application of two erosion models to a basin

Abstract Two mathematical models were used to estimate the annual sediment yield resulting from rainfall and runoff at the outlet of the Nestos River basin (Toxotes, Thrace, Greece). The models were applied to that part of the Nestos River basin (838 km2) which lies downstream of three dams. Both models consist of three submodels: a simplified rainfall-runoff submodel, a physically-based surface erosion submodel and a sediment transport submodel for streams. The two models differ only in the surface erosion submodel: that of the first model is based on the relationships of Poesen (1985) for splash detachment and splash transport, while the corresponding submodel of the second model is based on the relationships of Schmidt (1992) for the momentum flux exerted by the droplets and the momentum flux exerted by the overland flow. The degree of conformity between the annual values of sediment yield at the basin outlet according to both models is satisfactory.

Assessment of reservoir sedimentation effect on coastal erosion in the case of Nestos River, Greece

Coastal erosion that is generated by the reduction of the annual sediment yield at river outlets, due to the construction of reservoirs, constitutes one of the main environmental problems in many parts of the world. Nestos is one of the most important boundary rivers, flowing through Bulgaria and Greece, characterized by its great biodiversity. 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. The present paper constitutes the first attempt where the assessment of reservoir sedimentation effect on the coastal erosion for the case of the Nestos River delta and the adjacent shorelines is addressed in detail, through mathematical modeling, modern remote sensing techniques and field surveying. It is found that the construction and operation of the considered reservoirs have caused a dramatic decrease (about 83%) in the sediments supplied directly to the basin outlet and indirectly to the neighbouring coast and that this fact has almost inversed the erosion/accretion balance in the deltaic as well as the adjacent shorelines. Before the construction of the reservoirs, accretion predominated erosion by 25.36%, while just within five years after the construction of the reservoirs, erosion predominates accretion by 21.26%.

Simulation modeling of runoff, groundwater flow and sediment transport into Kastoria Lake, Greece

Kastoria Lake is located in northwestern Greece. The whole basin of the lake is about 253 km2. For the computation of the surface water volume inflowing into the lake from the main streams of the sub-basins located around Kastoria Lake, a rainfall-runoff sub-model is used. A quasi-three-dimensional simulation model of the Kastoria basin aquifer is also realized, in order to estimate the groundwater contribution to the volumetric budget of the lake and the whole basin as well. For the computation of sediment load inflowing into the lake from the main streams of the sub-basins, the rainfall-runoff sub-model is combinedwith a soil erosion sub-model and a sediment transport sub-model for streams. A GIS was developed in the hydrologic basin with all data needed for parameter identification and model application. The data base was enriched by a series of on site measurements of water discharge made in all main streams for one whole hydrologic year. By means of the resulting mathematical sediment model, those sub-basins, which deliver most sediment load to the lake, are identified. On the basis of this identification, a series of control measures, for the reduction of sediment inflowing into the lake, at certain places of the above mentioned sub-basins is proposed.

Estimate of sediment inflow into Vistonis Lake, Greece.

Abstract In the present study, the mean annual sediment inflow into Vistonis Lake (Thrace, northeastern Greece) was calculated. The sediment quantity originates mainly from the basins of Kossynthos, Kompsatos and Travos (Aspropotamos) Rivers. The whole basin area (mountainous part) contributing to the lake amounts to about 845 km 2 . The above mean annual sediment quantity was compared with the mean annual sediment accumulation in the lake. The latter quantity was estimated from the mean annual decrease of the lake water volume for a period of 22 years, which was determined by means of older and newer topographic maps (isobath contours). For the calculation of the mean annual sediment yield at the outlets of the three above mentioned basins, a mathematical model consisting of three submodels was used: a rainfall-runoff submodel, a soil erosion submodel and a sediment transport submodel for streams. The comparison of the computational results by means of the mathematical model with the estimation results by means of the topographic maps is satisfactory and encouraging.

Estimate of Continuous Sediment Graphs in a Basin, Using a Composite Mathematical Model

The aim of this study is to present continuous simulation efforts of soil and streambed erosion processes, as well as the comparison of computed sediment load values with field measurements. For this reason, a composite mathematical model, consisting of three submodels, is applied to the basin of Kosynthos River (district of Xanthi, Thrace, northeastern Greece): a rainfall-runoff submodel, a soil erosion submodel and a sediment transport submodel for streams. The rainfall-runoff submodel that is used for the computation of the surface runoff and the streamflow in the sub-basins, is the deterministic distributed hydrologic model HEC-HMS 3.5. For the estimate of soil erosion in a sub-basin, the model of Poesen (1985) is used, while for the estimate of sediment yield at the outlet of a sub-basin, and finally at the outlet of the whole basin, the stream sediment transport model of Yang and Stall (1976) is used. The statistic efficiency criteria utilized for the comparison between computed and measured sediment discharge values at the outlet of the whole basin, provide satisfactory values. Therefore, it is concluded that the continuous hydromorphologic modeling can be successfully applied to Kosynthos River basin.

Fuzzy Regression Analysis for Sediment Incipient Motion under Turbulent Flow Conditions

Sediment incipient motion in turbulent flow conditions is hard to be determined due to the random turbulence fluctuations as well as the random positioning and orientation of the sediment particles. The most common criteria for the initiation of motion of sediment particles, like the Shields diagram, have a general movement or no-movement functionality. In such criteria, the subjectivity of the researcher on the definition of sediment incipient motion influences the quantification of sediment transport; however, uncertainties in the selection of critical flow conditions may lead to large computational errors. In order to avoid the ambiguity of selecting a unique entrainment threshold, this paper employed fuzzy regression and set a fuzzy band, which offers a transition from sporadic to general sediment entrainment. Two approaches were used, namely the conventional fuzzy regression analysis and a goal programming-based fuzzy regression. In the latter, the modification of the fuzzy linear regression basis, by establishing a distance (error) associated with the underestimation of the left-hand boundary and a distance associated with the overestimation of the right-hand boundary, constitutes a significant improvement in the field since it avoids the significant influence of the outliers. The fuzzy regression analyses employ data from the pertinent literature and consider critical shear stress to depend on a particle Reynolds number and relative roughness. The results show that the terms that contain the particle Reynolds number do not have any uncertainty, while the fuzziness appears in both the coefficient of the relative roughness and the constant terms.

Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers

Three data-driven techniques, namely artificial neural networks, adaptive-network-based fuzzy inference system, and symbolic regression based on genetic programming, are employed for the prediction of bed load transport rates in gravel-bed steep mountainous streams and rivers in Idaho (U.S.A.), and the potential of several input variables is investigated. The input combinations that were tested are based, mainly, on unit stream power, stream power, and shear stress, and exhibited similarly good performance, with respect to the machine learning technique used, accentuating the importance of the regression model. The derived models are robust, generalize very well in unseen data, and generated results superior to those of some of the widely used bed load formulae, without the need to set a threshold for the initiation of motion, and consequently avoid predicting erroneous zero transport rates.

Numerical Simulation of Sediment Transport Following a Dam Break

For the computation of the morphological changes on reservoir beds, in scale model experiments, a hydrodynamic model is combined with the sediment continuity equation. The hydrodynamic model is based upon the equations of mass and momentum conservation. The sediment transport capacity appearing in the sediment continuity equation is calculated by using the relationship proposed by Smart. For the solution of the above system of three partial differential equations, the explicit numerical schemes of Lax and Wendroff and MacCormack are tested. The whole mathematical model is applied to the experimental case of sediment release from a reservoir after a dam break.

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed. Editor Z.W. Kundzewicz

Numerical Simulation of Morphological Changes in Rivers and Reservoirs

Abstract For the computation of the morphological changes in river or reservoir beds, due to erosion or deposition, a hydrodynamic model is combined with the sediment continuity equation. The hydrodynamic model is based upon the equations of mass and momentum conservation. For the solution of the above system of three partial differential equations, the explicit numerical schemes of MacCormack and Lax-Wendroff are tested. The whole mathematical model is applied to two experimental cases and to a real case. The first experimental case concerns the aggradation in a laboratory channel due to sediment overloading, and the second one the sediment release from a reservoir after a dam break. The real case concerns the morphological changes in the torrent Mallero (Northern Italy) during a flood event.