Gravity Recovery And Climate Experiment Data Assimilation Research Articles

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

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.

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model

Accurate estimation of snow mass or snow water equivalent (SWE) over space and time is required for global and regional predictions of the effects of climate change. This work investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from the Gravity Recovery and Climate Experiment (GRACE), can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5% and improved unbiased root-mean-square difference (ubRMSD) by 23%. At the grid scale, the DA method improved ubRMSD values and correlation coefficients for 85% and 97% of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it effectively improved the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced streamflow simulations.

Improved water storage estimates within the North China Plain by assimilating GRACE data into the CABLE model

As one of the most important crop-producing bases, the North China Plain (NCP) has suffered serious groundwater depletion due to drying climate and intensive human activities. An accurate estimation of groundwater storage (GWS) is of great importance for the sustainable development of local society and region’s economy. This study investigates a methodology to determine improved GWS variation throughout the NCP based on assimilation of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) data into the Community Atmosphere Biosphere Land Exchange (CABLE) model. We found that the CABLE model is not able to compute the prolonged decrease (and accelerated since 2013) in the GWS change around the southwestern region of the NCP where major cities are located. The model incorrectly characterizes the spatial patterns of interannual and annual changes of water storage variation, mainly caused by errors in the precipitation forcing data. The interannual TWS change is indicative of GWS, while the observed seasonal variation is primarily that of root-zone soil moisture. The GRACE data assimilation most effectively improves GWS computation. The GWS assimilation results were validated against a total of 156 in-situ groundwater level data in the NCP. Compared to the model computation, there was a significant improvement in terms of cross correlation, on average, from 0.12 (before assimilation) to 0.54 (after assimilation). This study demonstrates the effectiveness of GRACE data assimilation toward reliable estimation of ground water storage variation in the NCP, and its promise to quantify the potential implication of water supply from the South-to-North Water Transfer Project within the NCP.

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

AbstractWest Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite‐based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6‐month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE‐DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE‐DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water‐related disaster warning systems for Africa.

GRACE improves seasonal groundwater forecast initialization over the U.S.

We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the U.S., focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003-2016 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth-to-water-table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts towards an improved U.S. drought monitor and forecast system.

Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates

Assimilating remote sensing observations into land surface models has become common practice to improve the accuracy of terrestrial water storage (TWS) estimates such as soil moisture and groundwater, for understanding the land surface interaction with the climate system, as well as assessing regional and global water resources. Such remote sensing observations include soil moisture information from the L-band Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, and TWS information from the Gravity Recovery And Climate Experiment (GRACE). This study evaluates the benefit of assimilating them into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The evaluation is conducted in the Goulburn River catchment, South-East Australia, where various in situ soil moisture and groundwater level data are available for validating data assimilation (DA) approaches. It is found that the performance of DA mainly depends on the type of observations that are assimilated. The SMOS/SMAP-only assimilation (SM DA) improves the top soil moisture but degrades the groundwater storage estimates, whereas the GRACE-only assimilation (GRACE DA) improves only the groundwater component. Assimilating both observations (multivariate DA) results in increased accuracy of both soil moisture and groundwater storage estimates. These findings demonstrate the added value of multivariate DA for simultaneously improving different model states, thus leading to a more robust DA system.

Assimilating GRACE Into a Land Surface Model in the Presence of an Irrigation‐Induced Groundwater Trend

AbstractAssimilating terrestrial water storage observations from the Gravity Recovery and Climate Experiment (GRACE) mission into land surface models (LSMs) provides an opportunity to disaggregate and downscale GRACE information to finer scales and improve water component estimates in LSMs. However, the performance of GRACE data assimilation (GRACE‐DA) is limited by the lack of representation of human activities in most LSMs. To simultaneously improve GRACE‐DA and reduce the uncertainties in the modeled anthropogenic processes, we assimilate mascon‐based GRACE terrestrial water storage into the Noah‐Multiparameterization LSM that includes groundwater extraction for irrigation. Simulations with and without GRACE‐DA and with and without groundwater pumping for irrigation are performed to study the isolated and combined effects of groundwater irrigation and GRACE‐DA on water and energy fluxes over the High Plains Aquifer (HPA). The results reveal that the DA‐only simulation may erroneously distribute increments across water storage components and affect the related fluxes through biased feedbacks, while the irrigation‐only simulation may overestimate groundwater decline due to shortcomings in the irrigation and groundwater parameterizations. Assimilating GRACE when irrigation is simulated produces the best overall performance for water storage trends over the northern HPA. For the southern HPA, GRACE assimilation with irrigation performs similarly to irrigation‐only simulation for water storage components and evapotranspiration. GRACE assimilation also improves results in nonirrigated regions and can potentially alleviate the overestimation of groundwater trends in regions with greater irrigation uncertainties. This study highlights the potential to advance hydrological data assimilation in the context of anthropogenic water consumption and land‐atmosphere interactions.

Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges

AbstractThe scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low‐flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM‐simulated groundwater correlates strongly with 12‐month precipitation anomalies in low‐latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity.

On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

AbstractThe ensemble Kalman filter (EnKF) has been proved as a useful algorithm to merge coarse‐resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally, a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolutions of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude.

Improving surface soil moisture estimates in West Africa through GRACE data assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

Multi-sensor assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for soil moisture and shallow groundwater estimation

The Gravity Recovery and Climate Experiment (GRACE) mission provided monthly global estimates of the vertically integrated terrestrial water storage with about 300–400-km horizontal resolution between 2002 and 2017. Since 2009, the Soil Moisture and Ocean Salinity (SMOS) mission observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture, with a revisit time of 1–3 days at a nominal 43-km spatial resolution. This work investigates if the multi-sensor assimilation of these observations into the Catchment land surface model can improve the estimation of 0–5 cm “surface” soil moisture, 0–100 cm “rootzone” soil moisture, and shallow (unconfined) groundwater levels. Single-sensor GRACE or SMOS assimilation and multi-sensor GRACE+SMOS assimilation experiments were performed over the continental U.S. for 6 years (July 2010–June 2016). GRACE data assimilation mostly improves estimates of shallow groundwater, whereas SMOS data assimilation mainly improves estimates of surface soil moisture. The benefits introduced by the single-sensor assimilation are merged in the multi-sensor assimilation experiment, suggesting that better and more consistent soil moisture and groundwater estimates can be achieved when multiple observation types are assimilated. Interestingly, in the multi-sensor GRACE+SMOS experiment, the water storage increments introduced by the GRACE analysis and the SMOS analysis are anti-correlated. That is, when the GRACE assimilation increments remove water from the overall profile storage, the SMOS assimilation increments add water to it, and vice versa. This anti-correlation could be caused by the SMOS analysis trying to undo the increments from the GRACE analysis.

Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The large-scale ΔGWS can be simulated from a land surface model (LSM), but the high model uncertainty is a major drawback that reduces the reliability of the estimates. The evaluation of the model estimate is then very important to assess its accuracy. To improve the model performance, the terrestrial water storage variation derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is commonly assimilated into LSMs to enhance the accuracy of the ΔGWS estimate. This study assimilates GRACE data into the PCRaster Global Water Balance (PCR-GLOBWB) model. The GRACE data assimilation (DA) is developed based on the three-dimensional ensemble Kalman smoother (EnKS 3D), which considers the statistical correlation of all extents (spatial, temporal, vertical) in the DA process. The ΔGWS estimates from GRACE DA and four LSM simulations (PCR-GLOBWB, the Community Atmosphere Biosphere Land Exchange (CABLE), the Water Global Assessment and Prognosis Global Hydrology Model (WGHM), and World-Wide Water (W3)) are validated against the in situ groundwater data. The evaluation is conducted in terms of temporal correlation, seasonality, long-term trend, and detection of groundwater depletion. The GRACE DA estimate shows a significant improvement in all measures, notably the correlation coefficients (respect to the in situ data) are always higher than the values obtained from model simulations alone (e.g., ~0.15 greater in Australia, and ~0.1 greater in the NCP). GRACE DA also improves the estimation of groundwater depletion that the models cannot accurately capture due to the incorrect information of the groundwater demand (in, e.g., PCR-GLOBWB, WGHM) or the unavailability of a groundwater consumption routine (in, e.g., CABLE, W3). In addition, this study conducts the inter-comparison between four model simulations and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia (subject to the calibrated parameter) while PCR-GLOBWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors). The analysis can be used to declare the status of the ΔGWS estimate, as well as itemize the possible improvements of the future model development.

The Value of Remotely Sensed Information: The Case of GRACE-Enhanced Drought Severity Index.

A decision framework is developed for quantifying the economic value of information (VOI) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission for drought monitoring, with a focus on the potential contributions of groundwater storage and soil moisture measurements from the GRACE Data Assimilation (GRACE-DA) System. The study consists of: (a) the development of a conceptual framework to evaluate the socioeconomic value of GRACE-DA as a contributing source of information to drought monitoring; (b) structured listening sessions to understand the needs of stakeholders who are affected by drought monitoring; (c) econometric analysis based on the conceptual framework that characterizes the contribution of GRACE-DA to the US Drought Monitor (USDM) in capturing the effects of drought on the agricultural sector; and (d) a demonstration of how the improved characterization of drought conditions may influence decisions made in a real-world drought disaster assistance program. Results show that GRACE-DA has the potential to lower the uncertainty associated with our understanding of drought, and that this improved understanding has the potential to change policy decisions that lead to tangible societal benefits.

Benefits and Pitfalls of GRACE Data Assimilation: a Case Study of Terrestrial Water Storage Depletion in India.

This study investigates some of the benefits and drawbacks of assimilating Terrestrial Water Storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) into a land surface model over India. GRACE observes TWS depletion associated with anthropogenic groundwater extraction in northwest India. The model, however, does not represent anthropogenic groundwater withdrawals and is not skillful in reproducing the interannual variability of groundwater. Assimilation of GRACE TWS introduces long-term trends and improves the interannual variability in groundwater. But the assimilation also introduces a negative trend in simulated evapotranspiration whereas in reality evapotranspiration is likely enhanced by irrigation, which is also unmodeled. Moreover, in situ measurements of shallow groundwater show no trend, suggesting that the trends are erroneously introduced by the assimilation into the modeled shallow groundwater, when in reality the groundwater is depleted in deeper aquifers. The results emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems.

Improving estimates of water resources in a semi-arid region by assimilating GRACE data into the PCR-GLOBWB hydrological model

Abstract. An accurate estimation of water resources dynamics is crucial for proper management of both agriculture and the local ecology, particularly in semi-arid regions. Imperfections in model physics, uncertainties in model land parameters and meteorological data, as well as the human impact on land changes often limit the accuracy of hydrological models in estimating water storages. To mitigate this problem, this study investigated the assimilation of terrestrial water storage variation (TWSV) estimates derived from the Gravity Recovery And Climate Experiment (GRACE) data using an ensemble Kalman filter (EnKF) approach. The region considered was the Hexi Corridor in northern China. The hydrological model used for the analysis was PCR-GLOBWB, driven by satellite-based forcing data from April 2002 to December 2010. The impact of the GRACE data assimilation (DA) scheme was evaluated in terms of the TWSV, as well as the variation of individual hydrological storage estimates. The capability of GRACE DA to adjust the storage level was apparent not only for the entire TWSV but also for the groundwater component. In this study, spatially correlated errors in GRACE data were taken into account, utilizing the full error variance–covariance matrices provided as a part of the GRACE data product. The benefits of this approach were demonstrated by comparing the EnKF results obtained with and without taking into account error correlations. The results were validated against in situ groundwater data from five well sites. On average, the experiments showed that GRACE DA improved the accuracy of groundwater storage estimates by as much as 25 %. The inclusion of error correlations provided an equal or greater improvement in the estimates. In contrast, a validation against in situ streamflow data from two river gauges showed no significant benefits of GRACE DA. This is likely due to the limited spatial and temporal resolution of GRACE observations. Finally, results of the GRACE DA study were used to assess the status of water resources over the Hexi Corridor over the considered 9-year time interval. Areally averaged values revealed that TWS, soil moisture, and groundwater storages over the region decreased with an average rate of approximately 0.2, 0.1, and 0.1 cm yr−1 in terms of equivalent water heights, respectively. A particularly rapid decline in TWS (approximately −0.4 cm yr−1) was seen over the Shiyang River basin located in the southeastern part of Hexi Corridor. The reduction mostly occurred in the groundwater layer. An investigation of the relationship between water resources and agricultural activities suggested that groundwater consumption required to maintain crop yield in the growing season for this specific basin was likely the cause of the groundwater depletion.

GRACE terrestrial water storage data assimilation based on the ensemble four-dimensional variational method PODEn4DVar: Method and validation

Seasonal and interannual changes in the Earth’s gravity field are mainly due to mass exchange among the atmosphere, ocean, and continental water sources. The terrestrial water storage changes, detected as gravity changes by the Gravity Recovery and Climate Experiment (GRACE) satellites, are mainly caused by precipitation, evapotranspiration, river transportation and downward infiltration processes. In this study, a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage (TWS) data into the Community Land Model (CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4DVar, disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time, and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations. The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables, indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.

Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (>150,000 km2) and temporal (>10 days) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (DAS), based on the Catchment Land Surface Model (CLSM), enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture components (i.e., groundwater, soil moisture, and snow), which individually are more useful for scientific applications. In this study, GRACE DAS was applied to North America, and GRACE‐based drought indicators were developed as part of a larger effort to investigate the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally, and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the drought monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive data sets of groundwater storage from U.S. Geological Survey monitoring wells and soil moisture from the Soil Climate Analysis Network were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of a GRACE‐assimilated water storage field for improving drought detection.

Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe

A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

On the capabilities of the multi-channel singular spectrum method for extracting the main periodic and non-periodic variability from weekly GRACE data

We study the capabilities of the method of multi-channel singular spectrum analysis (MSSA) to extract periodic signals from the Gravity Recovery and Climate Experiment (GRACE) gravity field solutions. As a non-parametric method and because of the data-adaptive nature of the base functions, the MSSA allows for modelling of non-periodic variations. In addition, it can identify modulated oscillations in the presence of noise. In our study, we analyze a complete 6-year weekly time series of the GFZ-produced GRACE spherical harmonic coefficients of degree and order 30. The MSSA filtering reduces the average root-mean-square (RMS) of the mass variability over the oceans by more than 60% when all but the annual, semi-annual and long-term variations in the spherical harmonic coefficients are filtered out. While the high variance annual signal can be extracted from the GRACE data straightforwardly, the semi-annual and long-term modes identified in the low variance portion of the data eigen-spectrum are mixed. Moreover, the semi-annual variability is contaminated by the S2 tidal alias signal shown in an example for the Hudson Bay region to possibly alter the typical two-peak seasonal cycle of the water mass anomalies. Also, some long-term modes indicate that a variation exists with a 2.1–2.5 years period, possibly of a hydrology origin, which was found by previous studies in the GFZ GRACE data. On land, we analyze time series of basin averages in Amazon, Congo, Mississippi and Nelson rivers basins computed from the filtered GRACE and Global Land Data Assimilation System (GLDAS)/Noah model coefficients. The analysis is performed in two steps: (1) an approximation of the series of basin average is computed from the identified annual, semi-annual and long-term variations in the spherical harmonic coefficients and (2) to improve the approximation, the residual variability in the basin average series is analyzed by means of singular spectrum analysis. Time lags between the hydrology model and observations are found for the Amazon (4 weeks) and Mississippi and Nelson (2 weeks) basins, where GLDAS is generally ahead of GRACE. Significant differences in the water content for particular years, for example, during the 2007–2008 time period in Congo, are observed, as well. The use of the MSSA method in the analysis of the GRACE mass variations is not pertinent to the low resolution weekly solutions. The same approach can be applied to the monthly GRACE solutions of a much higher degree and order in the search for local signals. Here, we traded off the spatial resolution for the time resolution and we determined the time lag of the model water mass anomalies and observations with a nominal weekly resolution.