Seasonal snow is a critical component of the surface energy balance and hydrologic cycle, yet global maps of seasonal snow boundaries are not readily available. Snow persistence (SP), the fraction of ...a year that snow is present on the ground, is an easily globally observed snow metric that can be used to map snow zones globally. Here we map snow zones across the globe using SP calculated from the MODIS10A2 product; evaluate how SP relates to precipitation, temperature, and climate indices; and examine trends in annual SP for 2001–2016. In the Northern Hemisphere, intermittent, seasonal, and permanent snow zones occupy a far greater percent (63%) of the land surface than in the Southern Hemisphere (<5%) where the low snow zone dominates (>95%). SP is most variable from year to year near the snow line, which has a relatively consistent decrease in elevation with increasing latitude across all continents. At lower elevations, SP is typically best correlated with temperature, whereas precipitation has greater relative importance for SP at high elevations. SP is best correlated with the North Atlantic Oscillation in all continents except South America, where the Southern Annular Mode is a stronger influence, and Africa, where the strongest correlation is with the Oceanic Niño Index. Areas with decreasing SP trends cover 5.8% of snow zone areas, whereas those with increasing trends cover 1.0% of this area. The largest areas of declining SP are in the seasonal snow zones of the Northern Hemisphere. Trend patterns vary within individual regions, with elevation, and on windward‐leeward sides of the mountains. This study supplies a framework for comparing snow between regions, highlights areas with snow changes, and can facilitate analyses of why snow changes vary within and between regions.
Seasonal snow is a critical component of the surface energy balance and hydrologic cycle, yet global maps of seasonal snow boundaries are not readily available. Snow persistence, the fraction of a year that snow is present, is an easily globally observed snow metric that can be used to map snow zones globally. This study supplies a framework for comparing snow between regions, highlights areas with changes, and facilitates analysis of why snow changes vary within and between regions.
We present and develop a general dispersive framework allowing us to construct representations of the amplitudes for the processes Pπ →ππ, P=K, η, valid at the two-loop level in the low-energy ...expansion. The construction proceeds through a two-step iteration, starting from the tree-level amplitudes and their S and P partial-wave projections. The one-loop amplitudes are obtained for all possible configurations of pion masses. The second iteration is presented in detail in the cases where either all masses of charged and neutral pions are equal or for the decay into three neutral pions. Issues related to analyticity properties of the amplitudes and of their lowest partial-wave projections are given particular attention. This study is introduced by a brief survey of the situation, for both experimental and theoretical aspects, of the decay modes into three pions of charged and neutral kaons and of the eta meson.
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.
Abstract
Non-perennial streams are widespread, critical to ecosystems and society, and the subject of ongoing policy debate. Prior large-scale research on stream intermittency has been based on ...long-term averages, generally using annually aggregated data to characterize a highly variable process. As a result, it is not well understood if, how, or why the hydrology of non-perennial streams is changing. Here, we investigate trends and drivers of three intermittency signatures that describe the duration, timing, and dry-down period of stream intermittency across the continental United States (CONUS). Half of gages exhibited a significant trend through time in at least one of the three intermittency signatures, and changes in no-flow duration were most pervasive (41% of gages). Changes in intermittency were substantial for many streams, and 7% of gages exhibited changes in annual no-flow duration exceeding 100 days during the study period. Distinct regional patterns of change were evident, with widespread drying in southern CONUS and wetting in northern CONUS. These patterns are correlated with changes in aridity, though drivers of spatiotemporal variability were diverse across the three intermittency signatures. While the no-flow timing and duration were strongly related to climate, dry-down period was most strongly related to watershed land use and physiography. Our results indicate that non-perennial conditions are increasing in prevalence over much of CONUS and binary classifications of ‘perennial’ and ‘non-perennial’ are not an accurate reflection of this change. Water management and policy should reflect the changing nature and diverse drivers of changing intermittency both today and in the future.
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 term distributed model is widely applied to describe hydrologic models that can simulate broad classes of pathways of water through space, e.g., overland flow, saturated groundwater flow, and/or ...unsaturated flow in the vadose zone. Because existing distributed modeling approaches differ substantially from one another, we present a common framework from which to compare the many existing hillslope‐ and catchment‐scale models. To provide a context for understanding the structure of the current generation of distributed models, we briefly review the history of hydrologic modeling. We define relevant modeling terms and describe common physical, analytical, and empirical approaches for representing hydrologic processes in the subsurface, surface, atmosphere, and biosphere. We then introduce criteria for classifying existing distributed models based on the nature of their process representation, solution scheme, coupling between the surface and subsurface, and treatment of space and time. On the basis of these criteria we describe 19 representative distributed models and discuss how process, scale, solution, and logistical considerations can be incorporated into model selection and application.
Stream temperature is an important indicator of water quality, particularly in regions where endangered fish populations are sensitive to elevated water temperature. Regional assessment of stream ...temperatures from the ground is limited by sparse sampling in both space and time. Remotely sensed thermal-infrared (TIR) images are able to make spatially distributed measurements of the radiant skin temperature of streams. We quantify and discuss the accuracy and uncertainty limits to recovering stream temperatures in the Pacific Northwest for a range of stream widths (10–500 m), and TIR pixel sizes (5–1000 m) from remotely sensed airborne and satellite TIR images. Among locations with more than three pixels across the stream, the image temperature overestimated the in-stream temperature on average by 1.2 °C, which is 7% of the in-stream temperature (standard error (SE) of 0.2 °C,
n
=
21). The corresponding uncertainty (band weighted standard deviation in image temperature) for these locations averaged ±
0.3 °C (SE
<
0.1 °C,
n
=
21) which is 2% of in-stream temperatures. This overestimation by the image temperatures is likely to be due to thermal stratification between the stream surface and the location of the in-stream temperature measurements deeper in the water column. For streams with one to three pixels across, mixing with bank elements increased the overestimation by image temperatures to 2.2 °C (SE
=
0.3 °C,
n
=
23) on average (13% of in-stream temperatures), and the uncertainty increased to ±
0.4 °C (SE
=
0.1 °C,
n
=
23) which is 2% of in-stream temperatures. For a fraction of a pixel across the stream the overestimation by image temperatures was 7.6 °C (SE
=
1.2 °C,
n
=
23) on average (45% of in-stream temperatures), and the uncertainty was ±
0.5 °C (SE
=
0.1 °C,
n
=
23) which is 3% of in-stream temperatures. These results show that reliable satellite TIR measurement of stream temperatures is limited to large rivers (∼180-m across for Landsat ETM+), unless novel unmixing algorithms are used effectively.
Workshop summary: Kaons@CERN 2023 Anzivino, G.; Cuendis, Sergio Arguedas; Bernard, V. ...
The European physical journal. C, Particles and fields,
04/2024, Letnik:
84, Številka:
4
Journal Article
Recenzirano
Odprti dostop
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.
The radiative corrections to double-Dalitz $({P\rightarrow\overline ll\overline l'l'})$ decays are revisited and completed up to next-to-leading order in QED, finding mild differences with respect to ...previous studies. These might be relevant for extracting information about the mesons transition form factors, which play an important role in determining the hadronic light-by-light contribution to the anomalous magnetic moment of the muon.
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.
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