Hydrometeorological extreme events, such as heavy rainfall, can cause a rapid increase in local water storage over a short period of time. The duration, magnitude, and area of the event, as well as the runoff of the affected region, determine the life span of these water mass changes. This leads to temporal aliasing, which, along with instrument noise, poses a significant challenge to improving the accuracy and resolution of satellite gravimetry products from GRACE, GRACE Follow-On, and next-generation gravity missions (NGGM). The current Atmospheric Ocean Dealiasing (AOD) products can remove tidal and sub-monthly non-tidal mass variations in the ocean and atmosphere from the level-1 GRACE/-FO data. However, the reanalysis and forecast datasets underlying these AOD products only have a limited temporal and spatial resolution and do not include high-frequency hydrological mass variations and liquid cloud water content of atmospheric mass, that can significantly increase during hydrometeorological extreme events. The research group New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), funded by the German Research Foundation (DFG), aims at improving GRACE/-FO data products by developing new analysis methods and modeling approaches. This includes a revision of existing geophysical background models as well as their spatial-temporal parameterization. Within this research group we investigate how hydrometeorological extreme events are mapping into GRACE/-FO level-1 data. In this study, we provide statistics on events that may affect GRACE/-FO/NGGM observations and are not accounted for in current AOD products. Using a 3D connected component algorithm, we determine the duration, magnitude, and area of these events for multiple test years over the entire GRACE/-FO observation time span from 2002 to 2023. As expected, the majority of hydrometeorological extreme events take place in tropical regions and areas that are frequently impacted by typhoons, hurricanes, and monsoon rains such as Japan, northern India, and around the Gulf of Mexico. Additionally, we found an increasing number of events over Europe that could significantly impact GRACE/-FO/NGGM observations. Overall, the number of events per year more than doubles from the start to the end of the observation period, which is likely due to climate change. We suspect that the GRACE-FO LRI measurement is sensitive to instantaneous precipitation and liquid cloud water content during overfly for a significant number of these extreme events. Hence, we anticipate an increased probability of NGGM observations being affected by such events, particularly in the context of ongoing climate change. Therefore, we recommend that upcoming dealiasing procedures carefully consider these events.
Abstract Africa is particularly vulnerable to climate change impacts, which threatens food security, ecosystem protection and restoration initiatives, and fresh water resources availability and quality. Groundwater largely contributes to the mitigation of climate change effects by offering short- to long-term transient water storage. However, groundwater storage remains extremely difficult to monitor. In this paper, we review the strengths and weaknesses of satellite remote sensing techniques for addressing groundwater quantity issues with a focus on GRACE space gravimetry, as well as concepts to combine satellite observations with numerical models and ground observations. One particular focus is the quantification of changes in groundwater resources in the different climatic regions of Africa and the discussion of possible climatic and anthropogenic drivers. We include a thorough literature review on studies that use satellite observations for groundwater research in Africa. Finally, we identify gaps in research and possible future directions for employing satellite remote sensing to groundwater monitoring and management on the African continent. Article Highlights Overview on the distribution and characteristics of African groundwater resources including future projections Combination of satellite and in situ observations with numerical models allows us to obtain a synoptic view of groundwater-related processes Summary of current concepts and achievements of satellite remote sensing-based groundwater monitoring and decision making over Africa
Temporal aliasing of high-frequency mass variations is, next to instrument noise, the biggest obstacle to improving accuracy and resolution of satellite gravimetry products from the GRACE, GRACE Follow-On and next generation gravity missions. In current GRACE/-FO processing, tidal and sub-monthly non-tidal mass variations in ocean and atmosphere are removed from the level-1 data using the Atmospheric Ocean Dealiasing (AOD) data sets. However, these reanalysis- and forecast-based data sets are not perfect and therefore errors are introduced in derived short- and long-term science results. Hence, to a large extent, the quality of the GRACE/-FO level-2 data relies on the consistency of the AOD data. It is therefore an essential task to improve the background models, especially with regard to next generation gravity mission.The research group New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), funded by the German Research Foundation (DFG), aims at improving GRACE/-FO data products by developing new analysis methods and modeling approaches. This includes a revision of existing geophysical background models as well as their spatial-temporal parameterization. Within this research group we create a consistent global data set of short-term atmospheric and hydrological mass variations with higher temporal and spatial resolution over Europe. For computing the atmospheric dealiasing fields we apply the 3D integration approach developed by Forootan et al. (2013) that also accounts for more realistic approximations of the Earth’s physical and geometrical shape. We compared sub-monthly as well as long-term signals of global atmospheric fields from ERA-Interim and ERA5 to investigate differences arising from different spatial resolution (0.5° & ~0.1°). The regional non-hydrostatic atmospheric reanalysis COSMO-REA6 provides 3D fields on a grid sized even below 0.1° within the EURO-CORDEX domain. We nested COSMO-REA6 into ERA-Interim to obtain a global consistent atmospheric dealiasing data set, but with higher spatial and temporal resolution over Europe. Short-term hydrological signals are not included in the standard dealiasing products, yet, some studies indicate that removing them might further reduce aliasing errors in the monthly GRACE/-FO fields and might be inevitable for next generation gravity missions. Therefore, we developed a hydrological dealiasing product following the same approach as for our refined atmospheric data set by nesting the water storage changes derived by the regional CLM data set (forced by COSMO-REA6) into the global WGHM data set. In this presentation we show the impact of short-term mass changes over Europe on GRACE observations.
Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.
The GRACE (Gravity Recovery And Climate Experiment) satellite mission as well as its successor GRACE Follow-On have monitored global and regional variability of total water storage (TWS) for the past two decades. Assimilating observations from these missions into hydrological models helps to improve modeled water storages and fluxes, to overcome deficits arising from simplifications or processes that are not considered in the model (e.g. unmodeled anthropogenic impacts), and to disaggregate GRACE observations temporally and spatially. Determining the optimal approach for assimilating these observations into hydrological models remains an ongoing area of research. The choice often depends on specific applications and the characteristics of the model itself. In this study, we analyze the water storage dynamics of two versions of the Community Land Model (CLM) - versions 3.5 and 5 - within a GRACE data assimilation framework over a 12.5 km grid covering Europe. The analysis focuses on assessing (i) the skill of both models without data assimilation, (ii) the impact of GRACE data assimilation on the model performance and (iii) the distribution of assimilation increments to different storage compartments. We evaluate water storages and fluxes simulated by both models against independent observations such as discharge from river gauges and satellite derived soil moisture. The results offer valuable insights into the impact of advancements made in biophysical processes and the representation of the carbon cycle in CLM5. Furthermore, we discuss the effectiveness of GRACE data assimilation and its influence on the behavior of CLM3.5 and CLM5, analyzing whether the assimilation helps to address differences between the two model versions - particularly considering the advancements in CLM5 - which would underline the ability of GRACE data assimilation in mitigating model deficits.
The Global Positioning System (GPS) is routinely used to measure the elastic response of Earth to continental hydrological mass changes, occurring at various temporal and spatial scales. While long-term and seasonal height changes are well observed in GPS height time series, the subseasonal deformations are less well resolved. A suite of predictions based on a remote sensing based Gravity Recovery and Climate Experiment (GRACE)-assimilating land surface model suggest significant high-frequency changes observable in GPS height time series (with periods between 15 and 90 days). This physical model is then adopted for separating the deterministic component of time series from the stochastic noise and is compared with the conventional harmonic functions modeling, a commonly used approach with predefined annual and semiannual periods. The noise parameters (spectral index and amplitude) associated with the two methods are estimated using the maximum likelihood estimation. We conclude that the GRACE-assimilated model output removes the effect of high-frequency hydrological deformations, producing less correlated residuals. Among other aspects, our results highlight the importance of assimilating GRACE-based remotely sensed total water storage data into hydrological models to obtain unbiased estimates of GPS vertical velocity and its uncertainty which is in demand in a range of applications such as the upcoming reference frame realizations.
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