Effect of spatial scale on suspended sediment concentration in flood season in hilly loess region on the Loess Plateau in China

2007 ◽  
Vol 54 (6) ◽  
pp. 1261-1269 ◽  
Author(s):  
Haiyan Fang ◽  
Hao Chen ◽  
Qiangguo Cai ◽  
Xin Huang
Keyword(s):  
Spatial Scale ◽  
Suspended Sediment ◽  
Loess Plateau ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
The Loess Plateau ◽  
Flood Season ◽  
Loess Region ◽  
Hilly Loess Region

Formulating an elasticity approach to quantify the effects of ecological restoration and climate variability on streamflow and sediment discharge changes in the Loess Plateau of China

2020 ◽  
Author(s):  
Guangyao Gao
Keyword(s):  
Climate Variability ◽  
Suspended Sediment ◽  
Ecological Restoration ◽  
Loess Plateau ◽  
Yellow River ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Sediment Discharge ◽  
The Loess Plateau ◽  
Check Dams

<p>Ecological restoration (ER) has strong consequences on hydrological responses. The China’s Loess Plateau (LP) contributed nearly 90% of sediment load in the Yellow River, which was once the world’s largest carrier of fluvial sediment. ER efforts including the soil and water conservation measures (SWCMs, especially terracing and construction of check dams) since 1950s and large-scale ecological restoration campaigns such as Grain-for-Green project (i.e., returning sloping cropland to afforestation and pasture reestablishment) in 1999, has resulted in extensive land use/cover change, leading to considerable decreases of streamflow (Q), suspended sediment yield (SSY) and sediment concentration (C) in the LP over the past 60 years. However, it remains challenging to quantify the impacts of ER and climate variability on declines of Q and especially SSY. In this study, we formulate the notion of elasticity of sediment discharge, by associating SSY change to climate variability and ER over the period 1950s to 2014. Our results strongly support the hypothesis that changes to both streamflow volumes and to the suspended sediment concentration versus water discharge (C-Q) relationships result in reduced SSY, so that streamflow is reduced but runs clearer. We find that two of the ER strategies resulted in weaker relative impacts of climate variability, largely by reducing streamflow (by 55% to 75%). Meanwhile, ER predominantly decreased SSY (by 63% to 81%). Regarding ER practices, (i) the predominant measure acting to reduce SSY changed, over time, from engineering to reforestation; (ii) check-dams preferentially act to regulate the C-Q relationships whereas reforestation preferentially acts to moderate streamflow. Overall, our results suggest that a combination of engineering and vegetation measures is critical to achieving high-efficiency ER. While change to the ER strategy increased the efficiency of streamflow for SSY control, the lost water discharge per unit SSY reduction increased from 5.2 to 6.4 m<sup>3</sup>·t<sup>-1</sup>. Conflicting demands for water necessitate that further ER should target precision management by revegetation of targeted areas in the Loess Plateau.</p>

scholarly journals Influences of conservation measures on runoff and sediment yield in different intra-event-based flood regimes in the Chabagou watershed

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shan-Shan Wang ◽  
Zhan-Bin Li ◽  
Le-Tao Zhang ◽  
Bo Ma
Keyword(s):  
Soil Erosion ◽  
Loess Plateau ◽  
Sediment Yield ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
The Loess Plateau ◽  
Flood Duration ◽  
Conservation Measures ◽  
Event Based ◽  
Measurement Period

AbstractThe Loess Plateau in China has suffered severe soil erosion. To control soil erosion, extensive conservation measures aimed at redistributing rainfall, hindering flow velocity and intercepting sediment were implemented on the Loess Plateau. To accurately evaluate the combined effect of conservation measures in the Chabagou watershed, this study classified intra-event-based floods into four regimes via cluster and discriminant analyses. Regime A was characterized by short flood duration and low erosive energy, regime B was characterized by short flood duration and high erosive energy, regime C was characterized by long flood duration and low erosive energy, and regime D was characterized by long flood duration and high erosive energy. The results indicated that peak discharge (qp), runoff depth (H), mean discharge (qm), and runoff erosion power (E) decreased by 75.2%, 56.0%, 68.0% and 89.2%, respectively, in response to conservation measures. Moreover, area-specific sediment yield (SSY), average suspended sediment concentration (SCE), and maximum suspended sediment concentration (MSCE) decreased by 69.2%, 33.3% and 11.9%, respectively, due to conservation measures. The nonlinear regression analysis revealed a power function relationship between SSY and E in both the baseline (1961–1969) and measurement period (1971–1990) in all regimes. Conservation measures reduced sediment yield by not only reducing the runoff amount and soil erosion energy but also transforming the flood regime, for example, transforming a high-sediment-yield regime into a low-sediment-yield regime. Moreover, conservation measures altered the SSY-E relationship in regime A, whereas no obvious difference in regime B or C/D was observed between the measurement period and the baseline period. This study provides a better understanding of the mechanism of runoff regulation and the sediment yield reduction under comprehensive conservation measures in a small watershed on the Chinese Loess Plateau.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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