An Instrument With Constant Volume Approach for In Situ Measurement of Surface Runoff and Suspended Sediment Concentration

2021 ◽  
Vol 57 (2) ◽  
Author(s):  
Xiaoyun Zhan ◽  
Jun Zhao ◽  
Xia Zhu‐Barker ◽  
Junfeng Shui ◽  
Baoyuan Liu ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Surface Runoff ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Constant Volume ◽  
In Situ Measurement

scholarly journals Estimation of Suspended Sediment Concentration from Remote Sensing and In Situ Measurement over Lake Tana, Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zelalem R. Womber ◽  
Fasikaw A. Zimale ◽  
Mebrahtom G. Kebedew ◽  
Bekalu W. Asers ◽  
Nikole M. DeLuca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Temporal Distribution ◽  
Sediment Concentration ◽  
In Situ Measurement ◽  
Spatial And Temporal Distribution ◽  
Lake Tana

Discharge from basins joining a lake is the main factor determining the lake volume and sediment inflow to the lake. Suspended sediment is an important parameter for describing the water quality of aquatic ecosystems. Lake Tana is an important and the largest lake in Ethiopia for the local ecological system. However, environmental change and anthropogenic activities in the area threaten its water quality. The conventional methods of suspended sediment concentration (SSC) observation are unable to determine and compare spatial and temporal SSC patterns for the lake over a period of years. Remote sensing methods have made it possible to map SSC. The objective of this study is to characterize the spatial and temporal distribution of suspended sediment of Lake Tana using in situ measurement and remote sensing applications and specifically to develop a relationship between in situ and remote sensing observation to retrieve suspended sediment concentration and map the spatal distribution of SSC. This study used MODIS-Terra and in situ data to characterize the spatial and temporal distribution of SSC in the rainy season. Four sampling campaigns (20 samples per campaign) were carried out on Lake Tana, and the first three sampled campaigns on May 11–13, 2018, June 08–10, 2018, and July 15–17, 2018, were used for calibration of regression models. MODIS-Terra reflectance in NIR was found best related to in situ water quality data and varies linearly with SSC (r2 = 0.81) and turbidity (r2 = 0.85). Secchi disc depth (SDD) found the best fit for a power relation with NIR band reflectance (r2 = 0.74). The MODIS-Terra reflectance in red was found to be poorly related to in situ measurements. The relation in NIR reflectance was validated using the LOOCV (leave-one-out-cross-validation) technique and the fourth sampled data set collected on August 12–14, 2018. Developed models are validated with RMSE of 42.96 mg/l, 14.6 NTU, and 0.17 m, ARE of 23.3%, 27.6%, and 12.4%, and RRMSE of 25.1%, 44.5%, and 29.6% for SSC, turbidity, and SDD, respectively, using LOOCV. The equation was also validated using August 2018 collected data sets with RMSE of 87.6 mg/l, 11.7 NTU, 0.08 m, ARE of 20.8%, 25.9%, and 28.8%, and RRMSE of 17.8%, 20.5%, and 27.9% for SSC, turbidity, and SDD, respectively. Applying the developed regression model, a 10-year time series of SSC from 2008–2017 for May-August was estimated and the trend was tested using the Mann–Kendall trend test. It was found that an increasing trend was observed from the period 2008 to 2017. The result shows that satellite data like the MODIS-Terra imagery could be used to monitor and obtain past records of SSC with the developed equation. The increasing SSC can be reduced by implementing selected management practices in the surrounding watersheds of the lake to reduce nutrient and sediment inflow.

scholarly journals Quantifying Suspended Sediment using Acoustic Doppler Current Profiler in Tidung Island Seawaters

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Henry Munandar Manik ◽  
Randi Firdaus
Keyword(s):  
Water Column ◽  
Suspended Sediment ◽  
Suspended Solids ◽  
Acoustic Doppler Current Profiler ◽  
Sediment Concentration ◽  
In Situ Measurement ◽  
Ocean Current ◽  
Echo Intensity ◽  
Current Profiler

Tidung Island, located near Jakarta Bay, is a tourism and conservation area. It is necessary to keep these seawaters unpolluted. To calculate the level of pollution, it is necessary to know the sediment concentration. Quantifying concentration suspended sediment is important for knowledge of sediment transport. Researchers usually use water sample analysis and optical method for quantifying suspended sediment in seawater. Less accuracies of these methods are due to under sample of seawater and the existence of biological fouling. One promising method to measure concentration of suspended sediment is using Acoustic Doppler Current Profiler (ADCP). ADCP is usually used by oceanographer and hydrographer to measure ocean current. In this research, ADCP with 300 kHz operating frequency was used effectively to measure suspended sediment concentration (SSC) and ocean current simultaneously. The echo intensity received from suspended sediment was computed using sonar equations to quantify SSC. The empirical equation between echo intensity and SSC was found. The SSC value obtained by ADCP was also compared with in situ measurement. The result showed that quantified SSC value obtained by ADCP was nearly equal with SSC obtained from in situ measurement with coefficient correlation of 0.98. The high concentration ranged from 55 mg/L to 80 mg/L at the surface layer to a depth 12 m, moderate concentration ranged from 45 mg/L to 55 mg/L at a depth 12 m to 40 m, and low concentration less than 45 mg/L at a depth greater than 40 m. The distribution of SSC was correlated with ocean current condition. In small currents, suspended solids will settle faster so that the concentration in the water column will decrease. Conversely, if the velocity is high, suspended solids will continue to float carried by the current in the water column so that the concentration is high.

scholarly journals Calibrations of Suspended Sediment Concentrations in High-Turbidity Waters Using Different In Situ Optical Instruments

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3296
Author(s):  
Yunwei Wang ◽  
Yun Peng ◽  
Zhiyun Du ◽  
Hangjie Lin ◽  
Qian Yu
Keyword(s):  
Suspended Sediment ◽  
Inflection Point ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Fluid Mud ◽  
Optical Instruments ◽  
Suspended Sediment Concentrations ◽  
High Turbidity ◽  
The Right

In environments of high suspended sediment concentration (SSC > 1 kg/m3), efficient measurements of SSC through accurate calibration relationships between turbidity and SSC are necessary for studies on marine sediment dynamics. Here, we investigated the performance of three types of optical instrument (OBS-3A, AQUAlogger 310TY, and RBRsolo3Tu with Seapoint sensor) in observations carried out at the middle of the Jiangsu coast, China. These instruments were calibrated in the lab using the water and suspended sediment samples collected from the observation site. It was found that both the calibration curves of OBS-3A and RBRsolo3Tu have an inflection point (at SSC of ca. 15 kg/m3 for OBS-3A and ca. 2 kg/m3 for RBRsolo3Tu), on either side of which turbidity increases (the left side) or decreases (the right side) with the increasing SSC. Only under SSCs smaller than the inflection point can OBS-3A and RBRsolo3Tu be applied to continuous SSC measurements at a fixed point. However, the turbidity output of AQUAlogger 310TY has always a positive correlation with SSC, which applies for SSC up to 40 kg/m3; thus, three fluid-mud events are quantified during this observation. AQUAlogger 310TY has important prospects for field applications in high-SSC environments.

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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