To estimate snow mass across North America, brightness temperature observations collected by the Advanced Microwave Scanning Radiometer (AMSR‐E) from 2002 to 2011 were assimilated into the Catchment model using a support vector machine as the observation operator and a one‐dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground‐based measurements and reference snow products. In general, there are no statistically significant skill differences between the domain‐averaged, model‐only (open loop, or OL) snow estimates and assimilation estimates. The assessment of improvements (or degradations) in snow estimates is difficult because of limitations in the measurements (or products) used for evaluation. It is found that assimilation estimates agree slightly better in terms of root‐mean‐square error and Nash‐Sutcliffe model efficiency with ground‐based snow depth measurements than OL estimates in 82% (56 out of 62) of pixels that are colocated with at least two ground‐based stations. Assimilation estimates tend to agree slightly better in terms of mean difference with reference snow products over tundra snow, alpine snow, maritime snow, and sparsely vegetated, snow‐covered pixels. Changes in snow mass via assimilation translate into improvements (e.g., by 22% on average in terms of root‐mean‐square error, relative to OL) in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 snow‐dominated basins in Alaska. These results suggest that a support vector machine can potentially serve as an effective observation operator for snow mass estimation within a radiance assimilation system, but a better observational baseline is required to document a statistically significant improvement.

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Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0-10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10-40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

Agricultural runoff can be a source of P, a limiting factor for freshwater eutrophication. To develop a simple method to estimate P export from the cropland, we studied 1.2-p.m filtered dissolved phosphorus (DP) output from four tiles draining areas ranging from 8 to 25 ha, and from a river draining a 48 173 ha watershed in eastcentral Illinois during 1993 to 1996. The land was under maize (Zea mays L.)-soybean (Glycine max L.) rotation. The tiles were estimated to contribute more than 86% of the river flow and 65 to 69% of the river DP export during 1995 to 1996. The DP load from tiles followed consecutive pseudo first-order kinetics in terms of tile flow (DP load depended on the amount of DP remaining in the soil matrix). The kinetic curves indicated a soluble-inorganic-P pool that was quickly depleted and replenished. In contrast, for DP export from the river at the watershed scale we observed pseudo zero-order kinetics based on river flow (DP export was independent of how much DP remained in the watershed). The contribution from numerous tiles and surface runoff to the river may have stabilized DP export at the watershed scale and therefore could explain the different kinetic orders. For the study watershed, a one parameter equation could estimate watershedwide DP export: k' × (surface water discharge from the watershed) × (watershed area), with k' being 3.94 × 10 -6 mg P L -1 ha -1. Our approach should be tested in watersheds with different geographic and agricultural characteristics.Abbreviations: DP, dissolved phosphorus passed through 1.2 ~m filter;