Comment on egusphere-2022-106

Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

Mass distribution on Earth is continuously changing due to various physical processes beneath the Earth's surface or on the surface. Some of the primary sources for these mass displacements are tidal forces, atmospheric and oceanic loading, and seasonal changes in continental water distribution. The development of relative cryogenic gravimeters, the Superconducting Gravimeters (SGs), has made it possible to characterize and monitor such mass variations at orders of magnitudes as small as a few nm/s2 (1 nm/s2–10–10 g where g is the mean gravity at the Earth’s surface). Our study focuses on the hydrodynamics of the 900 m thick unsaturated zone of the low-noise underground research laboratory (Laboratoire Souterrain à Bas Bruit, LSBB) located in Rustrel (France) using a unique configuration of two SGs vertically arranged 520 m depth apart. The installation of an SG (iGrav31) at the site surface several years after installing the first (iOSG24) inside a tunnel has provided several new insights into the understanding of the hydrological processes occurring in the LSBB. By comparing differential and residual gravity time-series together with global hydrological loading models, we find that most water-storage changes occur in the unsaturated zone between both SGs. The misfit between the observed gravity time-series and the gravity effect corresponding to local hydrological contribution calculated from global hydrological models can be explained by large lateral fluxes and rapid runoff occurring in the LSBB site. Finally, we implement a rectangular prism method to compute forward gravity responses to water storage changes for a homogeneous water-layer following the site topography using a 5-m digital elevation model. In particular, we analyse the sensitivity of the differential record from both SGs to the extent and depth of the water storage changes by computing the corresponding 2D admittances. This gravity difference is sensitive to an extension up to about 2500 m laterally before tending towards an asymptotic value corresponding to the Bouguer plate approximation. We show that the zone of water-storage changes that best fits observed differential gravity signal is located at depths larger than 500 m (below iOSG24). This fitting is improving when the integration radius increases with depth. This is the first time that hydrological processes are investigated when the baseline configuration of two SGs is vertical.

<p>There is considerable concern about water depletion caused by climate extremes (e.g., drought) and human water use in the U.S. and globally. Major U.S. aquifers provide an ideal laboratory to assess water storage changes from GRACE satellites because the aquifers are intensively monitored and modeled. The objective of this study was to assess the relative importance of climate extremes and human water use on GRACE Total Water Storage Anomalies in 14 major U.S. aquifers and to evaluate the reliability of the GRACE data by comparing with groundwater level monitoring (~-23,000 wells) and regional and global models. We quantified total water and groundwater storage anomalies over 2002 – 2017 from GRACE satellites and compared GRACE data with groundwater level monitoring and regional and global modeling results.  </p> <p>The results show that water storage changes were controlled primarily by climate extremes and amplified or dampened by human water use, primarily irrigation. The results were somewhat surprising, with stable or rising long-term trends in the majority of aquifers with large scale depletion limited to agricultural areas in the semi-arid southwest and southcentral U.S. GRACE total water storage in the California Central Valley and Central/Southern High Plains aquifers was depleted by drought and amplified by groundwater irrigation, totaling ~70 km<sup>3</sup> (2002–2017), about 2× the capacity of Lake Mead, the largest surface reservoir in the U.S. In the Pacific Northwest and Northern High Plains aquifers, lower drought intensities were partially dampened by conjunctive use of surface water and groundwater for irrigation and managed aquifer recharge, increasing water storage by up to 22 km<sup>3</sup> in the Northern High Plains over the 15 yr period. GRACE-derived total water storage changes in the remaining aquifers were stable or slightly rising throughout the rest of the U.S.</p> <p>GRACE data compared favorably with composite groundwater level hydrographs for most aquifers except for those with very low signals, indicating that GRACE tracks groundwater storage dynamics. Comparison with regional models was restricted to the limited overlap periods but showed good correspondence for modeled aquifers with the exception of the Mississippi Embayment, where the modeled trend is 4x the GRACE trend. The discrepancy is attributed to uncertainties in model storage parameters and groundwater/surface water interactions. Global hydrologic models (WGHM-2d and PCR-GLOBWB-5.0 overestimated trends in groundwater storage in heavily exploited aquifers in the southwestern and southcentral U.S. Land surface models (CLSM-F2.5 and NOAH-MP) seem to track GRACE TWSAs better than global hydrologic models but underestimated TWS trends in aquifers dominated by irrigation.</p> <p>Intercomparing GRACE, traditional hydrologic monitoring, and modeling data underscore the importance of considering all data sources to constrain water storage changes.  GRACE satellite data have critical implications for many nationally important aquifers, highlighting the importance of conjunctively using surface-water and groundwater and managed aquifer recharge to enhance sustainable development.</p>

Global glacier mass loss is accelerating, yet considerable spread in projected glacier changes remains due to model structure, forcing, and parameter uncertainty. Most global glacier projections rely on relatively simple models where calibration approaches are essential to constrain uncertainty by using scarce observations. Probabilistic calibration strategies that help with quantifying uncertainities have recently incorporated into a limited number of global glacier models. As a next step, we suggest employing Bayesian data assimilation methods with untapped potential to integrate multi source observations (in-situ, satellite, reanalysis) when calibrating global glacier model projections.Here, we present a probabilistic calibration framework for the Python Glacier Evolution Model (PyGEM) based on ensemble-based data assimilation. Two data assimilation techniques (PBS and AdaPBS) are used to calibrate mass balance parameters to constrain future projections of glacio-hydrological variables (surface mass balance and runoff) between 2015 and 2100 under four SSP climate scenarios across the Scandinavian region. In this work, we compare our calibration results with commonly used deterministic and probabilistic glacier model calibration algorithms which are already in use in PyGEM. Our probabilistic calibration framework based on data assimilation has the potential to quantify and disentangle uncertainties from climate forcing, model structure, and parameters. Better constrained and uncertainty-aware glacier models increases confidence in projections of future glacier change and their relevant impacts. This work paves the way for producing policy relevant global glacier projections and scenarios with their uncertainty estimates within the ERC-AdG GLACMASS project.

<strong class="journal-contentHeaderColor">Abstract.</strong> The GRACE (Gravity Recovery And Climate Experiment) and GRACE Follow-On (FO) satellite gravity missions enable global monitoring of the mass transport within the Earth&rsquo;s system, leading to unprecedented advances in our understanding of the global water cycle in a changing climate. This study focuses on the quantification of changes in terrestrial water storage based on an ensemble of GRACE and GRACE-FO solutions and two global hydrological models. Significant changes in terrestrial water storage are detected at pluriannual and decadal time -scales in GRACE and GRACE-FO satellite gravity data, that are generally underestimated by global hydrological models. The largest differences (more than 20 cm in equivalent water height) are observed in South America (Amazon, Sao Francisco and Parana river basins) and tropical Africa (Congo, Zambezi and Okavango river basins). Significant differences (a few cm) are observed worldwide at similar timescales, and are generally well correlated with precipitation. While the origin of such differences is unknown, pa rt of it is likely to be climate-related and at least partially due to inaccurate predictions of hydrological models. Slow changes in the terrestrial water cycle may indeed be overlooked in global hydrological models due to inaccurate meteorological forcin g (e.g., precipitation), unresolved groundwater processes, anthropogenic influences, changing vegetation cover and limited calibration/validation datasets. Significant differences between GRACE satellite measurements and hydrological model predictions have been identified, quantified and characterised in the present study. Efforts must be made to better understand the gap between both methods at pluriannual and decadal time-scales, which challenges the use of global hydrological models for the prediction of the evolution of water resources in changing climate conditions.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

&amp;lt;p&amp;gt;Studies of the global sea-level budget (SLB) and ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. The SLB is considered closed if the observed sea-level change agrees with the sum of independently assessed steric and mass contributions. The OMB is considered closed if the observed ocean-mass change is compatible with the sum of assessed mass contributions.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we present results from the Sea-Level Budget Closure (SLBC_cci) project conducted in the framework of ESA&amp;amp;#8217;s Climate Change Initiative (CCI). We used data products from CCI projects as well as newly-developed products based on CCI products and on additional data sources. Our focus on products developed in the same framework allowed us to exercise a consistent uncertainty characterisation and its propagation to the budget closure analyses, where the SLB and the OMB are assessed simultaneously.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present time series of global mean sea-level changes from satellite altimetry; new time series of the global mean steric component generated from Argo drifter data with incorporation of sea surface temperature data; time series of ocean-mass change derived from GRACE satellite gravimetry; time series of global glacier mass change from a global glacier model; time series of mass changes of the Greenland Ice Sheet and the Antarctic Ice Sheet both from satellite radar altimetry and from GRACE; as well as time series of land water storage change from the WaterGAP global hydrological model. Our budget analyses address the periods 1993&amp;amp;#8211;2016 (covered by the satellite altimetry records) and 2003&amp;amp;#8211;2016 (covered by GRACE and the Argo drifter system). In terms of the mean rates of change (linear trends), the SLB is closed within uncertainties for both periods, and the OMB, assessable for 2003&amp;amp;#8211;2016 only, is also closed within uncertainties. Uncertainties (1-sigma) arising from the combined uncertainties of the elements of the different budgets considered are between 0.26 mm/yr and 0.40 mm/yr, that is, on the order of 10% of the magnitude of global mean sea-level rise, which is 3.05 &amp;amp;#177; 0.24 mm/yr and 3.65 &amp;amp;#177; 0.26 mm/yr for 1993-2016 and 2003-2016, respectively. We also assessed the budgets on a monthly time series basis. The statistics of monthly misclosure agrees with the combined uncertainties of the budget elements, which amount to typically 2-3 mm for the 2003&amp;amp;#8211;2016 period. We discuss possible origins of the residual misclosure.&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;Large scale or global hydrological models (GHMs) show promise in enabling us to accurately predict floods, droughts, navigation hazards, reservoir operations, and many more water related issues. As opposed to regional hydrological models that have many parameters that need to be calibrated or estimated using local observation data (Sood and Smakhtin 2015). GHMs are able to simulate regions that lack observation data, whilst applying a uniform approach for parameter estimation (D&amp;amp;#246;ll, Kaspar, and Lehner 2003; Wid&amp;amp;#233;n&amp;amp;#8208;Nilsson et al. 2009). Up until recently the GHMs used coarse modelling grids of around 0.5 to 1 degree spatial resolution. However, due to advances in satellite data, climate data, and computational resources, GHMs are modelling on higher resolutions (up to 200 meters) that raise questions about how these models can be adjusted in order to take advantage of the finer modelling grid.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we carry out an extensive assessment of how changes in spatial resolution affect the simulations of the Wflow SBM model for 8 basins in the Continental United States. This is done by comparing the model states and fluxes at three spatial resolutions, namely 3 km, 1km, and 200m. A hypothesis driven approach is used to investigate why changes in states and fluxes are taking place at different spatial resolutions and how they relate to model performance. The latter is determined by validating river discharge, snow extent, soil moisture, and actual evaporation. In addition, we make use of two sets of parameters that rely on different pedo-transfer functions. Further investigating the role parameterization in conjunction with changes in spatial resolution.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;By carrying out this study within the eWaterCycle II framework we showcase our ability to handle large datasets (forcing and validation) whilst always complying to the FAIR principles. Furthermore, this study is setup in such that it is scalable in terms of case study areas and hydrological models.&amp;lt;/p&amp;gt;

Global hydrological models (GHM) are indispensable tools for understanding hydrological dynamics in natural settings as well as for analysing complex hydrological human-nature systems in critical regions of the globe and for supporting sustainable management policies in current context and future climate change scenarios. However, GHMs suffer from high predictive uncertainties which stem from input data and climate forcing uncertainties, incomplete knowledge about hydrological processes and their imprecise mathematical description, unknown initial and boundary conditions, and uncertain parameters. Reduction of these uncertainty by model calibration has almost never been performed globally for any GHM due to the high number of parameters in these models, the limited availability of observations of critical hydrological variables at a scale suitable for these models, and the high computational complexity and demand of model calibration. To address these issues, we have developed and employed a parallel and scalable multi-criterial Pareto optimal calibration framework to estimate parameters of the state-of-the-art global hydrological model WaterGAP for 1509 drainage basins with available streamflow observations. Model calibration was done against gauge-based observations of streamflow (Q) and terrestrial water storage anomalies of GRACE/GRACE-FO (TWSA). The influential parameters of each basin were identified prior to calibration by a multi-variable sensitivity analysis for the variables Q, TWSA, and percentage snow cover in the case of basins with relevant snow accumulation. We expect that our study will advance methodologies for sensitivity and calibration analyses of GHMs.

Drought occurrences have become more frequent across all continents in recent years, leading to greater emphasis on understanding their impacts on water resources and socioeconomic conditions. Despite the existence of several global drought monitoring systems, a comprehensive multisectoral approach, that integrates the impact on water, agriculture, ecosystems and society, is still lacking. We therefore present a multi-sectoral global drought hazard monitoring dataset (histMDH) for the period of 1981-2020 covering five key sectors: water supply, riverine and non-agricultural land ecosystems, and both rainfed and irrigated agriculture. With a period of 40 years coverage, histMDH is suitable to be used as the baseline/reference period for a near real-time monitoring and forecasting system, part of which will be used in an operational system in future. The dataset is derived from a modelling chain using the ERA5 reanalysis data (produced by the European Centre for Medium-Range Weather Forecasts) as climate forcing for two global models: Global Crop Water Model (GCWM) and Global Hydrological Model (WaterGAP) to generate a suite of multi-sectoral drought hazard indicators (DHI). The resulting gridded monthly dataset comprises eleven DHIs (two meteorological, seven hydrological, and two agricultural), spanning 1981–2020. The DHIs defined can be used to identify droughts across different sectors and consequently define their characteristics and intersectoral impacts. The suitability of the DHIs for drought monitoring was assessed using multiple independent data sources at global and regional scales. As an open-access dataset, histMDH provides a critical baseline for near real-time drought hazard monitoring and forecasting within operational systems. It offers valuable support for decision-making in water management, agriculture, and food and water security monitoring. Furthermore, the spatio-temporal variability of DHIs at global and regional scales enables the identification of drought-prone regions, allowing to mitigate drought impacts and transition to more resilient agricultural, ecological and water supply systems.

Recent advances in global hydrological modeling yield many global runoff datasets that are extensively used in global hydrological analyses. Here, we provide a comprehensive evaluation of simulated runoff from 21 global models, including 12 climate models from CMIP6, six global hydrological models from the Inter-Sectoral Impact Model Inter-Comparison Project (ISMIP2a) and three land surface models from the Global Land Data Assimilation System (GLDAS), against observed streamflow in 840 unimpaired catchments globally. Our results show that (i) no model performs consistently better in estimating runoff from all aspects, and all models tend to perform better in more humid regions and non-cold areas; (ii) the interannual runoff variability is well represented in ISIMIP2a and GLDAS models, and no model performs satisfactorily in capturing the annual runoff trend; (iii) the runoff intra-annual cycle is reasonably captured by all models yet an overestimation of intra-annual variability and an early bias in peak flow timing are commonly found; and (iv) model uncertainty leads to a larger uncertainty in runoff estimates than that induced by forcing uncertainty in ISIMIP2a, and model uncertainty in GLDAS is larger than that in ISIMIP2a. Finally, we confirm that the multi-model ensemble is an effective way to reduce uncertainty in individual models except for CMIP6 regarding mean annual magnitude and annual runoff trend. Overall, our findings suggest that assessments/projections of runoff changes based on these global outputs contain great uncertainties and should be interpreted with caution, and call for more advanced, observation-guided ensemble techniques for better large-scale hydrological applications.

Variations in Earth’s rotation result from a range of geophysical processes, including gravitational forcing by celestial bodies and mass redistribution within the atmosphere, oceans, hydrosphere, and cryosphere. The contributions of these processes to variability in the planet’s rotational motion are commonly quantified using four components of effective angular momentum: atmospheric (AAM), oceanic (OAM), hydrological (HAM), and cryospheric (CAM).Hydrological angular momentum (HAM) describes the excitation of polar motion (PM) and length-of-day (LOD) variations caused by mass redistribution within the continental hydrosphere and can be estimated from global hydrological models, satellite-derived gravity field solutions, or climate model outputs. In this study, we reassess the mass-related excitation of PM by deriving the equatorial components (χ₁ and χ₂) of HAM from temporal variations in the C21 and S21 geopotential coefficients obtained from a new class of hybrid gravity field solutions. These solutions replace the conventional spherical harmonic representation with empirical orthogonal functions (EOFs) derived from Gravity Recovery and Climate Experiment (GRACE) data and fitted to Satellite Laser Ranging (SLR) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations, enabling the construction of an extended time series spanning from 1984 to the present.The resulting HAM time series are compared with estimates from global hydrological models and validated against the hydrological signal in geodetic angular momentum (GAO). The consistency between HAM and GAO is evaluated across multiple frequency bands, with particular emphasis on variability at periods longer than three years. In this low-frequency range, correlations between HAM derived from the hybrid solutions and GAO reach values of up to 0.9, indicating that hydrological signals inferred from temporal variations of the Earth’s gravity field account for a substantial fraction of the observed long-term PM excitation..Typically, the agreement between GAO and HAM time series is analysed by comparing the χ₁ and χ₂ components separately. Here, we perform the analysis along the direction of maximum correlation, providing a more robust and physically meaningful assessment of the agreement between HAM and GAO.These findings highlight the importance of gravimetry-based HAM for interpreting PM variability across multiple time scales and extend earlier GRACE- and model-based investigations of hydrological PM excitation. In addition, this study provides the first long-term HAM estimates derived from hybrid SLR+DORIS gravity solutions spanning the period from 1984 to the present.

This paper is devoted to the first results from the GHYRAF (Gravity and Hydrology in Africa) experiment conducted since 2008 in West Africa and is aimed at investigating the changes in water storage in different regions sampling a strong rainfall gradient from the Sahara to the monsoon zone. The analysis of GPS vertical displacement in Niamey (Niger) and Djougou (Benin) shows that there is a clear annual signature of the hydrological load in agreement with global hydrology models like GLDAS. The comparison of GRACE solutions in West Africa, and more specifically in the Niger and Lake Chad basins, reveals a good agreement for the large scale annual water storage changes between global hydrology models and space gravity observations. Ground gravity observations done with an FG5 absolute gravimeter also show signals which can be well related to measured changes in soil and ground water. We present the first results for two sites in the Sahelian band (Wankama and Diffa in Niger) and one (Djougou in Benin) in the Sudanian monsoon region related to the recharge–discharge processes due to the monsoonal event in summer 2008 and the following dry season. It is confirmed that ground gravimetry is a useful tool to constrain local water storage changes when associated to hydrological and subsurface geophysical in situ measurements.

Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River Basin

Streamflow prediction is a long‐standing hydrologic problem. Development of models for streamflow prediction often requires incorporation of catchment physical descriptors to characterize the associated complex hydrological processes. Across different scales of catchments, these physical descriptors also allow models to extrapolate hydrologic information from one catchment to others, a process referred to as “regionalization”. Recently, in gauged basin scenarios, deep learning models have been shown to achieve state of the art regionalization performance by building a global hydrologic model. These models predict streamflow given catchment physical descriptors and weather forcing data. However, these physical descriptors are by their nature uncertain, sometimes incomplete, or even unavailable in certain cases, which limits the applicability of this approach. In this paper, we show that by assigning a vector of random values as a surrogate for catchment physical descriptors, we can achieve robust regionalization performance under a gauged prediction scenario. Our results show that the deep learning model using our proposed random vector approach achieves a predictive performance comparable to that of the model using actual physical descriptors. The random vector approach yields robust performance under different data sparsity scenarios and deep learning model selections. Furthermore, based on the use of random vectors, high‐dimensional characterization improves regionalization performance in gauged basin scenario when physical descriptors are uncertain, or insufficient.