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 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 Adjusting altimetric sea surface height observations in coastal regions. Case study in the Greek Seas

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Ioannis Mintourakis
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Altimetry Data ◽  
Geoid Model ◽  
Sea Surface Topography ◽  
Marine Gravity ◽  
Mean Sea Surface ◽  
Process Strategy ◽  
Satellite Altimetry Data

AbstractWhen processing satellite altimetry data for Mean Sea Surface (MSS) modelling in coastal environments many problems arise. The degradation of the accuracy of the Sea Surface Height (SSH) observations close to the coastline and the usually irregular pattern and variability of the sea surface topography are the two dominant factors which have to be addressed. In the present paper, we study the statistical behavior of the SSH observations in relation to the range from the coastline for many satellite altimetry missions and we make an effort to minimize the effects of the ocean variability. Based on the above concepts we present a process strategy for the homogenization of multi satellite altimetry data that takes advantage ofweighted SSH observations and applies high degree polynomials for the adjustment and their uniffcation at a common epoch. At each step we present the contribution of each concept to MSS modelling and then we develop a MSS, a marine geoid model and a grid of gravity Free Air Anomalies (FAA) for the area under study. Finally, we evaluate the accuracy of the resulting models by comparisons to state of the art global models and other available data such as GPS/leveling points, marine GPS SSH’s and marine gravity FAA’s, in order to investigate any progress achieved by the presented strategy

scholarly journals ESTIMATING AND FUSING OPTICAL FLOW, GEOSTROPHIC CURRENTS AND SEA SURFACE WIND IN THE WATERS AROUND KISH ISLAND

2015 ◽  
Vol XL-1-W5 ◽  
pp. 221-226 ◽  
Author(s):  
E. Ghalenoei ◽  
M. A. Sharifi ◽  
M. Hasanlou
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Altimetry ◽  
Surface Wind ◽  
Sea Surface ◽  
Data Sets ◽  
Surface Currents ◽  
Altimetry Data ◽  
Sea Surface Wind ◽  
Satellite Altimetry Data

The aim of this study is calculation of sea surface currents (SSCs) which are estimated from satellite data sets and processed with the variance component estimation (VCE) algorithm to check role of each data set, in fused surface currents (FSCs). The satellite data used in this study are sea surface temperature (SST), satellite altimetry data and sea surface wind (SSW) that plays the important role to make the SSCs and is measured by Ascat satellite. We use optical flow (OF) method (Horn-Schunck algorithm) to extract sea surface movements from sequential SST imageries; in addition, geostrophic currents (GCs) are estimated by satellite altimetry data like sea surface height (SSH). Combining these data sets, has its pros and cons, the OF results are so dense and precise due to high spatial resolution of MODIS data (SST), but sometimes cloud covering over the sea, does not allow the MODIS sensor to measure the SST. In contrast the SST data, the altimetry data have poor spatial resolution and the GCs are not able to determine small scale SSCs. The VCE algorithm shows variances of our data sets and it can be shown their correlations with themselves and with the FSCs. We also calculate angular differences between FSCs and OF, GCs and SSW, and plot distributions of these angular differences. We discover that, the OF and SSW are homolographic, but OF and GCs are accordant to each other.

scholarly journals GEODYNAMICS

GEODYNAMICS ◽  
2011 ◽  
Vol 1(10)2011 (1(10)) ◽  
pp. 27-30
Author(s):  
N. Marchenko ◽  
◽  
N.P. Yarema ◽  
T.R. Pavliv ◽  
◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Surface Topography ◽  
Black Sea ◽  
Satellite Altimetry ◽  
Sea Surface ◽  
The Black Sea ◽  
Altimetry Data ◽  
Sea Surface Topography ◽  
Satellite Altimetry Data

The study of Black Sea and Mediterranean Sea surface altitudes was carried out based on satellite altimetry data. The model of the Black Sea and Mediterranean Sea surface topography (SST) was build. The comparison of received results with the European quasigeoid was done.

scholarly journals Using multi-source satellite data for lake level modelling in ungauged basins: a case study for Lake Turkana, East Africa

2011 ◽  
Vol 8 (3) ◽  
pp. 4851-4890 ◽  
Author(s):  
N. M. Velpuri ◽  
G. B. Senay ◽  
K. O. Asante
Keyword(s):  
Lake Water ◽  
Satellite Altimetry ◽  
Lake Level ◽  
Water Levels ◽  
Ungauged Basins ◽  
Altimetry Data ◽  
Lake Turkana ◽  
Lake Water Level ◽  
Surface Water Resources ◽  
Satellite Altimetry Data

Abstract. Managing limited surface water resources is a great challenge in areas where ground-based data are either limited or unavailable. Direct or indirect measurements of surface water resources through remote sensing offer several advantages of monitoring in ungauged basins. A physical based hydrologic technique to monitor lake water levels in ungauged basins using multi-source satellite data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, a digital elevation model, and other data is presented. This approach is applied to model Lake Turkana water levels from 1998 to 2009. Modelling results showed that the model can reasonably capture all the patterns and seasonal variations of the lake water level fluctuations. A composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data is used for model calibration (1998–2000) and model validation (2001–2009). Validation results showed that model-based lake levels are in good agreement with observed satellite altimetry data. Compared to satellite altimetry data, the Pearson's correlation coefficient was found to be 0.81 during the validation period. The model efficiency estimated using NSCE is found to be 0.93, 0.55 and 0.66 for calibration, validation and combined periods, respectively. Further, the model-based estimates showed a root mean square error of 0.62 m and mean absolute error of 0.46 m with a positive mean bias error of 0.36 m for the validation period (2001–2009). These error estimates were found to be less than 15 % of the natural variability of the lake, thus giving high confidence on the modelled lake level estimates. The approach presented in this paper can be used to (a) simulate patterns of lake water level variations in data scarce regions, (b) operationally monitor lake water levels in ungauged basins, (c) derive historical lake level information using satellite rainfall and evapotranspiration data, and (d) augment the information provided by the satellite altimetry systems on changes in lake water levels.

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