Seasonal and Interannual Variability of the Krasnodar Reservoir Water Level Based on Satellite Altimetry Data

2020 ◽  
Author(s):  
Sergey A. Lebedev ◽  
Olga P. Shevyakova ◽  
Murat K. Bedanokov
Keyword(s):  
Water Level ◽  
Interannual Variability ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Seasonal And Interannual Variability ◽  
Satellite Altimetry Data

scholarly journals Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2693
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Cross Sections ◽  
Mississippi River ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
Virtual Station ◽  
Lower Mississippi River ◽  
Satellite Altimetry Data

Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.

scholarly journals Relationships between pacific and atlantic ocean sea surface temperatures and water levels from satellite altimetry data in the Amazon rivers

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Mylena Vieira Silva ◽  
Adrien Paris ◽  
Stéphane Calmant ◽  
Luiz Antonio Cândido ◽  
Joecila Santos da Silva
Keyword(s):  
Water Level ◽  
Satellite Altimetry ◽  
Amazon Basin ◽  
Strong Relationship ◽  
Water Levels ◽  
Sea Surface ◽  
Altimetry Data ◽  
Negro River ◽  
Hydrological Extremes ◽  
Satellite Altimetry Data

ABSTRACT The influence of SST (Sea Surface Temperature) of adjacent oceans on the variability of water levels in the Amazon basin was investigated by using radar altimetry from the ENVISAT and Jason-2 missions. Data from the in situ network was used to compare the correlations of water level and SST anomalies in the sub-basins of the Amazonas-Peru, Solimões, Negro and Madeira Rivers. The analysis was made on the monthly and annual scales between 2003 and 2015. The correlations with anomalies of levels from altimetry presented higher accuracy indices than those from the conventional network. In general, ATN and PAC are better correlated with the entire basin. During the flood months, most of the sub-basins presented negative associations with ATN. In the months of ebb, the response to the indexes varies according to the region. The satellite altimetry data permitted to reach regions non-monitored by the conventional network. We also analyzed the impacts of hydrological extremes in all these sub-regions in the last 13 years. In Western Amazon, the drought of 2010 stands out, associated with the warming of the Tropical Atlantic and the El Niño. In the Negro River, the water level anomalies were the lowest in the basin during the 2005 drought. In the Purus River, the effects of the 2010 drought that affected the entire Amazon, were higher in 2011 due to its strong relationship with the Atlântic and Pacific oceans. In general, hydrological extremes are stronger or highlighted when SST increases simultaneously in both oceans.

scholarly journals The Monitoring-Based Analysis on Deformation-Controlling Factors and Slope Stability of Reservoir Landslide: Hongyanzi Landslide in the Southwest of China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Han ◽  
Bin Tong ◽  
Jinkai Yan ◽  
Chunrong Yin ◽  
Liang Chen ◽  
...  
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Monitoring Program ◽  
Significant Risk ◽  
Controlling Factors ◽  
Seepage Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
Reservoir Landslide

Reservoir landslide is a type of commonly seen geological hazards in reservoir area and could potentially cause significant risk to the routine operation of reservoir and hydropower station. It has been accepted that reservoir landslides are mainly induced by periodic variations of reservoir water level during the impoundment and drawdown process. In this study, to better understand the deformation characters and controlling factors of the reservoir landslide, a multiparameter-based monitoring program was conducted on a reservoir landslide—the Hongyanzi landslide located in Pubugou reservoir area in the southwest of China. The results indicated that significant deformation occurred to the landslide during the drawdown period; otherwise, the landslide remained stable. The major reason of reservoir landslide deformation is the generation of seepage water pressure caused by the rapidly growing water level difference inside and outside of the slope. The influences of precipitation and earthquake on the slope deformation of the Hongyanzi landslide were insignificant.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

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