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  1. Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs ( P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks. 
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  2. null (Ed.)
    Abstract When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees. 
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  3. null (Ed.)
    Temperature is widely known to influence the spatio-temporal dynamics of vector-borne disease transmission, particularly as temperatures vary across critical thermal thresholds. When temperature conditions exhibit such ‘transcritical variation’, abrupt spatial or temporal discontinuities may result, generating sharp geographical or seasonal boundaries in transmission. Here, we develop a spatio-temporal machine learning algorithm to examine the implications of transcritical variation for West Nile virus (WNV) transmission in the Los Angeles metropolitan area (LA). Analysing a large vector and WNV surveillance dataset spanning 2006–2016, we found that mean temperatures in the previous month strongly predicted the probability of WNV presence in pools of Culex quinquefasciatus mosquitoes, forming distinctive inhibitory (10.0–21.0°C) and favourable (22.7–30.2°C) mean temperature ranges that bound a narrow 1.7°C transitional zone (21–22.7°C). Temperatures during the most intense months of WNV transmission (August/September) were more strongly associated with infection probability in Cx. quinquefasciatus pools in coastal LA, where temperature variation more frequently traversed the narrow transitional temperature range compared to warmer inland locations. This contributed to a pronounced expansion in the geographical distribution of human cases near the coast during warmer-than-average periods. Our findings suggest that transcritical variation may influence the sensitivity of transmission to climate warming, and that especially vulnerable locations may occur where present climatic fluctuations traverse critical temperature thresholds. 
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  4. Abstract. The recent availability of freely and openly availablesatellite remote sensing products has enabled the implementation of globalsurface water monitoring at a level not previously possible. Here we presenta global set of satellite-derived time series of surface water storagevariations for lakes and reservoirs for a period that covers the satellitealtimetry era. Our goals are to promote the use of satellite-derived productsfor the study of large inland water bodies and to set the stage for theexpected availability of products from the Surface Water and OceanTopography (SWOT) mission, which will vastly expand the spatial coverage ofsuch products, expected from 2021 on. Our general strategy is to estimateglobal surface water storage changes (ΔV) in large lakes andreservoirs using a combination of paired water surface elevation (WSE) andwater surface area (WSA) extent products. Specifically, we use data producedby multiple satellite altimetry missions (TOPEX/Poseidon, Jason-1, Jason-2,Jason-3, and Envisat) from 1992 on, with surface extent estimated fromTerra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000on. We leverage relationships between elevation and surface area (i.e.,hypsometry) to produce estimates of ΔV even during periods wheneither of the variables was not available. This approach is successfulprovided that there are strong relationships between the two variablesduring an overlapping period. Our target is to produce time series ofΔV as well as of WSE and WSA for a set of 347 lakes and reservoirsglobally for the 1992–2018 period. The data sets presented and theirrespective algorithm theoretical basis documents are publicly available anddistributed via the Physical Oceanography Distributed Active Archive Center (PO DAAC;, last access: 13 May 2020) of NASA's Jet Propulsion Laboratory.Specifically, the WSE data set is available at (Birkett et al., 2019), the WSA dataset is available at and Kumar, 2019), and the ΔV data set is available at (Tortini et al., 2019). Therecords we describe represent the most complete global surface water timeseries available from the launch of TOPEX/Poseidon in 1992 (beginning of thesatellite altimetry era) to the near present. The production of long-term,consistent, and calibrated records of surface water cycle variables such as inthe data set presented here is of fundamental importance to baseline futureSWOT products. 
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  5. Abstract

    We characterize the sensitivity of atmospheric river (AR)‐derived seasonal snowfall estimates to their atmospheric reanalysis‐based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher‐resolution data sets identifying four more AR days per year, on average, during the 1985–2015 winters. However, this can lead to ~10% difference in AR attribution to the mean domain‐wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow‐bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher‐resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs.

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