Satellite‐Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS‐2)

Prigent, Catherine; Jiménez, Carlos; Bousquet, Philippe

doi:10.1029/2019jd030711

Cited by 80 publications

(105 citation statements)

References 45 publications

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“…Spatial and temporal coverage was greatly improved soon after (Blake and Rowland, 1986) with the addition of the Earth System Research Laboratory from US National Oceanic and Atmospheric Administration (NOAA/ESRL) flask network (Steele et al, 1987, Fig. 1) and of the Advanced Global Atmospheric Gases Experiment (AGAGE) (Cunnold et al, 2002;Prinn et al, 2000), the Commonwealth Scientific and Industrial Research Organisation (CSIRO; Francey et al, 1999), the University of California Irvine (UCI; Simpson et al, 2012), and in situ and flask measurements from regional networks, such as the ICOS (Integrated Carbon Observation System) network in Europe (INGOS, 2018; ICOS-RI, 2019; https://www.icos-ri.eu/, last access: 29 June 2020). The combined datasets provide the longest time series of globally averaged CH 4 abundance.…”

Section: Atmospheric Observationsmentioning

confidence: 99%

The Global Methane Budget 2000–2017

Saunois

Stavert

Poulter

et al. 2020

Earth Syst. Sci. Data

1,543

1,245

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Abstract. Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.

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Section: Atmospheric Observationsmentioning

confidence: 99%

The Global Methane Budget 2000–2017

Saunois

Stavert

Poulter

et al. 2020

Earth Syst. Sci. Data

1,543

1,245

View full text Add to dashboard Cite

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“…All data in this study are publicly available and were accessed at the links given above in the text (Landsat, https://global-surface-water.appspot.com; GRDC, https://portal.grdc.bafg.de; ESA CCI, http://maps.elie.ucl.ac.be/CCI/viewer/download.php), from the literature (Tree density map, Hansen et al., 2013; MERIT DEM, Yamazaki et al., 2017; MERIT Hydro Yamazaki et al., 2019; GIEMS‐2, Prigent et al., 2020). The output of water occurrence estimated from CaMa‐Flood is available from Zhou and Yamazaki (2021).…”

Section: Data Availability Statementmentioning

confidence: 99%

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Zhou

Prigent

Yamazaki

2021

Water Resources Research

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Land surface water area (hereafter LSWA) is of paramount importance to the survival of all life forms (Karpatne et al., 2016). Water not only provides habitat for aquatic organisms but also affects various aspects of human life, such as for agricultural, domestic and industrial purposes (Vörösmarty & Sahagian, 2000). LSWA is highly dynamic and variations therein can be used as a direct indicator of climate change (Williamson et al., 2009) or human-induced changes (Pekel et al., 2016). LSWA is thus an essential variable in ecological, hydrological, climatic, and economic studies (Hirabayashi et al., 2013;Raymond et al., 2013;Willner et al., 2018). For such applications, accurate water information at adequate spatiotemporal resolution is crucial.Estimation of LSWA relies on three methods: ground surveys, remote sensing, and models. Among these methods, ground surveys cannot fully describe the water dynamics due to their slow updating frequency (Carroll et al., 2009;Lehner & Döll, 2004) and the significant cost of covering a large spatial domain. Remote sensing using satellites is an outstanding method that can provide regular large-scale observations of water surfaces. Various satellites have been used to identify LSWA, including Landsat (

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“…The latter is visible in Figure 6B, where a snapshot of the two fields show the waterlogged field (blue) shows less uniformity than the non-waterlogged field (gray). A limitation of this technique is the inability to acquire observations under cloudy conditions [112]. Since waterlogging may also result from intensive rainfall events, the inability to have observations under cloudy conditions hampers the ability to continuously monitor waterlogging.…”

Section: Detection Of Waterlogging With Different Remote Sensing Techniquesmentioning

confidence: 99%

Towards Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated Agriculture

Besten

Steele‐Dunne

Jeu³

et al. 2021

Remote Sensing

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Waterlogging is an increasingly important issue in irrigated agriculture that has a detrimental impact on crop productivity. The above-ground effect of waterlogging on crops is hard to distinguish from water deficit stress with remote sensing, as responses such as stomatal closure and leaf wilting occur in both situations. Currently, waterlogging as a source of crop stress is not considered in remote sensing-based evaporation algorithms and this may therefore lead to erroneous interpretation for irrigation scheduling. Monitoring waterlogging can improve evaporation models to assist irrigation management. In addition, frequent spatial information on waterlogging will provide agriculturalists information on land trafficability, assist drainage design, and crop choice. This article provides a scientific perspective on the topic of waterlogging by consulting literature in the disciplines of agronomy, hydrology, and remote sensing. We find the solution to monitor waterlogging lies in a multi-sensor approach. Future scientific routes should focus on monitoring waterlogging by combining remote sensing and ancillary data. Here, drainage parameters deduced from high spatial resolution Digital Elevation Models (DEMs) can play a crucial role. The proposed approaches may provide a solution to monitor and prevent waterlogging in irrigated agriculture.

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Satellite‐Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS‐2)

Cited by 80 publications

References 45 publications

The Global Methane Budget 2000–2017

The Global Methane Budget 2000–2017

Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations

Towards Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated Agriculture

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