Stream power

About: Stream power is a research topic. Over the lifetime, 1135 publications have been published within this topic receiving 51324 citations.

Scour around bridge piers applying stream power approach

Abstract: The current research aims introducing the problem of scour around bridge piers from a new point of view. The classical approach for tackling this problem usually depends on applying empirical equations developed from the analysis of experimental results with the use of dimensional analysis. Thus, in the current research an intensive study for evaluating the existing scour predictor was performed. The comparison processes through comparing the results of some laboratory experiments performed for scour around bridge piers and the predicted scour depth from some selected available predictor equations. About 156 experiments were carried out to perform the current study in accordance with 15 predictor equations. The results show that the scour predictors have a low reliability for following a specific trend or giving accurate results. Thus, it has been considered that it is of great importance to study the scour problem around bridge piers through a new approach. Stream power approach has been selected for achieving this objective. A one dimensional numerical model for open channel hydraulic was selected to stimulate the problem of flow and scour around bridge piers. Stream power variation is studied versus several parameters such as scour depth, sediment type and flow conditions. Several laboratory experimental studies were tested and stream power values in these different cases were analyzed. Results show that there is a significant relationship between scour depth escalations and stream power value. The relationship between stream power and scour depth was found to have a specific trend through which a new predictor could be developed. The new predictor was applied on some experiments and the estimated scour depth was compared to the experimental results. It was found that there was a good agreement between the experimental results and the scour calculated using the stream power approach. Finally, it can be concluded that the new approach is considered more reliable than the classical methods for estimating the scour depth at bridge piers. However more studies are needed to perform an inclusive idea about the application of stream power approach for pier and abutment scour problem.

GIS Evaluation of Erosion-Sedimentation Risk Caused by Extreme Convective Rainstorms: Case Study of the Stonávka River Catchment, Czech Republic

Abstract: Timeliness of erosion-sedimentation processes is evident at both local and global scale. In Central Europe, problems of landscape management are often discussed in the relation to rainfall-runoff phenomena and the resultant processes of soil erosion and sediment deposition. Nowadays, these issues are well elaborated theoretically and with the use of information technologies (IT) and geographical information systems (GIS) potential, the evaluation of predisposition of a given area to erosion is quite easy. IT and GIS offer the effective use of a wide range of erosion models. In this chapter, a USPED (Unit Stream Power based Erosion/Deposition) model (Mitasova et al. 1996) is applied to the analysis of a rainstorm event in June 2009 within the Stonavka River catchment, Czech Republic. In comparison to the well-known USLE (Wishmeier and Smith 1965, 1978) model and its newer versions, the USPED model is more suitable for the use at a catchment scale and besides erosion, it is also able to calculate the rate of deposition of eroded material. Because of the convective character of the causal rainfall, the Onstad-Foster’s equation (Onstad and Foster 1975) was used to derive the R factor describing an erosive effect of rainfall/surface runoff.

The Role of Human Activities in Streambank Stability: Lower Sakarya River (NW Turkey)

Abstract: The main objective of this study is to determine historic and current human impacts on streambank stability in Lower Sakarya River. Remote sensing and Geographical Information System techniques with conjunction field works were performed to identify the impact of human alteration on streambank stability in the riverine environment of the eastern portion of Sakarya province. LULC (land use/cover) and historical streambank changes were analyzed from Landsat 1-5 Multispectral Scanner (MSS), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Google Earth images between 1995 and 2016. As results, a significant LULC changes have been observed along the buffered zone due to population growth. Recently, change in LULC type from agricultural to urban usage has changed river equilibrium. The stream channel also became more stable and straight as man-made modifications including a hydropower (HES) dam constructed in 2010, which primarily reduced flood frequency, water velocity, stream power, shear stress on sediment particles temporarily deposited along the streambank. After the year of 2010, downstream portion of the dam had experienced narrowing and expanding mid-channel bars. Moreover, the channel has been slightly moved towards east especially along urbanized and sinuous courses. The streambank displacement ranged from 2.9 m to 36 m in the region. Instream mining activities and bridge constructions in the region also disturb active streambanks, which raise a concern about instability of streambank and potential damage to infrastructures. Such studies are extremely important for understanding basic mechanisms of streambank evolution for further river restoration practices.

A Study on Development of Landslide Susceptibility Map in Malaysia Landslide Prone Areas by Using Geographic Information System (GIS) and Machine Learning

Abstract: Landslide is a natural disaster that is common and frequently occurring in Malaysia. Thus, to reduce the impact of the landslide’s tragedy, a landslide susceptibility map is needed. The ultimate goal of this paper is to use Geographic Information System (GIS) and machine learning to develop a landslide susceptibility map. In two different landslide-prone areas in Malaysia, the performance of the two different machine learning models, Random Forest and Extreme Gradient Boosting (XGBoost) are evaluated and cross-validated. The Cameron Highland and Penang Island, Malaysia which are the subjects of this study, have a total of 233 and 443 landslides locations, respectively. These landslide locations were randomly divided into 70% for training and 30% for testing. The Digital Elevation Model (DEM), slope angle, slope length, Normalized Vegetation Index (NDVI), plan curvature, profile curvature, distance from the stream, distance from roads, Topographic Wetness Index (TWI) and Stream Power Index (SPI) are among the ten landslide conditioning factors, for which the spatial databases were developed by using GIS software. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) had been applied to evaluate the machine learnings prediction accuracy. The result indicated that both XGBoost and Random Forest had a great performance across both study areas. For Penang Island, the AUC of XGBoost is 95.02% and the AUC of Random Forest is 94.99%. Meanwhile, for Cameron Highland, the AUC of XGBoost is 91.99% and the AUC of Random Forest is 92.32%. The final prediction map from this study might be useful for better planning in mitigating the occurrence of landslides.