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Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500–1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500–1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500–1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥30 m) globally. We based our mapping on Google Earth Engine’s high resolution images, which for many places have meter-scale resolution. The resulting Global River Obstruction Database (GROD) consists of 30,549 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured >90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities.

International audience; The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50–100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g., friction coefficient and bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.Measurements of river flow are essential for the allocation of water resources, flood and drought forecast and mitigation efforts, and others. Access to local measurements is not ubiquitous and is particularly difficult for rivers flowing in remote locations or across country borders. Measurements taken by satellites such as the upcoming Surface Water and Ocean Topography (SWOT) mission will offer freely available global data and methods to estimate discharge using such data have been in development. We conducted a comprehensive assessment of the accuracy and precision of five SWOT discharge inversion algorithms under three conditions: (a) ideal, that is if the measurements were available once a day and contained no error; (b) with no measurement error but changing how frequently the measurements were taken, and (c) under different levels of measurement error. We found that the methods consistently improved over the initial estimates of discharge and we identified river hydraulic properties that increase the chances of successful parameter estimation. We also found that despite the use of very similar discharge equations, the subtle differences in equations among the methods can be important. Finally, we found that at least two methods can work well with the expected amount of measurement error and frequency.

Locally advanced rectal cancer is treated with neoadjuvant-chemoradiotherapy, however, only ~22% of patients achieve a complete response, and resistance mechanisms are poorly understood. The role of inflammation and immune cell biology in this setting is under-investigated. In this study, we profiled the inflammatory protein secretome of normal (non-cancer) (n = 8) and malignant rectal tissue (n = 12) pre- and post-radiation in human ex vivo explant models and examined the influence of these untreated and treated secretomes on dendritic cell biology (n = 8 for cancer and normal). These resultant profiles were correlated with patient clinical characteristics. Nineteen factors were secreted at significantly higher levels from the rectal cancer secretome when compared to the normal rectal secretome, Flt-1, P1GF, IFN-γ, IL-6, IL-10, CCL20, CCL26, CCL22, CCL3, CCL4, CCL17, GM-CSF, IL-12/IL-23p40, IL-17A, IL-1α, IL-17A/F, IL-1RA, TSLP and CXCL10 (p &lt, 0.05). Radiation was found to have differential effects on normal rectal tissue and rectal cancer tissue with increased IL-15 and CCL22 secretion following radiation from normal rectal tissue explants (p &lt, 0.05), while no significant alterations were observed in the irradiated rectal cancer tissue. Interestingly, however, the irradiated rectal cancer secretome induced the most potent effect on dendritic cell maturation via upregulation of CD80 and PD-L1. Patient’s visceral fat area correlated with secreted factors including CCL20, suggesting that obesity status may alter the tumour microenvironment (TME). These results suggest that radiation does not have a negative effect on the ability of the rectal cancer TME to induce an immune response. Understanding these responses may unveil potential therapeutic targets to enhance radiation response and mitigate normal tissue injury. Tumour irradiation in this cohort enhances innate immune responses, which may be harnessed to improve patient treatment outcome.

Locally advanced rectal cancer is treated with neoadjuvant-chemoradiotherapy, however only 22% of patients achieve a complete response. Resistance mechanisms are poorly understood. Radiation-induced Bystander Effect (RIBE) describes the effect of radiation on neighbouring unirradiated cells. We investigated the effects of ex vivo RIBE-induction from normal and rectal cancer tissue on bystander cell metabolism, mitochondrial function and metabolomic profiling. We correlated bystander events to patient clinical characteristics. Ex vivo RIBE-induction caused metabolic alterations in bystander cells, specifically reductions in OXPHOS following RIBE-induction in normal (p = 0.01) and cancer tissue (p = 0.03) and reduced glycolysis following RIBE-induction in cancer tissue (p = 0.01). Visceral fat area correlated with glycolysis (p = 0.02) and ATP production (p = 0.03) following exposure of cells to TCM from irradiated cancer biopsies. Leucine levels were reduced in the irradiated cancer compared to the irradiated normal secretome (p = 0.04). ROS levels were higher in cells exposed to the cancer compared to the normal secretome (p = 0.04). RIBE-induction ex vivo causes alterations in the metabolome in normal and malignant rectal tissue along with metabolic alterations in bystander cellular metabolism. This may offer greater understanding of the effects of RIBE on metabolism, mitochondrial function and the secreted metabolome., Highlights • RIBE induction ex vivo alters mitochondrial metabolism in bystander cells. • Rectal cancer secretome increases ROS in bystander cells. • Higher leucine levels in the irradiated normal rectal secretome compared to the irradiated rectal cancer secretome • Glycolysis and ATP levels in bystander cells correlate with patient's visceral fat area.

Assessing the impact of climate change on floodplain productivity poses unique challenges for hydrodynamic models. For example, the dynamics of floodplain fisheries are governed both by inundation dynamics across thousands of km2, and water storage timing within small depressions (which serve as fish habitat) connected to the river network by meter-scale manmade canals, controlled by flow across fishing weirs. Here, we propose to represent these features as a system of effective, interconnected sub-grid elements within a coarse-scale model. We test this strategy over the Logone floodplain in Cameroon, and its floodplain fishery. We first validate this strategy for a local study area (30 km2); we find that hydraulic models at resolutions from 30 m to 500 m are able to reproduce hydraulic dynamics as documented by in situ water level observations. When applied to the entire floodplain (16,000 km2), we find that the proposed modeling strategy allows accurate prediction of observed pattern of recession in the depressions. Artificially removing floodplain canals in the model causes residence time of water in depressions to be overpredicted by approximately 30 days. This study supports the strategy of modeling fine-scale interconnected features as a system of sub-grid elements in a coarse resolution model for applications such as assessing the sensitivity of floodplain fisheries to future climate change.

Sub-Saharan floodplains are sensitive to climatic changes in their upstream drainage basin, a major concern is given the dependency of millions of people for their daily subsistence. Understanding hydroclimatic trends and variability is critical for developing integrated coupled human and natural system models to evaluate future scenarios of vulnerability. Here we describe the historical climatic changes in the Logone River basin using grid-based climatic data during a time of concomitant human hydrological modification of the floodplain. Temporal trends were analysed by comparing two periods i.e. 1901/1921–1961 (pre-1960) and 1961–2013 (post-1960). Trends were analysed spatially based on the basin's two Koppen climatic zones: savanna (Aw) covering the southern upstream areas and semi-arid hot (BSh) covering the northern downstream areas. The results show significant increasing trends in maximum and minimum temperatures and potential evapotranspiration (PET), and non-significant decreasing trends in annual rainfall and number of rainy days, across the study area. These climatic changes on temporal and spatial scales play an important role for the coupled natural and human systems in the Logone floodplain.

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization.

The Surface Water and Ocean Topography Mission (SWOT) will generate global, spatially continuous maps of water surface elevation and extent for large inland water bodies when it launches in 2021. We present an analysis of water surface elevation, width, and bathymetry timeseries data from a medium-sized (average annual discharge 14 m 3 /s) river to explore Manning’s equation, an empirical open channel flow equation, in the context of SWOT discharge algorithms. While this equation is in theory inapplicable to natural channels due to the non-uniform and spatially heterogeneous nature of river systems, we explored approaches to adapt it to this context using reach-averaged variables. At twenty sites along a 6.5 km stretch of the Olentangy River in Ohio, USA, we collected automated and manual measurements of water surface elevation and river width, undertook a full bathymetric survey of the study area, and built a hydraulic model. The stretch of river was divided into five reaches, and hydraulic variables were reach-averaged. Using these variables, we used a modified form of Manning’s equation to compute a reach-averaged roughness coefficient. Reach-averaged roughness coefficients varied nonlinearly with discharge and were 2-10 times larger at low flow than at high flow in the in-situ data, ranging from 0.06 to 0.61 in one of the study reaches. These results were compared with the output of an unsteady flow simulation using a calibrated 1-D hydraulic model which was run with constant roughness coefficients at each cross section. When reach-averaged data was used, model-derived roughness coefficient also varied by more than an order of magnitude, with a range of 0.02 to 0.82 for one reach. For both in-situ and model-derived datasets, using a two-parameter roughness coefficient which scaled with a power law on either discharge or stage reduced discharge estimation error, with error for one reach dropping from 81 % to 8 % relative root-mean square error (rRMSE) in the in-situ data and 58 % to 8 % nRMSE in the modeled data. These results imply that spatial averaging of hydraulic variables leads to large variations in reach averaged Manning’s n , which we term the reach’s “effective resistance”, and suggest that this variability can be accounted for with a simple parameterization in estimates of discharge that use spatially averaged data.

ArcticDEM is a collection of 2-m resolution, repeat digital surface models created from stereoscopic satellite imagery. To demonstrate the potential of ArcticDEM for measuring river stages and discharges, we estimate river surface heights along a reach of Tanana River near Fairbanks, Alaska, by the precise detection of river shorelines and mapping of shorelines to land surface elevation. The river height profiles over a 15-km reach agree with in situ measurements to a standard deviation less than 30 cm. The time series of ArcticDEM-derived river heights agree with the U.S. Geological Survey gage measurements with a standard deviation of 32 cm. Using the rating curve for that gage, we obtain discharges with a validation accuracy (root-mean-square error) of 234 m3/s (23% of the mean discharge). Our results demonstrate that ArcticDEM can accurately measure spatial and temporal variations of river surfaces, providing a new and powerful data set for hydrologic analysis.

The Surface Water and Ocean Topography satellite mission (SWOT), scheduled for launch in 2021, will deliver two-dimensional observations of water surface heights for lakes, rivers wider than 100 m and oceans. Even though the scientific literature has highlighted several fields of application for the expected products, detailed simulations of the SWOT radar performance for a realistic river scenario have not been presented in the literature. Understanding the error of the most fundamental “raw” SWOT hydrology product is important in order to have a greater awareness about strengths and limits of the forthcoming satellite observations. This study focuses on a reach (∼140 km in length) of the middle-lower portion of the Po River, in Northern Italy, and, to date, represents one of the few real-case analyses of the spatial patterns in water surface elevation accuracy expected from SWOT. The river stretch is characterized by a main channel varying from 100 to 500 m in width and a large floodplain (up to 5 km) delimited by a system of major embankments. The simulation of the water surface along the Po River for different flow conditions (high, low and mean annual flows) is performed with inputs from a quasi-2D model implemented using detailed topographic and bathymetric information (LiDAR, 2 m resolution). By employing a simulator that mimics many SWOT satellite sensor characteristics and generates proxies of the remotely sensed hydrometric data, this study characterizes the spatial observations potentially provided by SWOT. We evaluate SWOT performance under different hydraulic conditions and assess possible effects of river embankments, river width, river topography and distance from the satellite ground track. Despite analyzing errors from the raw radar pixel cloud, which receives minimal processing, the present study highlights the promising potential of this Ka-band interferometer for measuring water surface elevations, with mean elevation errors of 0.1 cm and 21 cm for high and low flows, respectively. Results of the study characterize the expected performance of the upcoming SWOT mission and provide additional insights into potential applications of SWOT observations.

An ultra-wideband radiometer has been developed to measure subsurface properties of the cryosphere including ice sheets and sea ice. The radiometer measures brightness temperature spectra from 0.5 to 2 GHz using 12 channels, each of which measures scene brightness temperatures over an ~88-MHz bandwidth resolved into 0.24-MHz intervals. The instrument was flown over northwestern Greenland in September 2016 and acquired the first, wideband, low-frequency brightness temperature spectra over the ice sheet and coastal region. The results reveal strong spatial and spectral variations that correlate well with the physical properties of the surface encountered along the flight path, which started over ocean, then passed the rock near the coast, and then up onto the ablation, wet, percolation, and dry snow zones of the interior ice sheet. In particular, strong spectral responses in percolation and dry snow zones are observed and plausibly explained by varying the distribution of horizontal density layers and isolated icy bodies in the upper portion of the firn. The success of the airborne deployment of the instrument and subsequent implementation of algorithms to limit radio frequency interference in unprotected bands is motivating continued airborne investigations as well as stimulating research into the feasibility of a spaceborne instrument.

We develop the first global ‘Bare-Earth’ Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) for all landmasses between 60N and 54S. Our new ‘Bare-Earth’ SRTM DEM combines multiple remote sensing datasets, including point-ground elevations from NASA's laser altimeter ICESat, a database of percentage of tree cover from the MODIS satellite as a proxy for penetration depth of SRTM and a global vegetation height map in order to remove the vegetation artefacts present in the original SRTM DEM. We test multiple methods of removing vegetation artefacts and investigate the use of regionalization. Our final ‘Bare-Earth’ SRTM product shows global improvements greater than 10 m in the bias over the original SRTM DEM in vegetated areas compared with ground elevations determined from ICESat data with a significant reduction in the root mean square error from over 14 m to 6 m globally. Therefore, our DEM will be valuable for any global applications, such as large scale flood modelling requiring a ‘Bare-Earth’ DEM.

International audience; We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the ‘‘true’’ hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning’s equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River.

International audience; The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relativeresiduals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.

Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow depth were derived using a low-cost photogrammetric approach on-board a micro unmanned aircraft system (UAS). Using commercial off-the-shelf (COTS) sensors mounted on a multi-rotor UAS and photogrammetric image processing techniques, the results demonstrate that snow depth can be accurately retrieved by differencing two surface models corresponding to the snow-free and snow-covered scenes, respectively. In addition to accurate snow depth retrieval, we show that high-resolution (50 cm) spatially continuous snow depth maps can be created using this methodology. Two types of photogrammetric bundle adjustment (BA) routines are implemented in this study to determine the optimal estimates of sensor position and orientation, in addition to 3D scene information, conventional BA (which relies on measured ground control points) and direct BA (which does not require ground control points). Error sources that affect the accuracy of the BA and subsequent snow depth reconstruction are discussed. The results indicate the UAS is capable of providing high-resolution and high-accuracy (&lt, 10 cm) estimates of snow depth over a small alpine area (~0.7 ha) with significant snow accumulation (depths greater than one meter) at a fraction of the cost of full-size aerial survey approaches. The RMSE of estimated snow depths using the conventional BA approach is 9.6 cm, whereas the direct BA is characterized by larger error, with an RMSE of 18.4 cm. If a simple affine transformation is applied to the point cloud derived from the direct BA, the overall RMSE is reduced to 8.8 cm RMSE.

We investigate the degree to which ultrahigh frequency radio emission can be used to estimate subsurface physical temperature in the polar ice sheets. We combine electromagnetic emission forward models with plausible models of depth-dependent physical properties in the ice sheet. Temperature models are parameterized with variables including accumulation rate, geothermal heat flux, and surface temperature. Scattering is parameterized using empirical observations of grain growth combined with measured densities. Electromagnetic absorption is modeled using dielectric dispersion processes and semiempirical models based on observations. Our models illustrate that information about East Antarctic ice sheet temperature from near the surface to near the base can be gleaned from ultrawideband radiometer data. Based on our modeling study, we illustrate an instrument concept to measure ice sheet temperature profiles comprising a novel ultrawideband radiometer.

International audience; Global surface water variations are still difficult to monitor with current satellite measurements. The future Surface Water and Ocean Topography (SWOT) mission is designed to address this issue. Its main payload will be a wide swath altimeter which will provide maps of water surface elevations between 78°S and 78°N over a 120 km swath. This study aims to combine coupled hydrologic/hydraulic modeling of an Arctic river with virtual SWOT observations using a local ensemble Kalman smoother to characterize river water depth variations. We assumed that modeling errors are only due to uncertainties in atmospheric forcing fields (precipitation and air temperature) and different SWOT orbits were tested. First, we tested orbits that all have a three day repeat period but differ in terms of their spatial coverage of the study reach; these orbits correspond to the first three months of the mission, which will be dedicated to calibration and validation experiments. For these orbits, the mean spatial Root Mean Square Error (RMSE) in modeled channel water depth decreased by between 29 % and 79 % compared to the modeled RMSE with no assimilation, depending on the spatial coverage. The corresponding mean temporal RMSE decrease was between 54 % and 91 %. We then tested the nominal orbit with a twenty two day repeat period which will be used during the remaining lifetime of the mission. Unlike the three day repeat orbits, this orbit will observe all continental surfaces (except Antartica and the northern part of Greenland) during one repeat period. The assimilation of SWOT observations computed with this nominal orbit into the hydraulic model leads to a decrease of 59 % and 66 % in the mean spatial and temporal RMSE in modeled channel water depth, respectively. These results show the huge potential of the future SWOT mission for land surface hydrology, especially at high latitudes which will be very well sampled during one orbit repeat period. Still, further work is needed to reduce current modeling uncertainties and to better characterize SWOT measurement errors.

International audience; River discharge and lake water storage are critical elements of land surface hydrology, but are poorly observed globally. The Surface Water and Ocean Topography (SWOT) satellite mission will provide high-resolution measurements of water surface elevations with global coverage. Feasibility studies have been undertaken to help define the orbit inclination and repeat period. Preliminary error budgets have been computed for estimating instantaneous and monthly river discharge from SWOT measurements (errors are assumed uncorrelated). Errors on monthly discharge due to SWOT temporal sampling were estimated using gauges and their observation times for two SWOT orbits with different inclinations (78° and 74°). These errors have then been extrapolated to rivers globally. The 78° and 74° orbital inclinations allow a good sampling frequency, avoid tidal aliasing and cover almost all the continental surface. For a 22 day repeat orbit, a single point at 72°N is sampled 11 and 16 times during one repeat period for the 78° and 74° inclination orbit, respectively. Errors in instantaneous discharge are below 25% for rivers wider than 50 m (48% of all rivers). Errors in monthly discharge are below 20% for rivers with drainage areas larger than 7 000 km2 (34% of all rivers). A rough estimate of global lake storage change has been computed. Currently available satellite nadir altimetry data can only monitor 15% of the global lake volume variation, whereas from 50% to more than 65% of this variation will be observed by SWOT, thus providing a significant increase in our knowledge of lake hydrology.

Thiosulfate is a sulfide metabolite and a biological marker, especially in urine, of exposure to hydrogen sulfide gas (H2S). In many suspected poisoning cases, victims are known to be exposed to low concentrations of H2S, but it is difficult to establish the degree of exposure to H2S responsible for the poisoning. In such cases it is necessary to account for a possible chronic exposure signal by subtracting any background thiosulfate from the measured total. However, no data exist on the background levels of thiosulfate in individuals exposed to relatively low levels of H2S. We obtained preexposure and postexposure urine thiosulfate data from eight individuals exposed to H2S in the ppb to low ppm range. Mean thiosulfate concentrations in urine increased from 4.6 to 11.5 μmol/l following exposure (P ≤ 0.05). When normalized against creatinine, mean thiosulfate increased from 7.2 to 9.8 μmol/mol (P ≤ 0.05). However, positive changes were not consistent between individuals. Whilst these findings support the idea that relatively minor exposures to H2S can be confirmed by thiosulfate in urine, it also suggests that the response of this biomarker to minor H2S exposure may vary significantly between individuals.