Last modified on Sept 04, 2023 16:39

Contributors: B. Calmettes (CLS), JF. Crétaux (CLS), L. Carrea (University of Reading), C.J. Merchant (University of Reading)

Issued by: B. Calmettes

Date: 21/04/2021

Ref:C3S_312b_Lot4.D2.LK.3-v3.0_LWL_Product_Quality_Assurance_Document_i1.0.docx

Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2

History of modifications

List of datasets covered by this document

Related documents

Acronyms

General definitions

Accuracy: The closeness between the measured value and the true quantity value.
Precision: Closeness between measured values obtained by replicate measurements on the same object under similar conditions
Bias: Estimate of a systematic error

Scope of the document

This document is the Product Quality Assurance Document (PQAD). It describes the dataset and validation methods as well as the strategies used for validation and characterisation of the C3S Lake Water Level product. It is a self-contained document which gathers all validation methods and analyses conducted to assess the quality of the C3S Lake Water Level product.

Executive summary

The C3S Lake production system (C3S ECV LK) provides an operational service, generating lake surface water temperature and lake water level climate datasets for a wide variety of users within the climate change community. The present document covers the lake water level system.

The Product Quality Assurance includes the definition and description of the datasets, validation methods and strategies used for the validation and characterisation of the accuracy and stability of the Lake Water Level product. This document is applicable to the Climate Data Record version 3.0 of the produced in January 2021 (product version v3.0). This Quality assurance work consists of two parts: ( i) validation of the generated data by analysing the error generated by the instruments and processing and (ii) comparison of generated products with external data from other altimetry products and in-situ data.

This document describes the methodology at the third version of the C3S LWL product.

1. Validated products

1.1. Product Specifications

Presently, this section relies on statements for the Lake ECV from GCOS, published literature, experience from other CDR projects, and requirements emerging from the definition of the service. The user requirements are indicated in Table 1.

Table 1: User Requirements for Water Level as described in GCOS

A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [D2], with further information on the product given in the Product User Guide and Specification (PUGS) [D4].

1.2. Available products

The C3S lakes products comprise a long-term climate data record (CDR). The time series has been computed since late 1992 to 2020. The altimetry data is provided by the missions:

• TOPEX/Poseidon from 1992 to 2002
• Jason-1 from 2001 to 2013
• Jason-2 from 2008 to 2015
• Jason-3 from 2016 to Present
• Envisat/RA-2 from 2002 to 2012
• SARAL/Altika: from 2013 to Present
• Geosat Follow On (GFO): from 1998 to 2008
• Sentinel-3A/SRAL from 2016 to Present
• Sentinel-3B from 2018 to Present

This current document is applicable to the Quality Assessment activities performed on the dataset generated in January 2021 (CDR v3.0).

1.3. Parameters and units

The products in this C3S version are shared with Theia-land program supported by CNES. The research leading to the current version of the product has received funding from CNES, LEGOS and CLS as well as the Copernicus Climate Change program permitting an increase in the spatial coverage.
The LWL product, in netCDF4 format, contains:

• The measure of the absolute height expressed in meters of the reflecting water surface beneath the satellite with respect to a vertical datum (GGM02C).
• The associated uncertainty expressed in meters.

Additional information is also included in the output file, concerning both the lake (name, country, basin, latitude, longitude) and the processing (processing mode, processing level).

2. Description of validating datasets

External altimetry-based data and in situ data could provide an accurate external estimate of water level, though it is not necessarily collocated in time and space, and also contains specific errors.
With this method, the lake water level products can be compared with external data in terms of root mean square error (rms) and correlation. Such results will be presented in the PQAR associated with v3.0 products.

2.1. Altimetry based datasets

2.1.1. G-Realm

The G-Realm dataset (https://ipad.fas.usda.gov/cropexplorer/global_reservoir) is produced by The United States Department of Agriculture (USDA) in cooperation with the National Aeronautics and Space Administration and the University of Maryland. This database utilises radar altimetry from different satellites (Topex/Jason, Envisat) to produce lake water level over global inland water bodies.
The result of the process is a time series of height variation. The accuracy is expected to be better than 10cm for the largest lakes and 20 cm for smaller lakes. Currently, only lakes larger than 100 km2 are available.

2.1.2. Dahiti

Dahiti is a global database. It currently contains water level series from 886 water bodies (https://dahiti.dgfi.tum.de/en). The processing for generating the Dahiti products, based on an extended outlier detection and Kalman filtering, is described in Schwatke et al. 2015.

2.2. In-situ datasets

Hydrolare (hydrolare.net), the International Data Centre on Hydrology of Lakes And Reservoirs provides data on mean monthly water level of nearly 1200 water bodies. The centre improves data collection, processing, analysing and monitoring lakes and reservoirs. It functions on the basis of free of charge dissemination of information for governmental, scientific, educational, projecting public and commercial institutions.
Other sources of in-situ information are being explored. Among them we can mention:

• Base de Datos Hidrologica Integrada (BDHI) in Argentina (bdhi.hidricosargentina.gob.ar)
• US Army Corps of Engineer with levels from the Great Lakes (https://www.lre.usace.army.mil)
• Direccion General de Aguas in Chile (https://dga.mop.gob.cl/)
• USGS water resources (https://waterdata.usgs.gov/)
• Federal Office for the Environment FOEN (https://www.bafu.admin.ch/bafu/en/home.html)

3. Description of product validation methodology

3.1. Overall procedure

The validation exercise consists of validating the local changes of water level as measured by altimetry. The uncertainties or errors in the products are two-fold: measurement errors and processing errors.

The following description of errors is inspired from [Bercher, 2008].

3.2. Instrument characteristics

Water Surface Height estimates are obtained by measurements of the satellite radar echo return time. The instrument monitors the reflected radar signal over a recording window (e.g. approximately 4.10^-7 seconds for Jason-3). This window can be determined by the on-board tracking system based on the analysis of the last range measurements to approximate the position of the useful information in the incoming waveform – this is known as close loop mode. It can also be imposed by the use of a Digital Elevation Model (DEM) stored onboard the satellite – this is known as open loop mode.

In close loop mode, in case of rapid and unexpected changes of the topography, the tracking system may not position the recording window accurately and fail to measure the range (“unhook”). In this case, the system misses between 1-3 seconds of measurements [Chelton et al., 2001]. This ultimately leads to the loss of 10 to 120 high frequency measurements, depending on the sampling rate of the altimeter, or 5 to 20 km. The open loop mode allows to avoid such problems provided an accurate enough DEM is uploaded.

Both Sentinel-3A and Jason-3 use these 2 modes. Switching between the modes is defined by patches positions uploaded onboard. The initial Jason-3 DEM included about 250 lakes from the Hydroweb product. It has been updated on 08/30/2017 to about 350 lakes after Biancamaria et al (2017) performed validation showing high quality measurements were obtained over DEM stations.

The present product contains both lakes for which open and close loop acquisition modes are used. An evaluation of the products has not been performed as a function of the acquisition mode, nevertheless the editing algorithm [ATBD, D2] ensures that the accepted Water Level values are compatible with the a priori elevation provided in the lake parameter files. Therefore, such a selection acts as a posteriori use of DEM. This ensures that altimeter hooking is detected on possible different targets. Such measurements are rejected by the water level processing chain.

Other parameters can also affect the water level retrieved by the altimeter. In the context of hydrology, the presence of land surfaces in the altimeter footprint may result in a significant error in the range estimate from which water level is computed. This effect is particularly significant when land surfaces contain echogenic targets (buildings, emerged lands, humid vegetation, dams…etc.). The surface of the footprint, thus, plays an important role.

All altimeters are not similarly affected by surface specificities or canopy penetration of the signal. As an example, it depends on the altimeter frequency band (e.g. C, Ka, Ku) and the technology of the altimeter. Two technologies are currently used. As a result, the products are obtained either for standard altimetry measurements (low resolution mode, LRM, for Jason-3) or Synthetic Aperture Radar measurements (SAR, for Sentinel-3A). Lakes time series can contain measurements derived from both technologies, provided both Sentinel-3A/Sentinel-3B  and Jason-3 sub-satellite track pass over the considered lake.

3.3. Processing characteristics

Though altimeters are perfectly fitted to the observation of sea level, their performance decreases over inland waters, and particularly small lakes. The smaller the ratio between water and emerged land surfaces in the footprint, the more complex the waveforms. This requires dedicated retracking algorithms which are not yet able to reach the quality of the retracking algorithm over ocean.

Instrumental, propagation and geophysical corrections are also used in the computation of water level and may contain errors. The most notable is the geoid correction, locally leading to uncertainties up to 35cm [Jekeli et Dumrongchai, 2003].

Consequently, several criteria are applied to remove segments of satellite passes (transects) which are not compliant with the quality requirements. The validation and quality assessment metrics presented in this document only refer to these pre-selected transects.

3.4. Validation approach

The main problem of water level validation is that one tries to quantify errors smaller than natural variability.

Thus, one needs to process separately the errors of different nature (see sections 3.2 and.3.3). Are they constant in time (for a given lake, but variable on another target), or in space (for a given pass/day, all targets "see" the same error), or both (bias), or neither (random)? Above all, does using a given methodology (difference at crossovers, in situ) to separate errors and/or natural variability achieve better results than performing an absolute validation (statistics)?

The validation methods presented below are, thus, based on absolute and relative approaches.

The first absolute validation method quantifies the dispersion of consecutive water level high frequency measurements for the same target by computing their standard deviation. NB: consecutive measurements are then averaged to compute the lake products. The more precise the product is, the smaller the dispersion is expected to be, provided that one can accurately correct for the natural spatial slope over the transect.

Relative validation methods are based on a comparison of lake products with external water level data. These are either equivalent altimetry data or in situ data.

3.5. Absolute Assessment

The validation exercise consists of validating the local changes of water level as measured by altimetry. The uncertainties or errors in the products are two-fold: measurement errors and processing errors. The following description of errors is inspired from [Bercher, 2008].

Four performance indicators are chosen to assess the quality of lake products:

• Dispersion: mean valid transect dispersion
• High-frequency variations: standard deviation of residuals from a high-pass Lanczos filter with an arbitrary 1-month cut-off period.
• Mean time step: average time between two valid measures
• The missing values: this is the percent of lake water level values that can't be estimated due to different reasons: quality of the signal, shift of the ground trajectory, fast change in the level that activates the editing of the estimate.

These performance indicators will be calculated for each lake (140 lakes in the current version) on two time periods: the full time series of ~25 year for most lakes and the last 10 years. These last indicators give the performance of recent and future quality products.

Lakes products can contain altimeter data from multiple satellites tracks as well as different missions. Transects (intersections between satellite tracks and lakes) are on average longer in big lakes. So, the precision of the level estimation is also a function of the size of the lake.

Since LWL products are derived from multiple missions, other interesting indicators involve the comparison of the performance between missions. Missing values and the uncertainty per mission will be calculated.

3.6. Relative Assessment: comparison to independent altimetry products

External altimetry products using different data processing are useful to assess the quality of the lake water level products. This data comes from two different products (G-REALM and Dahiti). These products use different datums at different dates, and the comparison is not straightforward. Several approaches are being considered:

• Comparison of monthly anomalies
• Comparison between measurements available at the same dates

3.7. Relative assessment: comparison to in situ data

In situ data provide an accurate external estimate of water level, though it is not necessarily collocated in time and space, and also contains specific errors.

With this method, the lake water level products can be compared with in situ data in terms of root mean square difference and correlation [Crétaux et al, 2016].

4. Summary of validation results

This section provides some keys results. The detailed results will be included in the Quality Assessment report [D3].

For the lake products, the time series quality is assessed by studying the dispersion characteristics as well as providing an overview of the series completeness (Table 2 contains the values evaluated in 2017). The second column presents the average of the dispersion of the LWL measurements over lake transects whereas the third column is the standard deviation. The last dispersion value indicated in column 4 is the value of the standard deviation over the last transect in the operational product. Its comparison with column 2 and 3 values allows checking that the accuracy is coherent with these of the historical time series. The fifth column references the longest period without data over the period covered by the time series (last column) and the sixth one the mean interval between two points.

Table 2: Absolute validation of lake level products.

Concerning in-situ comparisons, one example is presented in Figure 1 (performed by LEGOS). Altimetry results, given by Sentinel-3A and Jason missions and different retracking algorithms, are compared to in situ data from 2016 to mid-2017 on the Issykkul lake. Whatever retracking algorithm is used for Sentinel-3A, the root mean square (rms) values are lower than the Jason ones. On this lake, the Sentinel-3A results show that the use of ICE-1 retracking (see [D2]) instead of the ocean one (model fit according to a Brownian model) enables to be more consistent with in situ data. NB: Ocean retracking will be mostly used except for several lakes with small areas for which ICE-1 is used (this choice is permanent).

Figure 1: Comparison between Sentinel-3A, Jason and in situ data on the Issykkul lake

A generalisation of this kind of comparisons will allow a better characterisation of the altimetry errors over each lake. Inter-calibration between altimetry missions can also be checked.

References

Bercher, N. (2008). Précision de l'altimétrie satellitaire radar sur les cours d'eau: Développement d'une méthode standard de quantification de la qualité des produits alti-hydrologiques et applications (Doctoral dissertation).

Sylvain Biancamaria, Thomas Schaedele, Denis Blumstein, Frédéric Frappart, François Boy, et al.. Validation of Jason-3 tracking modes over French rivers. Remote Sensing of Environment, Elsevier, 2018, 209, pp.77-89. 10.1016/j.rse.2018.02.037 Chelton, D. B., Esbensen, S. K., Schlax, M. G., Thum, N., Freilich, M. H., Wentz, F. J., ... & Schopf, P. S. (2001). Observations of coupling between surface wind stress and sea surface temperature in the eastern tropical Pacific. Journal of Climate, 14(7), 1479-1498.

Crétaux, J. F., Abarca-del-Río, R., Berge-Nguyen, M., Arsen, A., Drolon, V., Clos, G., & Maisongrande, P. (2016). Lake volume monitoring from space. Surveys in Geophysics, 37(2), 269-305.

Jekeli, C., & Dumrongchai, P. (2003). On monitoring a vertical datum with satellite altimetry and water-level gauge data on large lakes. Journal of Geodesy, 77(7-8), 447-453.

Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345-4364, https://doi.org/10.5194/hess-19-4345-2015, 2015

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