Snowmelt Season Research Articles

Evaluation of effects on a viaduct affected by a snowmelt-induced landslide using AI-Driven Pattern Recognition

Over recent years, the worldwide increase in infrastructure failures due to natural hazards such as landslides and earthquakes has raised significant concern. Aggravated by the effects of climate change, this issue has had a considerable impact, particularly in Italy. This study focuses on the "Villa Ilii" Viaduct on the A24 highway to analyze landslide-structure interactions, emphasizing the significant deformation risks. The methodology includes comprehensive geological, morphometric, and geophysical analyses, with findings showing cumulative deformation rates up to +2.5 cm/year at critical points. Field surveys and high-resolution SAR data analysis have identified deformation patterns linked to seasonal snow melt and temperature variations, adding a quantitative dimension to the hazard assessment. A novel component of this study is the application of Gradient Boosting Machines (GBM) for automated pattern recognition within SAR data processed using the Differential Interferometry SAR (DInSAR) technique. By leveraging datasets on snow melting patterns and cumulative radar target deformation, GBMs uncover intricate, non-linear relationships between snowmelt dynamics and ground deformation. This integration of GBMs and DInSAR data advances landslide hazard assessment by providing a robust 87% accuracy predictive model, effectively capturing environmental triggers that impact infrastructure stability. These findings contribute to more effective hazard management strategies and inform resilient infrastructure design, highlighting the study's significance in addressing climate-driven geohazards.

Sensitivity of the Northern Hemisphere Warming Trend to Snowpack Variability

Abstract A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model’s SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates.

Climate change and land-use policies exacerbate run-off reduction in a semi-arid inland river basin

Semi-arid regions are characterized by scarce water resources and fragile ecosystems, making them highly sensitive to environmental changes. Assessing the impacts of climate change and human activity on hydrological processes in these areas is crucial for ecological conservation and social development. However, due to data limitations and the complex interactions between climate change and human activities, especially at seasonal scales, accurately quantifying their impacts on hydrological processes poses significant challenges. In the present study, the Budyko framework combined with the elasticity coefficient approach was adopted to assess the contribution of environmental changes to variations in annual and intra-annual runoff in the Xilin River Basin from 1963 to 2020. The results showed that annual runoff exhibited a significant declining trend at a rate of 0.05 mm/a (P < 0.05), with an abrupt change in 2001. The underlying surface characteristic parameters were the most sensitive factors affecting the changes in runoff; however, climate change was identified as the primary cause, accounting for 65.96 % of runoff reduction. Increased temperatures accelerated the melting of ice and snow, such that the highest runoff (36.24 % of the annual total) occurred during April. The impact of human activities on runoff varies across seasons, with the greatest impact observed during the snowmelt season, followed by the irrigation, rainy, and dry seasons, respectively. Based on our findings, we conclude that tailored land-use policies and science-based water management are required to mitigate the impacts of human activities on changes in runoff.

Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

AbstractSnow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Inferring sediment-discharge event types in an Alpine catchment from sub-daily time series

Abstract. Fluvial sediment dynamics in mountain rivers are changing rapidly in a degrading cryosphere, raising the potential for erosive rainfall and runoff and detrimental effects on downstream areas. Hence, we need to understand better what characterises and drives episodic pulses of water and suspended solids in rivers. Here, we infer different types of such sediment-discharge events from 959 automatically detected events based on 16 metrics derived from 15 min time series of streamflow and suspended sediment concentrations from Vent–Rofental in the high Ötztal Alps, Austria. We use principal component analysis to extract uncorrelated event characteristics and cluster event types with a Gaussian mixture model. We interpret the thus inferred event types with catchment metrics describing antecedent conditions, hydrometeorological forcing, and fraction of catchment area with freezing temperatures and snow cover. We find event magnitude, hysteresis, and event shape complexity to be the main factors characterising the overall event regime. The most important characteristics distinguishing the event types are suspended sediment and streamflow magnitude and complexity of the hydro- and sedigraphs. Sediment-discharge hysteresis is less relevant for discerning event types. We derive four event types that we attribute to (1) compound rainfall–melt extremes, (2) glacier and seasonal snowmelt, (3) freeze–thaw-modulated snowmelt and precipitation events, and (4) late-season glacier melt. Glacier and snowmelt events driven by warm conditions and high insolation were the most frequent and contributed some 40 % to annual suspended sediment yield on average; compound rainfall–melt extremes were the rarest but contributed the second-highest proportion (26 %). Our approach represents a reproducible method for objectively estimating the variety of event-scale suspended sediment transport conditions in mountain rivers, which can provide insights into the contribution of different drivers to annual sediment yields in current and future regimes. Our findings highlight the importance of both meltwater and rainfall–runoff as drivers of high-magnitude suspended sediment fluxes in mountain rivers.

Hydrological Drought‐To‐Flood Transitions Across Different Hydroclimates in the United States

AbstractFloods following on streamflow droughts can have severe impacts. While they have been prominently featured by the media in recent years, we know little about their spatio‐temporal variability. In this study, we analyze the occurrence and drivers of such drought‐to‐flood transitions by calculating transition lengths from droughts to floods for natural and regulated catchments across the Contiguous United States between 1970 and 2022. We find that drought‐to‐flood transitions strongly vary in their lengths and their spatial distribution. We identify snowmelt as the main driver of transitions in high‐elevation catchments, while transitions in low‐elevation catchments are more variable in their time of occurrence and drivers. Reservoir management reduces the number of short drought‐to‐flood transitions, particularly in catchments with a high amount of snow where snowmelt is crucial for filling reservoirs in early summer. These findings suggest that projected changes in the snowmelt season will lead to changes in transitions from streamflow droughts to floods and that reservoir management may be used to adapt to these changes.

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

ABSTRACT This study quantitatively investigated reservoir-effects on drought propagation in two semi-arid river basins: India’s Tapi basin with the Ukai reservoir and Uzbekistan’s Chirchik basin with the Charvak reservoir. Meteorological drought (MD) is analyzed using the Standardized Precipitation Index (SPI) and hydrological drought (HD) using the Standardized Streamflow Index (SSI) for the duration from 1980 to 2004. Both the river basins, especially in upstream reservoir areas, exhibited a notable correlation between HD and MD. Reservoir operation was observed to reduce the downstream MD–HD correlation at shorter SPI timescales. Hit-score-based evaluations indicated that reservoir operation has induced changes in the drought propagation patterns for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the drought propagation time (DPT), with distinct influences from monsoon (Tapi) and snow-melting (Chirchik). The average DPT (average DPT over 12 months) for the reservoir-influenced part of the Tapi (∼ six months) and Chirchik (∼ nine months) basins is higher than that of the natural parts of both basins (Tapi: ∼ four months; Chirchik: ∼ six months) as a result of the natural and anthropogenic storage influence.

Assessment of geo-disaster in the snowmelt season considering climate changes

In this study, we reproduced avalanches and debris flow phenomena using the debris flow analysis tool, iRIC Morpho2DH, and laboratory-test apparatus. In the laboratory tests, we reproduced aggregates of snow, water, and soil that form during avalanches and debris flows using a pot mill turntable. Consequently, the possibility of forming aggregates of snow, water, and soil during debris flow phenomena was analyzed. Evaluation of avalanche and debris flow phenomena during the snowmelt season was conducted by considering the characteristics of these aggregates. Based on the results, the validity of the analysis method for the Nozuka disaster in Japan was examined. Moreover, the risks associated with future avalanches and debris flow phenomena under changing climatic conditions were studied using the Policy Decision making for Future climate change (d4PDF) database.

Can we rely on drought‐ending “miracles” in the Colorado River Basin?

AbstractUnexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought‐busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard‐to‐predict, ultra‐high precipitation events occurring during the late‐snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry‐to‐wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less‐snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

A 20-Year Ecotone Study of Pacific Northwest Mountain Forest Vulnerability to Changing Snow Conditions

(1) Background: Global climate change is expected to significantly alter growing conditions along mountain gradients. Landscape ecological patterns are likely to shift significantly as species attempt to adapt to these changes. We evaluated the extent to which spatial (elevation and canopy cover) and temporal (decadal trend and El Niño–Southern Oscillation/Pacific Decadal Oscillation) factors impact seasonal snowmelt and forest community dynamics in the Western Hemlock–True Fir ecotone region of the Oregon Western Cascades, USA. (2) Methods: Tsuga heterophylla and Abies amabilis seedling locations were mapped three times over 20 years (2002–2022) on five sample transects strategically placed to cross the ecotone. Additionally, daily ground temperature readings were collected over 10 years for the five transects using 123 data loggers to estimate below-canopy snow metrics. (3) Results: Based on validation using time-lapse cameras, the data loggers proved highly reliable for estimating snow cover. The method reported fewer days of snow cover as compared to meteorological station-based snow products for the region, emphasizing the importance of direct under-canopy field observations of snow. Snow season variability was most significantly impacted temporally by cyclical ENSO/PDO climate patterns and spatially by differences in canopy cover within the ecotone. The associated seedling analysis identified clear sorting of species by elevation within the ecotone but reflected a lack of a long-term trend, as species dominance in the seedling strata did not significantly shift along the elevation gradient over the 20-year study. (4) Conclusions: The data logger-based approach provided estimates of snow cover at ecologically significant locations and fine enough spatial resolutions to allow for the study of forest regeneration dynamics. The results highlight the importance of long-term, understory snow measurements and the influence of climatic oscillations in understanding the vulnerability of mountain areas to the changing climate.

Managed aquifer recharge as a strategy to redistribute excess surface flow to baseflow in snowmelt hydrologic regimes

Water management in snowmelt hydrologic regimes, characterized by large annual fluctuations in stream flow driven by seasonal snow melt, faces the challenge of highly variable supply that often does not align with timing of demand. Climate change may exacerbate management challenges by significantly reducing snowpack or shifting snow melt earlier. Here, managed aquifer recharge (MAR) is evaluated as a potential strategy to reallocate excess early-season stream flow to time periods when less surface water is available. This strategy differs from traditional MAR, where the goal is to minimize loss to surface water. We assess how to site MAR operations such that groundwater recharge flows back to the surface water system in a lagged manor to benefit water management objectives, which we term “enhanced baseflow.” We use a regional groundwater model for the Treasure Valley aquifer located in southwestern Idaho, United States to demonstrate a generalizable approach using regional groundwater models as tools to identify favorable baseflow enhancement locations. Hypothetical MAR is simulated at 197 candidate locations, which are then evaluated for how effectively they meet potential management objectives. In addition to demonstrating the modeling and evaluation approach, we discuss lessons learned from applying a pre-existing regional groundwater model to MAR for enhanced baseflow and also describe important considerations, such as the physical and institutional availability of surface flows and specific management objectives, when assessing regional and site-specific suitability of MAR for enhanced baseflow as a potential management strategy.

Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western US from 2001 to 2022

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March–30th June) in all 22 years. We tested for long-term trend significance with the Mann–Kendall test and trend magnitude with Theil–Sen’s slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%–8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Sensitivity analysis in the wavelet domain: a comparison study

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

Predicted Contribution of Snowmelt to Subsurface Drainage Discharge in Two Subsurface-Drained Fields in Southern Quebec and Ontario

Highlights RZ-SHAW model delivered a satisfactory simulation for winter subsurface drainage discharge. Snowmelt dominated the drainage discharge during the winter and spring snowmelt seasons in southern Quebec. Snowmelt contributed to 29% and 18% of winter and annual drainage discharge, respectively, in southern Ontario. Abstract. Late winter/early spring has been recognized as a critical period for subsurface drainage discharge and associated nutrient losses due to snowmelt and rainfall in croplands under cold, humid climates in North America and Europe. Although the detachment and transport processes of soil particles under snowmelt and rainfall are known to be different, studies quantifying the contribution of snowmelt to winter drainage discharge are limited. This study aims to investigate the contribution of snowmelt to subsurface drainage discharge at two winterized experimental sites in southern Quebec and Ontario, Canada, using observed data and the Root Zone Water Quality Model-Simultaneous Heat and Water model (RZ-SHAW model). The RZ-SHAW model was calibrated and validated against the measured snow depth and year-round subsurface drainage discharge data from 2021 to 2023 at St. Emmanuel, Quebec, and from 2000 to 2003 at Harrow, Ontario. RZ-SHAW demonstrated satisfactory Nash-Sutcliffe model efficiency values (NSE = 0.50) for simulating snow depth, soil temperature, and winter subsurface drainage discharge. The calibrated RZ-SHAW model was used to simulate the contribution of snowmelt to subsurface drainage for the two sites from 1990 to 2022. The snowmelt was predicted to contribute 55% and 44% of the winter and annual subsurface drainage discharge for the cropland in Southern Quebec. In contrast, for the southern Ontario site, the contribution of snowmelt to winter and annual subsurface drainage discharge was 29% and 18%, respectively. Considering that snowmelt in Southern Quebec contributed a significant fraction of winter and annual subsurface drainage discharge, more attention should be devoted to adapting and developing new winter management for nutrient loss in future research in the region. Keywords: Cold region, Cropland drainage, Drainage partition, RZ-SHAW, RZWQM2, Snowmelt, Soil freeze and thaw, Winter subsurface drainage.

Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling

Abstract. Dynamical (i.e., model-based) methods are widely used by forecasting centers to generate seasonal streamflow forecasts, building upon process-based hydrological models that require parameter specification (i.e., calibration). Here, we investigate the extent to which the choice of calibration objective function affects the quality of seasonal (spring–summer) streamflow hindcasts produced with the traditional ensemble streamflow prediction (ESP) method and explore connections between hindcast skill and hydrological consistency – measured in terms of biases in hydrological signatures – obtained from the model parameter sets. To this end, we calibrate three popular conceptual rainfall-runoff models (GR4J, TUW, and Sacramento) using 12 different objective functions, including seasonal metrics that emphasize errors during the snowmelt period, and produce hindcasts for five initialization times over a 33-year period (April 1987–March 2020) in 22 mountain catchments that span diverse hydroclimatic conditions along the semiarid Andes Cordillera (28–37∘ S). The results show that the choice of calibration metric becomes relevant as the winter (snow accumulation) season begins (i.e., 1 July), enhancing inter-basin differences in hindcast skill as initializations approach the beginning of the snowmelt season (i.e., 1 September). The comparison of seasonal hindcasts shows that the hydrological consistency – quantified here through biases in streamflow signatures – obtained with some calibration metrics (e.g., Split KGE (Kling–Gupta efficiency), which gives equal weight to each water year in the calibration time series) does not ensure satisfactory seasonal ESP forecasts and that the metrics that provide skillful ESP forecasts (e.g., VE-Sep, which quantifies seasonal volume errors) do not necessarily yield hydrologically consistent model simulations. Among the options explored here, an objective function that combines the Kling–Gupta efficiency (KGE) and the Nash–Sutcliffe efficiency (NSE) with flows in log space provides the best compromise between hydrologically consistent simulations and hindcast performance. Finally, the choice of calibration metric generally affects the magnitude, rather than the sign, of correlations between hindcast quality attributes and catchment descriptors, the baseflow index and interannual runoff variability being the best predictors of forecast skill. Overall, this study highlights the need for careful parameter estimation strategies in the forecasting production chain to generate skillful forecasts from hydrologically consistent simulations and draw robust conclusions on streamflow predictability.

Nitrate losses from forest during snowmelt: An underestimated source in mid-high latitude watershed

The forest nitrate cycle is a crucial part of the watershed nitrate load but has received limited attention compared to that of agricultural and residential land. Here, we analyzed the status and sources of riverine nitrate fluxes and identified the characteristics and contribution of forest nitrate loss to the riverine system in a mid-high latitude forested watershed using monthly field sampling and a modified Soil and Water Assessment Tool (SWAT) with enhanced forest nutrient cycle representation. The results indicate that nitrate losses in the headwater stream and downstream exhibit different seasonal characteristics. The nitrate losses in the headwater stream show a bimodal pattern due to lower temperatures and snowmelt runoff. Redundancy analysis (RDA) revealed that, unlike nitrogen (N) fertilizer-induced nitrate loss in the rainy season, forest loss has a positive effect on headwater stream nitrate concentration during the snowmelt season. The modified SWAT was then utilized to simulate nitrate losses in forest lands. The forest nitrate export per unit area of the headwater stream (1.58 ± 1.78 kg/ha/yr) was observed to be higher than that of the downstream (0.67 ± 0.74 kg/ha/yr) due to high snowmelt and mineralization of active organic N. At watershed scale, forest lands contributed 8.18 ± 3.94 % of the total nitrate losses to the water system in the headwater watersheds during the snowmelt season, representing the highest level within the entire basin. A comparison with forest streams in similar low-temperature conditions worldwide revealed that increasing nitrate loss occurred after extreme cold weather or soil freezing events, with an average increment of 6.32 kg/ha/yr. Therefore, forest nitrate losses should be better characterized and included in future watershed N budgets in low-temperature regions, which might help to reduce the N budget uncertainty and improve watershed management.

Seasonal variation in microplankton communities in Suttsu, Hokkaido, northern Japan, from 2020 to 2022

Southwestern Hokkaido faces intricate physiological and chemical changes. Subsequently, changes in lower trophic levels are of concern, but fully understanding the relationship between microplankton and hydrography in this region is required. To examine the relationship between the microplankton community (diatoms, dinoflagellates, ciliates, and euglenoids) and environmental factors in Suttsu, Hokkaido, water samples were collected at 4–15-day intervals from August 2020 to August 2022 at Yokoma Port. Diatoms were dominant in the study area, with an apparent seasonal change in species composition. Lower phytoplankton cell density was observed in the second year than in the first year during autumn and winter because of low temperatures and light intensity in the second year. A significant decrease in the number of attached diatoms (Navicula spp.) was observed during the winter of the second year, possibly due to calm conditions (low tide and wind speed), which prevent the detachment of weakly attached species. Large blooms of euglenoids (Eutreptiella gymnastica and E. marina) in April and May were caused by the exclusive use of nutrients from the inflow of river water during the snowmelt season. Warm-water species sporadically occurred, suggesting their transportation by the Tsushima Warm Current. Temperature, nutrients, and light intensity primarily control microplankton communities, which vary seasonally. These findings led to the prediction of lower trophic levels due to the impact of warm water inflow.

Greenup Variability Impact on Seasonal Streamflow and Soil Moisture Dynamics in Humid, Temperate Forests

AbstractIn this study, we investigate how seasonal streamflow and soil moisture patterns have responded to variability in vegetation phenology in humid, temperate forested watersheds without significant seasonal snowmelt over the last four decades. We characterize spring streamflow peaks using 50th percentiles of cumulative daily precipitation, streamflow, and soil moisture measurements, and investigate interactions with remotely sensed, greenup anomalies. After removing a dominant precipitation control, 1‐day earlier greenup is usually associated with about 1‐day early spring flow peak at four low‐elevation deciduous catchments using both sequential and multiple linear regressions. This indicates that the strong dependency of seasonal flow regimes on precipitation is mediated by vegetation seasonality, especially by greenup variability. In contrast, we find less significant correlations of the greenup anomalies on flow percentiles from two paired evergreen and two high‐elevation deciduous catchments. At a plot scale, similar correlations were found only at an upslope topographic position, where precipitation also showed tighter coupling with moisture seasonal patterns than downslope. Our study suggests that rainfall‐runoff and rainfall‐soil moisture relations have been closely mediated by vegetation seasonality in deciduous forests, especially by greenup anomalies, but patterned along topoclimate and hillslope gradients. This study emphasizes that it is important to understand phenological responses to ongoing climate change (in both long‐term and interannual variability) for prediction of seasonal flow regimes especially in deciduous forested catchments.

Spatial distribution and variability of boundary layer aerosol particles observed in Ny-Ålesund during late spring in 2018

Abstract. This article aims to improve the understanding of the small-scale aerosol distribution affected by different atmospheric boundary layer (ABL) properties. In particular, transport and mixing of ultrafine aerosol particles (UFPs) are investigated as an indicator for possible sources triggering the appearance of new particle formation (NPF) at an Arctic coastal site. For this purpose, flexible measurements of uncrewed aerial systems (UASs) are combined with continuous ground-based observations at different altitudes, the Gruvebadet observatory close to the fjord at an altitude of 67 m above sea level (a.s.l.) and the observatory at Mount Zeppelin at an altitude of 472 m a.s.l. The two uncrewed research aircraft called ALADINA and MASC-3 were used for field activities at the polar research site Ny-Ålesund, Svalbard, between 24 April and 25 May 2018. The period was at the end of Arctic haze during the snowmelt season. A high frequency of occurrence of UFPs was observed, namely on 55 % of the airborne measurement days. With ALADINA, 230 vertical profiles were performed between the surface and the main typical maximum height of 850 m a.s.l., and the profiles were connected to surface measurements in order to obtain a 4-D picture of the aerosol particle distribution. Analyses of potential temperature, water vapor mixing ratio and aerosol particle number concentration of UFPs in the size range of 3–12 nm (N3−12) indicate a clear impact of the ABL's stability on the vertical mixing of the measured UFPs, which results in systematical differences of particle number concentrations at the two observatories. In general, higher concentrations of UFPs occurred near the surface, suggesting the open sea as the main source for NPF. Three different case studies show that the UFPs were rapidly mixed in the vertical and horizontal scale depending on atmospheric properties. In case of temperature inversions, the aerosol population remained confined to specific altitude ranges and was not always detected at the observatories. However, during another case study that was in relation to a persistent NPF event with subsequent growth rate, the occurrence of UFPs was identified to be a wide-spreading phenomenon in the vertical scale, as the observed UFPs exceeded the height of 850 m a.s.l. During a day with increased local pollution, enhanced equivalent black carbon mass concentration (eBC) coincided with an increase in the measured N3−12 in the lowermost 400 m but without subsequent growth rate. The local pollution was transported to higher altitudes, as measured by ALADINA. Thus, emissions from local pollution may play a role for potential sources of UFPs in the Arctic as well. In summary, a highly variable spatial and temporal aerosol distribution was observed with small scales at the polar site Ny-Ålesund, determined by atmospheric stability, contrasting surface and sources, and topographic flow effects. The UAS provides the link to understand differences measured at the two observatories at close distances but different altitudes.

Standing on the shoulder of a giant landslide: A six-year long InSAR look at a slow-moving hillslope in the western Karakoram

In this work, we investigate a slow-moving, large landslide (∼20 km2) in the Chitral district in Northern Pakistan, near several villages. The slow-moving landslide was reported more than four decades ago but has never been examined afterward. Interferometric Synthetic Aperture Radar (InSAR) analyses, using Sentinel-1 data that span a period of six years, allowed us to retrieve the spatio-temporal pattern of hillslope deformation. We combined both ascending and descending orbits to identify vertical and horizontal deformations. Our results showed that the crown is moving relatively fast in comparison to the nearby regions; 30 mm/year and 40 mm/year in downward and eastward directions, respectively. Also, step-like deformations observed over the crown reflect a deep-seated landslide. At the footslope, on the other hand, we captured relatively high deformations but in an upward direction; specifically 30 mm/year and 30 mm/year in upward and eastward directions, respectively. We have discussed the possible roles of meteorologic and anthropogenic factors causing hillslope deformation occurred during the six-year period under consideration. We observed a seasonal deformation patterns that might be mainly interpreted to be governed by the influence of snowmelt due to increasing temperatures during the start of spring. Overall, the same mechanism might be present in many other hillslopes across the whole Hindukush-Himalayan-Karakoram range, where seasonal snowmelt is an active agent. In this context, this research provides a case study shedding a light on the hillslope deformation mechanism at the western edge of the Himalayan range.