A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

High-resolution mapping of global surface water and its long-term changes

We explore the role of lakes in carbon cycling and global climate, examine the mechanisms influencing carbon pools and transformations in lakes, and discuss how the metabolism of carbon in the inland waters is likely to change in response to climate. Furthermore, we project changes as global climate change in the abundance and spatial distribution of lakes in the biosphere, and we revise the estimate for the global extent of carbon transformation in inland waters. This synthesis demonstrates that the global annual emissions of carbon dioxide from inland waters to the atmosphere are similar in magnitude to the carbon dioxide uptake by the oceans and that the global burial of organic carbon in inland water sediments exceeds organic carbon sequestration on the ocean floor. The role of inland waters in global carbon cycling and climate forcing may be changed by human activities, including construction of impoundments, which accumulate large amounts of carbon in sediments and emit large amounts of methane to the atmosphere. Methane emissions are also expected from lakes on melting permafrost. The synthesis presented here indicates that (1) inland waters constitute a significant component of the global carbon cycle, (2) their contribution to this cycle has significantly changed as a result of human activities, and (3) they will continue to change in response to future climate change causing decreased as well as increased abundance of lakes as well as increases in the number of aquatic impoundments.

/pdf/lakes-and-reservoirs-as-regulators-of-carbon-cycling-and-1nvh2w013k.pdf

Lakes and reservoirs as regulators of carbon cycling and climate

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Remote Sensing And Image Interpretation

Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery.

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.

/pdf/the-global-methane-budget-2000-2017-mbxchwmkf5.pdf

The global methane budget 2000–2017

An accurate description of the abundance and size distribution of lakes is critical to quantifying limnetic contributions to the global carbon cycle. However, estimates of global lake abundance are ...

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2014GL060641

A global inventory of lakes based on high‐resolution satellite imagery

https://dspace.stir.ac.uk/bitstream/1893/21536/1/RSE-IW-special-issue_Intro-FINAL-09-09-2014.pdf

Remote sensing of inland waters: challenges, progress and future directions

The extent of cyanobacterial blooms has been mapped using different satellite sensors from weather satellites to synthetic aperture radars. Quantitative detection of chlorophyll in cyanobacterial blooms by remote sensing, however, has been less successful. The first civilian hyperspectral sensor in space, Hyperion, acquired an image of cyanobacterial bloom in the western part of the Gulf of Finland on 14 July 2002. A chlorophyll concentration map was produced from this image using a spectral library that was created by running a bio-optical model with variable concentrations of chlorophyll. The results show that chlorophyll concentrations in the bloom area were much higher than reported by conventional water-monitoring programs, ships-of-opportunity, and satellite measurements. The reason why both in situ and satellite methods underestimate the amount of phytoplankton during cyanobacterial blooms is vertical and horizontal distribution of cyanobacteria, because cyanobacteria can regulate their buoyancy and are not uniformly distributed within the top mixed layer of water column in calm weather conditions. Aggregations of cyanobacteria form dense subsurface blooms and surface scums during extensive blooms. This study demonstrates that it is difficult to collect representative water samples from research vessels using standard methods because ships and water samplers destroy the natural distribution of cyanobacteria in the sampling process. Flowthrough systems take water samples from the depths at which the concentration of cyanobacteria is not correlated with the amount of phytoplankton that remote sensing instruments detect. The chlorophyll estimation accuracy in cyanobacterial blooms by many satellites is limited because of spatial resolution, as significant changes in chlorophyll concentration occur even at a smaller spatial scale than 30 m.

https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lo.2004.49.6.2179

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

The importance of lakes and reservoirs leads to the high need for monitoring lake water quality both at local and global scales. The aim of the study was to test suitability of Sentinel-2 Multispectral Imager’s (MSI) data for mapping different lake water quality parameters. In situ data of chlorophyll a (Chl a), water color, colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC) from nine small and two large lakes were compared with band ratio algorithms derived from Sentinel-2 Level-1C and atmospherically corrected (Sen2cor) Level-2A images. The height of the 705 nm peak was used for estimating Chl a. The suitability of the commonly used green to red band ratio was tested for estimating the CDOM, DOC and water color. Concurrent reflectance measurements were not available. Therefore, we were not able to validate the performance of Sen2cor atmospheric correction available in the Sentinel-2 Toolbox. The shape and magnitude of water reflectance were consistent with our field measurements from previous years. However, the atmospheric correction reduced the correlation between the band ratio algorithms and water quality parameters indicating the need in better atmospheric correction. We were able to show that there is good correlation between band ratio algorithms calculated from Sentinel-2 MSI data and lake water parameters like Chl a (R2 = 0.83), CDOM (R2 = 0.72) and DOC (R2 = 0.92) concentrations as well as water color (R2 = 0.52). The in situ dataset was limited in number, but covered a reasonably wide range of optical water properties. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for lake monitoring and research, especially taking into account that the data will be available routinely for many years, the imagery will be frequent, and free of charge.

First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery

Cyanobacterial blooms are attracting the increasing attention of environment agencies, water authorities, and human and animal health organizations, since they can present a range of amenity, water quality ant treatment problems, and hazards to human and animal health. The problem is especially acute in the Baltic Sea where cyanobacterial blooms occur every summer covering areas of more than 100 000 km2. It has been shown that quantitative mapping of cyanobacteria during bloom conditions is possible with hyperspectral instruments. These sensors, however, cannot provide synoptic spatial coverage and high revisit times needed for near real-time monitoring of potentially harmful blooms. The aim was to estimate whether spectral resolution of multispectral sensors, which can provide needed coverage, is adequate for quantitative mapping of cyanobacteria and whether it is possible to separate potentially harmful blooms of cyanobacteria from waters dominated by algae using ocean colour satellites. The modelling results show that multispectral sensors like ALI, Landsat or MODIS are not capable of separating waters dominated by cyanobacteria from waters dominated by other algae species, as their spectral band configuration does not allow detecting absorption features caused by phycocyanin (present primarily in cyanobacteria) or any other spectral features that are characteristic to cyanobacteria only. MERIS bands 6 and 7 allow detecting phycocyanin absorption feature near 630 nm and a small peak in reflectance spectra near 650 nm characteristic to only cyanobacteria. Thus, MERIS can be used in detecting cyanobacteria if they are present in relatively high quantities. Unfortunately it is not possible to use MERIS for early warning of emerging potentially harmful blooms as the minimum biomass needed to cause features in reflectance spectra typical to cyanobacteria is higher than the biomass already considered as a bloom in the Baltic Sea.

Tiit Kutser

Papers

A global inventory of lakes based on high‐resolution satellite imagery

Remote sensing of inland waters: challenges, progress and future directions

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery

Monitoring cyanobacterial blooms by satellite remote sensing