Description: Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate- to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a “big picture” or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission.

Description: Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).

Description: In the EU-funded project Global Gravity-based Groundwater Product (G3P), we strive to combine data on terrestrial water storage from satellite gravimetry by the GRACE and GRACE-FO missions with existing products on water storage compartments from the Copernicus portfolio to establish a new cross-cutting product on groundwater storage variations with global coverage on a monthly basis. While the focus of G3P lies on incorporating observation-based Copernicus products, some model data has to be added to fill spatial and temporal gaps. This especially applies to water storage variations in surface water bodies, i.e., lakes and rivers, where little observation-based data is available. Altimetry-based data bases such as HYSOPE and the MGB model are available for large surface water bodies. However, to account for smaller water bodies and rivers, and to have a basis for assessing uncertainties of the entire approach, the integration of well-established models is desirable. A model we deem fit to these ends is Lisflood, which underpins the Global Flood Awareness System (GloFAS) of the Copernicus Emergency Management Service, and for which a recent global re-calibration is available. In this study, we evaluate Lisflood’s capability of modeling surface water storage variations in comparison to the above-mentioned observational data sources as well as the WaterGAP Global Hydrology Model (WGHM). As the target output of G3P is a 0.5° grid with monthly resolution,Lisflood’s output data will undergo an upscaling and temporal aggregation procedure which will also be subject of this study.

Description: This study has been run in the context of the European Union research project G3P (Global Gravitybased Groundwater Product) on developing Groundwater storage (GW) as a new product for the EU Copernicus Services. GW variations can be derived on a global scale by subtracting from total water storage (TWS) variations based on the GRACE/GRACE-FO satellite missions variations in other water storage compartments such as soil moisture, snow, surface water bodies, and glaciers. Due to the nature of data acquisition by GRACE and GRACE-FO, the data need filtering in order to reduce North-South-oriented striping errors. However, this also leads to a spatially smoothed TWS signal. For a consistent subtraction of all individual storage compartments from GRACE-based TWS, the individual data sets for all other hydrological compartments need to be filtered in a similar way as GRACE-based TWS. In order to test different filter methods, we used compartmental water storage data of the global hydrological model WGHM. The decorrelation filter known as DDK filter that is routinely used for GRACE and GRACE-FO data introduced striping artifacts in the smoothed model data. Thus, we can conclude that the DDK filter is not suitable for filtering water storage data sets that do not exhibit GRACE-like correlated error patterns. Alternatively, an isotropic Gaussian filter might be used. The best filter width of the Gaussian filter is determined by minimizing the differences between the empirical spatial correlation functions of each water storage and the spatial correlation function of GRACE-based TWS. We also analyzed time variations of correlation lengths such as seasonal effects. Finally, the selected filter widths are applied to each compartmental storage data set to remove them from TWS and to obtain the GW variations.

Description: Groundwater is one of the most important freshwater resources for mankind and for ecosystems. Assessing groundwater resources and developing sustainable water management plans based on this resource is a major field of activity for science, water authorities and consultancies worldwide. Due to its fundamental role in the Earth’s water and energy cycles, groundwater has been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. However, within Copernicus - the European Earth Observation Programme – there is no service available yet to deliver data on this fundamental resource, nor is there any other data source worldwide that operationally provides information on changing groundwater resources in a consistent way, observation-based, and with global coverage. Filling this gap is the goal of the G3P (Global Gravity-based Groundwater Product) project, funded since the beginning of 2020 by the European Union. G3P aims at (1) capitalizing from the unique capability of GRACE and GRACE-FO satellite gravimetry as the only remote sensing technology to monitor subsurface mass variations and thus groundwater storage change for large areas with global coverage, and (2) incorporating and advancing a wealth of products on storage compartments of the water cycle that are already part of the Copernicus portfolio and will be used for separating out the groundwater storage variations from the gravity-based total terrestrial water storage, to finally (3) developing an operational global groundwater service prototype as a cross-cutting extension of the existing Copernicus portfolio. In this contribution, we present the concept of G3P and first results.

Description: A sprinkling experiment was conducted at the geodetic observatory Wettzell (Bavaria, Germany) with the intention to combine classical hydrological field observations of soil moisture with gravity data and electrical resistivity tomography (ERT). The setup consisted of 8 sprinkling units installed around a gravimeter in field enclosure. Artificial rainfall was applied for 6 hours. The sprinkling area of 15 x 15 m was equipped with 3 vertical soil moisture sensor profiles, 1 horizontal soil moisture transect, near-surface soil moisture sensors and 3 ERT profiles. The non-invasive gravity data and the ancillary monitoring data were used to infer water transport processes in the subsurface during the sprinkling experiment. To this end, the gravity data were used to identify the structure and the parameters of a subsurface flow model in an inverse modelling approach by optimizing the simulated gravity response with respect to the observations. The ancillary soil moisture and ERT data were used to evaluate the model outputs in terms of adequacy and dominant subsurface flow processes. Model data cover the following subtopics: • virtual experiments to show the theoretical relationships between subsurface water re-distribution processes and their corresponding gravity responses • an uncertainty analysis of the sprinkling experiment, e.g., with respect to water volumes and their spatial distribution, and the impact on the expected gravity response • inverse modelling to identify dominant subsurface water re-distribution processes • a synthetical model setup based on the ancillary datasets of soil moisture and ERT Monitoring and model output data used for this investigation is provided within this data repository. A detailed description and discussion can be found in Reich et al. (2021). The inverse modelling was carried out using the R-package gravityInf (Reich, 2021).

Description: Field scale subsurface flow processes are difficult to observe and monitor. We investigated the value of gravity time series to identify subsurface flow processes by carrying out a sprinkling experiment in the direct vicinity of a superconducting gravimeter. We demonstrate how different water mass distributions in the subsoil affect the gravity signal and show the benefit of using the shape of the gravity response curve to identify different subsurface flow processes. For this purpose, a simple hydro-gravimetric model was set up to test different scenarios in an optimization approach, including the processes macropore flow, preferential flow, wetting front advancement, bypass flow and perched water table rise. Besides the gravity observations, electrical resistivity and soil moisture data were used for evaluation. For the study site, the process combination of preferential flow and wetting front advancement led to the best correspondence to the observations in a multi-criteria assessment. We argue that the approach of combining field-scale sprinkling experiments in combination with gravity monitoring can be transferred to other sites for process identification, and discuss related uncertainties including limitations of the simple model used here. The study stresses the value of advancing terrestrial gravimetry as an integrative and non-invasive monitoring technique for assessing hydrological states and dynamics.

Description: Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth’s climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales.

Description: In the last three years Central Europe experienced an ongoing severe drought. With the data of the GRACE Follow-On (GRACE-FO) mission we are able to quantify the water deficit of these years. Since May 2018 GRACE-FO continues the observations of GRACE (2002-2017) allowing to compare the most recent drought with earlier droughts in 2003 and 2015. In July 2019 the water mass deficit in Central Europe amounted to -154 Gt, which has been the largest deficit in the whole GRACE and GRACE-FO time series. In November 2018 the deficit reached -138 Gt and in June 2020 -147 Gt. Comparing these deficits to the mean annual water storage variation of 162 Gt shows the severity of the ongoing drought. With such a water mass deficit, a fast recovery within one year cannot be expected. In comparison to this, the droughts of 2003 with a deficit of -55 Gt and of 2015 with a deficit of -111 Gt were less severe. The GRACE and GRACE-FO total water storage data set also allows for analysing spatio-temporal drought patterns. In 2018 the drought was centred in in the South-West of Germany and neighbouring countries while parts of Poland were hardly affected by the drought. In 2018 the drought reached its largest extent only in late autumn. However, the exact onset of drought is not determinable due to missing data between July and October. Both in 2019 and 2020 the centre of the drought is located further East and the months with the largest deficit were July and June, respectively. Also in the later years, the drought was more evenly spread out over the whole of Central Europe. Additionally, we compared the GRACE and GRACE-FO data to an external soil moisture index and to surface water drought indices for Lake Constance and Lake Müritz. To this end, we derive a drought index from the GRACE and GRACE-FO mass anomalies. For the whole time series, the GRACE drought index shows a high congruency to the soil moisture drought index. Overall, the surface water drought index also fits well together with the GRACE drought index. However, the comparison reveals the influence of regional effects on surface waters not observable with GRACE and GRACE-FO.