Development of rating curve estimators for suspended-sediment concentration and transport in the C-51 Canal based on surrogate technology, Palm Beach County, Florida, 2004-05

2005 ◽  
Author(s):  
A.C. Lietz ◽  
Elizabeth A. Debiak
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Palm Beach County ◽  
Rating Curve

Impact of Chelicorophium curvispinum on the concentration-discharge response of suspended sediment in the Rhine River

2021 ◽  
Author(s):  
Marcel van der Perk
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Sudden Increase ◽  
Rhine River ◽  
Rating Curve ◽  
Independent Evidence ◽  
Sediment Rating Curve ◽  
Suspended Sediment Concentrations

<p>In an ongoing study to the decline in suspended sediment concentrations and loads in the Rhine river since the mid-1950s, the temporal changes in the power-law sediment rating curve parameters were examined. This revealed that the rating exponent of the rating curve increased substantially between the early and late 1980s. Until the early 1980s, the ratings curves were relatively flat with values of the rating exponent b varying around 0.2. In the mid-1980s, the exponent suddenly increased to a value between 0.4 and 0.6 and since then has remained within this range. This change in the rating exponent was mainly caused by a decrease in suspended sediment concentrations during low discharges. During high discharges, the suspended sediment concentration initially increased during the late 1980s, but this increase was nullified soon afterwards due to the declining trend in suspended sediment concentration.</p><p>The sudden increase of the rating exponent coincided with the period that the Ponto-Caspian <em>Chelicorophium curvispinum</em> (Caspian mud shrimp) invaded the Rhine river basin. This suggests that this suspension-feeder species bears the prime responsibility for this increase, although this hypothesis requires further independent evidence. The sudden increase in the rating exponent does however not manifest itself in the long-term gradual trend of declining suspended sediment concentrations and vice versa. Apparently, the sequestration of sediment by <em>Chelicorophium curvispinum</em> is only temporary: the suspended sediment sequestered during periods of relatively low discharges is likely remobilised again during periods of high discharge. This implies that the invasion of <em>Chelicorophium curvispinum</em> has not played a significant role in the decline of suspended sediment concentrations. The precise reasons for the gradual long-term decline in suspended sediment concentration remain yet unknown.</p>

scholarly journals Hydroclimatic control on suspended sediment dynamics of a regulated Alpine catchment: a conceptual approach

2018 ◽  
Author(s):  
Anna Costa ◽  
Daniela Anghileri ◽  
Peter Molnar
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Sediment Dynamics ◽  
Time Lags ◽  
Rating Curve ◽  
Conceptual Approach ◽  
Alpine Catchments ◽  
Erosive Rainfall ◽  
Hydroclimatic Variables

Abstract. We analyse the control of hydroclimatic factors on suspended sediment concentration (SSC) in Alpine catchments by differentiating among the potential contributions of erosion and suspended sediment transport driven by erosive rainfall, defined as liquid precipitation over snow free surfaces, icemelt from glacierized areas, and snowmelt on hillslopes. We account for the potential impact of hydropower by intercepting sediment fluxes originated in areas diverted to hydropower reservoirs, and by considering the contribution of hydropower releases to SSC. We obtain the hydroclimatic variables from daily gridded datasets of precipitation and temperature, implementing a degree–day model to simulate spatially distributed snow accumulation and snow–ice melt. We estimate hydropower releases by a conceptual approach with a unique virtual reservoir regulated on the basis of a target–volume function, representing normal reservoir operating conditions throughout a hydrological year. An Iterative Input Selection algorithm is used to identify the variables with the highest predictive power for SSC, their explained variance, and characteristic time lags. On this basis, we develop a hydroclimatic multivariate rating curve (HMRC) which accounts for the contributions of the most relevant hydroclimatic input variables mentioned above. We calibrate the HMRC with a gradient–based nonlinear optimization method and we compare its performance with a traditional discharge–based rating curve. We apply the approach in the upper Rhone Basin, a large Swiss Alpine catchment, heavily regulated by hydropower. Our results show that the three hydroclimatic processes – erosive rainfall, icemelt, and snowmelt – are significant predictors of mean daily SSC, while hydropower release does not have a significant explanatory power for SSC. The characteristic time lags of the hydroclimatic variables correspond to the typical flow concentration times of the basin. Despite not including discharge, the HMRC performs better than the traditional rating curve in reproducing SSC seasonality, especially during validation at the daily scale. While erosive rainfall determines the daily variability of SSC and extremes, icemelt generates the highest SSC per unit of runoff, and represents the largest contribution to total suspended sediment yield. Finally, we show that the HMRC is capable of simulating climate–driven changes in fine sediment dynamics in Alpine catchments. In fact, HMRC can reproduce the changes in SSC in the past 40 years in the Rhone Basin connected to air temperature rise, even though the simulated changes are more gradual than those observed. The approach presented is this paper, based on the analysis of the hydroclimatic control on suspended sediment concentration, allows the exploration of climate–driven changes in fine sediment dynamics in Alpine catchments. The approach can be applied to any Alpine catchment with a pluvio–glacio–nival hydrological regime and adequate hydroclimatic datasets.

scholarly journals A Process–Based Rating Curve to model suspended sediment concentration in Alpine environments

2017 ◽  
Author(s):  
Anna Costa ◽  
Daniela Anghileri ◽  
Peter Molnar
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Snow Accumulation ◽  
Suspended Sediment Transport ◽  
Time Lags ◽  
Rating Curve ◽  
Power Functions ◽  
Erosive Rainfall ◽  
Hydroclimatic Variables

Abstract. A Process-Based Rating Curve (PBRC) approach to simulate mean daily suspended sediment concentration (SSC) as a function of different sediment sources and their activation by erosive rainfall (ER), snowmelt (SM), and icemelt (IM) in an Alpine catchment is presented. Similarly to the traditional rating curve, the PBRC relates SSC to the three main hydroclimatic variables through power functions. We obtained the hydroclimatic variables from daily gridded datasets of precipitation and temperature, implementing a degree-day model to simulate spatially distributed snow accumulation and snow-ice melt. We calibrated the PBRC parameters by an Iterative Input Selection algorithm to capture the characteristic response time lags, and by a gradient-based nonlinear optimization method to minimize the errors between SSC observations and simulations. We apply our approach in the upper Rhône Basin, a large Alpine catchment in Switzerland. Results show that all three hydroclimatic processes ER, SM, and IM are significant predictors of mean daily SSC (explaining 75 %, 12 % and 3 % of the total observed variance). Despite not using discharge in prediction, the PBRC performs better than the traditional rating curve, especially during validation at the daily scale and in reproducing SSC seasonality. The characteristic time lags of the three variables in contributing to SSC reflect the typical flow concentration times of the corresponding hydrological processes in the basin. Erosive rainfall determines the daily variability of SSC, icemelt generates the highest SSC per unit of runoff, and snowmelt-driven fluxes represent the largest contribution to total suspended sediment yield. Finally, we show that the PBRC is able to simulate changes in SSC in the past 40 years in the Rhône Basin connected to air temperature rise, even though these changes are more gradual than those detected in observations. We argue that a sediment source perspective on suspended sediment transport such as the PBRC may be more suitable than traditional discharge-based rating curves to explore climate-driven changes in fine sediment dynamics in Alpine catchments. The PBRC approach can be applied to any Alpine catchment with a pluvio-glacio-nival hydrological regime and adequate hydroclimatic datasets.

scholarly journals A new approach for estimation suspended sediment yield

1999 ◽  
Vol 3 (2) ◽  
pp. 285-294 ◽  
Author(s):  
R. Lidén
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Model Parameters ◽  
Rating Curve ◽  
Sediment Rating Curve ◽  
Daily Series ◽  
Suspended Sediment Concentrations ◽  
Model Equations

Abstract. A semi-distributed conceptual model, HBV-SED, for estimation of total suspended sediment concentration and yield at the outlet of a catchment was developed and tested through a case study. The base of the suspended sediment model is a dynamic hydrological model, which produces daily series of areal runoff and rainfall for each sub-basin as input to the sediment routine. A lumped measure of available sediment is accumulated continuously based on a linear relationship between log-transformed values of rainfall and erosion, while discharge of suspended sediment at the sub-basin outlet is dependent on runoff and amount of stored available sediment. Four model parameter are empirically determined through calibration against observed records of suspended sediment concentration. The model was applied to a 200 km2 catchment with high altitude differences in the tropical parts of Bolivia, where recorded suspended sediment concentrations were available during a two-year period. 10,000 parameter sets were generated through a Monte Carlo procedure to evaluate the parameter sensitivity and interdependence. The predictability of the model was assessed through dividing the data record into a calibration and an independent period for which the model was validated and compared to the sediment rating curve technique. The results showed that the slope coefficients of the log-transformed model equations for accumulation and release were much stronger than the intercept coefficients. Despite and existing interdependence between the model parameters, the HBV-SED model gave clearly better results than the sediment rating curve technique for the validation period, indication that the supply-based approached has a promising future as a tool for basic engineering applications.

scholarly journals Development and Interpretation of New Sediment Rating Curve Considering the Effect of Vegetation Cover for Asian Basins

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Wang ◽  
Hiroshi Ishidaira ◽  
Wenchao Sun ◽  
Shaowei Ning
Keyword(s):  
Suspended Sediment ◽  
Sediment Yield ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Suspended Sediment Concentration ◽  
Hydrological Cycle ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Rating Curve ◽  
Sediment Rating Curve

Suspended sediment concentration of a river can provide very important perspective on erosion or soil loss of one river basin ecosystem. The changes of land use and land cover, such as deforestation or afforestation, affect sediment yield process of a catchment through changing the hydrological cycle of the area. A sediment rating curve can describe the average relation between discharge and suspended sediment concentration for a certain location. However, the sediment load of a river is likely to be undersimulated from water discharge using least squares regression of log-transformed variables and the sediment rating curve does not consider temporal changes of vegetation cover. The Normalized Difference Vegetation Index (NDVI) can well be used to analyze the status of the vegetation cover well. Thus long time monthly NDVI data was used to detect vegetation change in the past 19 years in this study. Then monthly suspended sediment concentration and discharge from 1988 to 2006 in Laichau station were used to develop one new sediment rating curve and were validated in other Asian basins. The new sediment model can describe the relationship among sediment yield, streamflow, and vegetation cover, which can be the basis for soil conservation and sustainable ecosystem management.

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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