WaterGAP Global Hydrology Model Research Articles

Independent patterns of water mass anomalies over Australia from satellite data and models

The Gravity Recovery and Climate Experiment (GRACE) products allow the quantification of total water storage (TWS) changes at global to regional scales. However, the quantity measured by GRACE represents mass signals integrated over vertical columns, requiring their separation into their original sources. Such a separation is vital for Australia, for which GRACE estimates are affected by leakage from the surrounding oceans. The independent component analysis (ICA) method that uses higher-order statistics, is implemented here to separate GRACE-derived water storage signals over the Australian continent from its surrounding oceans, covering from October 2002 to May 2011. The performance of ICA applied to GRACE is then compared to the ICA of WaterGAP Global Hydrology Model (WGHM) and the ICA of the Australian Water Resources Assessment (AWRA) system. To study the influence of rainfall variability on the derived independent patterns, use is made of Tropical Rainfall Measuring Mission (TRMM) data set, from January 2000 to May 2011. Implementing ICA on GRACE-TWS showed a remarkable improvement in separating the continental hydrological signals from the surrounding oceanic anomalies, which was not achievable using a conventional principle component analysis. Reconstructing the continental TWS changes using only those independent components of GRACE that were located over the continent showed a high correlation with WGHM-TWS and AWRA-TWS. Mass concentrations over the oceans and particularly S2 semi-diurnal aliased pattern were separated as independent modes. Correlation analysis between the independent components of GRACE and climate teleconnections showed that the mass anomalies over the northern ocean, Gulf of Carpentaria and north-eastern parts of Australia were significantly correlated with the El Niño-Southern Oscillation, while those over south and south-eastern parts of Australia were mainly linked to the Indian Ocean Dipole.

Gravity Recovery and Climate Experiment (GRACE) detection of water storage changes in the Three Gorges Reservoir of China and comparison with in situ measurements

Water impoundment in the Three Gorges Reservoir (TGR) of China caused a large mass redistribution from the oceans to a concentrated land area in a short time period. We show that this mass shift is captured by the Gravity Recovery and Climate Experiment (GRACE) unconstrained global solutions at a 400 km spatial resolution after removing correlated errors. The WaterGAP Global Hydrology Model (WGHM) is selected to isolate the TGR contribution from regional water storage changes. For the first time, this study compares the GRACE (minus WGHM) estimated TGR volume changes with in situ measurements from April 2002 to May 2010 at a monthly time scale. During the 8 year study period, GRACE‐WGHM estimated TGR volume changes show an increasing trend consistent with the TGR in situ measurements and lead to similar estimates of impounded water volume. GRACE‐WGHM estimated total volume increase agrees to within 14% (3.2 km3) of the in situ measurements. This indicates that GRACE can retrieve the true amplitudes of large surface water storage changes in a concentrated area that is much smaller than the spatial resolution of its global harmonic solutions. The GRACE‐WGHM estimated TGR monthly volume changes explain 76% (r2 = 0.76) of in situ measurement monthly variability and have an uncertainty of 4.62 km3. Our results also indicate reservoir leakage and groundwater recharge due to TGR filling and contamination from neighboring lakes are nonnegligible in the GRACE total water storage changes. Moreover, GRACE observations could provide a relatively accurate estimate of global water volume withheld by newly constructed large reservoirs and their impacts on global sea level rise since 2002.

Global-scale validation of model-based load deformation of the Earth's crust from continental watermass and atmospheric pressure variations using GPS

AbstractTemporal mass variations in the continental hydrosphere and in the atmosphere lead to changes in the gravitational potential field that are associated with load-induced deformation of the Earth’s crust. Therefore, models that compute continental water storage and atmospheric pressure can be validated by measured load deformation time series. In this study, water mass variations as computed by the WaterGAP Global Hydrology Model (WGHM) and surface pressure as provided by the reanalysis product of the National Centers for Environmental Prediction describe the hydrological and atmospheric pressure loading, respectively. GPS observations from 14 years at 208 stations world-wide were reprocessed to estimate admittance factors for the associated load deformation time series in order to determine how well the model-based deformation fits to real data. We found that such site-specific scaling factors can be identified separately for water mass and air pressure loading. Regarding water storage variation as computed by WGHM, weighted global mean admittances are 0.74±0.09, 0.66±0.10, 0.90±0.06 for the north, east and vertical component, respectively. For the dominant vertical component, there is a rather good fit to the observed displacements, and, averaged over all sites, WGHM is found to slightly overestimate temporal variations of water storage. For Europe and North America, with a dense GPS network, site-specific admittances show a good spatial coherence. Regarding regional over- or underestimation of WGHM water storage variations, they agree well with GRACE gravity field data. Globally averaged admittance estimates of pre-computed atmospheric loading displacements provided by the Goddard Geodetic VLBI Group were determined to be 0.88±0.04, 0.97±0.08, 1.13±0.01 for the north, east and vertical, respectively. Here, a relatively large discrepancy for the dominant vertical component indicates an underestimation of corresponding loading predictions.

Sensitivity of superconducting gravimeters in central Europe on variations in regional river and drainage basins

As underpinned by various studies in the last years, temporal changes of the Earth’s gravity field contain a wealth of information on mass redistribution processes in the Earth’s system particularly associated with variations in continental water storage. By combining satellite and terrestrial observations with superconducting gravimeters (SG) a maximum of information can be gained due to the different temporal and spatial sampling. Esp. the cluster of superconducting gravimeters in central Europe is well suited for studies related to spatial and temporal changes in continental water storage. Due to the distribution of SG sites different sensitivities of the instruments are to be expected on changes in the various river and drainage basins which could, if sufficiently pronounced, be deployed to pinpoint areas in which main discrepancies between modelled and actual water storage changes occur and would thus allow us to fine-tune hydrological models. Based on the WaterGap Global Hydrological Model (WGHM), this sensitivity of the SG observations is investigated. One compartment of the WGHM is surface water, thus comprising rivers, flooding areas, and major reservoirs. This contribution is given for the total cell of 0.5° × 0.5° and not localized, e.g. in a riverbed, therefore the question arises to which extent localization or non-localization of this compartment affects the estimate if the respective surface waters are in the vicinity of 50 km around the SG stations. It can be shown, however, that the lateral distribution of the surface water masses plays only a negligible role for the central European stations meaning distributed surface water masses are an acceptable simplification when estimating hydrological effects. It emerges that variations in water storage in regions outside central Europe produce comparable effects on gravity at all sites and the impact of basins within central Europe is clearly distinguishable among the SG stations.

On the suitability of the 4° × 4° GRACE mascon solutions for remote sensing Australian hydrology

Hydrological monitoring is essential for meaningful water-management policies and actions, especially where water resources are scarce and/or dwindling, as is the case in Australia. In this paper, we investigate the regional 4° × 4° mascon (mass concentration) GRACE solutions for Australia provided by GSFC (Goddard Space Flight Center, NASA) for their suitability in monitoring Australian hydrology, with a particular focus on the Murray-Darling Basin (MDB). Using principal component analysis (PCA) and multi-linear regression analysis (MLRA), the main components of spatial and temporal variability in the mascon solutions are analysed over the whole Australian continent and the MDB. The results are compared to those from global solutions provided by CSR (Center for Space Research, University of Texas at Austin, USA) and CNES/GRGS (Centre National d'Études Spatiales/Groupe de Recherche de Geodesie Spatiale, France) and validated using data from the Tropical Rainfall Measuring Mission (TRMM), water storage changes predicted by the WaterGap Global Hydrological Model (WGHM) and the Global Land Data Assimilation System (GLDAS), and ground-truth (river-gauge) observations. For the challenging Australian case with generally weak hydrological signals, the mascon solutions provide similar results to those from the global solutions, with the advantage of not requiring additional filtering (destriping and smoothing) as, for example, is necessary for the CSR solutions. A further advantage of the mascon solutions is that they offer a higher temporal resolution (i.e., 10 days) compared to approximately monthly CSR solutions. Examining equivalent water volume (EWV) time series for the MDB shows a good cross-correlation (generally > 0.7) among the GRACE solutions when considering the whole basin, although lower (generally < 0.5) when all the GRACE solutions are compared to the TRMM, WGHM and GLDAS time series. Examining smaller portions of the MDB see the correlation among the GRACE solutions and the TRMM, WGHM and GLDAS EWV time series increase slightly (> 0.6), with all time series appearing to visually follow the general behaviour of the river-gauge data, although the cross-correlations are relatively low (between 0.3 and 0.6).

Calibration analysis for water storage variability of the global hydrological model WGHM

Abstract. The aim of this study is to provide an improved global simulation of continental water storage variations by calibrating the WaterGAP Global Hydrology Model (WGHM) for 28 of the largest river basins worldwide. Five years (January 2003–December 2007) of satellite-based estimates of the total water storage changes from the GRACE mission were combined with river discharge data in a multi-objective calibration framework that uses the most sensitive WGHM model parameters. The uncertainty and significance of the calibration results were analysed with respect to errors in the observation data. An independent simulation period (January 2008–December 2008) was used for validation. The contribution of single storage compartments to the total water budget before and after calibration was analysed in detail. A multi-objective improvement of the model states was obtained for most of the river basins, with mean error reductions of up to 110 km3/month for discharge and up to 24 mm of a water mass equivalent column for total water storage changes, such as for the Amazon basin. Errors in the phase and signal variability of seasonal water mass changes were reduced. The calibration is shown to primarily affect soil water storage in most river basins. The variability of groundwater storage variations was reduced on a global scale after calibration. Structural model errors were identified from a small contribution of surface water storage including wetlands in river basins with large inundation areas, such as the Amazon or the Mississippi. Our results demonstrate the value of both the GRACE data and the multi-objective calibration approach for improving large-scale hydrological simulations, and they provide a starting-point for improving model structures. The integration of complimentary observation data to further constrain the simulation of single storage compartments is encouraged.

Time series of superconducting gravimeters and water storage variations from the global hydrology model WGHM

The gravimetric time series achieved from the combination of superconducting and absolute gravimeters are characterized by highest precision and long-term stability. If the effects of Earth and ocean tides, atmosphere and polar motion are removed, the residual curve is dominated by hydrological mass variations. A major source of these variations is water storage changes in the vicinity of the sensor. However, global variations contribute to the signal significantly. For three stations of superconducting gravimeters, a comparison of the principal components obtained from the residual gravity curve on the one hand and continental water storage from the WaterGAP Global Hydrological Model (WGHM) on the other hand is carried out. The results demonstrate a coherence of seasonal variations but a difference in the contribution of the local zone at the individual stations, which point out the need for a careful and site-specific examination of local effects.

Integration of GRACE mass variations into a global hydrological model

Time-variable gravity data of the GRACE (Gravity Recovery And Climate Experiment) satellite mission provide global information on temporal variations of continental water storage. In this study, we incorporate GRACE data for the first time directly into the tuning process of a global hydrological model to improve simulations of the continental water cycle. For the WaterGAP Global Hydrology Model (WGHM), we adopt a multi-objective calibration framework to constrain model predictions by both measured river discharge and water storage variations from GRACE and illustrate it on the example of three large river basins: Amazon, Mississippi and Congo. The approach leads to improved simulation results with regard to both objectives. In case of monthly total water storage variations we obtained a RMSE reduction of about 25 mm for the Amazon, 6 mm for the Mississippi and 1 mm for the Congo river basin. The results highlight the valuable nature of GRACE data when merged into large-scale hydrological modeling. Furthermore, they reveal the utility of the multi-objective calibration framework for the integration of remote sensing data into hydrological models.

Value of river discharge data for global-scale hydrological modeling

Abstract. This paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are e.g. required for assessing water resources, flood risk and habitat alteration of aquatic ecosystems. An improved version of the WaterGAP Global Hydrology Model (WGHM) was tuned against measured discharge using either the 724-station dataset (V1) against which former model versions were tuned or an extended dataset (V2) of 1235 stations. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas in order to fit simulated and observed long-term average discharge at tuning stations. In basins where γ does not suffice to tune the model, two correction factors are applied successively: the areal correction factor corrects local runoff in a basin and the station correction factor adjusts discharge directly the gauge. Using station correction is unfavorable, as it makes discharge discontinuous at the gauge and inconsistent with runoff in the upstream basin. The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5% and the area where the model can be tuned by only adjusting γ increases by 8%. However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and in regions where the refined dataset provides a significant subdivision of formerly extended tuning basins (average V2 basin size less than half the V1 basin size). If the additional discharge information were not used for tuning, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 in the formerly untuned basins and 1.3 in the subdivided basins. The benefits tend to be higher in semi-arid and snow-dominated regions where the model is less reliable than in humid areas and refined tuning compensates for uncertainties with regard to climate input data and for specific processes of the water cycle that cannot be represented yet by WGHM. Regarding other flow characteristics like low flow, inter-annual variability and seasonality, the deviation between simulated and observed values also decreases significantly, which, however, is mainly due to the better representation of average discharge but not of variability. (3) The choice of the optimal sub-basin size for tuning depends on the modeling purpose. While basins over 60 000 km2 are performing best, improvements in V2 model performance are strongest in small basins between 9000 and 20 000 km2, which is primarily related to a low level of V1 performance. Increasing the density of tuning stations provides a better spatial representation of discharge, but it also decreases model consistency, as almost half of the basins below 20 000 km2 require station correction.

Global-scale modeling of groundwater recharge

Abstract. Long-term average groundwater recharge, which is equivalent to renewable groundwater resources, is the major limiting factor for the sustainable use of groundwater. Compared to surface water resources, groundwater resources are more protected from pollution, and their use is less restricted by seasonal and inter-annual flow variations. To support water management in a globalized world, it is necessary to estimate groundwater recharge at the global scale. Here, we present a best estimate of global-scale long-term average diffuse groundwater recharge (i.e. renewable groundwater resources) that has been calculated by the most recent version of the WaterGAP Global Hydrology Model WGHM (spatial resolution of 0.5° by 0.5°, daily time steps). The estimate was obtained using two state-of-the-art global data sets of gridded observed precipitation that we corrected for measurement errors, which also allowed to quantify the uncertainty due to these equally uncertain data sets. The standard WGHM groundwater recharge algorithm was modified for semi-arid and arid regions, based on independent estimates of diffuse groundwater recharge, which lead to an unbiased estimation of groundwater recharge in these regions. WGHM was tuned against observed long-term average river discharge at 1235 gauging stations by adjusting, individually for each basin, the partitioning of precipitation into evapotranspiration and total runoff. We estimate that global groundwater recharge was 12 666 km3/yr for the climate normal 1961–1990, i.e. 32% of total renewable water resources. In semi-arid and arid regions, mountainous regions, permafrost regions and in the Asian Monsoon region, groundwater recharge accounts for a lower fraction of total runoff, which makes these regions particularly vulnerable to seasonal and inter-annual precipitation variability and water pollution. Average per-capita renewable groundwater resources of countries vary between 8 m3/(capita yr) for Egypt to more than 1 million m3/(capita yr) for the Falkland Islands, the global average in the year 2000 being 2091 m3/(capita yr). Regarding the uncertainty of estimated groundwater resources due to the two precipitation data sets, deviation from the mean is 1.1% for the global value, and less than 1% for 50 out of the 165 countries considered, between 1 and 5% for 62, between 5 and 20% for 43 and between 20 and 80% for 10 countries. Deviations at the grid scale can be much larger, ranging between 0 and 186 mm/yr.

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.

A global analysis of temporal and spatial variations in continental water storage

While continental water storage plays a key role in the Earth's water, energy, and biogeochemical cycles, its temporal and spatial variations are poorly known, in particular, for large areas. This study analyzes water storage simulated with the Watergap Global Hydrology Model. The model represents four major storage compartments: surface water, snow, soil, and groundwater. Water storage variations are analyzed for the period 1961–1995 with 0.5° resolution, for the major global climate zones, and for the 30 largest river basins worldwide. Seasonal variations are the dominant storage change signal with maximum values in the marginal tropics and in snow‐dominated high‐latitude areas. Interannual variations are associated with large‐scale oscillations such as El Niño Southern Oscillation. The contribution of individual water storage compartments to total storage change varies with the climate region and the timescale under consideration. In most regions, a prominent role of storage variations in surface water bodies is found. Surface water reduces markedly the spatial correlation lengths of water storage fields. The simulation results are evaluated against storage variations of combined atmospheric‐terrestrial water balance studies and other global models. This study contributes to an improved understanding of continental water storage for which the consistent integration of model results and new observations such as from time‐variable gravity data of the Gravity Recovery and Climate Experiment (GRACE) satellite mission is required.

ENSO impact on simulated South American hydro-climatology

Abstract. The variability of the simulated hydro-climatology of the WaterGAP Global Hydrology Model (WGHM) is analysed. Main object of this study is the ENSO-driven variability of the water storage of South America. The horizontal model resolution amounts to 0.5 degree and it is forced with monthly climate variables for 1961-1995 of the Tyndall Centre Climate Research Unit dataset (CRU TS 2.0) as a representation of the observed climate state. Secondly, the model is also forced by the model output of a global circulation model, the ECHAM4-T42 GCM. This model itself is driven by observed monthly means of the global Sea Surface Temperatures (SST) and the sea ice coverage for the period of 1903 to 1994 (GISST). Thus, the climate model and the hydrological model represent a realistic simulated realisation of the hydro-climatologic state of the last century. Since four simulations of the ECHAM4 model with the same forcing, but with different initial conditions are carried out, an analysis of variance (ANOVA) gives an impression of the impact of the varying SST on the hydro-climatology, because the variance can be separated into a SST-explained and a model internal variability (noise). Also regional multivariate analyses, like Empirical Orthogonal Functions (EOF) and Canonical Correlation Analysis (CCA) provide information of the complex time-space variability. In particular the Amazon region and the South of Brazil are significantly influenced by the ENSO-variability, but also the Pacific coastal areas of Ecuador and Peru are affected. Additionally, different ENSO-indices, based on SST anomalies (e.g. NINO3.4, NINO1+2), and its influence on the South American hydro-climatology are analysed. Especially, the Pacific coast regions of Ecuador, Peru and Chile show a very different behaviour dependant on those indices.

A global hydrological model for deriving water availability indicators: model tuning and validation

Freshwater availability has been recognized as a global issue, and its consistent quantification not only in individual river basins but also at the global scale is required to support the sustainable use of water. The WaterGAP Global Hydrology Model WGHM, which is a submodel of the global water use and availability model WaterGAP 2, computes surface runoff, groundwater recharge and river discharge at a spatial resolution of 0.5°. WGHM is based on the best global data sets currently available, and simulates the reduction of river discharge by human water consumption. In order to obtain a reliable estimate of water availability, it is tuned against observed discharge at 724 gauging stations, which represent 50% of the global land area and 70% of the actively discharging area. For 50% of these stations, the tuning of one model parameter was sufficient to achieve that simulated and observed long-term average discharges agree within 1%. For the rest, however, additional corrections had to be applied to the simulated runoff and discharge values. WGHM not only computes the long-term average water resources of a country or a drainage basin but also water availability indicators that take into account the interannual and seasonal variability of runoff and discharge. The reliability of the modeling results is assessed by comparing observed and simulated discharges at the tuning stations and at selected other stations. The comparison shows that WGHM is able to calculate reliable and meaningful indicators of water availability at a high spatial resolution. In particular, the 90% reliable monthly discharge is simulated well. Therefore, WGHM is suited for application in global assessments related to water security, food security and freshwater ecosystems.