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Recent efforts have sought to estimate river discharge and other surface water-related quantities using spaceborne sensors, with better spatial coverage but worse temporal sampling as compared with in situ measurements. The Surface Water and Ocean Topography (SWOT) mission will provide river discharge estimates globally from space. However, questions on how to optimally use the spatially distributed but asynchronous satellite observations to generate continuous fields still exist. This paper presents a statistical model (River Kriging-RK), for estimating discharge time series in a river network in the context of the SWOT mission. RK uses discharge estimates at different locations and times to produce a continuous field using spatiotemporal kriging. A key component of RK is the space-time river discharge covariance, which was derived analytically from the diffusive wave approximation of Saint Venant's equations. The RK covariance also accounts for the loss of correlation at confluences. The model performed well in a case study on Ganges-Brahmaputra-Meghna (GBM) River system in Bangladesh using synthetic SWOT observations. The correlation model reproduced empirically derived values. RK (R2=0.83) outperformed other kriging-based methods (R2=0.80), as well as a simple time series linear interpolation (R2=0.72). RK was used to combine discharge from SWOT and in situ observations, improving estimates when the latter is included (R2=0.91). The proposed statistical concepts may eventually provide a feasible framework to estimate continuous discharge time series across a river network based on SWOT data, other altimetry missions, and/or in situ data.

We have developed the HydroCube mission concept with a constellation of small satellites to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The HydroCube satellites would operate at sun-synchronous 3- day repeat polar orbits with a spatial resolution of about 1-3 Km. The mission goals would be to improve the estimation of terrestrial water storage and weather forecasts. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The HydroCube Signals of Opportunity (SoOp) concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals from geostationary Mobile Use Objective System (MUOS) communication satellites. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE. To address the ionospheric delay at P-band frequencies, the signals from both MUOS bands (360-380 MHz and 250-270 MHz) would be used. We have conducted an analysis to trade off the spatial resolution for a space-based sensor and measurement accuracy. Through modeling analysis, we find that the dual-band MUOS signals would allow estimation of soil moisture and surface roughness together. From the two MUOS frequencies at 260 MHz and 370 MHz, we can retrieve the soil moisture from the reflectivity ratio scaled by wavenumbers using the two P-band frequencies for MUOS. A modeling analysis using layered stratified model has been completed to determine the sensitivity requirements of HydroCube measurements. For mission concept demonstration, a field campaign has been conducted at the Fraser Experimental Forest in Colorado since February 2016. The data acquired has provided support to the HydroCube concept.

International audience; Satellite observations, combined with algorithms borrowed from river engineering, could fill large gaps in our knowledge of global river flows where field data are lacking.

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.

Summary Despite its importance as a major element of the global hydrologic cycle, runoff remains poorly constrained except at the largest spatial scales due to limitations of the global stream gauge network and inadequate data sharing. Efforts using remote sensing to infer runoff from discharge estimates are limited by characteristics of present-day sensors. The proposed Surface Water and Ocean Topography (SWOT) mission, a joint project between the United States and France, aims to substantially improve space-based estimates of river discharge. However, the extent of rivers observable by SWOT, likely limited to those wider than 50–100 m, remains unknown. Here, we estimate the extent of SWOT river observability globally using a downstream hydraulic geometry (DHG) approach combining basin areas from the Hydro1k and Hydrosheds elevation products, discharge from the Global Runoff Data Centre (GRDC), and width estimates from a global width–discharge relationship. We do not explicitly consider SWOT-specific errors associated with layover and other phenomena in this analysis, although they have been considered in formulation of the 50–100 m width thresholds. We compare the extent of SWOT-observable rivers with GRDC and USGS gauge datasets, the most complete datasets freely available to the global scientific community. In the continental US, SWOT would match USGS river basin coverage only at large scales (>25,000 km 2 ). Globally, SWOT would substantially improve on GRDC observation extent: SWOT observation of 100 m (50 m) rivers will allow discharge estimation in >60% of 50,000 km 2 (10,000 km 2 ) river basins. In contrast, the GRDC observes fewer than 30% (15%) of these basins. SWOT could improve characterization of global runoff processes, especially with a 50 m observability threshold, but in situ gauge data remains essential and must be shared more freely with the international scientific community.

Mathematical and Physical Sciences: 2nd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)

This paper used a data assimilation framework to estimate spatially and temporally continuous snow water equivalent (SWE) from a 27 year reanalysis (from water year 1985 to 2011) of the Landsat-5 record for the Kern River watershed in the Sierra Nevada, California. The data assimilation approach explicitly treats sources of uncertainty from model parameters, meteorological inputs, and observations. The method is comprised of two main components: (1) a coupled land surface model (LSM) and snow depletion curve (SDC) model, which is used to generate an ensemble of predictions of SWE and fractional snow cover area (FSCA) for a given set of prior (uncertain) inputs, and (2) a retrospective reanalysis step, which updates estimation variables to be consistent with the observed fractional snow cover time series. The final posterior SWE estimate is generated from the LSM-SDC using the posterior estimation variables consistently with all postulated sources of uncertainty in the model, inputs, and observations. A reasonable agreement was found between the SWE reanalysis and in situ SWE observations and streamflow data. The data set was studied to evaluate factors controlling SWE spatial and temporal variability. Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak. The easting coordinate had additional explanatory power, which is hypothesized to be related to rain shadow effects due to the prevailing storm track directions. The spatial patterns were found to be interannually inconsistent. However, drier years and lower elevations were found more variable than wetter years and higher elevations, respectively. Only a very small percentage of the Kern River watershed had a significant trend in peak-SWE and day-of-peak. Trends deemed to be significant were found to be positive (peak-SWE is increasing and day-of-peak occurs later) at higher elevations, but negative (peak-SWE is decreasing and day-of-peak occurs earlier) at lower elevations. The reanalysis approach proved to be useful in terms of identifying subwatershed variability and trends, and could be extended to larger regions and areas where in situ data are sparse or unavailable.

Summary An algorithm is presented that calculates a best estimate of river bathymetry, roughness coefficient, and discharge based on input measurements of river water surface elevation (h) and slope (S) using the Metropolis algorithm in a Bayesian Markov Chain Monte Carlo scheme, providing an inverse solution to the diffusive approximation to the shallow water equations. This algorithm has potential application to river h and S measurements from the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission. The algorithm was tested using in situ data as a proxy for satellite measurements along a 22.4 km reach of the River Severn, UK. First, the algorithm was run with gage measurements of h and S during a small, in-bank event in June 2007. Second, the algorithm was run with measurements of h and S estimated from four remote sensing images during a major out-of-bank flood event in July 2007. River width was assumed to be known for both events. Algorithm-derived estimates of river bathymetry were validated using in situ measurements, and estimates of roughness coefficient were compared to those used in an operational hydraulic model. Algorithm-derived estimates of river discharge were evaluated using gaged discharge. For the in-bank event, when lateral inflows from smaller tributaries were assumed to be known, the method provided an accurate discharge estimate (10% RMSE). When lateral inflows were assumed unknown, discharge RMSE increased to 36%. Finally, if just one of the three river reaches was assumed to be have known bathymetry, solutions for bathymetry, roughness and discharge for all three reaches were accurately retrieved, with a corresponding discharge RMSE of 15.6%. For the out-of-bank flood event, the lateral inflows were unknown, and the final discharge RMSE was 19%. These results suggest that it should be possible to estimate river discharge via SWOT observations of river water surface elevation, slope and width.

Passive microwave (PM) remote sensing measurements are routinely utilized to estimate snow depth and water equivalent (SWE). Both vegetation and physical snowpack variables including snowpack grain size, snow depth, and stratigraphy influence the observed brightness temperature. The natural heterogeneity of snowpack and vegetation states within the microwave footprint occurs at spatial scales shorter than PM observation scales. In this study, we analyze the relationship between PM brightness temperature measurements and the heterogeneity of snowpack and vegetation. Specifically, we explore the question of whether PM observations are sensitive to changes in snow depth even given sub-pixel variability in snow and vegetation. To examine this question, densely sampled, spatially distributed in situ snow properties from multiple study areas made during the NASA Cold Land Processes Experiment (CLPX) are employed in a forward modeling scheme to study the effect of highly variable snow and vegetation properties on the observed PM measurement. In all test cases, this study finds that there exists sensitivity of microwave brightness temperature ( T b ) to total snow depth contained within the measurement footprint, regardless of the heterogeneous nature of snow pack properties. Across three study areas, T b decreases by 23–35 K as depth increased up to the signal saturation depth, which ranged from 70 to 120 cm. With regard to vegetation sensitivity, forest fractions (F) as little as 0.2 can modify the PM measurement by up to 10 K, and F greater than 0.6 mask virtually all of the microwave signal attributable to snow. Finally, with respect to the measurement scale, our results indicate that the scale at which the PM measurement is made does not affect the sensitivity of the T b to mean snow depth, to the scale (1 km) examined herein.

Snow accumulation and melt is highly variable in space and time in complex mountainous environments. Therefore, it is necessary to provide high-resolution spatially and temporally distributed estimates of sub-basin snow water equivalent (SWE) to accurately predict the timing and magnitude of snowmelt runoff. In this study, we compare two reconstruction techniques (a commonly used deterministic reconstruction vs a probabilistic data assimilation framework). The methods retrospectively estimate SWE from a time series of remotely sensed maps of fractional snow-covered area (FSCA). In testing both methods over the Tokopah watershed in the Sierra Nevada (California), the probabilistic reconstruction approach is shown to be a more robust generalization of the deterministic reconstruction. Under idealized conditions, both probabilistic and deterministic approaches perform reasonably well and yield similar results when compared with in situ verification data, whereas the probabilistic reconstruction was found to be in slightly better agreement with snow-pit observations. More importantly, the probabilistic approach was found to be more robust: unaccounted for biases in solar radiation impacted the probabilistic SWE estimates less than the deterministic case (4% vs 7% errors for water year (WY)1997 and 0% vs 3% errors for WY1999); the probabilistic reconstruction was found to be less sensitive to the number of available observations (6% vs 10% errors in WY1997 and 13% vs 44% errors in WY1999 from the nominal cases when four fewer FSCA images were available). Finally, results from the probabilistic reconstruction approach, which requires precipitation inputs (unlike the deterministic approach), were found to be relatively robust to bias in prior precipitation estimates, where the nominal case mean estimates were recovered even when an underestimated prior precipitation was used. The additional robustness of the probabilistic SWE reconstruction technique should prove useful in future applications over larger basins and longer periods in mountainous terrain. Copyright © 2013 John Wiley & Sons, Ltd.

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission will measure water surface elevation, its spatial and temporal derivatives, and inundated area. These observations can be used to estimate river discharge at a global scale. SWOT will measure a given area on mid-latitude rivers two or three times per 22-day repeat cycle. In this paper, we suggest an interpolation-based method of estimating water height for times without SWOT observations (i.e., in between SWOT overpasses). A local space-time ordinary kriging (LSTOK) method is developed. Two sets of synthetic SWOT observations are generated by corrupting two different types of true river height with the instrument error. The true river heights are extracted from: 1) simulation of the LISFLOOD-FP hydrodynamic model, and from 2) in situ gage measurements from five USGS gages. Both of these synthetic SWOT observations datasets are important for the following reasons. The model-based dataset provides a complete spatiotemporal picture of river height that is unavailable from in situ measurements, but neglects the effects of e.g. human management actions on river dynamics. On the other hand, the gage-based dataset samples only five locations on the river (1,050 km in length), but represents all effects of human management, tributaries, or other influences on river heights, which are not included in the model. The results are evaluated by a comparison with truth and simple linear interpolation estimates as a first-guess. The model-based experiment shows the LSTOK recovered the river heights with a mean spatial and temporal root mean square error (RMSE) of 11 cm and 12 cm, respectively; these accuracies show a 46% and 54% improvement compared to the RMSEs of the linear interpolation estimates. The gage-based experiment shows a temporal RMSE of 32 cm on average; the LSTOK estimates show a 23% improvement over the linear interpolation estimates. The degradation in performance of the LSTOK for the gage-based analysis as compared to the model-based analysis is apparently due to the effects of human management on river dynamics. Further work is needed to model the effects of human management, and to extend the analysis to consider river tributaries and the main stem of the river simultaneously.

[1] The Surface Water and Ocean Topography (SWOT) radar interferometer satellite mission will provide unprecedented global measurements of water surface elevation (h) for inland water bodies. However, like most remote sensing technologies SWOT will not observe river channel bathymetry below the lowest observed water surface, thus limiting its value for estimating river depth and/or discharge. This study explores if remotely sensed observations of river inundation width and h alone, when accumulated over time, may be used to estimate this unmeasurable flow depth. To test this possibility, synthetic values of h and either cross-sectional flow width (w) or effective width (We, inundation area divided by reach length) are extracted from 1495 previously surveyed channel cross-sections for the Upper Mississippi, Illinois, Rio Grande, and Ganges-Brahmaputra river systems, and from 62 km of continuously acquired sonar data for the Upper Mississippi. Two proposed methods (called “Linear” and “Slope-Break”) are tested that seek to identify a small subset of geomorphically “optimal” locations where w or We covary strongly with h, such that they may be usefully extrapolated to estimate mean cross-sectional flow depth (d). While the simplest Linear Method is found to have considerable uncertainty, the Slope-Break Method, identifying locations where two distinct hydraulic relationships are identified (one for moderate to high flows and one for low flows), holds promise. Useful slope breaks were discovered in all four river systems, ranging from 6 (0.04%) to 242 (16%) of the 1495 studied cross-sections, assuming channel bathymetric exposures ranging from 20% to 95% of bankfull conditions, respectively. For all four rivers, the derived depth estimates from the Slope-Break Method have root mean squared errors (RMSEs) of

We present a portable spectral profiler probe to measure snow grain size stratigraphy in mountain snowpack at up to 5 mm vertical resolution, without the need for snow pit excavation. The probe infers grain size using near-infrared reflectance spectroscopy by inserting into the snowpack an optical package consisting of a light source and fiber optic receiver, which views the snow laterally and sends the collected reflected light to a spectrometer at the surface. The instrument can be easily dismantled and transported in a backpack, and rapidly deployed in the field. Grain size profiles from the probe, along with snow-pit contact spectroscopy and hand lens measurements were gathered and compared during winter and spring 2010 field campaigns in Colorado. Results from the probe agree to within 30% with snow-pit contact spectroscopy measurements, except when thin layers are present, which are detected at better vertical resolution by the profiler probe. The results highlight the lateral heterogeneity inherent in most mountain snowpacks, which is impractical to measure with conventional techniques. This type of measurement, along with density measurements, can greatly impact the accuracy of remote passive and active microwave retrievals.

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.

The ultra-wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature by measuring low frequency microwave emission. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb). Experiment results showed feasibility to estimate ice sheet internal temperature and improvement of priors.

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.

Two sets of experiments were performed to identify, respectively, the impacts of using different grain size models and simplified representations of snowpack stratigraphy on the predicted grain size evolution and the resulting radiance predictions. Three different grain size models were examined with the grain size prediction used as inputs to the Microwave Emission Model of Layered Snowpacks (MEMLS) model for predicting radiobrightness (at different frequencies) from the snowpack. The varying mechanisms and treatment of grain size growth lead to differences between the three models. Despite the differences, when using best-fit relationships between grain size and the exponential correlation length parameter needed by MEMLS, the predicted brightness temperatures are similar. For all three models, it was found that a proportional model between grain size and correlation length outperformed a more physically based model and that the regression coefficients differed from those in previous studies. The predicted V-pol brightness temperature measurements from the three grain size models showed good agreement with each other, with inter-model differences on the order of ~ 5 K. At H-pol the biases significantly increase, which is most likely due to errors in predicted density and grain size in melt-freeze layers. In the experiments aimed at assessing the impact of stratigraphy on predicted radiances, it was found that there were limited additional errors introduced in using a pre-specified one-, three-, or five-layer scheme at 18.7 and 36.5 GHz (V-pol), while sizeable errors were introduced at 89 GHz (V-pol). The additional errors at H-pol were relatively small, yet the overall errors were still larger than V-pol.

Microwave brightness temperatures (Tbs) contain information about snow properties, including the thickness, density, grain size, and temperature of stratigraphic layers; each of these properties generally varies vertically in the snowpack. Inverse solutions to characterize snow via Tbs have met with only limited success, not least because multiple sets of snow properties can lead to the same Tbs; the problem becomes more complex with increasing number of layers. Our objective is to address the question: Can an inverse solution be obtained with only very minimal prior information on snow properties under ideal conditions? We developed a new inverse algorithm: a Bayesian Markov Chain Monte Carlo scheme solved using the Metropolis algorithm. We allowed the number of snowpack layers itself to be unknown by generating different chains for each possible number of layers (up to a maximum of four), then selecting the optimal chain using a model selection criterion. We generated synthetic Tb observations using the microwave emission model for layered snow (MEMLS), and then used the Metropolis algorithm, MEMLS, and the synthetic observations to estimate the snow properties for 150 snowpits measurements made during the NASA Cold Land Processes Experiment (CLPX) field campaign. We evaluated algorithm results using the accuracy criteria on snow water equivalent (SWE) imposed for the planned CoreH2O satellite mission. We obtained an accurate solution to number of layers, layer thickness, density, grain size, snow temperature and ground temperature from microwave measurements for shallow snow (less than 300 mm), with an RMSE of 18.2 mm. We examined the sensitivity of these results to error in MEMLS, and found that for model error less than 8 K standard deviation, the SWE RMSE is uniformly less than 30 mm. For deep snow, however, the problem appears to be intractable, with RMS errors greater than 20% even for an assumption of zero model error. We conclude that for deep snow, additional prior information is required for accurate SWE estimation from microwave Tbs. We suggest that such prior information could come from physical snowpack models.

Summary This paper focuses on estimating river bathymetry for retrieving river discharge from the upcoming Surface Water and Ocean Topography (SWOT) satellite mission using a data assimilation algorithm coupled with a hydrodynamic model. The SWOT observations will include water surface elevation (WSE), its spatial and temporal derivatives, and inundated area. We assimilated synthetic SWOT observations into the LISFLOOD-FP hydrodynamic model using a local ensemble batch smoother (LEnBS), simultaneously estimating river bathymetry and flow depth. SWOT observations were obtained by sampling a “true” LISFLOOD-FP simulation based on the SWOT instrument design; the “true” discharge boundary condition was derived from USGS gages. The first-guess discharge boundary conditions were produced by the Variable Infiltration Capacity model, with discharge uncertainty controlled via precipitation uncertainty. First-guess estimates of bathymetry were derived from SWOT observations assuming a uniform spatial depth; bathymetric variability was modeled using an exponential correlation function. Thus, discharge and bathymetry errors were modeled realistically. The LEnBS recovered the bathymetry from SWOT observations with 0.52 m reach-average root mean square error (RMSE), which was 67.8% less than the first-guess RMSE. The RMSE of bathymetry estimates decreased sequentially as more SWOT observations were used in the estimate; we illustrate sequential processing of 6 months of SWOT observations. The better estimates of bathymetry lead to improved discharge estimates. The normalized RMSE of the river discharge estimates was 10.5%, 71.2% less than the first-guess error.

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to -7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Large-scale snow hydrology has been studied via spaceborne passive microwave (PM) measurements for decades. Forward and inverse radiative transfer (RT) models of snow are utilized in this context but typically neglect snow stratigraphy. Our objective in this paper is to characterize the expected model error in PM brightness temperature (Tb) predictions due to neglecting stratigraphy over a range of snow cover conditions. For 191 snowpits ranging from prairie to alpine, we performed side-by-side RT model runs including and ignoring stratigraphy via mass-weighted averages across stratigraphic layers; error was estimated by comparing the two RT model runs. Neglecting stratigraphy at 37 GHz led to approximately 10-K root mean square error (RMSE) for moderately deep (alpine) snow cover and to approximately 5-K RMSE for shallower (prairie) snow. RMSE across all types of snow was 1.67 and 26.9 K at 18.7 and 89 GHz, respectively. At 37 GHz, there was a low bias for deep snowpacks and a high bias for moderate-to-shallow snowpacks. Bias magnitude bias was dependent on vertical grain size variability. Based on these results and estimates of sensitivity of Tb to snow depth, we estimated that snow depth RMSE due to neglecting stratigraphy approaches 50%.

In this paper, three satellite derived precipitation datasets (TRMM, CMORPH, PERSIANN) are used to drive the Hillslope River Routing (HRR) model in the Congo Basin. The precipitation data are compared spatially and temporally in two forms: (1) precipitation magnitudes, and (2) resulting streamflow and water storages. Simulated streamflow is assessed using historical monthly discharge data from in situ stream gauges and recent stage data based on water surface elevations derived from ENVISAT radar altimetry data. Simulated total water storage is assessed using monthly storage change values derived from GRACE data. The results show that the three precipitation datasets vary significantly in terms of magnitudes but generally produce a reasonable hydrograph throughout much of the basin, with the exception of the equatorial regions of the watershed. The satellite datasets provide unreasonably high values for specific periods (e.g. all three in Oct–Nov; only CMORPH and PERSIANN in Mar–Apr) in the equatorial regions. Overall, TRMM (3B42) provides the best spatial and temporal distributions and magnitudes or rainfall based on the assessment measures used here. Both CMORPH and PERSIANN tend to overestimate magnitudes, especially in the equatorial regions of the Basin. Copyright © 2011 John Wiley & Sons, Ltd.

The planned Surface Water and Ocean Topography (SWOT) satellite mission will measure freshwater storage changes in global lakes. Herein, the anticipated SWOT storage change accuracy is evaluated for the lakes in the Peace-Athabasca Delta, Northern Alaska and Western Siberia. Because of the significant lack of Arctic lake measurements, we simulated realistic daily to seasonal changes in water elevations in the study region using a combination of data from lake gauges, satellite radar altimeter, and satellite imagery. This 'truth' dataset is sampled with several candidate SWOT orbits and then corrupted with expected instrument errors to simulate SWOT observed storage changes. The number of revisits increases with increasing or decreasing latitude for a given repeat cycle (e.g. four to eight revisits for a 22-day cycle), allowing us to investigate storage change errors at monthly sampling. SWOT storage change accuracy is primarily controlled by lake size. Lakes larger than 1 km2 have relative errors generally less than 5% whereas one-hectare size lakes are about 20%. We concluded that the storage change accuracy is insensitive to the orbital inclination or repeat periods, but is sensitive to lake shapes.

This special issue of the International Journal of Remote Sensing is based on papers presented in a special session at the American Geophysical Union (AGU) 2008 Fall Meeting in San Francisco. The session consisted of 85 contributions from the research, operational and user communities that addressed various applications of remote sensing observations in the development, improvement, validation and use of hydrological models. Of the papers presented, 15 were eventually selected for publication. They cover a range of applications of remote sensing in hydrological study and have been loosely assembled into three categories, focusing on (a) hydrological state variables, (b) moisture fluxes and (c) water storage and surface water budget.

The elevation of the ocean surface has been measured for over two decades from spaceborne altimeters. However, existing altimeter measurements are not adequate to characterize the dynamic variations of most inland water bodies, nor of ocean eddies at scales of less than about 100 km, notwithstanding that such eddies play a key role in ocean circulation and climate change. For terrestrial hydrology, in situ and spaceborne measurements of water surface elevation form the basis for estimates of water storage change in lakes, reservoirs, and wetlands, and of river discharge. However, storage in most inland water bodies, e.g., millions of Arctic lakes, is not readily measured using existing technologies. A solution to the needs of both surface water hydrology and physical oceanography communities is the measurement of water elevations along rivers, lakes, streams, and wetlands and over the ocean surface using swath altimetry. The proposed surface water and ocean topography (SWOT) mission will make such measurements. The core technology for SWOT is the Ka-band radar interferometer (KaRIN), which would achieve spatial resolution on the order of tens of meters and centimetric vertical precision when averaged over targets of interest. Average revisit times will depend upon latitude, with two to four revisits at low to mid latitudes and up to ten revisits at high latitudes per ~20-day orbit repeat period.

The Surface Water and Ocean Topography (SWOT) satellite mission will provide global, space-based estimates of water elevation, its temporal change, and its spatial slope in fluvial environments, as well as across lakes, reservoirs, wetlands, and floodplains. This paper illustrates the utility of existing remote sensing measurements of water temporal changes and spatial slope to characterize two complex fluvial environments. First, repeat-pass interferometric SAR measurements from the Japanese Earth Resources Satellite are used to compare and contrast floodplain processes in the Amazon and Congo River basins. Measurements of temporal water level changes over the two areas reveal clearly different hydraulic processes at work. The Amazon is highly interconnected by floodplain channels, resulting in complex flow patterns. In contrast, the Congo does not show similar floodplain channels and the flow patterns are not well defined and have diffuse boundaries. During inundation, the Amazon floodplain often shows sharp hydraulic changes across floodplain channels. The Congo, however, does not show similar sharp changes during either infilling or evacuation. Second, Shuttle Radar Topography Mission measurements of water elevation are used to derive water slope over the braided Brahmaputra river system. In combination with in situ bathymetry measurements, water elevation and slope allow one to calculate discharge estimates within 2.3% accuracy. These two studies illustrate the utility of satellite-based measurements of water elevation for characterizing complex fluvial environments, and highlight the potential of SWOT measurements for fluvial hydrology. Copyright © 2010 John Wiley & Sons, Ltd.

The Surface Water and Ocean Topography (SWOT) mission is a swath mapping radar interferometer that would provide new measurements of inland water surface elevation (WSE) for rivers, lakes, wetlands, and reservoirs. SWOT WSE estimates would provide a source of information for characterizing streamflow globally and would complement existing in situ gage networks. In this paper, we evaluate the accuracy of river discharge estimates that would be obtained from SWOT measurements over the Ohio river and eleven of its major tributaries within the context of a virtual mission (VM). SWOT VM measurements are obtained by using an instrument measurement model coupled to simulated WSE from the hydrodynamic model LISFLOOD-FP, using USGS streamflow gages as boundary conditions and validation data. Most model pixels were estimated two or three times per 22-day orbit period. These measurements are then input into an algorithm to obtain estimates of river depth and discharge. The algorithm is based on Manning's equation, in which river width and slope are obtained from SWOT, and roughness is estimated a priori. SWOT discharge estimates are compared to the discharge simulated by LISFLOOD-FP. Instantaneous discharge estimates over the one-year evaluation period had median normalized root mean square error of 10.9%, and 86% of all instantaneous errors are less than 25%.

Much research has examined associations between particulate pollution and health. The majority of this research has focused on outdoor air. Yet it is known that people spend most of their time indoors. This research has examined concentrations of PM10, PM2.5 and PM1 inside and outside of a boarding school in Christchurch, New Zealand. Christchurch is a city with a known wintertime particulate pollution problem as a result of burning wood for domestic heating. Data were collected over the winter of 2004 (May–September) using TEOM and GRIMM dust analysers. Results show that there is a close relationship between the fine fraction (PM2.5 and PM1) of indoor and outdoor particles. However, the activities of the building occupants resulted in the resuspension of coarse particulate fractions resulting in weaker indoor–outdoor relationships for PM10.

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km 2 ). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼ daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from − 0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction.

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.

This paper discusses the little-known but potentially serious indoor air quality problems that may occur where buildings are constructed on geothermal ground. The main problems are related to seepage of carbon dioxide, hydrogen sulphide, radon and other gases from soil cavities directly into indoor air through perforations in the structure. These gases present a health hazard, and hydrogen sulphide, which is particularly corrosive, may cause problems electrical and electronic systems. Counter-measures are not always effective, so developments in such areas should only be undertaken with a clear understanding of site-specific issues and their possible solutions.

A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (RTM). The land surface model (LSM) was given biased precipitation to represent typical errors introduced in modeling, yet was still able to recover the true value of SWE with a seasonally integrated rmse of only 2.95 cm, despite a snow depth of around 3 m and the presence of liquid water in the snowpack. This ensemble approach is ideal for investigating the complex theoretical relationships between the snowpack properties and the observations, and exploring the implications of these relationships for the inversion of remote sensing measurements for estimating snowpack properties. The contributions of each channel to recovering the true SWE are computed, and it was found that the low-frequency 10.67-GHz AMSR-E channels contain information even for very deep snow. The effect of vegetation thickness on assimilation results is explored. Results from the assimilation are compared to those from a pure modeling approach and from a remote sensing inversion approach, and the effects of measurement error and ensemble size are investigated.

This paper compares PM10 mass concentrations data generated by three co-located particulate monitors, the tapered element oscillating microbalance (TEOM40) (operating at 40 °C), MiniVol and DustTrak, operating outdoors during winter woodsmoke air pollution (Christchurch, New Zealand). TEOM40 data were adjusted to adjTEOM40 with an algorithm derived from a co-located TEOM40 and HiVol at a second field site. Corrected and corrected-loge-transformed data were analyzed with reduced major axis regression. Logged adjTEOM40 and DustTrak data correlated best ( r 2 = 0.8 1 ) but the DustTrak required a substantial correction factor to make data comparable in real terms. Consistent over-recording by the DustTrak and under-recording by the TEOM40 were consistent with previously published work. The MiniVol did not correlate well with other instruments ( r 2 = 0.6 2 and 0.53 against the adjTEOM40 and DustTrak, respectively) but reasons for this cannot be ascertained.

The emission of toxic gases from the soil is a hazard in geothermal regions that are also urbanized because buildings constructed on geothermal ground may be subject to the ingress of gases from the soil directly into the structure. The Rotorua geothermal field, New Zealand, is extensively urbanized but to date no studies have evaluated the extent of the ground gas hazard. The main gases emitted are hydrogen sulphide (H2S) and carbon dioxide (CO2), both of which are highly toxic and denser than air. This paper reports preliminary findings from a study of selected buildings constructed in the gas anomaly area. Properties were investigated for evidence of ingress by H2S, CO2, and 222Rn, with a view to determine the means and rates of gas entry and the nature of any consequent hazard. H2S and CO2 were investigated using infrared active gas analysers and passive detector tubes left in place for 10-48 h. 222Rn was measured over a period of 3 months by poly-allyl diglycol carbonate sensors. Eight of the nine buildings studied were found to suffer problems with soil gases entering the indoor air through the structure. The primary means of gas entry was directly from the ground through the floors, walls, and subsurface pipes. Indoor vents were located and found emitting up to approximately 200 ppm H2S and approximately 15% CO2, concentrations high enough to present an acute respiratory hazard to persons close to the vent (e.g., children playing at floor level). In some properties, gas problems occurred despite preventative measures having been made during construction or during later renovations. Typically, these measures include the under-laying of concrete floors with a gas-proof butanol seal, under-floor ventilation systems or the installation of positive-pressure air conditioning. Recently constructed buildings (10 years) with butanol seals were nevertheless affected by ground gas emissions, and we conclude that such measures are not always effective in the long term.

Biological monitoring of exposure to gases and respirable particles is common in industry, when urine or blood samples are analysed for elevated levels of various trace elements, but this is almost unheard of in volcanology. In this work, 10 volunteers undertook 20 min of acute gas exposure downwind of fumaroles on White Island, New Zealand. Pre- and post-exposure urine samples were analysed for aluminium, arsenic, rubidium and mercury—elements which are known to be present in volcanic gases—in order to test if any may be used as markers for gas exposure. Statistically significant (p

Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due to the complex interaction between the microwave radiance and the mountain environment; a better knowledge of the interaction is requisite to improve the characterization of mountain SWE via PM. In this study, we modeled the 36.5GHz V-Pol microwave radiance at 90m spatial resolution over the Upper Kern Basin. We replicated the AMSR-E observations with the modeling results, and used the modeled radiance to interpret the satellite observations, as well as to explore the impacts of the mountain environment on microwave radiance. We found snow grain size, model stratigraphic representation, liquid water content and intense snowfall event close related with the modeling accuracy; the calibration to these parameters reduced the error in the modeled radiance by 81%. We aggregated the modeled radiance to AMSR-E footprint scale using the antenna sampling pattern to facilitate the comparison between the modeled radiance and the AMSR-E observations. The RMSE of the basin-scale modeling was 3.1K during the dry snow season. Using the modeling results, we classified the environmental variables in terms of their influence on microwave radiance in the study area; from high to low: SWE (significant only when snowpack is deep), vegetation, elevation, aspect, slope.

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.