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  • Area; Error, absolute; GRACE satellite data, processed; International Polar Year (2007-2008); ipy; IPY; ORDINAL NUMBER; River; Water storage, trend  (1)
  • Observational gaps  (1)
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  • 1
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    PANGAEA
    In:  Supplement to: Llovel, William; Becker, Melanie; Cazenave, Anny; Crétaux, Jean-François; Ramillien, Guillaume (2010): Global land water storage change from GRACE over 2002-2009; Inference on sea level. Comptes Rendus Geoscience, 342(2), 179-188, https://doi.org/10.1016/j.crte.2009.12.004
    Publication Date: 2023-12-13
    Description: Global change in land water storage and its effect on sea level is estimated over a 7-year time span (August 2002 to July 2009) using space gravimetry data from GRACE. The 33 World largest river basins are considered. We focus on the year-to-year variability and construct a total land water storage time series that we further express in equivalent sea level time series. The short-term trend in total water storage adjusted over this 7-year time span is positive and amounts to 80.6 ± 15.7 km**3/yr (net water storage excess). Most of the positive contribution arises from the Amazon and Siberian basins (Lena and Yenisei), followed by the Zambezi, Orinoco and Ob basins. The largest negative contributions (water deficit) come from the Mississippi, Ganges, Brahmaputra, Aral, Euphrates, Indus and Parana. Expressed in terms of equivalent sea level, total water volume change over 2002-2009 leads to a small negative contribution to sea level of -0.22 ± 0.05 mm/yr. The time series for each basin clearly show that year-to-year variability dominates so that the value estimated in this study cannot be considered as representative of a long-term trend. We also compare the interannual variability of total land water storage (removing the mean trend over the studied time span) with interannual variability in sea level (corrected for thermal expansion). A correlation of ~0.6 is found. Phasing, in particular, is correct. Thus, at least part of the interannual variability of the global mean sea level can be attributed to land water storage fluctuations.
    Keywords: Area; Error, absolute; GRACE satellite data, processed; International Polar Year (2007-2008); ipy; IPY; ORDINAL NUMBER; River; Water storage, trend
    Type: Dataset
    Format: text/tab-separated-values, 132 data points
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., Mitchum, G., van de Wal, R. S. W., Woodworth, P. L., Ablain, M., Ardhuin, F., Ballu, V., Becker, M., Benveniste, J., Birol, F., Bradshaw, E., Cazenave, A., De Mey-Fremaux, P., Durand, F., Ezer, T., Fu, L., Fukumori, I., Gordon, K., Gravelle, M., Griffies, S. M., Han, W., Hibbert, A., Hughes, C. W., Idier, D., Kourafalou, V. H., Little, C. M., Matthews, A., Melet, A., Merrifield, M., Meyssignac, B., Minobe, S., Penduff, T., Picot, N., Piecuch, C., Ray, R. D., Rickards, L., Santamaria-Gomez, A., Stammer, D., Staneva, J., Testut, L., Thompson, K., Thompson, P., Vignudelli, S., Williams, J., Williams, S. D. P., Woppelmann, G., Zanna, L., & Zhang, X. Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level. Frontiers in Marine Science, 6, (2019): 437, doi:10.3389/fmars.2019.00437.
    Description: A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.
    Description: RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos.
    Keywords: Coastal sea level ; Sea-level trends ; Coastal ocean modeling ; Coastal impacts ; Coastal adaptation ; Observational gaps ; Integrated observing system
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Limitation Availability
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