Assessing global water mass transfers from continents to oceans over the period 1948–2016

Cáceres, Denise; Marzeion, Ben; Malles, Jan-Hendrik; Gutknecht, Benjamin D.; Schmied, Hannes Müller; Döll, Petra

doi:10.5194/hess-2019-664

Cited by 12 publications

(31 citation statements)

References 58 publications

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“…Loss of groundwater was partially balanced by increased impoundment of water in man-made reservoirs, but impoundment has decelerated. However, WaterGAP2.2d underestimates water storage increases because only the largest reservoirs are simulated as reservoirs including their commissioning year and because the GRanD v1.1 database used in WaterGAP2.2d does not include some of the major reservoirs that were put into operation after 2000 (Cáceres et al, 2020). Soil water storage also contributes significantly to total water storage changes, showing increases since 1981.…”

Section: Water Storage Componentsmentioning

confidence: 99%

“…Ongoing WaterGAP development aims to fully integrate a gradient-based groundwater model (Reinecke et al, 2019), improve the floodplain dynamics of large river basins (e.g. the Amazon) as proposed by Adam (2017) and to integrate glacier mass data (Cáceres et al, 2020). In addition, an update of the data basis for water use computations is planned.…”

Section: Water Use Componentsmentioning

confidence: 99%

See 1 more Smart Citation

The global water resources and use model WaterGAP v2.2d: Model description and evaluation

Schmied¹,

Cáceres²,

Eisner³

et al. 2020

Preprint

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Abstract. WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model WGHM. Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be done with WaterGAP2.2d model output.

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Section: Water Storage Componentsmentioning

confidence: 99%

Section: Water Use Componentsmentioning

confidence: 99%

The global water resources and use model WaterGAP v2.2d: Model description and evaluation

Schmied¹,

Cáceres²,

Eisner³

et al. 2020

Preprint

Self Cite

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“…We use estimates of the contributions of mass changes of the Antarctic and Greenland ice sheets (AIS and GIS, respectively), glaciers (GLA), and land water storage (LWS). We define LWS anomalies as water mass changes outside glacierized areas: the sum of water stored in rivers, lakes, wetlands, artificial reservoirs, snow pack, canopy and soil (groundwater) (Cáceres et al, 2020). For each of the barystatic contributions we use four different estimates (Table 1, and discuss in more detail in Supplementary Text A).…”

Section: Datasetsmentioning

confidence: 99%

Trends and Uncertainties of Regional Barystatic Sea-level Change in the Satellite Altimetry Era

Camargo

Riva

Hermans

et al. 2021

Preprint

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Abstract. Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, it is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides a comprehensive analysis of regional barystatic SLC trends since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself); temporal (related to the temporal variability in the time series); and spatial-structural (related to the location/distribution of the mass change sources). We collect a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic SLC and analyse the different types of uncertainty. When all the contributions are combined, we find that the barystatic sea-level trends regionally ranges from −0.43 to 2.55 mm year−1 for 2003–2016, and from −0.39 to 2.00 mm year−1 for 1993–2016, depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.62 to 1.29 mm year−1 for 2003–2016, and from 0.35 to 0.90 mm year−1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, although the spatial-structural also has a significant contribution. On average, the intrinsic uncertainty is almost negligible. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for at least 50 % of the total uncertainty, depending on the region of interest. The second main contributions come from the spatial-structural uncertainty from Antarctica and land water storage, which show that different locations of mass change can lead to trend deviations larger than 20 %. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning.

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“…As the main storage component of the freshwater hydrologic cycle, groundwater systems support baseflow levels in streams and rivers, and thereby ecosystems and agricultural productivity and other ecosystem services in both irrigated and rainfed systems (Scanlon et al, 2012;Qiu et al, 2019;Visser, 1959;Zipper et al, 2015Zipper et al, , 2017. When pumped groundwater is transferred to oceans Wada et al, 2012;Döll et al, 2014a;Wada, 2016;Caceres et al, 2020;Luijendijk et al 2020), resulting sealevel rise can impact salinity levels in coastal aquifers, and freshwater and solute inputs to the https://doi.org/10.5194/hess-2020-378 Preprint. Discussion started: 24 August 2020 c Author(s) 2020.…”

Section: Why and How Is Groundwater Modeled At Continental To Global mentioning

confidence: 99%

HESS Opinions: Improving the evaluation of groundwater representation in continental to global scale models

Gleeson

Wagener

Doell

et al. 2020

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Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system, driven by crucial Earth science and sustainability problems. These models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. A key question for this nascent and rapidly developing field is how to evaluate the realism and performance of such large-scale groundwater models given limitations in data availability and commensurability. Our objective is to provide clear recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We identify three evaluation approaches, including comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); comparing several models with each other with or without reference to actual observations (model-based evaluation); and comparing model behavior with expert expectations of hydrologic behaviors that we expect to see in particular regions or at particular times (expert-based evaluation). Based on current and evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches may significantly improve the realism of groundwater representation in large-scale models, and thus our quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on these challenges, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

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Assessing global water mass transfers from continents to oceans over the period 1948–2016

Cited by 12 publications

References 58 publications

The global water resources and use model WaterGAP v2.2d: Model description and evaluation

The global water resources and use model WaterGAP v2.2d: Model description and evaluation

Trends and Uncertainties of Regional Barystatic Sea-level Change in the Satellite Altimetry Era

HESS Opinions: Improving the evaluation of groundwater representation in continental to global scale models

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