Global terrestrial water storage connectivity revealed using complex climate network analyses

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.

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Global terrestrial water storage connectivity revealed using complex climate network analyses

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-2-781-2015 ◽

2015 ◽

Vol 2 (2) ◽

pp. 781-809 ◽

Cited By ~ 1

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0112.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 625-649 ◽

Cited By ~ 31

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.

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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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A reexamination of the dry gets drier and wet gets wetter paradigm over global land: insight from terrestrial water storage changes

10.5194/hess-2021-645 ◽

2022 ◽

Author(s):

Jinghua Xiong ◽

Shenglian Guo ◽

Jie Chen ◽

Jiabo Yin

Keyword(s):

Climate Change ◽

Land Surface ◽

Climate Models ◽

Water Storage ◽

Global Climate Models ◽

Historical Period ◽

Terrestrial Water Storage ◽

Wetting And Drying ◽

Global Land ◽

Terrestrial Water

Abstract. The “dry gets drier and wet gets wetter” (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is challenged over land due to different measures and datasets, and is still unexplored from the perspective of terrestrial water storage anomaly (TWSA). Considering the essential role of TWSA in wetting and drying of the land surface, here we built upon a large ensemble of TWSA datasets including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios. We find that 27.1 % of global land confirms the DDWW paradigm, while 22.4 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged with the percentage supporting the pattern lower than 20 %, and both the DDWW-validated and DDWW-opposed proportion increase along with the intensification of emission scenarios. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change.

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Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data

Hydrology and Earth System Sciences ◽

10.5194/hess-25-4185-2021 ◽

2021 ◽

Vol 25 (7) ◽

pp. 4185-4208

Author(s):

Natthachet Tangdamrongsub ◽

Michael F. Jasinski ◽

Peter J. Shellito

Keyword(s):

Land Surface ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Data Sets ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water ◽

Land Exchange

Abstract. Accurate estimation of terrestrial water storage (TWS) at a high spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5∘ by the resolution of soil and vegetation data sets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5 to 0.05∘) and time span (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05∘ developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. Using improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated on in this paper relies only on publicly accessible global data sets, allowing the reproduction of the 0.05∘ TWS estimates in any study region.

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Links between global terrestrial water storage and large-scale modes of climatic variability

10.5194/egusphere-egu2020-6613 ◽

2020 ◽

Author(s):

Tiewei Li

Keyword(s):

Land Surface ◽

Large Scale ◽

Water Cycle ◽

Southern Oscillation ◽

Global Climate ◽

Climatic Variability ◽

Water Storage ◽

Terrestrial Water Storage ◽

Global Land ◽

Terrestrial Water

Large-scale modes of climatic variability, or teleconnections, influence global patterns of climate variability and provide a framework for understanding complex responses of the global water cycle to global climate. Here, we examine how Terrestrial Water Storage (TWS) responds to 14 major teleconnections (TCs) during the 2003&#8211;2016 period based on data obtained from the Gravity Recovery and Climate Experiment (GRACE). By examining correlations between the teleconnections and TWS anomalies (TWSA) data, we find these teleconnections significantly influence TWSA over more than 80.8% of the global land surface. The El Ni&#241;o-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO) are significantly correlated with TWSA variations in 55.8%&#65292;56.2% and 60% the global land surface, while other teleconnections affect TWSA at regional scales. We also explore the TCs&#8217; effect on three key hydrological components, including precipitation (P), evapotranspiration (ET) and runoff (R), and their contribution to TWSA variations in 225 river basins. It&#8217;s found the TCs generally exert the comprehensive but not equally impact on all three components (P, ET and R). Our findings demonstrate a significant and varying effect of multiple TCs in terrestrial hydrological balance.

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

10.5194/hess-2021-323 ◽

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.

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Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

Author(s):

Mustafa Berk Duygu ◽

Zuhal Akyürek

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

High Temporal Resolution ◽

Land Surface Models ◽

Statistical Measures ◽

Surface Models ◽

Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

The Cryosphere Discussions ◽

10.5194/tcd-7-2333-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 2333-2372

Author(s):

E. Kantzas ◽

M. Lomas ◽

S. Quegan ◽

E. Zakharova

Keyword(s):

Land Surface ◽

Field Data ◽

Climate Models ◽

Optimization Techniques ◽

The Arctic ◽

Surface Model ◽

Land Surface Models ◽

Snow Density ◽

Data Set ◽

Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

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