New Estimates of Variations in Water Flux and Storage over Europe Based on Regional (Re)Analyses and Multisensor Observations

Abstract Precipitation minus evapotranspiration, the net flux of water between the atmosphere and Earth’s surface, links atmospheric and terrestrial water budgets and thus represents an important boundary condition for both climate modeling and hydrological studies. However, the atmospheric–terrestrial flux is poorly constrained by direct observations because of a lack of unbiased measurements. Thus, it is usually reconstructed from atmospheric reanalyses. Via the terrestrial water budget equation, water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) combined with measured river discharge can be used to assess the realism of the atmospheric–terrestrial flux in models. In this contribution, the closure of the terrestrial water budget is assessed over a number of European river basins using the recently reprocessed GRACE release 05 data, together with precipitation and evapotranspiration from the operational analyses of high-resolution, limited-area NWP models [Consortium for Small-Scale Modelling, German version (COSMO-DE) and European version (COSMO-EU)] and the new COSMO 6-km reanalysis (COSMO-REA6) for the European Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. These closures are compared to those obtained with global reanalyses, land surface models, and observation-based datasets. The spatial resolution achieved with the recent GRACE data allows for better evaluation of the water budget in smaller river basins than before and for the identification of biases up to 25 mm month−1 in the different products. Variations of deseasoned and detrended atmospheric–terrestrial flux are found to agree notably well with flux derived from GRACE and discharge data with correlations up to 0.75. Finally, bias-corrected fluxes are derived from various data combinations, and from this, a 20-yr time series of catchment-integrated water storage variations is reconstructed.

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Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

10.5194/egusphere-egu21-6380 ◽

2021 ◽

Author(s):

Ann Scheliga ◽

Manuela Girotto

Keyword(s):

Data Assimilation ◽

Sea Level Rise ◽

Sea Level ◽

Land Surface ◽

Water Budget ◽

Water Storage ◽

Surface Model ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Land Hydrology

Sea level rise (SLR) projections rely on the accurate and precise closure of Earth&#8217;s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution. &#160;Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.

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Intercomparison of terrestrial water budgets in EURO-CORDEX and TSMP evaluation runs

10.5194/egusphere-egu2020-5745 ◽

2020 ◽

Author(s):

Mohamed Eltahan ◽

Klaus Goergen ◽

Carina Furusho-Percot ◽

Stefan Kollet

Keyword(s):

Land Surface ◽

Water Budget ◽

Climate Models ◽

Water Cycle ◽

Weather Prediction ◽

Regional Climate ◽

Shallow Groundwater ◽

Surface Model ◽

Frequency Distributions ◽

Terrestrial Water

Water is one of Earth&#8217;s most important geo-ecosystem components. Here we present an evaluation of water cycle components using 12 EURO-CORDEX Regional Climate Models (RCMs) and the Terrestrial Systems Modeling Platform (TSMP) from ERA-Interim driven evaluation runs. Unlike the other RCMs, TSMP provides an integrated representation of the terrestrial water cycle by coupling the numerical weather prediction model COSMO, the land surface model CLM and the surface-subsurface hydrological model ParFlow, which simulates shallow groundwater states and fluxes. The study analyses precipitation (P), evapotranspiration (E), runoff (R), and terrestrial water storage (TWS=P-E-R) at a 0.11degree spatial resolution (about 12km) on EUR-11 CORDEX grid from 1996 to 2008. As reference datasets, we use ERA5 reanalysis to represent the complete terrestrial water budget, as well as the E-OBS, GLEAM and E-Run datasets for precipitation, evapotranspiration and runoff, respectively. The terrestrial water budget is investigated for twenty catchments over Europe (Guadalquivir, Guadiana, Tagus, Douro, Ebro, Garonne, Rhone, Po, Seine, Rhine, Loire, Maas, Weser, Elbe, Oder, Vistuala, Danube, Dniester, Dnieper, and Neman). Annual cycles, seasonal variations, empirical frequency distributions, spatial distributions for the water cycle components and budgets over the catchments are assessed. The analysis demonstrates the capability of the RCMs and TSMP to reproduce the overall characteristics of the water cycle over the EURO-CORDEX domain, which is a prerequisite if, e.g., climate change projections with the CORDEX RCMs or TSMP are to be used for vulnerability, impacts, and adaptation studies.

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Soil Control on Runoff Response to Climate Change in Regional Climate Model Simulations

Journal of Climate ◽

10.1175/jcli3471.1 ◽

2005 ◽

Vol 18 (17) ◽

pp. 3536-3551 ◽

Cited By ~ 60

Author(s):

Bart van den Hurk ◽

Martin Hirschi ◽

Christoph Schär ◽

Geert Lenderink ◽

Erik van Meijgaard ◽

...

Keyword(s):

Climate Change ◽

Annual Cycle ◽

Land Surface ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Water Storage ◽

Climate Simulation ◽

Discharge Data ◽

And Storage

Abstract Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections.

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Closing the water budget at the global scale using observations, remote sensing, and reanalyses

10.5194/egusphere-egu21-10270 ◽

2021 ◽

Author(s):

Fanny Lehmann ◽

Brahma Dutt Vishwakarma ◽

Jonathan Bamber

Keyword(s):

Land Surface ◽

Water Budget ◽

Water Storage ◽

Global Scale ◽

Terrestrial Water Storage ◽

Land Surface Models ◽

Cold Regions ◽

Climate Zones ◽

Surface Models ◽

Terrestrial Water

Despite the accuracy of GRACE terrestrial water storage estimates and the variety of global hydrological datasets providing precipitations, evapotranspiration, and runoff data, it remains challenging to find datasets satisfying the water budget equation at the global scale.We select commonly used and widely-assessed datasets. We use several precipitations (CPC, CRU, GPCC, GPCP, GPM, MSWEP, TRMM, ERA5 Land, MERRA2), evapotranspiration (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GLEAM, MOD16, SSEBop, ERA5 Land, MERRA2), and runoff (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GRUN, ERA5 Land, MERRA2) datasets to assess the water storage change over more than 150 hydrological basins. Both mascons and spherical harmonics coefficients are used as the reference terrestrial water storage from different centres processing GRACE data. The analysis covers a wide range of climate zones over the globe and is conducted over 2003-2014.The water budget closure is evaluated with Root Mean Square Deviation (RMSD), Nash-Sutcliffe Efficiency (NSE), and seasonal decomposition. Each dataset is assessed individually across all basins and dataset combinations are also ranked according to their performances. We obtain a total of 1080 combinations, among which several are suitable to close the water budget. Although none of the combinations performs consistently well over all basins, GPCP precipitations provide generally good results, together with GPCC and GPM. A better water budget closure is generally obtained when using evapotranspiration from Catchment Land Surface Models (GLDAS CLSM), while reanalyses ERA5 Land and MERRA2 are especially suitable in cold regions. Concerning runoff, the machine learning GRUN dataset performs remarkably well across climate zones, followed by ERA5 Land and MERRA2 in cold regions. We also highlight highly unrealistic values in evapotranspiration computed with version 2.2 of GLDAS (using data assimilation from GRACE) in most of the cold basins. Our results are robust as changing the GRACE product from one centre to the other does not affect our conclusions.

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Quantification of the Uncertainties in Soil and Vegetation Parameterizations for Regional Climate Simulations in Europe

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0226.1 ◽

2017 ◽

Vol 18 (5) ◽

pp. 1535-1548 ◽

Cited By ~ 4

Author(s):

Marcus Breil ◽

Gerd Schädler

Keyword(s):

Land Surface ◽

Regional Climate ◽

Small Scale ◽

Surface Model ◽

Limited Area ◽

Model Parameters ◽

Subgrid Scale ◽

Local Soil ◽

Atmosphere Interaction ◽

The Impact

Abstract The deterministic description of the subgrid-scale land–atmosphere interaction in regional climate model (RCM) simulations is changed by using stochastic soil and vegetation parameterizations. For this, the land–atmosphere interaction parameterized in a land surface model (LSM) is perturbed stochastically by adding a random value to the input parameters using a random number generator. In this way, a stochastic ensemble is created that represents the impact of the uncertainties in these subgrid-scale processes on the resolved scale circulation. In a first step, stochastic stand-alone simulations with the VEG3D LSM are performed to identify sensitive model parameters. Afterward, VEG3D is coupled to the Consortium for Small-Scale Modeling–Climate Limited-Area Modeling (COSMO-CLM) RCM and stochastically perturbed simulations driven by ERA-Interim (2001–10) are performed for the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) at a horizontal resolution of 0.22°. The simulation results reveal that the impact of stochastically varied soil and vegetation parameterizations on the simulated climate conditions differs regionally. In central Europe the impact on the mean temperature and precipitation characteristics is very weak. In southern Europe and North Africa, however, the resolved scale circulation is very sensitive to the local soil water conditions. Furthermore, it is demonstrated that the use of stochastic soil and vegetation parameterizations considerably improves the variability of monthly rainfall sums all over Europe by improving the representation of the land–atmosphere interaction in the stochastic ensemble on a daily basis. In particular, inland rainfall during summer is simulated much better.

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Assessing Water Balance Closure Using Multiple Data Assimilation and Remote Sensing-Based Datasets for Canada

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0131.1 ◽

2021 ◽

Author(s):

Jefferson S. Wong ◽

Xuebin Zhang ◽

Shervan Gharari ◽

Rajesh R. Shrestha ◽

Howard S. Wheater ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Water Balance ◽

River Basin ◽

Land Surface ◽

Water Storage ◽

River Basins ◽

Multiple Sources ◽

Discharge Data ◽

Multiple Data

AbstractObtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing and data assimilation-based products and quantifies the non-closure errors for river basins across Canada, ranging from 90,900 to 1,679,100 km2, for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products - the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products - MODIS, and Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM); one source of water storage data - GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The non-closure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7, 33.2, 17.8, and 12.2 percent, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month-1, respectively. In particular, non-closure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

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Toward continental hydrologic–hydrodynamic modeling in South America

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4815-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4815-4842 ◽

Cited By ~ 29

Author(s):

Vinícius A. Siqueira ◽

Rodrigo C. D. Paiva ◽

Ayan S. Fleischmann ◽

Fernando M. Fan ◽

Anderson L. Ruhoff ◽

...

Keyword(s):

South America ◽

Land Surface ◽

River Discharge ◽

Hydrological Modeling ◽

Water Levels ◽

Scale Model ◽

Global Models ◽

Discharge Data ◽

Continental Scale ◽

Terrestrial Water

Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

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Exploration of Terrestrial Water Storage Characterization via Assimilation of Ground-based GPS Observations of Vertical Displacement and GRACE TWS Retrievals

10.5194/gstm2020-2 ◽

2020 ◽

Author(s):

Gaohong Yin ◽

Barton Forman ◽

Jing Wang

Keyword(s):

Great Basin ◽

Land Surface ◽

Water Storage ◽

Vertical Displacement ◽

Estimation Accuracy ◽

Terrestrial Water Storage ◽

Drought Period ◽

Terrestrial Water ◽

Gps Observations

Accurate estimation of terrestrial water storage (TWS) is crucial in the characterization of the terrestrial hydrologic cycle. The launch of GRACE and GRACE Follow-On (GRACE-FO) missions provide an unprecedented opportunity to monitor the change in TWS across the globe. However, the spatial and temporal resolutions provided by GRACE/GRACE-FO are often too coarse for many hydrologic applications. Land surface models (LSMs) provide estimates of TWS at a finer spatio-temporal resolution, but most LSMs lack complete, all-encompassing physical representations of the hydrological system such as deep groundwater storage or anthropogenic influences (e.g., groundwater pumping and surface water regulation). In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth&#8217;s surface to mass redistribution. This study explores the potential of improving our knowledge in TWS change via merging the information provided by ground-based GPS, GRACE, and the NASA Catchment Land Surface Model (Catchment), especially for the TWS change during an extended drought period. &#160; Ground-based GPS observations of vertical displacement and GRACE TWS retrievals were assimilated into the Catchment LSM, respectively, using an ensemble Kalman filter (EnKF) in order to improve the estimation accuracy of TWS change. The data assimilation (DA) framework effectively downscaled TWS into its constituent components (e.g., snow and soil moisture) as well as improved estimates of hydrologic fluxes (e.g., runoff). Estimated TWS change from the open loop (OL; without assimilation) and GPS DA (i.e., using GPS-based vertical displacement during assimilation) simulations were evaluated against GRACE TWS retrievals. Results show that GPS DA improved estimation accuracy of TWS change relative to the OL, especially during an extended drought period post-2011 in the western United States (e.g., the correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). The performance of GPS DA and GRACE DA in estimating TWS constituent components and hydrologic fluxes were evaluated against in situ measurements. Results show that GPS DA improves snow water equivalent (SWE) estimates with improved R values found over 76% of all pixels that are collocated with in situ stations in the Great Basin. The findings in this study indicate the potential use of GPS DA and GRACE DA for TWS characterization. Both GRACE and ground-based GPS provide complementary TWS change information, which helps correct for missing physics in the LSM. Additionally, this study provides motivation for a multi-variate assimilation approach to simultaneously merge both GRACE and ground-based GPS into an LSM to further improve modeled TWS and its constituent components.

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