Global modelling of continental water storage changes – sensitivity to different climate data sets

Abstract. Since 2002, the GRACE satellite mission provides estimates of the Earth's dynamic gravity field with unprecedented accuracy. Differences between monthly gravity fields contain a clear hydrological signal due to continental water storage changes. In order to evaluate GRACE results, the state-of-the-art WaterGAP Global Hydrological Model (WGHM) is applied to calculate terrestrial water storage changes on a global scale. WGHM is driven by different climate data sets to analyse especially the influence of different precipitation data on calculated water storage. The data sets used are the CRU TS 2.1 climate data set, the GPCC Full Data Product for precipitation and data from the ECMWF integrated forecast system. A simple approach for precipitation correction is introduced. WGHM results are then compared with GRACE data. The use of different precipitation data sets leads to considerable differences in computed water storage change for a large number of river basins. Comparing model results with GRACE observations shows a good spatial correlation and also a good agreement in phase. However, seasonal variations of water storage as derived from GRACE tend to be significantly larger than those computed by WGHM, regardless of which climate data set is used.

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Global terrestrial water storage connectivity revealed using complex climate network analyses

Nonlinear Processes in Geophysics ◽

10.5194/npg-22-433-2015 ◽

2015 ◽

Vol 22 (4) ◽

pp. 433-446 ◽

Cited By ~ 5

Author(s):

A. Y. Sun ◽

J. Chen ◽

J. Donges

Keyword(s):

Spatial Patterns ◽

Land Surface ◽

Water Storage ◽

Data Sets ◽

Terrestrial Water Storage ◽

Land Surface Models ◽

Data Set ◽

Surface Models ◽

Global Land ◽

Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

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Evaluating short-term hydro-meteorological fluxes in global atmospheric reanalyses using daily GRACE data

10.5194/egusphere-egu2020-4502 ◽

2020 ◽

Author(s):

Viviana Wöhnke ◽

Annette Eicker ◽

Laura Jensen ◽

Andreas Kvas ◽

Torsten Mayer-Gürr ◽

...

Keyword(s):

Water Storage ◽

Numerical Differentiation ◽

Evaluation Tool ◽

Satellite Gravity ◽

Data Set ◽

Weather Forecasts ◽

Grace Data ◽

Terrestrial Water ◽

Band Pass ◽

Net Flux

Changes in terrestrial water storage as observed by the satellite gravity mission GRACE represent a new and completely independent data set for constraining the net flux deficit of precipitation (P), evapotranspiration (E), and lateral runoff (R) in atmospheric reanalyses.In this study we use daily GRACE gravity field changes to investigate high-frequency hydro-meteorological fluxes over the continents. Band-pass filtered water fluxes are derived from GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days.We can show that on these time scales GRACE is able to identify quality differences between different reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim. We will therefore use GRACE as an evaluation tool to compare hydro-meteorological fluxes in various global atmospheric reanalyses, such as ERA5(-Land), ERA-Interim, Merra2, JRA-55, or NCEP.

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Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations

Hydrology and Earth System Sciences ◽

10.5194/hess-21-821-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 821-837 ◽

Cited By ~ 23

Author(s):

Liangjing Zhang ◽

Henryk Dobslaw ◽

Tobias Stacke ◽

Andreas Güntner ◽

Robert Dill ◽

...

Keyword(s):

Large Scale ◽

Water Cycle ◽

Numerical Models ◽

Water Storage ◽

Terrestrial Water Storage ◽

Satellite Mission ◽

Validation Data ◽

Data Set ◽

Terrestrial Water ◽

Grace Satellite

Abstract. Estimates of terrestrial water storage (TWS) variations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to assess the accuracy of four global numerical model realizations that simulate the continental branch of the global water cycle. Based on four different validation metrics, we demonstrate that for the 31 largest discharge basins worldwide all model runs agree with the observations to a very limited degree only, together with large spreads among the models themselves. Since we apply a common atmospheric forcing data set to all hydrological models considered, we conclude that those discrepancies are not entirely related to uncertainties in meteorologic input, but instead to the model structure and parametrization, and in particular to the representation of individual storage components with different spatial characteristics in each of the models. TWS as monitored by the GRACE mission is therefore a valuable validation data set for global numerical simulations of the terrestrial water storage since it is sensitive to very different model physics in individual basins, which offers helpful insight to modellers for the future improvement of large-scale numerical models of the global terrestrial water cycle.

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On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe

10.5194/egusphere-egu21-125 ◽

2021 ◽

Author(s):

Jannis Hoch ◽

Edwin Sutanudjaja ◽

Rens van Beek ◽

Marc Bierkens

Keyword(s):

Global Scale ◽

Data Sets ◽

The Novel ◽

Data Set ◽

Meteorological Forcing ◽

Recent Developments ◽

Terrestrial Water ◽

Multiple Challenges ◽

Global Data ◽

Development Application

Developing and applying hyper-resolution models over larger extents has long been a quest in hydrological sciences. With the recent developments of global-scale yet fine data sets and advances in computational power, achieving this goal becomes increasingly feasible.We here present the development, application, and results of the novel 1 km version of PCR-GLOBWB for the period 1981 until 2020. Even though employing global data sets only, we developed, ran, and evaluated the 1 km model for the continent Europe only. In comparison to past versions of PCR-GLOBWB, input data was replaced with sufficiently fine data, for example the recent SoilGrids and MERIT-DEM data. Preliminary results indicate an improvement of model outcome when evaluating simulated discharge, evaporation, and terrestrial water storage.Additionally, we aim to answer the question to what extent developing hyper-resolution models is actually needed of whether the run times could be saved by using hyper-resolution state-of-the-art meteorological forcing. Therefore, the relative importance of model resolution and forcing resolution was cross-compared. To that end, the ERA5-Land data set was employed at different resolutions, matching the model resolutions at 1 km, 10 km, and 50 km.Despite multiple challenges still lying ahead before achieve true hyper-resolution, this application of a 1 km model across an entire continent can form the basis for the next steps to be taken.

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Can daily GRACE gravity field models be used to evaluate short-term hydro-meteorological signals over the continents?

10.5194/egusphere-egu2020-18050 ◽

2020 ◽

Author(s):

Annette Eicker ◽

Laura Jensen ◽

Viviana Wöhnke ◽

Andreas Kvas ◽

Henryk Dobslaw ◽

...

Keyword(s):

Time Series ◽

Gravity Field ◽

Time Scales ◽

Water Flux ◽

Water Storage ◽

Numerical Differentiation ◽

Data Set ◽

Grace Data ◽

Terrestrial Water ◽

Gravity Field Models

Over the recent years, the computation of temporally high-resolution (daily) GRACE gravity field solutions has advanced as an alternative to the processing of monthly models. In this presentation we will show that recent processing improvements incorporated in the latest version of daily gravity field models (ITSG-Grace2018) now allow for the investigation of water flux signals on the continents down to time scales of a few days.Time variations in terrestrial water storage derived from GRACE can be related to atmospheric net-fluxes of precipitation (P), evapotranspiration (E) and lateral runoff (R) via the terrestrial water balance equation, which makes GRACE a new and completely independent data set for constraining hydro-meteorological observations and the output of atmospheric reanalyses.In our study, band-pass filtered water fluxes are derived from the daily GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days. We can show that on these time scales GRACE is able to identify quality differences between different global reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim.We can further demonstrate that only the very recent progress in GRACE data processing has enabled the use of daily GRACE time series for such an evaluation of high-frequency atmospheric fluxes. The accuracy of the previous daily GRACE time series ITSG-Grace2016 would not have been sufficient to carry out such an assessment.

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Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data

Hydrology and Earth System Sciences ◽

10.5194/hess-25-4185-2021 ◽

2021 ◽

Vol 25 (7) ◽

pp. 4185-4208

Author(s):

Natthachet Tangdamrongsub ◽

Michael F. Jasinski ◽

Peter J. Shellito

Keyword(s):

Land Surface ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Data Sets ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water ◽

Land Exchange

Abstract. Accurate estimation of terrestrial water storage (TWS) at a high spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5∘ by the resolution of soil and vegetation data sets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5 to 0.05∘) and time span (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05∘ developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. Using improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated on in this paper relies only on publicly accessible global data sets, allowing the reproduction of the 0.05∘ TWS estimates in any study region.

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Validation of Terrestrial Water Storage Variations as Simulated by Different Global Numerical Models with GRACE Satellite Observations

10.5194/hess-2016-330 ◽

2016 ◽

Author(s):

Liangjing Zhang ◽

Henryk Dobslaw ◽

Tobias Stacke ◽

Andreas Güntner ◽

Robert Dill ◽

...

Keyword(s):

Large Scale ◽

Water Cycle ◽

Numerical Models ◽

Water Storage ◽

Terrestrial Water Storage ◽

Satellite Mission ◽

Validation Data ◽

Data Set ◽

Terrestrial Water ◽

Limited Degree

Abstract. Estimates of terrestrial water storage (TWS) variations from the satellite mission GRACE are used to assess the accuracy of four global numerical model realizations that simulate the continental branch of the global water cycle. Based on four different validation metrics, we demonstrate that for the 31 largest discharge basins worldwide all model runs agree with the observations to a very limited degree only, together with large spreads among the models themselves. Since we apply a common atmospheric forcing data-set to all hydrological models considered, we conclude that those discrepancies are not entirely related to uncertainties in meteorologic input, but instead to the model structure and parametrization, and in particular to the representation of individual storage compartments with different spatial characteristics in each of the models. TWS as monitored by the GRACE mission is therefore a valuable validation data-set for global numerical simulations of the terrestrial water storage since it is sensitive to very different model physics in individual basins, which offers helpful insight to modellers for the future improvement of large-scale numerical models of the global terrestrial water cycle.

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Identification of prominent spatio-temporal signals in GRACE derived terrestrial water storage for India

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

10.5194/isprsarchives-xl-8-333-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 333-338

Author(s):

C. Banerjee ◽

D. Nagesh Kumar

Keyword(s):

Water Resources ◽

Fresh Water ◽

Water Storage ◽

Component Analysis ◽

Blind Signal Separation ◽

Terrestrial Water Storage ◽

Satellite Mission ◽

Rapid Urbanization ◽

Data Set ◽

Terrestrial Water

Fresh water is a necessity of the human civilization. But with the increasing global population, the quantity and quality of available fresh water is getting compromised. To mitigate this subliminal problem, it is essential to enhance our level of understanding about the dynamics of global and regional fresh water resources which include surface and ground water reserves. With development in remote sensing technology, traditional and much localized in-situ observations are augmented with satellite data to get a holistic picture of the terrestrial water resources. For this reason, Gravity Recovery And Climate Experiment (GRACE) satellite mission was jointly implemented by NASA and German Aerospace Research Agency – DLR to map the variation of gravitational potential, which after removing atmospheric and oceanic effects is majorly caused by changes in Terrestrial Water Storage (TWS). India also faces the challenge of rejuvenating the fast deteriorating and exhausting water resources due to the rapid urbanization. In the present study we try to identify physically meaningful major spatial and temporal patterns or signals of changes in TWS for India. TWS data set over India for a period of 90 months, from June 2003 to December 2010 is use to isolate spatial and temporal signals using Principal Component Analysis (PCA), an extensively used method in meteorological studies. To achieve better disintegration of the data into more physically meaningful components we use a blind signal separation technique, Independent Component Analysis (ICA).

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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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