scholarly journals Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States

2017 ◽  
Vol 18 (3) ◽  
pp. 625-649 ◽  
Author(s):  
Youlong Xia ◽  
David Mocko ◽  
Maoyi Huang ◽  
Bailing Li ◽  
Matthew Rodell ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Water Storage ◽  
The United States ◽  
Major Contributor ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Community Land Model ◽  
Surface Models ◽  
Terrestrial Water

Abstract To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

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

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

<p>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.</p><p>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.</p><p>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.</p>

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 2 (2) ◽  
pp. 781-809 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Pairwise Correlation ◽  
Network Analyses ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationships exist between precipitation and TWS, the latter also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and hydrologic cycle, but also provide new model calibration constraints for improving the current land surface models. In this work, the connectivity of TWS is quantified using the climate network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS datasets, a remote-sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated dataset from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both datasets have 1 ° × 1 ° resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a statistical cutoff threshold to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show TWS hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two datasets indicate that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide important insights for constraining land surface models, especially in data sparse regions.

scholarly journals Benchmarking global hydrological and land surface models against GRACE in a medium-size tropical basin

2021 ◽  
Author(s):  
Silvana Bolaños Chavarría ◽  
Micha Werner ◽  
Juan Fernando Salazar
Keyword(s):  
Water Resources ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Models ◽  
Global Models ◽  
Grace Data ◽  
Surface Models ◽  
Terrestrial Water ◽  
Long Term Trends

Abstract. The increasing reliance on global models to address climate and human stresses on hydrology and water resources underlines the necessity for assessing the reliability of these models. In river basins where availability of gauging information from terrestrial networks is poor, models are increasingly proving to be a powerful tool to support hydrological studies and water resources assessments. However, the lack of in-situ data hampers rigorous performance assessment, particularly in tropical basins where discordance between global models is considerable. Remotely sensed data of the terrestrial water storage obtained from the GRACE satellite mission can, however, provide independent data against which the performance of such global models can be evaluated. Here we assess the reliability of six global hydrological models (GHM) and four land surface models (LSM) available at two resolutions. We compare Total Water Storage (TWS)'s modelled dynamics with TWS derived from GRACE data over the Magdalena-Cauca basin in Colombia, a medium-sized tropical basin with a comparatively well-developed gauging network. We benchmark monthly TWS changes from each model against GRACE data for 2002–2014, evaluating monthly variability, seasonality, and long-term trends. TWS changes are evaluated at basin level, as well as for selected sub-basins with decreasing basin size. We find that the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. The high-resolution GHM W3RA model forced by the Multi-Source Weighted Ensemble Precipitation (MSWEP) is most consistent at providing the best performance at almost all basin scales, with higher-resolution models generally outperforming lower-resolution counterparts. This is, however, not the case for all models. Results highlight the importance of basin scale in the representation of TWS by the models, as with decreasing basin area, we note a commensurate decrease in the model performance. A marked reduction in performance is found for basins smaller than 60,000 km2. Although uncertainties in the GRACE measurement increase for smaller catchments, the models are clearly challenged in representing the complex hydrological processes of this tropical basin, as well as human influences. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, in particular for those models with explicit representation of the internal dynamics of hydrological stocks, offering useful information for the continued improvement of large-scale hydrological and land-surface models of the global terrestrial water cycle, including in tropical basins.

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

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

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’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.  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.</p>

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

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

<p>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’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.</p> <p> </p> <p>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 R<sub>OL</sub> = 0.46 and R<sub>GPSDA</sub> = 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.</p>

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract 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).

A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations

2020 ◽  
Vol 143 ◽  
pp. 103683 ◽  
Author(s):  
Wenlong Jing ◽  
Liping Di ◽  
Xiaodan Zhao ◽  
Ling Yao ◽  
Xiaolin Xia ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Data Driven ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Model Simulations ◽  
Data Driven Approach ◽  
Terrestrial Water ◽  
Storage Solution

scholarly journals Evaluation of leaf-level optical properties employed in land surface models – example with CLM 5.0

2019 ◽  
Author(s):  
Titta Majasalmi ◽  
Ryan M. Bright
Keyword(s):  
Optical Properties ◽  
Optical Property ◽  
Land Surface ◽  
Surface Fluxes ◽  
Estimation Methods ◽  
Leaf Angle ◽  
Land Surface Models ◽  
Community Land Model ◽  
Canopy Volume ◽  
Surface Models

Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although Plant Functional Type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the time-invariant optical properties table of the Simple Biosphere (SiB) model (later referred as SiB-table) presented 30-years ago by Dorman and Sellers (1989) which has since become adopted by many LSMs. This revisit was needed as much of the data underlying the SiB-table was not formally reviewed or published or was based on older papers or personal communications (i.e. the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g. Community Land Model (CLM), Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB-table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e. tropical, boreal, temperate) and phenology (i.e. deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops and grasses and by photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to be appropriate. However, in the near-infrared and shortwave infrared bands (NIR+SWIR; e.g. 701–2500 nm, referred as NIR) notable differences between CLM default and measured estimates were observed, thus suggesting that NIR optical properties need updating in the model. For example, for conifer PFTs, the measured mean needle albedo estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat-leaves, the measured mean leaf albedo values in NIR were 20 %, 14 % and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle definitions were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and introduce the concept and application of photon recollision probability (p). The p may be used to upscale needle spectra into shoot spectra to meet the common assumption that foliage is located randomly within the canopy volume (behind canopy radiative transfer calculation) to account for multiple scattering effects caused by needles clustered into shoots.

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