Kalman filter approach for estimating water level time series over inland water using multi-mission satellite altimetry

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

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Satellite altimetry over small reservoirs in the Brazilian semiarid region

RBRH ◽

10.1590/2318-0331.262120210038 ◽

2021 ◽

Vol 26 ◽

Author(s):

Alfredo Ribeiro Neto ◽

Sajedeh Behnia ◽

Mohammad J. Tourian ◽

Fábio Araújo da Costa ◽

Nico Sneeuw

Keyword(s):

Time Series ◽

Water Level ◽

Satellite Altimetry ◽

Water Storage ◽

Semiarid Region ◽

Northeast Brazil ◽

Drought Period ◽

Small Reservoirs ◽

Brazilian Semiarid

ABSTRACT Northeast Brazil is one of the most populated semiarid regions in the world. The region is highly dependent on reservoirs for human water supply, irrigation, industry, and livestock. The objective of this study was to validate water level time series from the satellites Envisat, SARAL, Sentinel-3A/-3B, Jason-2/-3 in small reservoirs in Northeast Brazil. In total, we evaluated the water level time series of 20 reservoirs. The Sentinel-3B outperforms the other altimeters with a maximum RMSE of 0.21 m. In seven reservoirs with updated depth-area-volume curves, the altimetric water level was used to calculate the corresponding volume. The obtained volume was then compared to the volume given by the same curve by using in situ stage. Our investigations showed that, in the case of small reservoirs, the precision of water level time series derived from satellite altimetry is mainly governed by the seasonal variability of the water storage especially at the end of the 2012-2017 drought period.

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Multivariate statistical modelling of extreme coastal water levels and the effect of climate variability: a case study in the Netherlands

10.5194/hess-2020-536 ◽

2020 ◽

Author(s):

Victor M. Santos ◽

Mercè Casas-Prat ◽

Benjamin Poschlod ◽

Elisa Ragno ◽

Bart van den Hurk ◽

...

Keyword(s):

Time Series ◽

Water Level ◽

Climate Model ◽

Regional Climate ◽

Water Levels ◽

Multivariate Statistical ◽

Inland Water ◽

Impact Function ◽

The Impact

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

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AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-327-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 327-334 ◽

Cited By ~ 1

Author(s):

Paulo Henrique Costa ◽

Eric Oliveira Pereira ◽

Philippe Maillard

Keyword(s):

Water Level ◽

Water Surface ◽

Satellite Altimetry ◽

Water Levels ◽

Small Lakes ◽

Water Surface Area ◽

Volume Fluctuations ◽

Calm Water ◽

Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of in situ gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby in situ gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

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Inland Water Bodies Monitoring using Satellite Altimetry over Indian Region

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-1035-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 1035-1041 ◽

Cited By ~ 8

Author(s):

S. Chander ◽

D. Ganguly ◽

A. K. Dubey ◽

P. K. Gupta ◽

R. P. Singh ◽

...

Keyword(s):

Water Level ◽

Satellite Altimetry ◽

Tide Gauge ◽

Water Bodies ◽

High Rate ◽

Inland Water ◽

Gps Survey ◽

Level Information ◽

Inland Water Bodies

Satellite altimetry for inland water applications has evolved from investigation of water height retrieval to monitoring since last two decades. Altimetry derived reservoir/ river levels can subsequently be used to deal with key inland water resources problems such as flood, rating curve generation for remote locations, reservoir operations, and calibration of river/lake models. In this work 29 inland water bodies were selected over Indian region to monitor from satellite altimetry. First cut selection of potential water bodies was based on availability of altimeter tracks and geographic locations. Then feasibility study was carried out to check the potential of availability of in-situ measurement and scope of GPS survey for final selection. An algorithm is proposed and tested for the waterlevel retrieval over the Ukai Reservoir which fulfil all the necessary requirements. The methodology is based on averaged high rate waveforms, modified retracker and range corrections. The results were then validated with the GPS survey and in-situ tide gauge dataset. SARAL derived water-level information for six different retrackers were compared with the in-situ tide-gauge dataset installed close to the Ukai Dam. Averaged high rate waveforms were analysed for better performance, i.e. single 40 Hz, and multiple 40-Hz. A field trip was conducted on 17th January 2014, same day on the SARAL pass, using two Dual frequency GPS instruments. New improved retracker work best with overall RMSE within the range of 8 cm. The results supports that AltiKa dataset can be utilized for more accurate water level information over inland water bodies.

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Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

10.5194/egusphere-egu2020-2650 ◽

2020 ◽

Author(s):

Daniel Scherer ◽

Christian Schwatke ◽

Denise Dettmering

Keyword(s):

Remote Sensing ◽

Time Series ◽

Water Level ◽

River Discharge ◽

Water Surface ◽

Satellite Altimetry ◽

Remote Sensing Data ◽

Sensing Data

Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&#8217;s &#8220;Database of Hydrological Time series of Inland Waters&#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, 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 computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.

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Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery

Remote Sensing ◽

10.3390/rs12101606 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1606 ◽

Cited By ~ 2

Author(s):

Christian Schwatke ◽

Denise Dettmering ◽

Florian Seitz

Keyword(s):

Time Series ◽

Water Level ◽

Surface Area ◽

Satellite Altimetry ◽

Water Levels ◽

Inland Waters ◽

Data Sets ◽

Surface Areas ◽

Lakes And Reservoirs ◽

Optical Imagery

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by the Database of Hydrological Time Series of Inland Waters (DAHITI), developed and maintained by the Deutsches Geodätisches Forschungsinsitut der Technischen Universität München (DGFI-TUM). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.062 km 3 and 6.041 km 3 . The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters.

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AN ORIGINAL PROCESSING METHOD OF SATELLITE ALTIMETRY FOR ESTIMATING WATER LEVELS AND VOLUME FLUCTUATIONS IN A SERIES OF SMALL LAKES OF THE PANTANAL WETLAND COMPLEX IN BRAZIL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-327-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 327-334

Author(s):

Paulo Henrique Costa ◽

Eric Oliveira Pereira ◽

Philippe Maillard

Keyword(s):

Water Level ◽

Water Surface ◽

Satellite Altimetry ◽

Water Levels ◽

Small Lakes ◽

Water Surface Area ◽

Volume Fluctuations ◽

Calm Water ◽

Gauging Station

Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of in situ gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often “contaminated” by the surrounding land. To try to solve this, we created a “lake” retracker that rejects waveforms that cannot be attributed to “calm water” and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby in situ gauging station. Although the “lake” retracker worked well with calm water, the presence of waves and other factors was such that the standard “ice1” retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

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Comparison of Water Level Changes in the Mekong River Using Gnss Reflectometry, Satellite Altimetry and in-Situ Tide/River Gauges

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518977 ◽

2018 ◽

Cited By ~ 1

Author(s):

Phuong-Lan Vu ◽

Frederic Frappart ◽

Jose Darrozes ◽

Minh-Cuong Ha ◽

Thi-Bao-Hoa Dinh ◽

...

Keyword(s):

Water Level ◽

Satellite Altimetry ◽

Mekong River ◽

Water Level Changes ◽

Gnss Reflectometry

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Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

Remote Sensing ◽

10.3390/rs11091010 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1010 ◽

Cited By ~ 16

Author(s):

Christian Schwatke ◽

Daniel Scherer ◽

Denise Dettmering

Keyword(s):

Time Series ◽

Surface Area ◽

Time Variable ◽

New Approach ◽

Data Gaps ◽

Lakes And Reservoirs ◽

Variable Water ◽

Land Water ◽

Sentinel 2

In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).

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