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Streamflow generation and deep groundwater recharge may be vulnerable to loss of snow, making it important to quantify how snowmelt is partitioned between soil storage, deep drainage, evapotranspiration, and runoff. Based on previous findings, we hypothesize that snowmelt produces greater streamflow and deep drainage than rainfall and that this effect is greatest in dry climates. To test this hypothesis we examine how snowmelt and rainfall partitioning vary with climate and soil properties using a physically based variably saturated subsurface flow model, HYDRUS-1D. We developed model experiments using observed climate from mountain regions and artificial climate inputs that convert all precipitation to rain, and then evaluated how climate variability affects partitioning in soils with different hydraulic properties and depths. Results indicate that event-scale runoff is higher for snowmelt than for rainfall due to higher antecedent moisture and input rates in both wet and dry climates. Annual runoff also increases with snowmelt fraction, whereas deep drainage is not correlated with snowmelt fraction. Deep drainage is less affected by changes from snowmelt to rainfall because it is controlled by deep soil moisture changes over longer timescales. Soil texture modifies daily wetting and drying patterns but has limited effect on annual water budget partitioning, whereas increases in soil depth lead to lower runoff and greater deep drainage. Overall these results indicate that runoff may be substantially reduced with seasonal snowpack decline in all climates, whereas the effects of snowpack decline on deep drainage are less consistent. These mechanisms help explain recent observations of streamflow sensitivity to changing snowpack and highlight the importance of developing strategies to plan for changes in water budgets in areas most at risk for shifts from snow to rain.

The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36∘ S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann–Kendall and Theil–Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño–Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29∘ S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2) with persistent snow cover between 29 and 36∘ S experienced a significant loss of snow cover (2–5 fewer days of snow year−1). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10–30 m year−1 south of 29–30∘ S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31∘ S, whereas the SAM better predicted SP south of 31∘ S.

Streamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes. Plain Language Summary Most small streams in the world are not monitored, so we know little about when they are flowing or dry. Yet, the amount of time streams flow can determine whether they are protected by water quality legislation and streamside management plans. In this study we used visual observations of stream flow/no flow and stream sensors to develop a model that predicts the fraction of time that streams flow. At a study area in northern Colorado, volunteer observers documented stream flow/no flow at 177 stream segments, and we placed sensors in 11 headwater streams at different elevations. We found that streams needed to be visited approximately weekly to determine how long they flow each year. Streams that rarely flow needed to be visited more often than those that flow most of the time. Our model shows that most (98%) of the streams in the study area do not flow continuously. The amount of time that streams flow is sensitive to changing climate and water demands. Ongoing monitoring of these streams will help us track and predict the range of flow conditions that are possible throughout the vast networks of small streams that feed larger rivers and lakes. Key Points Predicted April–September fraction of time with flow using sensors, crowd‐sourced observations, and statistical models in Colorado streams Snow persistence, summer precipitation, and drainage area are dominant predictors of flow fractions in the Northern Colorado study area Developing a reliable model of flow fraction requires sampling diverse streams that span the full spectrum of flow fractions (0–1)

Kaon physics is at a turning point – while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop, held in September 2023 at CERN.

Continued climate warming is reducing seasonal snowpacks in the western United States, where >50% of historical water supplies were snowmelt‐derived. In the Upper Colorado River Basin, declining snow water equivalent (SWE) and altered surface water input (SWI, rainfall and snowmelt available to enter the soil) timing and magnitude affect streamflow generation and water availability. To adapt effectively to future conditions, we need to understand current spatiotemporal distributions of SWE and SWI and how they may change in future decades. We developed 100‐m SnowModel simulations for water years 2001–2013 and two scenarios: control (CTL) and pseudo‐global‐warming (PGW). The PGW fraction of precipitation falling as snow was lower relative to CTL, except for November–April at high elevations. PGW peak SWE was lower for low (−45%) and mid elevations (−14%), while the date of peak SWE was uniformly earlier in the year for all elevations (17–23 days). Currently unmonitored high elevation snow represented a greater fraction of total PGW SWE. PGW peak daily SWI was higher for all elevations (30%–42%), while the dates of SWI peaks and centroids were earlier in the year for all elevations under PGW. PGW displayed elevated winter SWI, lower summer SWI, and changes in spring SWI timing were elevation‐dependent. Although PGW peak SWI was elevated and earlier compared to CTL, SWI was more evenly distributed throughout the year for PGW. These simulated shifts in the timing and magnitude of SWE and SWI have broad implications for water management in dry, snow‐dominated regions. Plain Language Summary Snowpack water storage has historically functioned as a reliable extension of manmade reservoir storage. Loss of this storage has consequences for water resource management, ecological communities, and natural hazards including wildfire. We modeled snow accumulation and melt at high spatial resolution in the Upper Colorado River Basin to assess patterns in the timing and magnitude of snow storage and snowmelt for historical and future scenarios. We analyze these patterns in relation to existing snow monitoring station coverage, and ask how this coverage may need to change in future decades to better represent water availability. Our results indicate widespread future snow storage losses at lower elevations, but limited change at higher elevations that will likely remain conducive to seasonal snow accumulation and melt for decades to come. Peak snow storage and peak snowmelt occurred earlier for all elevations in future years, with increased peak surface water input noted at all elevations. A greater fraction of future snow storage will be in currently unmonitored high elevations. Projected elevation dependent changes from this study have implications for other dry, snow dominated regions, and additional work is needed to evaluate combined effects of widespread snow loss and earlier, flashier input on coordinated water management. Key Points Projections show lower peak snow water equivalent (SWE) below 3,000 m and earlier peak SWE, peak surface water input (SWI) at all elevations Greater future peak SWI and reduced annual snow‐derived SWI for all elevations, with a more even SWI distribution throughout the year A greater fraction of future SWE will be in high elevations that are currently unmonitored

Wildfires can dramatically alter water quality, resulting in severe implications for human and freshwater systems. However, regional-scale assessments of these impacts are often limited by data scarcity. Here, we unify observations from 1984–2021 in 245 burned watersheds across the western United States, comparing post-fire signals to baseline levels from 293 unburned basins. Organic carbon and phosphorus exhibit significantly elevated levels ( p  ≤ 0.05) in the first 1–5 years post-fire, while nitrogen and sediment show significant increases up to 8 years post-fire. During peak post-fire response years, average carbon, nitrogen, and phosphorus concentrations are 3–103 times pre-fire levels, and sediment 19–286 times pre-fire concentrations. Higher responses are linked with greater forested and developed areas, which respectively explain up to 31 and 33% of inter-basin response variability. Overall, this analysis provides strong evidence of multi-year water quality degradation following wildfires in the western United States and highlights the influence of basin and wildfire features. These insights may aid water managers in preparation efforts, increasing resilience of water systems to wildfire impacts. Water quality in the western United States is affected by wildfires, with sediment and turbidity levels increasing up to 8 years post-fire, according to an analysis of sediment, dissolved organic matter, and nutrients in 538 watersheds.

Wildfire area has been increasing in most ecoregions across the western United States, including snow-dominated regions. These fires modify snow accumulation, ablation, and duration, but the sign and magnitude of these impacts can vary substantially between regions. This study compares spatiotemporal patterns of western United States wildfires between ecoregions and snow zones. Results demonstrate significant increases in wildfire area from 1984 to 2020 throughout the West, including the Sierra Nevada, Cascades, Basin and Range, and Northern to Southern Rockies. In the late snow zone, where mean annual snow-free date is in May or later, 70% of ecoregions experienced significant increases in wildfire area since 1984. The distribution of burned area shifted from earlier melt zones to later-melt snow zones in several ecoregions, including the Southern Rockies, where the area burned in the late snow zone during 2020 exceeded the total burned area over the previous 36 y combined. Snow measurements at a large Southern Rockies fire revealed that burning caused lower magnitude and earlier peak snow-water equivalent as well as an 18–24 d estimated advance in snow-free dates. Latitude, a proxy for solar radiation, is a dominant driver of snow-free date, and fire advances snow-free timing through a more-positive net shortwave radiation balance. This loss of snow can reduce both ecosystem water availability and streamflow generation in a region that relies heavily on mountain snowpack for water supply.

Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the most data deficient. Datasets of surface water presence exist across multiple data collection groups in the United States but are not well aligned for easy integration. Given the value of these data, a unified approach for organizing information on surface water presence and absence collected by diverse surveys would facilitate more effective and broad application of these data and address the gap in streamflow data in headwaters. In this paper, we highlight the numerous existing datasets on surface water presence in headwater streams, including recently developed crowdsourcing approaches. We identify the challenges of integrating multiple surface water presence/absence datasets that include differences in the definitions and categories of streamflow status, data collection method, spatial and temporal resolution, and accuracy of geographic location. Finally, we provide a list of critical and useful components that could be used to integrate different streamflow permanence datasets.

Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.

Developing accurate stream maps requires both an improved understanding of the drivers of streamflow spatial patterns and field verification. This study examined streamflow locations in three semiarid catchments across an elevation gradient in the Colorado Front Range, USA. The locations of surface flow throughout each channel network were mapped in the field and used to compute active drainage densities. Field surveys of active flow were compared to National Hydrography Dataset High Resolution (NHD HR) flowlines, digital topographic data, and geologic maps. The length of active flow declined with stream discharge in each of the catchments, with the greatest decline in the driest catchment. Of the tributaries that did not dry completely, 60% had stable flow heads and the remaining tributaries had flow heads that moved downstream with drying. The flow heads were initiated at mean contributing areas of 0.1 km2 at the lowest elevation catchment and 0.5 km2 at the highest elevation catchment, leading to active drainage densities that declined with elevation and snow persistence. The field mapped drainage densities were less than half the drainage densities that were represented using NHD HR. Geologic structures influenced the flow locations, with multiple flow heads initiated along faults and some tributaries following either fault lines or lithologic contacts.