Interannual variations of the terrestrial water storage in the Lower Ob' basin from a multisatellite approach

Abstract. Temporal variations of surface water volume over inundated areas of the Lower Ob' basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derivec from the TOPEX/POSEIDON (T/P) altimetry satellite. We computed time-series of monthly maps of surface water volume over the period of common availability of T/P and the multisatellite data (1993–2004). The results exhibit similar interannual variabilities with precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis using multisatellite observations and hydrological models. Water stored in aquifer is isolated from the total water storage measured by GRACE by removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by hydrological models.

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Interannual variations of the terrestrial water storage in the Lower Ob' Basin from a multisatellite approach

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2443-2010 ◽

2010 ◽

Vol 14 (12) ◽

pp. 2443-2453 ◽

Cited By ~ 31

Author(s):

F. Frappart ◽

F. Papa ◽

A. Güntner ◽

S. Werth ◽

G. Ramillien ◽

...

Keyword(s):

Surface Water ◽

Root Zone ◽

Water Storage ◽

Temporal Variations ◽

Water Levels ◽

The Arctic ◽

Water Volume ◽

Hydrological Models ◽

Radar Altimetry ◽

Terrestrial Water

Abstract. Temporal variations of surface water volume over inundated areas of the Lower Ob' Basin in Siberia, one of the largest contributor of freshwater to the Arctic Ocean, are estimated using combined observations from a multisatellite inundation dataset and water levels over rivers and floodplains derived from the TOPEX/POSEIDON (T/P) radar altimetry. We computed time-series of monthly maps of surface water volume over the common period of available T/P and multisatellite data (1993–2004). The results exhibit interannual variabilities similar to precipitation estimates and river discharge observations. This study also presents monthly estimates of groundwater and permafrost mass anomalies during 2003–2004 based on a synergistic analysis of multisatellite observations and hydrological models. Water stored in the soil is isolated from the total water storage measured by GRACE when removing the contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the snow estimated by inversion of GRACE measurements. The time variations of groundwater and permafrost are then obtained when removing the water content of the root zone reservoir simulated by hydrological models.

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RECOG RL01: Correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

10.5194/essd-2020-256 ◽

2020 ◽

Author(s):

Simon Deggim ◽

Annette Eicker ◽

Lennart Schawohl ◽

Helena Gerdener ◽

Kerstin Schulze ◽

...

Keyword(s):

Time Series ◽

Surface Water ◽

Regional Scale ◽

Water Storage ◽

Water Volume ◽

Optical Remote Sensing ◽

Hydrological Models ◽

Total Water ◽

Satellite Mission ◽

The Impact

Abstract. Observations of changes in terrestrial water storage obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighboring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes & reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a re-location of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction RECOG-EQ includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above 9 Mw. We can show that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, even in neighboring river basins, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available as open access via the Pangea database (RECOG-LR: Deggim et al. (2020a) https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al. (2020b, under revision), https://doi.pangaea.de/10.1594/PANGAEA.921923).

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Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1543-2018 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1543-1561 ◽

Cited By ~ 14

Author(s):

Cassandra Normandin ◽

Frédéric Frappart ◽

Bertrand Lubac ◽

Simon Bélanger ◽

Vincent Marieu ◽

...

Keyword(s):

Surface Water ◽

Water Levels ◽

The Arctic ◽

Water Volume ◽

Mackenzie Delta ◽

Flood Peak ◽

Upstream Part ◽

Flooded Area ◽

Water Height

Abstract. Quantification of surface water storage in extensive floodplains and their dynamics are crucial for a better understanding of global hydrological and biogeochemical cycles. In this study, we present estimates of both surface water extent and storage combining multi-mission remotely sensed observations and their temporal evolution over more than 15 years in the Mackenzie Delta. The Mackenzie Delta is located in the northwest of Canada and is the second largest delta in the Arctic Ocean. The delta is frozen from October to May and the recurrent ice break-up provokes an increase in the river's flows. Thus, this phenomenon causes intensive floods along the delta every year, with dramatic environmental impacts. In this study, the dynamics of surface water extent and volume are analysed from 2000 to 2015 by combining multi-satellite information from MODIS multispectral images at 500 m spatial resolution and river stages derived from ERS-2 (1995–2003), ENVISAT (2002–2010) and SARAL (since 2013) altimetry data. The surface water extent (permanent water and flooded area) peaked in June with an area of 9600 km2 (±200 km2) on average, representing approximately 70 % of the delta's total surface. Altimetry-based water levels exhibit annual amplitudes ranging from 4 m in the downstream part to more than 10 m in the upstream part of the Mackenzie Delta. A high overall correlation between the satellite-derived and in situ water heights (R > 0.84) is found for the three altimetry missions. Finally, using altimetry-based water levels and MODIS-derived surface water extents, maps of interpolated water heights over the surface water extents are produced. Results indicate a high variability of the water height magnitude that can reach 10 m compared to the lowest water height in the upstream part of the delta during the flood peak in June. Furthermore, the total surface water volume is estimated and shows an annual variation of approximately 8.5 km3 during the whole study period, with a maximum of 14.4 km3 observed in 2006. The good agreement between the total surface water volume retrievals and in situ river discharges (R= 0.66) allows for validation of this innovative multi-mission approach and highlights the high potential to study the surface water extent dynamics.

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Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin

Remote Sensing ◽

10.3390/rs13193804 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3804

Author(s):

Frédéric Frappart ◽

Pierre Zeiger ◽

Julie Betbeder ◽

Valéry Gond ◽

Régis Bellot ◽

...

Keyword(s):

Time Series ◽

Surface Water ◽

Water Storage ◽

Temporal Variations ◽

Water Levels ◽

Congo Basin ◽

Radar Altimetry ◽

Surface Water Storage ◽

Cuvette Centrale ◽

Good Agreement

Surface water storage in floodplains and wetlands is poorly known from regional to global scales, in spite of its importance in the hydrological and the carbon balances, as the wet areas are an important water compartment which delays water transfer, modifies the sediment transport through sedimentation and erosion processes, and are a source for greenhouse gases. Remote sensing is a powerful tool for monitoring temporal variations in both the extent, level, and volume, of water using the synergy between satellite images and radar altimetry. Estimating water levels over flooded area using radar altimetry observation is difficult. In this study, an unsupervised classification approach is applied on the radar altimetry backscattering coefficients to discriminate between flooded and non-flooded areas in the Cuvette Centrale of Congo. Good detection of water (open water, permanent and seasonal inundation) is above 0.9 using radar altimetry backscattering from ENVISAT and Jason-2. Based on these results, the time series of water levels were automatically produced. They exhibit temporal variations in good agreement with the hydrological regime of the Cuvette Centrale. Comparisons against a manually generated time series of water levels from the same missions at the same locations show a very good agreement between the two processes (i.e., RMSE ≤ 0.25 m in more than 80%/90% of the cases and R ≥ 0.95 in more than 95%/75% of the cases for ENVISAT and Jason-2, respectively). The use of the time series of water levels over rivers and wetlands improves the spatial pattern of the annual amplitude of water storage in the Cuvette Centrale. It also leads to a decrease by a factor of four for the surface water estimates in this area, compared with a case where only time series over rivers are considered.

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RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

Earth System Science Data ◽

10.5194/essd-13-2227-2021 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2227-2244

Author(s):

Simon Deggim ◽

Annette Eicker ◽

Lennart Schawohl ◽

Helena Gerdener ◽

Kerstin Schulze ◽

...

Keyword(s):

Time Series ◽

Surface Water ◽

Regional Scale ◽

Water Storage ◽

Water Volume ◽

Optical Remote Sensing ◽

Hydrological Models ◽

Total Water ◽

Lakes And Reservoirs ◽

The Impact

Abstract. Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).

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Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data

10.5194/hess-2017-170 ◽

2017 ◽

Author(s):

Cassandra Normandin ◽

Frédéric Frappart ◽

Bertrand Lubac ◽

Simon Bélanger ◽

Vincent Marieu ◽

...

Keyword(s):

Surface Water ◽

Water Levels ◽

The Arctic ◽

Water Volume ◽

Mackenzie Delta ◽

North West ◽

Flood Peak ◽

Upstream Part ◽

Water Height

Abstract. Quantification of surface water storage in extensive floodplains and their dynamics are crucial for a better understanding of global hydrological and biogeochemical cycles. In this study, we present estimates of both surface water extent and storage combining multi-missions remotely-sensed observations and their temporal evolution over more than 15 years in the Mackenzie Delta. The Mackenzie Delta is located in the North West of Canada and is the second largest delta in the Arctic Ocean. The delta is frozen from October to May and the recurrent ice break up provokes an increase of the river's flows. Thus, this phenomenon causes intensive floods along the delta every year with dramatic environmental impacts. In this study, the dynamics of surface water extent and volume analyzed from 2000 to 2015 by combining multi-satellite information from MODIS multispectral images at 500 m spatial resolution and river stages derived from ERS-2 (1995–2003), ENVISAT (2002–2010) and SARAL (since 2013) altimetry data. The surface water extent (permanent water and flooded area) peaked in June with an area of 9,600 km2 (±200 km2) on average, representing approximately 70 % of the delta's total surface. Altimetry-based water levels exhibit annual amplitudes ranging from 4 m in the downstream part to more than 10 m in the upstream part of the Mackenzie Delta. A high overall correlation between the satellite-derived and in situ water heights (R>0.84) is found for the three altimetry missions. Finally, using altimetry-based water levels and MODIS-derived surface water extents, maps of interpolated water heights over the surface water extents are produced. Results indicate a high variability of the water height magnitude that can reach 10 meters compared to the lowest water height in the upstream part of the delta during the flood peak in June. Furthermore, the total surface water volume is estimated and shows an annual variation of approximately 8.5 km3 during the whole study period, with a maximum of 14.4 km3 observed in 2006. The good agreement between the total surface water volume retrievals and in situ river discharges (R=0.66) allows validating this innovative multi-mission approach and highlights the high potential to study the surface water extent dynamics.

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The importance of vegetation to understand terrestrial water storage variations

10.5194/hess-2021-394 ◽

2021 ◽

Author(s):

Tina Trautmann ◽

Sujan Koirala ◽

Nuno Carvalhais ◽

Andreas Güntner ◽

Martin Jung

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Water Storage ◽

Model Parameters ◽

Intermediate Water ◽

Model Structure ◽

Hydrological Models ◽

Terrestrial Water Storage ◽

Terrestrial Water

Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

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Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

Hydrology and Earth System Sciences ◽

10.5194/hess-19-2079-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 2079-2100 ◽

Cited By ~ 52

Author(s):

N. Tangdamrongsub ◽

S. C. Steele-Dunne ◽

B. C. Gunter ◽

P. G. Ditmar ◽

A. H. Weerts

Keyword(s):

Correlation Coefficient ◽

River Basin ◽

Hydrological Cycle ◽

Water Storage ◽

Model Parameters ◽

Hydrological Models ◽

Rhine River ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

Hydrology and Earth System Sciences ◽

10.5194/hess-18-2007-2014 ◽

2014 ◽

Vol 18 (5) ◽

pp. 2007-2020 ◽

Cited By ~ 64

Author(s):

F. Baup ◽

F. Frappart ◽

J. Maubant

Keyword(s):

High Resolution ◽

Satellite Data ◽

Satellite Images ◽

Temporal Variations ◽

Water Levels ◽

Water Volume ◽

Small Scale ◽

Altimetry Data ◽

Small Lakes ◽

High Resolution Satellite Images

Abstract. This study presents an approach to determining the volume of water in small lakes (<100 ha) by combining satellite altimetry data and high-resolution (HR) images. In spite of the strong interest in monitoring surface water resources on a small scale using radar altimetry and satellite imagery, no information is available about the limits of the remote-sensing technologies for small lakes mainly used for irrigation purposes. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by an RA-2 sensor onboard Envisat, and the high-resolution images (<10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) antenna (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (77) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches (HRBV and ABV) are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R2ABV = 0.98, RMSEABV = 5%, R2HRBV = 0.90, and RMSEABV = 7.4%), assuming a time-varying triangular shape for the shore slope of the lake (this form is well adapted since it implies a difference inferior to 2% between the theoretical volume of the lake and the one estimated from bathymetry). The third method (AHRBVC) combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2AHRBVC = 0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

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A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry

10.5194/hess-2018-21 ◽

2018 ◽

Cited By ~ 2

Author(s):

Tim Busker ◽

Ad de Roo ◽

Emiliano Gelati ◽

Christian Schwatke ◽

Marko Adamovic ◽

...

Keyword(s):

Surface Water ◽

Satellite Altimetry ◽

Google Earth ◽

Water Levels ◽

Water Volume ◽

Lake Area ◽

Reservoir Volume ◽

Long Time ◽

Lakes And Reservoirs ◽

Computationally Intensive

Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R2 > 0.8 and an average R2 of 0.91. For these lakes and for lakes with a nearly constant lake area (coefficient of variation

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