Annette Eicker

Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites

Groundwater depletion (GWD) compromises crop production in major global agricultural areas and has negative ecological consequences. To derive GWD at the grid cell, country, and global levels, we applied a new version of the global hydrological model WaterGAP that simulates not only net groundwater abstractions and groundwater recharge from soils but also groundwater recharge from surface water bodies in dry regions. A large number of independent estimates of GWD as well as total water storage (TWS) trends determined from GRACE satellite data by three analysis centers were compared to model results. GWD and TWS trends are simulated best assuming that farmers in GWD areas irrigate at 70% of optimal water requirement. India, United States, Iran, Saudi Arabia, and China had the highest GWD rates in the first decade of the 21st century. On the Arabian Peninsula, in Libya, Egypt, Mali, Mozambique, and Mongolia, at least 30% of the abstracted groundwater was taken from nonrenewable groundwater during this time period. The rate of global GWD has likely more than doubled since the period 1960–2000. Estimated GWD of 113 km3/yr during 2000–2009, corresponding to a sea level rise of 0.31 mm/yr, is much smaller than most previous estimates. About 15% of the globally abstracted groundwater was taken from nonrenewable groundwater during this period. To monitor recent temporal dynamics of GWD and related water abstractions, GRACE data are best evaluated with a hydrological model that, like WaterGAP, simulates the impact of abstractions on water storage, but the low spatial resolution of GRACE remains a challenge.

Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results

We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at

The ITG-Goce02 gravity field model from GOCE orbit and gradiometer data based on the short arc approach

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Covariance analysis and sensitivity studies for GRACE assimilation into WGHM

5∘ resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period.

Mapping probabilities of extreme continental water storage changes from space gravimetry

More than 4 years of GRACE data were used to determine the gravity field model ITG-Grace03 s. The solution consists of three parts: a static high resolution model up to a spherical harmonic degree of 180, temporal variations up to degree 40 and the full variance-covariance matrix for the static solution. The temporal gravity field variations are parameterized by continuous basis functions in the time domain. The physical model of the gravity field recovery technique is based on Newton’s equation of motion, formulated as a boundary value problem in the form of a Fredholm type integral equation. The principal characteristic of this method is the use of short arcs of the satellite’s orbit in order to avoid the accumulation of modeling errors and a rigorous consideration of correlations between the range observations in the subsequent adjustment procedure.

Global Gravity Field Solutions Based on a Simulation Scenario of GRACE SST Data and Regional Refinements by GOCE SGG Observations

In this contribution, we describe the global GOCE-only gravity field model ITG-Goce02 derived from 7.5 months of gradiometer and orbit data. This model represents an alternative to the official ESA products as it is computed completely independently, using a different processing strategy and a separate software package. Our model is derived using the short arc approach, which allows a very effective decorrelation of the highly correlated GOCE gradiometer and orbit data noise by introducing a full empirical covariance matrix for each arc, and gives the possibility to downweight ‘bad’ arcs. For the processing of the orbit data we rely on the integral equation approach instead of the energy integral method, which has been applied in several other GOCE models. An evaluation against high-resolution global gravity field models shows very similar differences of our model compared to the official GOCE results published by ESA (release 2), especially to the model derived by the time-wise approach. This conclusion is confirmed by comparison of the GOCE models to GPS/levelling and altimetry data.

Improved Resolution of a GRACE Gravity Field Model by Regional Refinements

The signal content in the low-low SST observables of the gravity field twin-satellite mission GRACE (Gravity Recovery And Climate Experiment) varies in the space domain depending on the roughness of the gravity field features. On the one hand, the maximum degree of the spherical harmonic expansion has to be selected as high as possible to bring out the maximum of gravity field information out of the data. On the other hand, an increasing maximal degree deteriorates the stability of the normal equations to solve for the gravity field parameters. Therefore, a trade-off is necessary between the selection of a maximal degree adequate for representing the signal content in the observables, on the one hand, and a maximal degree which can still be recovered without causing instabilities, on the other hand. We propose to integrate the global gravity field recovery with regional gravity field refinements tailored to the specific gravity field features in these regions: In a first step, the gravity field only up to a moderate safely determinable degree is recovered; the specific analysis features tailored to the individual gravity field characteristics in areas of rough gravity field signal will be modelled subsequently by space localizing base functions in a second step. In a final third step, a spherical harmonic expansion up to an (in principle) arbitrary degree can be derived based on a numerical Gauss — Legendre - quadrature procedure without any stability problems. The procedure will be applied in a first example to observations of a GRACE simulation scenario to test the potential capabilities of the approach. A second application demonstrates the determination of a global gravity field model and regional refinements based on low-low SST data of the GRACE twin satellite mission for the August 2003 observations.

Water budget analysis within the surrounding of prominent lakes and reservoirs from multi-sensor Earth observation data and hydrological models: case studies of the Aral Sea and Lake Mead

An ensemble Kalman filter approach for improving the WaterGAP Global Hydrology Model (WGHM) has been developed, which assimilates Gravity Recovery And Climate Experiment (GRACE) data and calibrates the model parameters, simultaneously. The method uses the model-derived states and satellite measurements and their error information to determine updated water storage states. However, due to the fact that hydrological models do not provide any error information, an empirical covariance matrix needs to be calculated. In this paper, therefore, we analyse the combined state and parameter covariance matrix of WGHM. We found that high correlations of up to 0.75 exist between calibration parameters and storage compartments, and that these allow for an efficient calibration. In addition, a sensitivity analysis is performed to identify those parameters that the water compartments are most sensitive to. The performed analysis is important, since GRACE cannot observe the model parameters directly. We found that those parameters, which the water storage is most sensitive to, differ not only regionally, but also with respect to the water compartments. Not unexpected, some climate input multipliers implemented in our model version have an overall strong influence. We also found that the degree of sensitivity changes temporally, e.g. between 0 (in summer) and 0.5 (in winter) for the snow storage.