The speciation of As and Fe was studied during the oxidation of Fe(II)-As(III) solutions by combining XAS analysis at both the Fe and As K-edges. Fe(II) and As(III) were first hydrolyzed to pH 7 under anoxic conditions; the precipitate was then allowed to oxidize in ambient air for 33 h under vigorous stirring. EXAFS analysis at the As K-edge shows clear evidence of formation of inner-sphere complexes between As(III) and Fe(II), i.e., before any oxidation. Inner-sphere complexes were also observed when Fe became sufficiently oxidized, in the form of edge-sharing and double-corner linkages between AsIIIO3 pyramids and FeIIIO6 octahedra. XAS analyses at the Fe K-edge reveal that the presence of As(III) in the solution limits the polymerization of Fe(II) and the formation of green rust and inhibits the formation of goethite and lepidocrocite. Indeed, As(III) accelerates the Fe(II) oxidation kinetics and leads to the formation of nanosized Fe-As subunits of amorphous aggregates. These observations, rather than a presumed weaker affinity of As(III) for iron oxyhydroxides, might explain why As(III) is more difficult to remove than As(V) by aerating reducing groundwater.

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The new Global Digital Elevation Model (GDEM v2) has been available since 17 October 2011. With a resolution of approximately 30 m, this model should provide more accurate information than the latest version of Shuttle Radar Topographic Mission (SRTM v4) with a resolution of 90 m outside of the USA. The accuracies of these two recently released digital elevation models (DEMs) were assessed over the Altiplano watershed in South America using ICESat/GLAS data (Ice, Cloud and Land Elevation Satellite/Geoscience Laser Altimeter System). On the global scale, GDEM v2 is more accurate than SRTM v4, which presents a negative bias of approximately 8.8 m. Strong correlations between the DEMs' accuracies and mean slope values occurred. Regarding land cover, SRTM v4 could be more accurate or easier to correct on a smaller scale than GDEM v2. Finally, a merged and corrected DEM that considers all of these observations was built to provide more accurate information for this region. The new model featured lower absolute mean errors, standard deviations, and root mean square errors relative to SRTM v4 or GDEM v2.

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In the Amazon basin, floodplains form a complex mosaic of freshwater systems with differing morphologies, resulting in varied inundation patterns and heterogeneous chemical and ecological characteristics. In this study, we focused on the Janauacá floodplain, a medium‐sized system (786 km2, including the local watershed) located along the Solimões River. Based on in situ and satellite observations acquired from November 2006 to November 2011, we computed water fluxes between the mainstream and the floodplain and examined the temporal dynamics of floodplain storage from river flooding, rainfall, runoff, and exchanges with groundwater through bank seepage for the 5 years from 2006 to 2011. The mainstream was the main input of water to the flooded area, accounting on average for 93% of total water inputs by the end of the water year. Direct precipitation and runoff from uplands contributed less than or equal to 5% and 10%, respectively. The seepage contribution was less than 1%. Model uncertainties, evaluated using Monte Carlo analysis of the input data and model parameters, showed that all water fluxes were relatively well constrained except for outflow through seepage, which had a standard deviation across simulations greater than 60%. The water balance computation was verified using electrical conductivity as an assumed non‐reactive tracer. Except during periods of very low water, the simulated and measured conductivities agreed well. Moreover, conductivity data analysis confirmed that the Janauacá system can be considered homogeneous in terms of electrical conductivity for filling percentages equal to or greater than 40% (i.e., when the water level is above 19.5 m, generally from April to August) but presented large heterogeneities during the rest of the hydrological cycle.

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Abstract:In 2015, an emergency state was declared in Bolivia when Poopó Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopó Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopó Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopó Lake.

Abstract:The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1 • , these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25 • ) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.