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Spatial Patterns in CO 2 Evasion from the Global River Network Ronny Lauerwald, Goulven Laruelle, Jens Hartmann, Philippe Ciais, Pierre A.G

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Spatial Patterns in CO 2 Evasion from the Global River Network Ronny Lauerwald, Goulven Laruelle, Jens Hartmann, Philippe Ciais, Pierre A.G Spatial patterns in CO 2 evasion from the global river network Ronny Lauerwald, Goulven Laruelle, Jens Hartmann, Philippe Ciais, Pierre A.G. Regnier To cite this version: Ronny Lauerwald, Goulven Laruelle, Jens Hartmann, Philippe Ciais, Pierre A.G. Regnier. Spatial patterns in CO 2 evasion from the global river network. Global Biogeochemical Cycles, American Geophysical Union, 2015, 29 (5), pp.534 - 554. 10.1002/2014GB004941. hal-01806195 HAL Id: hal-01806195 https://hal.archives-ouvertes.fr/hal-01806195 Submitted on 28 Oct 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. PUBLICATIONS Global Biogeochemical Cycles RESEARCH ARTICLE Spatial patterns in CO2 evasion from the global 10.1002/2014GB004941 river network Key Points: Ronny Lauerwald1,2,3, Goulven G. Laruelle1,4, Jens Hartmann3, Philippe Ciais5, and Pierre A. G. Regnier1 • First global maps of river CO2 partial pressures and evasion at 0.5° 1Department of Earth and Environmental Sciences, Université Libre de Bruxelles, Brussels, Belgium, 2Institut Pierre-Simon resolution Laplace, Paris, France, 3Institute for Geology, University of Hamburg, Hamburg, Germany, 4Department of Earth Sciences- • Global river CO2 evasion estimated at À 5 650 (483–846) Tg C yr 1 Geochemistry, Utrecht University, Utrecht, Netherlands, LSCE IPSL, Gif Sur Yvette, France • Latitudes between 10°N and 10°S contribute half of the global CO2 evasion Abstract CO2 evasion from rivers (FCO2) is an important component of the global carbon budget. Here we present the first global maps of CO2 partial pressures (pCO2) in rivers of stream orders 3 and higher and the Supporting Information: resulting FCO2 at 0.5° resolution constructed with a statistical model. A geographic information system based • Supporting Information approach is used to derive a pCO2 prediction function trained on data from 1182 sampling locations. While • Text S1 data from Asia and Africa are scarce and the training data set is dominated by sampling locations from the • Table S1 • Data set S1 Americas, Europe, and Australia, the sampling locations cover the full spectrum from high to low latitudes. • Data set S2 The predictors of pCO2 are net primary production, population density, and slope gradient within the river 2 catchment as well as mean air temperature at the sampling location (r = 0.47). The predicted pCO2 map Correspondence to: was then combined with spatially explicit estimates of stream surface area A and gas exchange velocity k R. Lauerwald, river [email protected] calculated from published empirical equations and data sets to derive the FCO2 map. Using Monte Carlo simulations, we assessed the uncertainties of our estimates. At the global scale, we estimate an average river À1 pCO2 of 2400 (2019–2826) μatm and a FCO2 of 650 (483–846) Tg C yr (5th and 95th percentiles of confidence Citation: À1 Lauerwald, R., G. G. Laruelle, J. Hartmann, interval). Our global CO2 evasion is substantially lower than the recent estimate of 1800 Tg C yr although the P. Ciais, and P. A. G. Regnier (2015), training set of pCO2 is very similar in both studies, mainly due to lower tropical pCO2 estimates in the present Spatial patterns in CO2 evasion from the study. Our maps reveal strong latitudinal gradients in pCO2, Ariver,andFCO2. The zone between 10°N and 10°S global river network, Global Biogeochem. Cycles, 29,534–554, doi:10.1002/ contributes about half of the global CO2 evasion. Collection of pCO2 data in this zone, in particular, for African and 2014GB004941. Southeast Asian rivers is a high priority to reduce uncertainty on FCO2. Received 19 JUL 2014 Accepted 2 APR 2015 Accepted article online 7 APR 2015 1. Introduction Published online 7 MAY 2015 Following several syntheses published over the last decade [Cole et al., 2007; Battin et al., 2009; Tranvik et al., 2009; Aufdenkampe et al., 2011], inland waters (streams, rivers, lakes, and reservoirs) are presented in the fifth assessment report of the Intergovernmental Panel on Climate Change [Ciais et al., 2013] as an active component of the global carbon cycle with substantial net evasion of CO2 (FCO2)totheatmosphere.Thisis a major paradigm shift from earlier assessments, which represented inland waters as a passive conduit of carbon from land to ocean [Denman et al., 2007], though inland waters were already known to be net sources of CO2 to the atmosphere [e.g., Kempe, 1982]. Published global estimates of FCO2 vary nevertheless substantially, and the quantitative contribution of inland waters to the atmospheric CO2 budget has large uncertainties [Regnier et al., 2013]. Reported values of FCO2 for the fluvial network alone range from 0.26 Pg À À Cyr 1 to 1.8 Pg C yr 1 [Richey et al., 2002; Cole et al., 2007; Tranvik et al., 2009; Aufdenkampe et al., 2011; À Regnier et al., 2013; Raymond et al., 2013], the latest published estimate (1.8 Pg C yr 1)[Raymond et al., 2013] being about twice as large as the lateral export of carbon to the coastal ocean from the global river network (see the reviews by Bauer et al.[2013]andRegnier et al. [2013]). However, even the most recent assessments of FCO2 do not resolve well its important spatial variability. So far, extrapolations of field data were performed to achieve a lumped global estimate [Cole et al., 2007], a segmentation according to three latitudinal zones (tropical, temperate, and boreal to arctic) [Aufdenkampe et al., 2011], and a regionalization based on the COSCAT units of Meybeck et al. [2006] [Raymond et al., 2013]. This study takes the next step forward and resolves FCO2 at a much higher resolution of 0.5°. This makes our estimation directly comparable to typical outputs of global land surface models such as ecosystem productivity and terrestrial carbon stocks. ©2015. American Geophysical Union. All A major challenge for achieving a spatially explicit assessment of river FCO2 at half a degree resolution is the Rights Reserved. uneven coverage of water chemistry data (alkalinity, pH, and water temperature) required for the upscaling LAUERWALD ET AL. SPATIAL PATTERNS OF RIVERINE PCO2 AND FCO2 534 Global Biogeochemical Cycles 10.1002/2014GB004941 of the river pCO2 on a grid over the entire globe and, subsequently, of FCO2. While some rivers in industrialized countries are covered by a dense network of sampling locations, a very large fraction of the global river network remains only sparsely or not at all surveyed [Regnier et al., 2013]. Thus, suitable interpolation techniques are required to fill the numerous spatial gaps that appear at the targeted resolution of half a degree and the uncertainty due to the limited sampling needs to be quantified. In a continental scale study over North America, it was shown that the river pCO2 can, to some extent, be estimated from high-resolution geodata representing the major environmental controls of the CO2 exchange between rivers and the atmosphere [Lauerwald et al., 2013]. In this study, we expand this approach to derive empirical equations that allow us to predict the spatial distribution of pCO2 and FCO2 from rivers of stream orders 3 and higher at the global scale on a 0.5° grid. In addition, a Monte Carlo analysis is applied to our empirical predictors to quantify the uncertainties in pCO2 and FCO2 estimations which were not considered in earlier studies. 2. Methods The global river chemistry database GloRiCh [Hartmann et al., 2014] (section 2.1.1), which was already used by Raymond et al. [2013], and geodata on river catchment properties were used to derive a functional relationship between river pCO2 and environmental variables. The resulting prediction model was then applied to derive a global 0.5° map of yearly averaged river pCO2 (section 2.1.2). This map was used in combination with spatially resolved estimates of gas exchange velocity k and surface lake and stream water area Ariver to calculate FCO2 from the global river network at the same resolution (section 2.2). Using Monte Carlo simulations, we quantified the uncertainty of our estimates (section 2.3). 2.1. River pCO2 2.1.1. Calculation of River pCO2 From Observed Data 3 Using PhreeqC v2 [Parkhurst and Appelo, 1999], we calculated 101 × 10 pCO2 values from alkalinity, pH, water temperature, and, where available, concentrations of major inorganic solutes reported in the hydrochemical database GloRiCh [Hartmann et al., 2014]. Data were selected according to the following criteria: (1) The delineation of the catchment of the sampling location is available in GloRiCh; (2) The sample dates from 1990 or later to avoid the high river pollution period in Europe; (3) Samples with a pH lower than 5.4 (1.7% of the considered water samples) are discarded because calculating pCO2 from alkalinity and pH is highly error prone in low-pH waters [Raymond et al., 2013], mainly due to contributions of noncarbonate alkalinity [Hunt et al., 2011]; (4) In order to calculate robust averages of pCO2, only sampling locations were retained where monthly median pCO2 values could be calculated from at least three single values and without leaving gaps of more than three consecutive months without a median pCO2 value. Given the small number of data for each location month, the median was preferred over the mean to limit the effect of single, probably erroneous outliers. To ensure an equilibrated seasonal weighting, gaps in the seasonal cycle were closed by linear interpolation between the previous and next following median monthly pCO2 values [cf.
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