The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model

We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5{\deg}, covering the time frame 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the Ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the global average over more than 1000 stations, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands. While some differences exist, overall GLWS2.0 agrees quite well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.Comment: Preprin

The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features

Water – Global Assessment and Prognosis (WaterGAP) is a modelling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms and new data of the most recent model version v2.2e are described, together with a thorough evaluation of simulated water use, streamflow and total water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small man-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended dataset of streamflow observations at 1509 gauging stations. The model can now be started using pre-scribed water storages and other conditions, which facilitates data assimilation as well as near real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework

GLWS 2.0: A global product that provides total water storage anomalies, groundwater, soil moisture and surface water with a spatial resolution of 0.5° from 2003 to 2019

The global land water storage (GLWS) data set is produced by assimilating (Eicker at al., 2014) gridded GRACE and GRACE-FO-derived total water storage anomalies (TWSA) into the WaterGAP global hydrological model using the Parallel Data Assimilation Framework (PDAF, Nerger and Hiller, 2013). The resulting data set represents thus an optimal synthesis of GRACE data and all data sets that went into the hydrological model. This synthesis seeks to fit GRACE (-FO) TWSA grids within error bars, and at the same time it solves the horizontal and vertical water balances as represented in the hydrological model, again within error bars. To this end, the uncertainty of the hydrological model simulation is represented via an n-member ensemble, where we take into account the uncertainty of forcing (precipitation and radiation) data as well as the uncertainty of some model calibration parameters. As a result, when no GRACE (-FO) data is available, the GLWS data set represents the mean – or the median additionally provided - of an ensemble where each member is dynamically consistent with the model. It is important to understand that this mean/median depends on the ensemble creation and thus will differ from published WaterGAP standard runs, even if there is no GRACE data within a particular month. It is also important to understand the assimilation-derived GLWS data set does not represent a simple downscaling of the GRACE data, i.e. spatial smoothing of GLWS does not necessarily correspond to GRACE (-FO) TWSA. The monthly level 3 GLWS data represent the total water storage anomaly (TWSA) on 0.5° grids and level 2 GLWS data represent groundwater, soil moisture and surface water. They are provided now for 01/2003 to 12/2019. Additionally, the standard deviation is provided (computed from the ensemble). As default, GLWS is derived from the ensemble mean, here, we additionally provide the ensemble median. The main updates with respect to the release 001 were the use of an updated version of WaterGAP as well as minor bug fixes in the assimilation

RECOG-EQ RL01 Earthquake correction for CSR, GFZ and ITSG solutions of GRACE level 3 total water storage anomalies from 2003-01 to 2016-12 correcting for the Sumatra-Andaman (2004) and Tohoku (2011) earthquakes

RECOG - REgional COrrections for GRACE: The dataset contains a monthly global earthquake correction for GRACE-level 3 total water storage anomalies (TWSA) for CSR, GFZ and ITSG solutions. The correction is monthly available from January 2003 to December 2016 and is used to correct for the Sumatra-Andaman (2004) and Tohoku (2011) earthquake by using a method described in Einarsson et al. (2010), where a co- and a post-seismic signal is modeled. The files contain longitude, latitude, time and the correction field of TWSA on an 0.5° grid. In the processing of the data, we included replacing lower degree coefficients, DDK3 filtering, reduced a temporal mean from 2003 to2016 and applied a correction for glacial isostatic adjustment. More information about the computation and application of the earthquake correction can be found in Deggim et al. (2020, in preparation). For more information about the GRACE processing visit following website: https://www.apmg.uni-bonn.de/daten-und-modelle/grace_level3_monthly_solutions, last access 12.08.2020

Veränderungen der Wasserspeicherung in Deutschland seit 2002 aus Beobachtungen der Satellitengravimetrie

Seit dem Jahr 2018 traten in weiten Teilen Europas unterdurchschnittliche Jahresniederschläge und überdurchschnittliche Lufttem-peraturen auf, einhergehend mit fallenden Wasserständen in Seen und im Grundwasser, Niedrigwasserbedingungen in Flüssen,Schäden an Ökosystemen und negativen Auswirkungen in verschiedenen Wirtschaftssektoren. Dies führte zu einer öffentlichenDiskussion zur gegenwärtigen und künftigen Verfügbarkeit der Wasserressourcen. In Deutschland wurde diese Debatte zudem ver-stärkt durch Medienberichte über die drastisch abnehmende Gesamtwasserspeicherung (Terrestrial Water Storage (TWS)) basierendauf Daten der Satellitengravimetriemissionen GRACE und GRACE-FO. Der dort verwendete Datensatz des amerikanischen Analyse-zentrums JPL (JPL-Mascons-Datensatz) zeigt einen TWS-Rückgang von 2,4 Gigatonnen pro Jahr (Gt/Jahr) für die Fläche von Deutsch-land im Zeitraum von November 2002 bis Oktober 2021. Um der Diskussion eine breitere wissenschaftliche Grundlage zu liefern, wirdhier zunächst das Konzept sowie die Möglichkeiten und Einschränkungen der Satellitengravimetrie vorgestellt. Anschließend werdenneben den JPL-Mascons-Daten drei weitere GRACE/GRACE-FO-Datenprodukte (COST-G, GFZ und ITSG Graz/Universität Bonn), dieeinen über Deutschland gemittelten TWS-Trend von -0,7 bis -1,3 Gt/Jahr für die genannte Periode ergeben. Da satellitengravi-metrische Messungen aufgrund des Mess- und Auswerteprinzips auch zu einem gewissen Anteil Massenveränderungen räumlichaußerhalb des eigentlich interessierenden Bereichs erfassen, führt der Massenverlust der Alpengletscher zu einem etwas stärker nega-tiven Trend in Deutschland, der hier herausgerechnet wurde. Die unterschiedlichen Ergebnisse für die verschiedenen Datenprodukteweisen auf die Unsicherheiten in den GRACE-Daten hin und die vergleichende Betrachtung mehrerer Datensätze wird somit empfohlen.Die deutlich anderen Ergebnisse des JPL-Mascons-Datensatztes sind möglicherweise mit seiner in mehreren Punkten abweichendenProzessierungsmethode begründet. Angesichts extrem positiver TWS-Anomalien im niederschlagsreichen Jahr 2002 und sehr negativerAnomalien in den Dürrejahren 2018 und 2019 ist weiterhin zu betonen, dass die resultierenden Trendwerte stark von dem gewähltenZeitraum abhängen. Eine längere, mit einem hydrologischen Modell simulierte TWS-Zeitreihe von Deutschland zeigt zum einen einegute Übereinstimmung mit den TWS-Beobachtungen, zum anderen dass die Trendwerte der Periode der Satellitengravimetrie nichtrepräsentativ für die langfristige Entwicklung sind und nicht zur Extrapolation auf die zukünftigen Trends der Gesamtwasserspeiche-rung verwendet werden sollten

WaterGAP v2.2e

<p>This version of the source code from the WaterGAP Global Hydrological Model (WGHM) is used in version v2.2e.</p&gt

RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

International audienceObservations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923)

Global-scale drought risk assessment for agricultural systems

Droughts continue to affect ecosystems, communities and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk for both irrigated and rainfed agricultural systems at the global scale. Composite hazard indicators were calculated for irrigated and rainfed systems separately using different drought indices based on historical climate conditions (1980–2016). Exposure was analyzed for irrigated and non-irrigated crops. Vulnerability was assessed through a socioecological-system (SES) perspective, using socioecological susceptibility and lack of coping-capacity indicators that were weighted by drought experts from around the world. The analysis shows that drought risk of rainfed and irrigated agricultural systems displays a heterogeneous pattern at the global level, with higher risk for southeastern Europe as well as northern and southern Africa. By providing information on the drivers and spatial patterns of drought risk in all dimensions of hazard, exposure and vulnerability, the presented analysis can support the identification of tailored measures to reduce drought risk and increase the resilience of agricultural systems.JRC.E.1-Disaster Risk Managemen