Predicting the impact of land use on the major element and nutrient fluxes in coastal Mediterranean rivers: The case of the Teˆt River (Southern France)

This study presents a detailed discrimination between the natural and anthropogenic sources of dissolved major elements in the Teˆt River, a typical small coastal river in the south of France. The main objectives were to quantify the materials that were released by human activities in the basin, and to determine the specific element inputs for the major land use forms. The dissolved material fluxes were estimated by weekly monitoring over a hydrological year (2000–2001) along the major water gauging stations, and the flux relationships were examined in the context of anthropogenic and natural basin characteristics as determined by a Geographical Information System (GIS). Intensive agricultural land use in the form of fruit tree cultures and vineyards has a strong control on the dissolved element fluxes in the river. Area specific element releases for these cultures are greatest for SO4, with an estimated average of about 430 ± 18 keq km2 a1. This is P11 times the natural SO4 release by rock weathering. Also for K, NO3, PO4 and Mg, the specific releases were P6 times the natural weathering rates (respectively about 44, 60, 4 and 265 keq km2 a1). Waste-waters are the other major source of anthropogenic elements in the river. They have an important role for the fluxes of inorganic P and N, but they are also a considerable source of Cl and Na to the river. For example, the average annual release of Cl is around 150 moles/inhabitant in the rural basin parts. Further downstream, however, where population density strongly increases, industrial effluents can enhance this value (>300 moles/inhabitant). The waste-waters contribute more than 70% of the dissolved inorganic N export to the sea, although their contribution to the average DOC export is almost negligible (3%)

Hydrological and climatic uncertainties associated with modeling the impact of climate change on water resources of small Mediterranean coastal rivers

International audienceThis paper investigates the uncertainties associated with using regional climate models and one hydrological model calibrated from non-stationary hydroclimatic time series to simulate future water resources of six Mediterranean French coastal river basins. First, a conceptual hydrological model (the GR2M model) was implemented in order to reproduce the observed river discharge regimes. Climatic scenarios were then constructed from a set of Regional Climate Models (RCMs) outputs and fed into the hydrological model in order to produce water discharge scenarios for the 2071–2100 period. At last, an assessment of uncertainties associated with the hydrological scenarios is given.With respect to the 1961–1990 period, RCMs project a mean annual temperature increase of 4.3–4.5 °C (3.1–3.2 °C) under the IPCC A2 (B2) scenario. Precipitation changes, although more variable, indicate a decrease between −10% and −15.6% for A2 and between −6.1% and −11.6% for B2. As a result, the GR2M model simulates a general water discharge decrease between −26% (−14%) and −54% (−41%) for the A2 (B2) scenario, depending on the basin of interest.Sensitivity tests on the hydrological modelling revealed that the hydrological scenarios are sensitive to the choice of the PE formulation, although this climatic input is negligible in the model calibration. Also, a slight but significant drift between the modelled and observed time series was detected for most basins, indicating that the hydrological model fails to adapt to non-stationary discharge conditions. A simple correction method based on a dynamical parametrization of one model parameter with temperature data considerably reduces the model drift in half of the investigated basins. When extrapolated this new parametrization to the future climate scenarios, decrease of water discharge is found to be twice as great as estimated from the standard parametrization. Our results suggest that the uncertainties stemming from hydrological models with fixed parametrizations should be further addressed in any climate change impact study

Modeling flash floods in southern France for road management purposes

International audienceFlash-floods are among the most devastating hazards in the Mediterranean. A major subset of damage and casualties caused by flooding is related to road submersion. Distributed hydrological nowcasting can be used for road flooding monitoring. This requires rainfall–runoff simulations at a high space and time resolution.Distributed hydrological models, such as the ISBA-TOP coupled system used in this study, are designed to simulate discharges for any cross-section of a river but they are generally calibrated for certain outlets and give deteriorated results for the sub-catchment outlets. The paper first analyses ISBA-TOP discharge simulations in the French Mediterranean region for target points different from the outlets used for calibration. The sensitivity of the model to its governing factors is examined to highlight the validity of results obtained for ungauged river sections compared with those obtained for the main gauged outlets. The use of improved model inputs is found beneficial for sub-catchments simulation. The calibration procedure however provides the parameters’ values for the main outlets only and these choices influence the simulations for ungauged catchments or sub-catchments. As a result, a new version of ISBA-TOP system without any parameter to calibrate is used to produce diagnostics relevant for quantifying the risk of road submersion. A first diagnostic is the simulated runoff spatial distribution, it provides a useful information about areas with a high risk of submersion. Then an indicator of the flood severity is given by simulated discharges presented with respect to return periods. The latter has to be used together with information about the vulnerability of road-river cross-sections

PMIF v1.0: assessing the potential of satellite observations to constrain CO<sub>2</sub> emissions from large cities and point sources over the globe using synthetic data

Abstract. This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the “posterior uncertainty”) of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr−1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements

The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2emissions from large cities and point sources

International audienceBackground: Satellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO2 concentration plumes generated by emission hotspots. CO2 emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO2 emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO2 emissions. This study assesses the uncertainties in the inversion estimates of emissions ("posterior uncertainties"). Results: The posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO2 plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO2 retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance. Conclusions: For all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30-11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps

A global map of emission clumps for future monitoring of fossil fuel CO2 emissions from space

A large fraction of fossil fuel CO 2 emissions emanate from “hotspots”, such as cities (where direct CO2 emissions related to fossil fuel combustion in transport, residential, commercial sectors, etc., excluding emissions from electricity-producing power plants, occur), isolated power plants, and manufacturing facilities, which cover a small fraction of the land surface. The coverage of all high-emitting cities and point sources across the globe by bottom-up inventories is far from complete, and for most of those covered, the uncertainties in CO2 emission estimates in bottom-up inventories are too large to allow continuous and rigorous assessment of emission changes (Gurney et al., 2019). Space-borne imagery of atmospheric CO2 has the potential to provide independent estimates of CO2 emissions from hotspots. But first, what a hotspot is needs to be defined for the purpose of satellite observations. The proposed space-borne imagers with global coverage planned for the coming decade have a pixel size on the order of a few square kilometers and a XCO2 accuracy and precision of < 1ppm for individual measurements of vertically integrated columns of dry-air mole fractions of CO 2 (XCO2). This resolution and precision is insufficient to provide a cartography of emissions for each individual pixel. Rather, the integrated emission of diffuse emitting areas and intense point sources is sought. In this study, we characterize area and point fossil fuel CO 2 emitting sources which generate coherent XCO2 plumes that may be observed from space. We characterize these emitting sources around the globe and they are referred to as “emission clumps” hereafter. An algorithm is proposed to identify emission clumps worldwide, based on the ODIAC global high-resolution 1km fossil fuel emission data product. The clump algorithm selects the major urban areas from a GIS (geographic information system) file and two emission thresholds. The selected urban areas and a high emission threshold are used to identify clump cores such as inner city areas or large power plants. A low threshold and a random walker (RW) scheme are then used to aggregate all grid cells contiguous to cores in order to define a single clump. With our definition of the thresholds, which are appropriate for a space imagery with 0.5ppm precision for a single XCO 2 measurement, a total of 11314 individual clumps, with 5088 area clumps, and 6226 point-source clumps (power plants) are identified. These clumps contribute 72% of the global fossil fuel CO2 emissions according to the ODIAC inventory. The emission clumps is a new tool for comparing fossil fuel CO2 emissions from different inventories and objectively identifying emitting areas that have a potential to be detected by future global satellite imagery of XCO2 . The emission clump data product is distributed from https://doi.org/10.6084/m9.figshare.7217726.v1.JRC.D.6-Knowledge for Sustainable Development and Food Securit