Snowmelt Season Research Articles

Comparing Bayesian and traditional end-member mixing approaches for hydrograph separation in a glacierized basin

Abstract. Tracer data have been successfully used for hydrograph separation in glacierized basins. However, in these basins uncertainties of the hydrograph separation are large and are caused by the spatiotemporal variability in the tracer signatures of water sources, the uncertainty of water sampling, and the mixing model uncertainty. In this study, we used electrical conductivity (EC) measurements and two isotope signatures (δ18O and δ2H) to label the runoff components, including groundwater, snow and glacier meltwater, and rainfall, in a Central Asian glacierized basin. The contributions of runoff components (CRCs) to the total runoff and the corresponding uncertainty were quantified by two mixing approaches, namely a traditional end-member mixing approach (abbreviated as EMMA) and a Bayesian end-member mixing approach. The performance of the two mixing approaches was compared in three seasons that are distinguished as the cold season, snowmelt season, and glacier melt season. The results show the following points. (1) The Bayesian approach generally estimated smaller uncertainty ranges for the CRC when compared to the EMMA. (2) The Bayesian approach tended to be less sensitive to the sampling uncertainties of meltwater than the EMMA. (3) Ignoring the model uncertainty caused by the isotope fractionation likely led to an overestimated rainfall contribution and an underestimated meltwater share in the melt seasons. Our study provides the first comparison of the two end-member mixing approaches for hydrograph separation in glacierized basins and gives insight into the application of tracer-based mixing approaches in similar basins.

Snow Samples Combined With Long-Range Transport Modeling to Reveal the Origin and Temporal Variability of Black Carbon in Seasonal Snow in Sodankylä (67°N)

In the Arctic areas, the influence of global climate change is enhanced. Enabling a better understanding of the changes in the Arctic environment is of the utmost importance. The deposition of local and long-range transported air pollutants includes light-absorbing aerosols, such as black carbon (BC), which darken bright surfaces and induce snow melt. In 2009–2013, surface snow was sampled on a weekly basis during autumn, winter and spring at the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, which is located north of the Arctic Circle (67.37o N, 26.63o E). Snow samples were analyzed for BC with an Organic/Elemental Carbon Aerosol (OCEC) analyzer. These data were combined with SILAM modeling (System for Integrated modeLing of Atmospheric coMposition) to reveal the origin and temporal variability of BC in seasonal snow. Quantitative footprint calculations for the BC observations were performed with the SILAM -model considering emission sources at all heights and including also the sensitivity to the local and near-surface sources. The median BC concentration in snow was 25 µg/kg (n = 107, skewness γ1 = 0.12, 75th percentile Q3 = 42 µg/kg), determined as [μg-EC/L-H2O]. During snow accumulation season, the median surface snow BC concentration was 21µg/kg (n = 78, γ1 = 1.5, Q3 = 33 µg/kg), and during melt season, it was 57 µg/kg (n = 29, γ1 = 2.2, Q3 = 85 µg/kg). The melt period was identified using snow depth data from the Sodankylä station. The highest BC values in spring represented the enrichment of BC to the snow surface due to seasonal snow melt. The spring melt BC enrichment ratio was 2.7 (calculated as the ratio of median melt season concentration to median accumulation season snow concentration). The results showed that increased surface snow BC concentrations (> 30 µg/kg) were due to air masses originating from the Murmansk region in Russia, where smelting and mining industries are located. The temporal variability of BC in snow was high and depended on atmospheric and cryospheric processes, mostly the origin of BC due to atmospheric transport and dry and wet deposition processes, as well as post-depositional snow processes.

Multiple Ecosystem Effects of Extreme Weather Events in the Arctic

The Arctic is getting warmer and wetter. Here, we document two independent examples of how associated extreme precipitation patterns have severe implications for high Arctic ecosystems. The events stand out in a 23-year record of continuous observations of a wide range of ecosystem parameters and act as an early indication of conditions projected to increase in the future. In NE Greenland, August 2015, one-quarter of the average annual precipitation fell during a 9-day intensive rain event. This ranked number one for daily sums during the 1996–2018 period and caused a strong and prolonged reduction in solar radiation decreasing CO2 uptake in the order of 18–23 g C m−2, a reduction comparable to typical annual C budgets in Arctic tundra. In a different type of event, but also due to changed weather patterns, an extreme snow melt season in 2018 triggered a dramatic gully thermokarst causing rapid transformation in ecosystem functioning from consistent annual ecosystem CO2 uptake and low methane exchange to highly elevated methane release, net source of CO2, and substantial export of organic carbon downstream as riverine and coastal input. In addition to climate warming alone, more frequent occurrence of extreme weather patterns will have large implications for otherwise undisturbed tundra ecosystems including their element transport and carbon interactions with the atmosphere and ocean.

Assessment of glacier‐ and snowmelt‐driven streamflow in the arid middle Tianshan Mountains of China

AbstractDue to limitations in transport and communication infrastructures and difficulties in accessing glaciers, it is challenging to monitor snow and glaciers. In this study, the enhanced Utah Energy Balance (UEB) with a glacier melt model and snow above and below the forest ablation algorithm is used to assess the contributions of snow and glacier melting in three typical inland river basins (MRB, URB and KRB) in the middle Tianshan Mountains of China from 2002 to 2014. Forced by the spatial downscaling of the China meteorological forcing dataset (CMFD) coupled with other parameters, the model simulates the total surface water balance using surface water input from snowmelt, glacial melt and rainfall. Model simulations reveal that although the MRB, URB and KRB are all located on the northern slopes of the Tianshan Mountains, there are obvious differences in their water resource composition characteristics. Different from the URB, which is mainly replenished by glacial melt and had an average annual percentage of glacial melt of approximately 39% of the total surface water from 2009 to 2014, the MRB and KRB are mainly supplied by snowmelt and rainfall and both had an average annual percentage of snowmelt of approximately 37%. Although snowmelt is an important source of water to inland rivers, especially during the snowmelt season, the contributions of snowmelt in these three basins are very small especially for the URB, which had a contribution of 17%. This study effectively verifies the applicability of the CMFD and provides important scientific and technological support for determining the spatiotemporal variations in snow and glacial melt in the middle Tianshan Mountains, where meteorological observation data are scarce and some observational data, such as radiation data, are incomplete.

Information content of snow hydrological signatures based on streamflow, precipitation and air temperature

AbstractHydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter‐annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter‐annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter‐catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.

Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Abstract Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations.

Effects of valley reshaping and damming on surface and groundwater nitrate on the Chinese Loess Plateau

Remote rural areas, which are generally the source regions for drinking water in small towns in China, experience nitrate pollution due to intensive agricultural activities. However, little is known about the nitrate sources of these waterbodies and how human activities affect the water quality in these rural areas. Here, a pair of rural watersheds on the Chinese Loess Plateau (CLP) were selected for study. One of the watersheds, which provides drinking water for local villages and a nearby small town, has experienced valley reshaping, damming and agricultural land consolidation, whereas the other one was not affected by human activities. Water chemistry monitoring and environmental isotope analysis were combined to identify the nitrate sources of the surface and groundwater in the dammed and natural watersheds and to assess the effects of landform transformation and agricultural and living activities on water quality. The results demonstrated that nitrate concentrations in both watersheds were lower than 10 mg L−1. However, the seasonal nitrate contents in the surface (0.42–0.52 mg L−1) and groundwater (1.86–3.65 mg L−1) of the dammed watershed were significantly lower than those of the natural watershed (surface water: 0.92–2.07 mg L−1; groundwater: 2.73–8.21 mg L−1). Isotope analysis demonstrated that nitrate fertilizer, soil ammonium, and manure and septic waste were the main sources of nitrate in the surface water of the two watersheds, while the nitrate in the groundwater mainly originated from the nitrification of soil ammonium and manure and septic waste. Compared to the natural watershed, the dammed watershed had increased inputs of agricultural fertilizers in the surface water. However, there was no significant difference in the groundwater nitrate sources between the two watersheds. The results also demonstrated that denitrification and dissimilatory nitrate reduction to ammonium (DNRA) probably occurred in the groundwater during the wet season, while nitrification likely occurred during the dry and snowmelt seasons. Hierarchical cluster analysis (HCA) showed that there were strong interactions between the surface runoff and the reservoir water in the dammed watershed, while in the natural watershed, streamflow had a close relationship with the groundwater. We concluded that valley reshaping, damming and agricultural land consolidation increased the nitrate input from mineral fertilizers in the watershed, but decreased the nitrate concentrations in the surface water through changing the transformation and transportation processes of nitrate. However, these activities had little influence on the groundwater, while the time lag between land consolidation and the groundwater quality response should be considered in the future. This study aids in developing strategies for the comprehensive management of watershed and drinking water sources in rural regions.

Hydrologic connectivity at the hillslope scale through intra‐snowpack flow paths during snowmelt

AbstractThe seasonal snowmelt period is a critical component of the hydrologic cycle for many mountainous areas. Changes in the timing and rate of snowmelt as a result of physical hydrologic flow paths, such as longitudinal intra‐snowpack flow paths, can have strong implications on the partitioning of meltwater amongst streamflow, groundwater recharge, and soil moisture storage. However, intra‐snowpack flow paths are highly spatially and temporally variable and thus difficult to observe. This study utilizes new methods to non‐destructively observe spatio‐temporal changes in the liquid water content of snow in combination with plot experiments to address the research question: What is the scale of influence that intra‐snowpack flow paths have on the downslope movement of liquid water during snowmelt across an elevational gradient? This research took place in northern Colorado with study plots spanning from the rain‐snow transition zone up to the high alpine. Results indicate an increasing scale of influence from intra‐snowpack flow paths with elevation, showing higher hillslope connectivity producing larger intra‐snowpack contributing areas for meltwater accumulation, quantified as the upslope contributing area required to produce observed changes in liquid water content from melt rate estimates. The total effective intra‐snowpack contributing area of accumulating liquid water was found to be 17, 6, and 0 m2 for the above tree line, near tree line, and below tree line plots, respectively. Dye tracer experiments show capillary and permeability barriers result in increased number and thickness of intra‐snowpack flow paths at higher elevations. We additionally utilized aerial photogrammetry in combination with ground penetrating radar surveys to investigate the role of this hydrologic process at the small watershed scale. Results here indicate that intra‐snowpack flow paths have influence beyond the plot scale, impacting the storage and transmission of liquid water within the snowpack at the small watershed scale.

Comparison of snow water equivalent estimates calculated by SnoWE and ICON models on the example of the Kama river basin

More recently, snow accumulation and snowmelt models for their calculations are forced to apply data from numerical weather prediction (NWP) models. This approach allows improvement the accuracy of calculating snow water equivalent (SWE) values especially in remote and mountain regions. In this study, we compared the numerical results of SWE calculations performed by two independent models. The first one is the SnoWE model and the second one is the ICON NWP model. During the period from November 2018 to May 2019, the simulation results of SWE compared with in-situ data from 64 snow surveys, which are located in the Kama river basin. We found that both models (SnoWE and ICON) allow getting satisfactory estimates of the maximum values of SWE (the accuracy of data is sufficient for their practical using). The root mean square error was equal 14-18% from the average measured SWE. Moreover, we got reliable maximum values of SWE for forested areas. At the same time, both models underestimate SWE values during spring snowmelt season. Probably, this underestimation is due to the shortcomings of the models and a sparse snow course-measuring network.

Streamflow variability and the role of snowmelt in a marginal snow environment

ABSTRACT Snowmelt in alpine regions supports hydroelectric power generation, water supply, and agricultural production. These regions are warming, and the impact on streamflow of changes in precipitation and the proportion falling as snow is of interest. We investigate the seasonality and interannual variability of streamflow in the Australian Alps, a key location due to the marginal snowpack with winter air temperatures close to 0°C, and focus on a small subalpine catchment with properties representative of an important part of the broader snow-affected region. Streamflow was highly responsive to precipitation inputs with little autocorrelation observed. Water years were divided into four hydrological seasons based on the mean properties of normalized cumulative inflows. The spring snowmelt season accounted for the greatest proportion of annual inflows (mean = 39 percent). However, correlations between seasonal and annual inflows were only significant in the other three seasons, and winter inflows were the most important contributor to annual variability. The present snowpack is highly variable and sensitive to synoptic-scale influences. Although significant future reductions in snow-covered area have been predicted, we find that water resources are more susceptible to observed declines in total precipitation and likely increases in evapotranspiration than to a shift to proportionally less snowfall.

Evaluating climate and water regime transformation in the European part of Russia using observation and reanalysis data for the 1945–2015 period

ABSTRACT The river runoff regime significantly changed worldwide in the end of twentieth – the beginning of twenty-first century. In the European part of Russia the current changes are manifested in different ways in various regions. The main trend that is observed for most river basins is the ‘levelling’ of annual hydrographs. The interception of melt water during occasional thaw floods has resulted in a decrease in river runoff during the spring months. Simultaneously an increase in the low water period during winter and summer occurred. Reducing in the depth of soil freezing, caused by warm winters, lead to higher groundwater replenishment. At the same time, the loss of melt water to surface retention as well as evaporation has increased. Almost in all seasons of the year the number of occasional flood peaks increases. Occasional floods are superimposed both on the rise and the decline of seasonal snowmelt flood wave, making it difficult to separate it as an independent phase of the water regime. Changes in seasonal characteristics of river runoff are the most evident in the Don and Oka basins. A significant transformation of the water regime is observed in the north-west of European Russia as well. To a lesser extent, the described patterns are typical for the northern rivers and the eastern part of the European Russia – the Kama and the Ural Basin.

Simulated single-layer forest canopies delay Northern Hemisphere snowmelt

Abstract. Single-layer vegetation schemes in modern land surface models have been found to overestimate diurnal cycles in longwave radiation beneath forest canopies. This study introduces an empirical correction, based on forest-stand-scale simulations, which reduces diurnal cycles of sub-canopy longwave radiation. The correction is subsequently implemented in land-only simulations of the Community Land Model version 4.5 (CLM4.5) in order to assess the impact on snow cover. Nighttime underestimations of sub-canopy longwave radiation outweigh daytime overestimations, which leads to underestimated averages over the snow cover season. As a result, snow temperatures are underestimated and snowmelt is delayed in CLM4.5 across evergreen boreal forests. Comparison with global observations confirms this delay and its reduction by correction of sub-canopy longwave radiation. Increasing insolation and day length change the impact of overestimated diurnal cycles on daily average sub-canopy longwave radiation throughout the snowmelt season. Consequently, delay of snowmelt in land-only simulations is more substantial where snowmelt occurs early.

Mercury and dissolved organic matter dynamics during snowmelt runoff in a montane watershed, Provo River, Utah, USA

The mechanisms of Hg and dissolved organic matter (DOM) transport from watersheds to streams remain unclear, especially in snowmelt dominated montane systems. We characterized total Hg concentrations and DOM characteristics during snowmelt by weekly and/or monthly sampling at three locations in the upper Provo River, northern Utah, over two water years (2016 and 2017). Total Hg concentrations increased from <1 ng/L during baseflow to >7 ng/L during the snowmelt period (April–June), with decreasing concentrations from upstream to downstream. Filtered THg concentrations accounted for 65–75% of the unfiltered concentrations, suggesting that Hg is primarily transported in the dissolved phase. Annual THg loading in the upper Provo River was approximately 1 kg/yr, with ~90% of the flux occurring during the snowmelt period. Filtered THg concentrations were strongly correlated with in-situ fluorescence DOM (fDOM) measurements, allowing for the development of high-resolution proxy THg concentrations. Further, DOM characteristics, evaluated using excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC), identified the dominance of soil humic and fulvic acid fractions of DOM during runoff. Total Hg concentrations were low in snowpack but elevated in ephemeral streams during snowmelt runoff, indicating that Hg originated from shallow soil water rather than snow. Concentration—discharge relationships revealed clockwise hysteresis patterns, suggesting that Hg was flushed from soils on the rising limb of the hydrograph. Our results demonstrate that a majority of Hg transport occurs with a flux of DOM during the short snowmelt season as shallow soils are flushed by meltwater. The snowmelt-driven Hg pulse is a substantial source to downstream reservoirs and potentially contributes to a fish consumption advisory.

Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin

The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment.

On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal)

In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method.

Subsurface Water Dominates Sierra Nevada Seasonal Hydrologic Storage

AbstractVertical displacements (dz) in permanent Global Positioning System (GPS) station positions enable estimation of water storage changes (ΔS), which historically have been impossible to measure directly. We use dz from 924 GPS stations in the western United States to estimate daily ΔS in California's Sierra Nevada and compare it to seasonal snow accumulation and melt over water years 2008–2017. Seasonal variations in GPS‐based ΔS are ~1,000 mm. Typically, only ~30% of ΔS is attributable to snow water equivalent (SWE). ΔS lags the snow cycle, peaking after maximum SWE and remaining positive when all snow has melted (SWE = 0). We conclude that seasonal ΔS fluctuations are not primarily driven by SWE but by rainfall and snowmelt stored in the shallow subsurface (as soil moisture and/or groundwater) and released predominantly through evapotranspiration. Seasonal peak GPS ΔS is larger than accumulated precipitation from the Parameter‐elevation Relationships on Independent Slopes Model and North American Land Data Assimilation System, indicating that these standard inputs underestimate mountain precipitation.

Quantifying terrain controls on runoff retention and routing in the Northern Prairies

AbstractThe role of hummocky terrain in governing runoff routing and focussing groundwater recharge in the Northern Prairies of North America is widely recognised. However, most hydrological studies in the region have not effectively utilised information on the surficial geology and associated landforms in large‐scale hydrological characterization. The present study uses an automated digital elevation model (DEM) analysis of a 6500‐km2 area in the Northern Prairies to quantify hydrologically relevant terrain parameters for the common types of terrains in the prairies with different surficial deposits widespread in the prairies, namely, moraines and glaciolacustrine deposits. Runoff retention (and storage) capacity within depressions varies greatly between different surficial deposits and is comparable in magnitude with a typical amount of seasonal snowmelt runoff generation. The terrain constraint on potential runoff retention varies from a few millimetres in areas classified as moraine to tens of millimetres in areas classified as stagnant ice moraine deposits. Fluted moraine and glaciolacustrine deposits have intermediate storage capacity values. The study also identified the probability density function describing a number of immediate upstream neighbours for each depression in a fill‐and‐spill network. A relationship between depression parameters and surficial deposits, as well as identified depression network structure, allows parametrisation of hydrologic models outside of the high‐resolution DEM coverage, which can still account for terrain variation in the Prairies.

Snowmelt modeling using two melt-rate models in the Urumqi River watershed, Xinjiang Uyghur Autonomous Region, China

In this paper, the performance of the classic snowmelt runoff model (SRM) is evaluated in a daily discharge simulation with two different melt models, the empirical temperature-index melt model and the energy-based radiation melt model, through a case study from the data-sparse mountainous watershed of the Urumqi River basin in Xinjiang Uyghur Autonomous Region of China. Theclassic SRM, which uses the empirical temperature-index method, and a radiation-based SRM, incorporating shortwave solar radiation and snow albedo, were developed to simulate daily runoff for the spring and summer snowmelt seasons from 2005 to 2012, respectively. Dailymeteorological and hydrological data were collected from three stations located in the watershed. Snowcover area (SCA) was extracted from satellite images. Solarradiation inputs were estimated based on a digital elevation model (DEM). Theresults showed that the overall accuracy of the classic SRM and radiation-based SRM for simulating snowmelt discharge was relatively high. Theclassic SRM outperformed the radiation-based SRM due to the robust performance of the temperature-index model in the watershed snowmelt computation. Nosignificant improvement was achieved by employing solar radiation and snow albedo in the snowmelt runoff simulation due to the inclusion of solar radiation as a temperature-dependent energy source and the local pattern of snowmelt behavior throughout the melting season. Ourresults suggest that the classic SRM simulates daily runoff with favorable accuracy and that the performance of the radiation-based SRM needs to be further improved by more ground-measured data for snowmelt energy input.

Hydrogeomorphic controls on soil carbon composition in two classes of subalpine wetlands

Wetlands play a vital role in terrestrial carbon (C) sequestration, but the sensitivity of their C stocks to disturbance remains uncertain, requiring enhanced understanding of the processes that govern C storage and removal. The unique conditions in wetlands from different hydrogeomorphic (HGM) classes likely regulate the cycling, storage and vulnerabilities of wetland soil C stocks. To determine how differences in hydrogeomorphic setting influence soil organic carbon (SOC) processing, we compared C content and composition between depressional and slope wetlands located in the Colorado Rocky Mountains. Isolated depressional wetlands were characterized by seasonally declining water tables, slow discharge, high clay content, and thick organic horizons. Slope wetlands received perennial groundwater inputs and had coarser soil textures and thinner organic horizons. Seasonal snowmelt inputs coupled with low hydrologic discharge and higher clay content in depressional wetlands were predicted to sustain anoxic conditions, leading to high SOC content and chemically reduced C compounds. Depressional wetland soils had higher SOC content at depth and higher porewater DOC concentrations compared to slope wetland soils. Solid-state 13C nuclear magnetic resonance spectroscopy demonstrated that aliphatic compounds were the dominant SOC component in depressional wetlands compared to aromatic C forms in the slope wetlands. The higher prevalence of aliphatic carbon in depressional wetland soils suggests that stored SOC is protected by anaerobic conditions to a greater extent than in the slope wetlands, and that this SOC may be more vulnerable to drying and oxic conditions associated with wetland drainage and climate change.

Implications of observation-enhanced energy-balance snowmelt simulations for runoff modeling of Alpine catchments

Snow is an important component of the water balance of many mountain watersheds worldwide. In a warming climate, snowmelt modeling and consequent soil water input, is often challenged by complex conditions such as rain-on-snow situations. This is why detailed physics-based snow models are increasingly being used. These models however have much higher input data requirements, where in many cases accurate forcing fields are very difficult to provide.This study investigates whether the latest advances in the development of snow model framework actually translate into improved discharge simulations. To this end we integrated a distributed multi-layer energy-balance snow model with two recently developed methods of updating snow model mass and energy fluxes using snow observations to improve snow accumulation and depletion predictions. Surface water input from these simulations was used as input for subsequent streamflow modeling of 25 catchments in the Swiss Alps over four hydrological years.Our analysis clearly demonstrates the benefits of accurate snow simulations for hydrological modeling in Alpine catchments. Simulations that included the flux updates improved streamflow predictions, and offered best performance at high elevation, where snow most prominently affected watershed hydrology. These results were consistently achieved when analyzing model performance over entire hydrological years, over the snowmelt season only, and for individual events.