Impact of fire on montane snowpack energy balance in Snow Gum forest stands

Agricultural and Forest Meteorology 294 (2020) 108164

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Impact of fire on montane snowpack energy balance in Snow Gum forest stands

T

Andrew J. Schwartza, , Hamish McGowana, Nik Callowb,c ⁎

a

Atmospheric Observations Research Group, University of Queensland, Brisbane 4072, Australia UWA School of Agriculture and Environment, University of Western Australia, Perth 6009, Australia c Department of Geography, University of Western Australia, Perth 6009, Australia b

ARTICLE INFO

ABSTRACT

Keywords: Snowpack Energy balance Eddy covariance Australia Bushfire Forest Fire

Forest stands fundamentally alter hydrology of a region through impacting area micrometeorology and corre­ sponding variability in snow accumulation and melt. Bushfires significantly change these interactions through removal of forest canopy, darkening of tree stems, and post-fire stem decay. This study quantified the impact of pre- and post-bushfire E. pauciflora (Snow Gum) forest stands on snowpack energy balance in the Snowy Mountains of Southeast Australia. Forest canopy cover in undisturbed forest stands moderated snowpack energy exchange through reductions to incoming shortwave radiation and turbulent fluxes. Energy flux to the snowpack was at a maximum in the fire-disturbed forest due to lower snowpack albedo resulting in greater net shortwave radiation and an increased sensible heat emission from decaying tree stems. The fire-disturbed forest also ex­ perienced the largest evaporation rate with 8.1% of snowpack SWE being lost to the atmosphere. Dominant energy fluxes to the snowpack were shortwave radiation in the unforested area and fire-disturbed forest stand, and sensible heat in the undisturbed forest stand.

1. Introduction Water from mountainous regions supports over 50% of the world's population (Beniston, 2003), with montane snowpacks referred to as “water towers” (Viviroli et al., 2007). While many factors are known to impact seasonal snowpack, Varhola et al. (2010) found forest cover was the most highly correlated with snow accumulation and melt. Most research on the impacts of forests on snowpack energy balance has been conducted in the cold, continental climates of boreal and montane forests of the Northern Hemisphere (Varhola et al., 2010), with limited research from more marginal snowpacks of warmer climates. This is significant, as montane forests found in warmer alpine landscapes are now experiencing increased frequency of disturbance from land use change and fire (Abatzoglou and Williams, 2016). The impact of fire disturbance on forest canopy in these regions and how it affects snowpack-atmosphere energetics is at present poorly understood, but critical to water management. Forest snowpack energetics depend heavily on the structure and form of the forest canopy, captured by metrics such as Leaf Area Index (LAI) and stem density. Forest canopies act to reduce incoming short­ wave radiation and increase longwave radiation to the surface (Male and Granger, 1981). Lundquist et al. (2013) showed that

⁎

longwave radiation emission from forest canopies, as a function of air temperature in warmer climates, can exceed the reduction in shortwave radiation and act to increase snowmelt, reducing snowpack longevity. However, different canopy structures and densities affect alteration of these radiative transfers and the subsequent effect on snow ablation (Faria et al., 2000; Pomeroy et al., 2002) in addition to topographic controls on radiative exchanges (Sanmiguel-Vallelado et al., 2020) Tree canopies have shown a wide variety of impacts on latent and sensible heat fluxes to the snowpack. Harding and Pomeroy, 1996 ob­ served that sensible and latent heat fluxes were either positive or ne­ gative based on whether a coniferous canopy had intercepted snow remaining on it. In the coniferous forests of Oregon's western Cascade Mountains, turbulent energy flux contributions to the snowpack were an average of two to three times lower than that of a nearby open area (Berris and Harr, 1987). Gryning et al. (2001) found that changing the amount of forest cover in a northern Finland boreal forest by 30%, only changed the sensible heat flux by ~10% with minimal effect on latent heat flux. While the majority of research on forest effects on snowpack has been conducted in undisturbed forests, several studies have in­ vestigated areas with fire-disturbed or altered forests. These studies found that shifts in snowpack spatial distribution and ablation char­ acteristics occurred following forest disturbance by fire (Burles and

Corresponding author. E-mail address: [email protected] (A.J. Schwartz).

https://doi.org/10.1016/j.agrformet.2020.108164 Received 19 March 2020; Received in revised form 23 June 2020; Accepted 23 August 2020 Available online 02 September 2020 0168-1923/ © 2020 Elsevier B.V. All rights reserved.

Agricultural and Forest Meteorology 294 (2020) 108164

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Fig. 1. Energy balance instrumentation at the unforested site (a), undisturbed forest stand (b), and fire-disturbed forest stand (c).

Boon, 2011; Gleason et al., 2013; Harpold et al., 2014), deforestation (Berris and Harr, 1987; Gary, 1974, 1979; Golding and Swanson, 1986; Schelker et al., 2013), and pine beetle infestation (Boon, 2009, 2012; Pomeroy et al., 2012; Welch et al., 2016). Boon (2012) noted that snowpack density was lowest in fire-disturbed forested areas due to a lack of canopy drip from interception and melt that occurred in un­ disturbed forests. Burles and Boon (2011) showed lower snowpack al­ bedo in fire-disturbed forests six years after fire, compared to a nearby unburned control area. This was shown to be partially due to burned woody debris that fell onto the snowpack resulting in lower albedo that allowed for absorption of over double the normal amount of incoming solar radiation (Gleason et al., 2013). These changes in radiative energy transfers show the complexity and dynamisms of the snowpack in these regions. These effects would likely be amplified in more temperate mountain regions where snowpacks are almost isothermal, such as Australia.

The region was also impacted by recent wildfires of high intensity, during 2003 and most recently in the summer of 2019/20. The tree canopy of the snow-covered area is dominated by Eucalyptus pauciflora (Snow Gum) woodland, which accounts for 57% of the forest in the broader Snowy Mountains region (Gellie, 2005). Pure stands of Snow Gums are found above 1500 m a.s.l. in the Snowy Mountains (Slatyer and Morrow, 1977) and cover approximately 23% (1401 km2) of the broader 6000 km2 of land above that altitude, which coincides with the elevations that commonly experience snowfall. Snowpack contained within these forests is a significant source of water for the Murray Darling Basin, which accounts for 62% of Australia's total irrigation area (Australian Bureau of Statistics, 2020). Unlike other Eucalypt woodlands and forests, Snow Gum is not well adapted to fire and stands damaged by high-intensity wildfire experience limited im­ mediate regrowth (Pickering and Barry, 2005). To date, no research has explicitly investigated the effects that fire has on forests and how they modulate energy dynamics of marginal snowpack environments including the fire-prone Snow Gum ecosystem of the Snowy Mountains. However, understanding the impact that fire has on snowpack energetics in the Snow Gum forests is essential to inform debate on water resource management, alpine recreation, and conservation of snow dependent ecosystems in a warming world. This paper contributes to this discussion as it aims to identify differences in energy balance across three different site types, evaluating melt drivers and differences between an open (no over-storey vegetation) site and E. pauciflora forest stands not impacted and impacted by fire. The research addresses three specific objectives: 1) to identify and validate a method for partitioning of sub-canopy sensible heat fluxes, 2) determination of changes to energy balance resulting from an increase or reduction of forest canopy and, 3) to identify dominant sources of energy flux to the snowpack in each area.

1.1. Snowpack energy balance in warm regions Shortwave radiation has been identified as a significant control on snowpack energetics in warmer environments (Fayad et al., 2017; López Moreno et al., 2008) and contributions of shortwave radiation to snowmelt are modulated by changes to snow surface albedo (Lundquist et al., 2013; Sicart et al., 2004). Fayad et al. (2017) found that turbulent fluxes contributed more energy to snowmelt in warm Mediterranean regions and Marks et al. (1998) showed that 60-90% of energy available for snowmelt came from turbulent fluxes during a rain-on-snow event in Oregon's Cascade Mountains. Forest canopies act to modulate these processes through interception and emission of ra­ diative fluxes (Lundquist et al., 2013) and changes to wind speeds and turbulence (Reba et al., 2012). Australia's Snowy Mountains have been identified as having one of the most marginal alpine snowpack's globally (Bilish et al., 2018; Bilish et al., 2019). The region is characterised by reductions in snow cover since 1962, attributed to higher air temperatures and climate change (Nicholls, 2005), with further declines of up to 85% in annual areal snow cover projected to occur by 2050 (Hennessy et al., 2008).

2. Study location and characteristics 2.1. Study site characteristics and climate The relatively warm climate of the Snowy Mountains is favourable 2

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for a relatively warm and marginal snowpack. Mean maximum daily temperatures during winter (JJA) for the Snowy Mountains range from 3°C to 4.6°C and mean maximum daily spring (SON) temperatures ranging from 6.8°C in September to 14.5°C in November (Bureau of Meteorology, 2018). As such, the Australian snowpack is distinct in that it doesn't have typical accumulation and melt periods related to sea­ sonality and, instead, melt occurs frequently across both the winter and spring periods (Bilish et al., 2019). Energy balance measurements were made at three sites (Fig. 1); an unforested area (-36.418, 148.422; 1847 m a.s.l.), an undisturbed by fire E. pauciflora forest stand (-36.432, 148.368; 1851 m a.s.l.), and a fire-disturbed E. pauciflora forest stand (-36.410, 148.421; 1842 m a.s.l.), in Kosciuszko National Park, New South Wales, Australia. Fire impact on the area occurred in 2003 and instrumentation deployment and operation occurred from May 2018 to November 2019. Therefore, some immediate characteristics noted from burned forest were not present (i.e. charred wood and debris falling onto the snowpack in the subsequent season), although rotting trees did contribute debris to the snow surface, altering surface albedo, and the regrowth of the vegeta­ tion was muted with maximum height of ~2 m (See Fig. 1c). As a result, in this study we use the term “fire-disturbed forest stand” rather than “burned forest” to avoid confusion. The locations of the three sites were chosen as they are, 1) located in homogeneous forest stands, 2) represent the key ecosystems of the subalpine landscape of the Snowy Mountains, 3) are proximal (within 5 km of each other), and 4) nearly identical in elevation (1828 ± 4 m a.s.l.) and aspect. The fire-disturbed stand was a result of the 2003 bushfires that burned approximately 1.73 million hectares in the region (Worboys, 2003) including 70% of Kosciuszko National Park's sub­ alpine zone (Pickering and Barry, 2005). The unforested study site was located in alpine grassland, bog, and herb fields that represents 8% of the broader Snowy Mountains region (Gellie, 2005). E. pauciflora woodland covers up to 57% of the broader area as it is present in one third of the dominant vegetation formations (Gellie, 2005) and, was chosen as the site for measurements of the undisturbed forest stand. The highest stem density (8400 stems ha−1), measured by counting the stems in a 10 m by 10 m plot and then scaling to stems ha−1, was found in the fire-disturbed site due to regrowth of new stems and ex­ isting stems killed by the 2003 fire. By comparison, the undisturbed forest had 77% fewer stems than the fire-disturbed forest (1900 stems ha−1). Despite a lower stem density, the undisturbed stand had a much higher Vegetation Area Index (VAI) (Fassnacht et al., 1994) (Table 1) due to increased leaf coverage, whereas the primary source of sky ob­ scuration in the fire-disturbed stand was the burned stems. The differ­ ences in canopy and, therefore, VAI at each site resulted in different wind characteristics at each with the fire-disturbed forest stand having slightly faster winter wind speeds (median: 1.3 ms−1; IQR: 1.8 ms−1) than the undisturbed forest stand (median: 1.1 ms−1; IQR: 1.5 ms−1), but both were considerably lower than the unforested site (median: 3.3 ms−1; IQR: 2.7 ms−1). Climatological data from a proximal long-term Automated Weather Station (Bureau of Meteorology station, Perisher Valley: -36.41, 148.41; 1738 m a.s.l.), showed that conditions during the 2018 and 2019 study seasons were close to average conditions from 2010, when the site was

installed, to 2017. The 2018 and 2019 winter and spring seasons had below average precipitation accumulations of 969 mm (2018) and 922 mm (2019) compared to the climatological mean of 1171 mm (Bureau of Meteorology, 2018). Most of the mean maximum daily temperatures during the 2018 winter and spring months were 0.3°C to 1.9°C below the climatological averages except for October that was 0.6°C higher (Bureau of Meteorology, 2018). By comparison, 2019 had warmer winter and spring seasons with mean maximum daily tem­ peratures being 0.5°C to 1.6°C above the climatological averages ex­ of cluding November that was 1.0°C colder (Bureau Meteorology, 2018). 3. Methods 3.1. Instrumentation The unforested site (Fig. 2) was originally installed in June of 2016 as part of a study by Bilish et al. (2018) with the two forested energy balance sites established in May of 2018 (Fig. 2). Each of the sites consisted of nearly identical instrumentation to reduce any measure­ ment uncertainty. Turbulent fluxes were measured at 10 Hz using Campbell Scientific eddy covariance (EC) systems and Kipp and Zonen radiometers monitored radiative fluxes, both systems were mounted at 3 m. Hukseflux heat flux plates were placed on the soil surface to measure soil heat flux into/out of the bottom of the snowpack. Snow surface temperatures were monitored with Apogee Instruments SI-111 infrared radiometers at the unforested and undisturbed forest sites. Snowy Hydro Limited (SHL) provided precipitation data from a nearby ETI Instrument Systems NOAH II weighing precipitation gauge with a 6 m diameter (half-size) Double Fence Intercomparison Reference (DFIR) shield approximately 1 km to the northwest of the unforested site at an elevation of 1761 m. Data was collected with Campbell Scientific CR3000 microloggers running EasyFlux DL software (Campbell Scientific, 2018b) that re­ corded 30-minute block average measurements for all variables and additional 10 Hz measurements of EC variables. Sites were serviced approximately every four months and were monitored via real-time telemeted data. Details on the specific instrumentation used at each site is presented in Table 2. 3.2. Snowpack energy balance Total net radiation (Q*) can be used to summarise energy dynamics (Fayad et al., 2017), but the analysis in this paper also presents con­ stituent components that allows for greater insight into the way that the forest influences the individual radiative exchanges and snowpack en­ ergetics:

Q * = (K

K ) + (L

L )

where shortwave (K) and longwave (L) radiation are broken down into incoming (↓) and outgoing (↑) components, and can be reported as net terms of shortwave (K*) and longwave (L*). We explore these radiative terms within Section 4.2.1 of the results. Closure of the snowpack energy balance is particularly problematic as calm conditions can lead to under-measurement of turbulent fluxes (Helgason and Pomeroy, 2012) and internal snowpack processes aren't always measured. This means it is necessary to include an error in closure term (Qec), when calculating snowpack energy balance:

Table 1 Forest and soil metrics for each energy balance measurement site. Forest and soil data was collected between 18 November and 20 November, 2019.

Qres = Q * + Qh + Qe + Q g + Qr + Qec

Parameter

Site Unforested

Undisturbed

Fire-disturbed

Vegetation area index Stem density (stems ha−1) Diameter at breast height (cm) Soil bulk density (g cm−3) Soil organic matter (%)

N/A N/A N/A 1.21 24

1.85 1900 58 1.56 22

0.4 8400 51 1.55 24

(1)

(2)

where Qres is the residual energy flux after taking the sum of the net radiation (Q*), sensible and latent heat fluxes (Qh and Qe, respectively), ground heat flux (Qg), heat flux from liquid precipitation (Qr). Within the energy balance all energy fluxes are net terms, accounting for in­ coming and outgoing fluxes for conservation of energy within the system (Marks and Dozier, 1992; Stoy et al., 2018; Welch et al., 2016). 3

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Fig. 2. Map of Southeast Australia, Perisher Valley Automated Weather Station (AWS), and energy balance site locations in the Snowy Mountains.

Turbulent fluxes were calculated using the equations:

Qh =

Cp (w

Qe =

L v (w q )

the sonic anemometer and gas analyser. Air density was corrected (Webb et al., 1980) to remove vertical velocities from density change due to water vapour and heat air mass changes. Quality validation of Qh and Qe values (0-2) followed Mauder and Foken (2011), with “0” used for all applications, “1” for long-term observations, and “2” was re­ moved and gap-filled. Qh and Qe values were also removed from periods where the water vapour signal strength of the gas analyser was < 0.70 to reduce incorrect values from precipitation on the lenses (Campbell Scientific, 2018a; Gray et al., 2018). Finally, Qh and Qe data was despiked and erroneous values > 3 standard deviations from the median were removed using a 13 point moving-median filter.

(3)

)

(4) −3

where air density is ρ expressed in kg m , the specific heat of air is Cp (1005 J kg−1 deg−1), the latent heat of vaporization or sublimation of water is Lv (J kg−1), and w and w q are the average covariances between the wind vertical velocity w (ms−1) and potential temperature θ (K) or specific humidity q (kg kg−1) (Reba et al., 2009). We explore these turbulence terms within Section 4.2.2 of the results. 3.3. Energy flux measurements and data quality control

3.3.1. Gap filling Two methods were used to fill Qh and Qe gaps coded as “2” as a result of quality control (QC) procedures. Gaps < 90 minutes were filled using linear interpolation (LI) (Falge et al., 2001a, Falge et al., 2001b). Gaps > 90 minutes were filled using the Random Forest re­ gression technique that uses a supervised machine learning algorithm to create advanced multivariate regression. Root Mean Squared Error

Raw 10 Hz EC data was corrected using the double coordinate ro­ tation method of Stiperski and Rotach, 2016, to address any potential errors from imprecise levelling and for removal of larger-scale signals from thermally driven anabatic and katabatic flows that can result from sloping terrain (though study sites were fairly level). EC data was also corrected for sensor response delay and separation distance between Table 2 Details on instrumentation used at each site during the course of the study. Instrument

Manufacturer

Variables measured

Accuracy

Site

CNR4 CSAT3A

Kipp and Zonen Campbell Scientific

K < 5% Daily Total; L < 10% Daily Total ± 5 cm s−1

All All

CS650 EC150 HFP01 HMP155 NOAH II PTB110 SI-111

Campbell Scientific Campbell Scientific Hukseflux Vaisala ETI Instrument Systems Vaisala Apogee Instruments

K↓, K↑, L↓, L↑ Wind Components (ux, uy, uz); Wind Speed (u) and Direction (°); and Sonic Temperature Soil Water Content (SWC); Soil Temperature H2O Gas Density Soil Heat Flux Air Temperature (Td); Relative Humidity (RH) Precipitation Accumulation Barometric Pressure Surface Temperature (Tsfc)

SR50A

Campbell Scientific

Snow Depth

± 3% SWC; ± 5°C 2% < 3% < 0.3°C; <1.8% RH ± 0.254 mm ± 0.15 kPa ± 0.2°C -10°C
USH-8

Sommer

Snow Depth

0.1% of range 0-8m

All All All All All All Unforested Undisturbed Undisturbed Fire-disturbed Unforested

4

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during a 7 week field campaign from 26 July to 5 September, 2018, which resulted in 12 measurements at the fire-disturbed site and 11 measurements at the unforested and undisturbed sites. Three mea­ surements were made in the immediate vicinity (< 15 m diameter) of each instrument site to determine average snowpack characteristics. Mean snowpack density measurements during the 2018 snow season were determined and used for calculations of snow water equivalent (SWE). Correct measurement of snowpack energy balance is largely de­ pendent on a homogeneous snow surface (Reba et al., 2009) and only periods with >10 cm of snow at all sites as measured by the sonic snow depth sensors were used for analysis. Additionally, periods during the day (07:00-17:00 AEST) where K* was negative were removed from each dataset as negative values indicate snow build-up on top of the radiation sensors. A total of 6090 30-minute periods (~127 days) of energy fluxes that were used for comparison during the 2018 and 2019 winter seasons.

Table 3 Gap-filling statistics for Random Forest (RF) and Linear Interpolation (LI) methods at each site.

RF Qh RMSE (Wm−2) RF Qh Model R2 RF Qe RMSE (Wm−2) RF Qe Model R2 Qh Gaps filled by RF Qh Gaps filled by LI Qe Gaps filled by RF Qe Gaps filled by LI

Site Unforested

Undisturbed

Fire-disturbed

7.07 0.97 3.12 0.97 635 2561 711 3029

3.92 0.98 3.81 0.97 1015 1794 940 2355

5.93 0.99 4.36 0.98 796 1664 895 2180

(RMSE) and Coefficient of Determination (R2) were calculated for the random forest gap-filling method for each variable (Table 3). The lowest gap-fill RMSE value for Qh was established to be at the un­ disturbed forest stand (3.92 Wm−2) and the lowest Qe RMSE was at the unforested site (3.12 Wm−2). The highest RMSE was also at the un­ forested site with a Qh value of 7.07 Wm−2. Overall, Qe had lower RMSEs at all sites when compared to the Qh values of the same site, although there was a maximum range of only 3.95 Wm−2 RMSE be­ tween all the sites. The unforested site required the highest number of values to be gapfilled due to occasional build-up of snow or ice on the instrumentation from its increased exposure to intense winds. A total of 2894 Qh (48%) and 2350 Qe (39%) measurements remained of the original flux dataset once QC procedures had been implemented on the unforested site data. Higher retention of original values occurred with the other two sites with 54% of Qh values and 46% of Qe values remaining from the ori­ ginal undisturbed forest stand data and 60% of Qh values and 50% of Qe values from the fire-disturbed forest stand data. Specifics on the number of gaps filled by each method for each site are shown in Table 3.

3.4. Flux partitioning considerations Reduction of canopy allowed for higher amounts of incoming shortwave radiation (Section 4.2.1) incident on the tree stems of the fire-disturbed forest stand that then emitted significant quantities of sensible heat (Fig. 3). Gryning et al. (2001) found that nearly all of the measured sensible heat flux above a snowpack within a northern Fin­ land coniferous forest resulted from warming of the tree stems during periods with low solar angle. The relatively warmer air temperatures and high solar angles of Australia (compared to those of northern

3.3.2. Ground and precipitation flux QC The ground heat flux plate at the undisturbed forest stand site malfunctioned in 2019 and erroneous values were replaced by values from a Random Forest multivariate regression model developed from measurements obtained in 2018 that had a RMSE of 0.02 Wm−2 for the training data. A similar model for the fire-disturbed forest site showed a variance of 8% between measured and modelled daily Qg values during the 2019 snow season. Precipitation quantities were identical for all areas as only one gauge was used, but differences in Qr were still pos­ sible due to use of the calculation methods outlined by Bilish et al. (2018). As such, it was still a worthwhile pursuit to compare the rain fluxes between regions to determine the effects of canopy on otherwise similar precipitation characteristics. 3.3.3. Snow depth measurements and identification of snow cover periods Automated 30-minute measurements of snowpack depth (ds) were made at each of the two forested sites during the 2018 and 2019 snow cover seasons. SHL provided 10-minute measurements of snowpack depth at the unforested site and the 30-minute samples that matched the collection times at other sites were used in analysis. Diurnal signals in the ds measurements, caused by changes in snowpack top-layer density, relative humidity, and ambient air temperature, were corrected using a 24 hour (48 measurement) rolling mean. Correction factors were also needed to remove bias from inaccurate measurement of sensor height and occasional movement of the sensors due to strong winds. Corrections were made by determining median depth values at times with bare ground and subtracting the values from the snow depth measurements. One correction factor was developed per year of data collection at each site so that there were a total of six. Manual measurements of snowpack depth and mass at the three instrumentation sites were also obtained using a Federal Snow Sampler

Fig. 3. Standard and infrared photographs of the same section of the fire-dis­ turbed forest stand at 15:56 AEST 22 August, 2019. Ambient air temperature at the instrumentation site at the time of photo was 1.28°C, while the infrared surface temperatures are scaled with respect to the colour bar in degrees Celsius. 5

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Fig. 4. SWE ground truth measurements, calculated SWE models with original and canopy-corrected Qh values, and soil volumetric water content from 11 July to 4 September, 2019.

Finland) results in higher sensible heat emission from the snowpack. As such, sub-canopy measurement of turbulent fluxes included Qh emission from tree stems and needed to be partitioned into constituent parts to ensure correct measurement and analysis of snowpack energy fluxes. Two methods of sensible heat flux partitioning/correction were tested for data obtained in the fire-disturbed stand. Both methods se­ parated the data into day (07:00 AEST to 16:30 AEST) and night (17:00 AEST to 06:30 AEST) periods and developed corrections for the sensible heat fluxes. The first method calculated the mean percent difference (MPD) between the sensible heat fluxes in the unforested and fire-dis­ turbed areas for the 30-minute measurement periods and then in­ creased the sensible heat values of the fire-disturbed forest by the dif­ ference. The second method was to determine the mean difference between sensible heat values (MVD) in Wm−2 and apply a uniform correction of that value to the fire-disturbed forest stand sensible heat fluxes for all 30-minute periods. The results of both methods were used to calculate snowmelt models for the fire-disturbed site, the snowpack SWE that was calculated from measured depth, and average 2018 snowpack density was used as the truth. The most accurate model as determined by RMSE and used to correct the fire-disturbed sensible heat fluxes. Similar to the energy balance comparisons, only periods with > 10 cm of snowpack were used to develop model corrections to avoid contamination by heterogeneous surfaces with patchy snow.

Snow accumulation was identified by developing 90-minute rolling standard deviations (σ) of SWE and determining a median σ for each site and was then calculated for each value using the following criteria:

( ds < dsn

Accum = dsn

dsn

1

where ds is the median standard deviation of the SWE over the entire period, dsn is the current SWE being compared, and dsn 1is the previous SWE value. This gave two separate datasets, MLT and Accum, that were then combined and cumulative sums for the snowpack at each site were developed to give complete accumulation and melt over the study period. RMSE was chosen as the preferred metric to evaluate the snowmelt models as it has a higher penalty of a model for large errors than Mean Absolute Error (MAE). 4. Results Correction of Qh (Section 4.1) is discussed first within the results as it identifies the amount of sensible heat being contributed by the firedisturbed canopy to the snowpack energy balance measurements and allows corrected values to be used in subsequent energy balance com­ parisons. Section 4.2 addresses key energy balance results, addressing radiative fluxes (Section 4.2.1), turbulent fluxes (Section 4.2.2), soil and liquid precipitation fluxes (Section 4.2.3), and summarizing area residual energy fluxes (Section 4.2.4). Closure of area energy balances is discussed last (Section 4.2.5) to give context to the accuracy of the measurements and their ability to represent snowmelt.

3.4.1. Calculation of snowpack SWE model Snowpack SWE models were calculated and compared to ground truth measurements to determine accuracy of predicting snowpack processes using the collected energy balances. Thirty minute block averages of snowmelt were calculated at each location from positive Qres values using the methods of Pomeroy and Goodison (1997):

MLT = Qres /(

dsn 1)

4.1. Assessment of sensible heat flux correction methods and error Qh corrections improved calculation of snowpack melt models and significantly reduced RMSE values that allowed for an approximation of canopy contributions to sensible heat flux. The MPD method of cor­ rection was determined to be optimal after comparison of SWE calcu­ lations using the modified Qh values resulted in 17% more reduction in model RMSE than MVD correction. Each of the sensible heat flux par­ titioning methods showed improvement over the standard snowpack melt model (Fig. 4) with the MVD correction improving the RMSE by

w F Bi )

where Qres is the residual energy after calculation of energy balance terms in J m−2 0.5 h−1, ρw is the density of liquid water (~1000 kg m−3), λF is the latent heat of fusion (0.355 × 106 J kg−1), and Bi is the thermal quality of the snow (Gray and Male, 1981). All snowmelt models used a Bi 0.96 as differences in this value had little impact on model output similar to what was noted by Burles and Boon (2011). 6

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54.4 mm SWE (99.7 mm to 45.3 mm). MPD was slightly superior and showed a reduction in model RMSE from 99.7 mm to 28.4 mm SWE (72%). There was initial improvement in the MVD model's ability to determine snowpack SWE, but it became less accurate over time and, eventually, became less accurate than the MPD model towards the end of the period. While there is still disagreement between the ground truth and MPD method, the melt rates are much better represented than with the MVD correction or no-correction model, and allowed for an approximation of canopy contributions to sensible heat flux. The percentage difference was calculated between the unaltered Qh measurements and those used in the MPD method to determine the approximate percentage of sensible heat contribution from the firedisturbed canopy. On average, the canopy was responsible for a dif­ ference of 22% in the sensible heat release (Qh) that was measured based on the Qh corrections. Effects of solar radiation on tree stem Qh emission were noticeable as day periods showed a higher contribution of the tree stems to sensible heat release accounting for a difference of 28% of the overall measurement, while there was a drop in the con­ tribution to 16% during night. The additional heat release by the ca­ nopy was enough to contaminate the snowpack Qh signal that then led to erroneous estimates of the energy balance and the miscalculation of the snowmelt model for the fire-disturbed forest stand. The corrected energy balance of the fire-disturbed forest stand that follows uses the MPD method for correction of Qh to remove the sen­ sible heat release from the canopy for better representation of snow­ pack energy fluxes. The sensible heat flux corrections for the fire-dis­ turbed forest stand are based on the 28% daytime and 16% night-time values that were found using the MPD method. The energy balance results of the fire-disturbed forest stand include both uncorrected and corrected Qh values for side-by-side comparison of the impact that ca­ nopy emission had on the measured energy balance.

radiative exchange. Forest canopy showed significant effects on shortwave radiative exchange in both forest stands and altered longwave radiation at the sites. Increased L↓values were observed at both forested sites when compared to the unforested site due to emission of L from the forest canopies. L* at the fire-disturbed forest stand was 86% lower than at the undisturbed forest stand and was primarily due to increased L↑ rather than a reduction in L↓. Mean daily maximum L↑ (0.69 MJ m−2) from the two seasons in the fire-disturbed forest stand occurred at 13:00 AEST just after maximum daily K* at 12:30 AEST. Maximum daily L↓ (0.61 MJ m−2) occurred closer to sunset at 15:00 AEST and gradually decreased to a daily minimum (0.58 MJ m−2) just before sunrise. The undisturbed forest stand showed a similar pattern in L↑ and L↓ across the two seasons of observations, but the maximum in both mean terms (both 0.57 MJ m−2) occurred at 14:00 AEST. Overall, the fire-disturbed stand also showed a faster decrease in the amount of L↓ overnight from 0.60 MJ m−2 at 17:00 AEST to 0.58 MJ m−2 at 06:30 AEST than the undisturbed forest which had little decrease (-0.0036 MJ m−2) during the same period. Effects on radiative fluxes were the primary impact of forest canopy and showed considerable changes to turbulent fluxes even after corrections to Qh in the fire-disturbed forest stand. 4.2.2. Turbulent fluxes Daily Qh flux was 3% higher in the fire-disturbed forest stand than the undisturbed forest stand when the correction from Section 4.1 was applied to fire-disturbed forest stand Qh, however, both forest stands had lower values than the unforested site (undisturbed: -14%, firedisturbed: -11%). Median ambient air temperatures were similar be­ tween areas, ranging from -0.40°C in the undisturbed forest stand to +0.23°C in the unforested stand with a value of -0.02°C at the firedisturbed site (Fig. 7b). However, median wind speeds (Fig. 7a) showed significant increases as canopy cover reduced, which likely contributed to the highest Qh values existing in the unforested site. On average, wind speeds were 57% higher in the unforested area when compared to the undisturbed site and were 45% higher than those in the fire-dis­ turbed forest stand. Qe was negative throughout the day at all sites with the fire-dis­ turbed forest stand having the highest loss of -1.73 MJ m−2 day−1, which was 36% higher than Qe at the undisturbed forest stand (-1.27 MJ m−2 day−1). Calculation of snowpack evaporation based on Qe showed that the fire-disturbed forest stand had the greatest evaporation over the course of the winter seasons losing 8.1% (89 mm) of total accumulated SWE. Evaporation rates were lower at the undisturbed forest stand (6%; 69 mm) and unforested site (5.6%; 46 mm). Median relative humidity (RH) values (Fig. 7c) showed the smallest spread and highest median value (86%) in the undisturbed forest stand, although all RH values were similar with medians of 81% and 83% for the un­ forested site and fire-disturbed forest site, respectively. Turbulent fluxes clearly displayed a diurnal pattern in the fire-dis­ turbed forest stand, but little change through the day in the undisturbed forest stand. Qh values in the undisturbed stand remained positive throughout the day, while the other two sites had negative values, each with a minimum between 12:00 and 13:00 AEST. Qe values did show a slight decrease during the day in the undisturbed forest stand from -0.01 MJ m−2 at 07:00 AEST to -0.04 MJ m−2 at 13:00 AEST with a

4.2. Energy balances 4.2.1. Radiative fluxes Across the three sites instrumented over the winter of 2018-2019, significant differences in the energy balance between the two forested and unforested site were found, highlighting the role of tree canopy in modulating energy fluxes. Across the snow season, net shortwave (K*) was 347% higher in the fire-disturbed forest stand compared to the undisturbed forest stand (Table 4). Common periods that the up-facing pyranometer at the fire-disturbed forest stand encountered shadow from the vegetation can be seen at ~11:30-12:30 AEST and 13:00-14:00 AEST in Fig. 5c. Despite some obstruction of incoming radiation, the increase in K* in the fire-disturbed stand was primarily due to a snow surface albedo that was 37% lower (0.71 to 0.45) than the unforested area, which also had 30% less K*. A lower albedo was also noted in the undisturbed area (0.51) as a result of forest debris (twigs, leaves, dust, etc.) on the snow surface, but canopy blocking of incoming shortwave radiation prevented an increase in shortwave absorption in this forest stand. Changes to albedo in the fire-disturbed stand was generally from woody debris that had fallen on the snow surface from the surrounding decaying tree stems (Fig. 6) similar to the effects noted by Gleason et al. (2013). As such, forest canopy shows multiple controls on shortwave energy flux along with expected changes to longwave

Table 4 Mean daily energy balance terms for each site in MJ m−2 day−1 during the 2018 and 2019 snow cover seasons when > 10 cm of snow existed on the ground at all sites. Site

K*

Radiative fluxes K↓ K↑

L*

L↓

L↑

Q*

Turbulent fluxes Qh Qe

Qg

Qr

Qres

Unforested Undisturbed Fire-disturbed Fire-disturbed (Qh Corrected)

3.25 1.04 4.65 4.65

11.88 3.55 10.48 10.48

-4.36 -0.43 -3.06 -3.06

21.73 26.28 28.57 28.57

-26.09 -26.71 -31.63 -31.63

-1.11 0.61 1.59 1.59

1.62 1.40 0.06 1.44

0.17 0.38 0.31 0.31

0.01 0.01 0.01 0.01

-0.12 1.13 0.24 1.62

-8.63 -2.51 -5.83 -5.83

7

-0.81 -1.27 -1.73 -1.73

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Fig. 5. Mean daily snowpack energy fluxes for the unforested site (a), undisturbed forest stand (b), fire-disturbed forest stand without Qh correction (c), and firedisturbed forest stand with Qh correction (d).

compared to the other energy balance terms. The two forest stand sites had similar Qg values with 0.38 MJ m−2 day−1at the undisturbed forest stand and the fire-disturbed forest stand having an average Qg of 0.31 MJ m−2 day−1. Large variability in daily values of Qr during pre­ cipitation events resulted from infrequent rain-on-snow events at each of the sites during the two seasons. The fire-disturbed forest stand had the highest summed Qr contribution of 1.32 MJ m−2 during the entire study period followed by the undisturbed stand with 1.07 MJ m−2 and the unforested site with 0.87 MJ m−2. On days when rain-on-snow was occurring, Qr mean values were 0.07 MJ m−2 day−1 for the un­ disturbed forest stand and were at a minimum at the fire-disturbed forest stand (0.05 MJ m−2 day−1). 4.2.4. Area residual energy fluxes Mean daily Qres was positive in both of the forest stands, but due to different processes. Turbulent fluxes were slightly positive in the un­ disturbed forest stand and it had the smallest emission of L*, which allowed for a mean Qres value of 1.13 MJ m−2 day−1 when combined with K*, Qg, and Qr values. Turbulent fluxes were negative in the firedisturbed forest stand, but a high K* and an increase in L↓ resulted in the highest mean Qres value of 1.62 MJ m−2 day−1. Despite having largely positive turbulent fluxes, the unforested site was the only area that had a negative daily mean Qres value (-0.12 MJ m−2 day−1), which was due to its largely negative L* and reduced Qg. The unforested area also had the largest variation relative to its mean value suggesting that changes to synoptic conditions would have a bigger impact on the snowpack melt in this area than those contained in the forest stands.

Fig. 6. Image of the snowpack with woody debris in fire-disturbed forest on 26 July, 2019 at 13:06 AEST.

similar range in daily values (0.03 MJ m−2) at unforested site. The most significant diurnal signals in both Qh and Qe was at the fire-disturbed forest stand with Qh having the lowest values of any turbulent flux at any site. However, once Qh corrections were made (Fig. 5d), its trend was similar to those of the undisturbed forest stand and unforested sites with positive values overnight and slightly negative values during the day. A sharp decline in corrected Qh can be seen at 07:00 AEST in Fig. 5d when the trends at the other two areas at the same time are more gradual. This is primarily due to the methods used for correction of Qh that were discussed in Section 4.1, as 07:00 AEST was the tran­ sition time from night-period corrections to day-period corrections.

4.2.5. Snowpack and soil moisture evolution Snowpack SWE and soil volumetric water content at the forest stands during the 2018 and 2019 winter seasons are shown in Fig. 8.

4.2.3. Soil and liquid precipitation fluxes Qg and Qr contributed relatively little energy to the snowpack 8

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Fig. 7. Box plots of wind speed (a), ambient air temperature (b), relative humidity (c), and soil temperature (d) at all sites during the 2018 and 2019 comparison periods.

Gaps in data existed in the undisturbed forest stand at the beginning of 2018 and the end of 2019 due to power issues at the site. Soil volu­ metric water content was not available at the unforested site due to instrumentation failure. The undisturbed forest stand had a maximum SWE 26% greater than that of the fire-disturbed forest stand during 2018 and both had higher SWE than the unforested site. Ablation and accumulation periods show similar patterns for all sites during the 2019 season, but differences between ablation rates at the two forest stands and the unforested site existed during 2018. Trends in snowpack depth between the fire-disturbed and unforested sites were similar until 6 July 2018 when a rain-on-snow event led to melt and refreezing of the snowpack surface. The ice layer that formed prevented cohesion of the existing snowpack surface with subsequent falling snow and allowed for wind scour of newly fallen snow while the other two sites continued to accumulate snow. Similar events on 17 July, 15 August, and 6 Sep­ tember resulted in multiple ice layers and accumulation spikes that were quickly ablated from wind scour. While wind scour at the forested sites also likely occurred, the intensity and amount of snow removed was lower due to reduced wind speeds in each of the forest stands. Soil moisture was lower at the fire-disturbed forest site (0.19 ± 0.07) than the undisturbed forest stand (0.32 ± 0.03), and both sites had a gradual increasing trend during the snow seasons. Greater variation in soil moisture occurred at the fire-disturbed stand, particularly during periods with lower SWE and during September and October when pronounced diurnal signals developed. Values at the

undisturbed forest stand still showed diurnal signals but to a lesser extent. 4.2.6. Energy balance closure Energy balance closure was determined using 30-minute data during daytime periods when snow depth sensors identified melt through change in snowpack depth (Fig. 9). Daily averages of closure were determined by comparing measured Qres values to calculated en­ ergy required for the identified melt amount (SWE based on median 2018 snow density values) on days where 50% or more daytime periods had snowmelt, outliers were removed. Higher wind speeds and asso­ ciated scouring of the snow surface at the unforested site (Fig. 7a) re­ sulted in the lowest average energy balance closure and had high variability (0.61 ± 1.07) as snow being removed wasn't always due to melt. As such, Qec at the unforested site represented an average of 39% of daily fluxes based on snowmelt. Closure was higher and variability was lowest at the undisturbed forest stand (0.69 ± 0.67) and fire-dis­ turbed forest stand (0.63 ± 0.89), which resulted in Qec values that represented 31% and 37% of daily snowpack energy fluxes, respec­ tively. The fire-disturbed forest stand with Qh correction had a closure higher than one (1.61 ± 1.11) with the highest variability of all sites due to the reduction of sensible heat release through the correction method. An average energy balance closure of 61-69% for sites with initial data (no correction) is similar to closure amounts over a snow­ pack reported by Welch et al. (2016). 9

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Fig. 8. Snowpack SWE at all sites and soil volumetric water content at the undisturbed and fire-disturbed forest stands 13 June 2018 to 20 October 2018 and 8 May 2019 to 26 September 2019.

forest stand suggests consistently cool snow surface temperatures with less daily melt. The diurnal L* trend in the fire-disturbed area showed a longer response time to the removal of solar radiation compared to the unforested site (Fig. 5a and c/d) and this gradual increase in L* suggests warmer snowpack surface temperatures. These increased temperatures existed over extended periods after solar radiation had been removed as a result of continued canopy emission of L↓ and Qh to the snow surface The reduction of canopy in the fire-disturbed forest stand allowed for greater shortwave radiation incident on the tree stems that was absorbed and then re-emitted as longwave radiation and sensible heat, primarily during daytime periods, but overnight as well. Contributions of Qh from fire-disturbed tree stems could not be precisely measured using existing methods. However, the high performance of the snow­ melt models in the other two areas created confidence that the tree stem Qh signal could be isolated by determining differences between the modelled snowmelt and the ground truth, and then correcting Qh ac­ cordingly. This resulted in identifying that approximately 21% of Qh in the fire-disturbed forest stand can be attributed to emission from the tree stems. This estimate of stem contribution is similar to the 20-30% understory contribution of Qh that Baldocchi and Vogel (1996) noted in a Jack Pine forest during summer. This method of determining tree stem contributions to Qh includes potential error in the energy balance measurements and, as such, can be used only as an approximation. Therefore, change in Qh between the undisturbed and fire-disturbed forest stands cannot be unequivocally attributed to fire-disturbance as it is possible that the correction method may have obscured the actual difference. Therefore, future work should be undertaken to determine exact contributions of the tree stems to snowmelt in the fire-disturbed stand as this is a limitation of the current research. Turbulent fluxes showed positive contributions to the snowpack energy budget at the unforested and undisturbed forest sites as Qh was dominant in those areas that had Bowen Ratios ( = |Qh |/|Qe |) of 2.0 and 1.1, respectively. The fire-disturbed stand was the only area dominated by Qe ( = 0.81), which resulted in a net loss of energy from

5. Discussion The influence of forest canopy cover on snowpack energy fluxes and a summary of the differences in fluxes between areas is shown in Fig. 10. A reduction of 68% in K↓ at the undisturbed forest stand was similar to the 65% reduction seen in the leafless deciduous forest measured by (Lafleur and Adams, 1986a). The fire-disturbed forest stand had much less of a reduction to K↓ obscuring only 12% when compared to the measurements at the unforested site. However, both snowpacks reacted differently in their interactions with overall short­ wave radiation as the undisturbed forest stand had a reduction in K* and the fire-disturbed forest stand had an increase in K*. The canopy of the undisturbed forest stand intercepted a significant amount of K↓ that led to an overall decrease in K* compared to the unforested area despite a lower snowpack albedo than the unforested area. The 30% increase of K* in the fire-disturbed forest stand was largely due to the lower albedo of the area. This was primarily the result of woody debris being shed from the tree stems that have been decaying since the fire occurred. The darker snow surface resulted in a higher percentage of shortwave ra­ diation being absorbed by the snow surface and converted into snow­ melt similar to the results of Gleason et al. (2013). Similar to Ellis et al. (2011) and Lafleur and Adams, 1986a, Lafleur and Adams, 1986b, L↓ showed increases in both areas with canopy cover with a maximum occurring in the fire-disturbed forest stand. Little change in L↓ in the undisturbed forest stand occurred overnight compared to the gradual reduction noted in the fire-disturbed forest stand and was likely due to different wood densities of the tree stems and associated stem specific heat at each site. The greater density of the stems in the undisturbed forest allowed for storage of heat and continued emission, while the lower densities of the decaying stems in the fire-disturbed stand stored less heat, the dead stems emitted this energy faster. Emission of longwave radiation by a melting snow sur­ face should be relatively uniform as the surface will stay near 0°C. As a result, the low L↑ with little variation that occurred in the undisturbed 10

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Fig. 9. Energy balance closure during daytime periods where at least 50% of periods were experiencing snowmelt from June 17, 2018 to September 14, 2018 and May 27, 2019 to September 4, 2019. The dashed line is 1:1 and the red line shows the linear regression model for each area. n is the number of daily measurements used for the regression statistics. Unforested site (a) R2 = 0.33, Qres = 0.55Ereq + 0.07 , n = 72; Undisturbed forest site (b) R2 = 0.44, Qres = 0.47Ereq + 0.13, n = 66; Fire-disturbed forest site (c) R2 = 0.30, Qres = 0.84Ereq 0.22 , n = 87; Fire-disturbed forest site with corrected Qh (d) R2 = 0.16, Qres = 0.86Ereq + 0.97 , n = 34; All pvalues < 0.001.

the snowpack based on daily turbulent fluxes. Enhanced evaporation/ sublimation in the fire-disturbed forest stand acted to cool the snow­ pack and reduce the overall impact of turbulent fluxes in the area as a result of increased absorption of solar radiation from the snowpack's lower albedo, increased exposure to sensible heat fluxes contributed from the tree stems, and increased wind speeds compared to the un­ disturbed forest stand. Qr contributions to snowmelt were negligible for all sites accounting for 0.1% to 0.3% of fluxes to the snowpacks on average over both winters, although, individual rain-on-snow events can have significant impacts on the snowpack of the Snowy Mountains (McGowan et al., 2020). Despite consistently positive values of Qg for all sites from the relatively warm soil (> 0°C) (Fig. 7d) beneath the snowpack, it only accounted for 3% and 5% of daily positive fluxes to the snowpack for the unforested and fire-disturbed forest stand sites, respectively. This is consistent with prior findings of Bilish et al. (2018) who found Qg

contributions of 1-5% and 0-1.5%, respectively, in other studies of the snowpack in the Snowy Mountains region. However, reductions in other energy balance terms in the undisturbed forest meant that 13% of daily energy fluxes to the snowpack were from Qg. Though soil char­ acteristics were similar at both forest stand sites, the specific values for Qg at each site may not be suitable for application to the larger region as the Snow Gum forests cover areas with differing soil and ground cover characteristics and, therefore, potential Qg contributions to the snow­ pack. As such, differences in Qg may not always be due to fire-dis­ turbance but, rather, a combination of soil characteristics and fire-dis­ turbance. It is important to note that positive Qg values led to caverns developing at the snowpack-ground interface at all sites when manual measurements of the snowpack were taken in 2018. As such, this study highlights the important role that melting driven by both atmosphere and ground surfaces plays in marginal snow environments. The gradual increase of soil moisture during the 2018 and 2019 11

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Fig. 10. Conceptual summary of how forest canopy and fire modifies daily marginal snowpack energetics. Energy fluxes are mean daily values in MJ m−2 day−1 from data collected 16 June to 14 September, 2018 and 27 May to 4 September, 2019.

study periods supports the statement that the snowpack is in a constant state of ablation with only short periods where melt is not occurring. The soil moisture data from the forest stands shows that this occurs regardless of whether the snowpack is contained within an undisturbed or fire-disturbed forest stand. The lower variability and more consistent soil moisture of the undisturbed forest stand is the result of the canopy acting to modulate energy fluxes and, hence, the melt in that area. Though the fire-disturbed forest stand had generally lower soil moisture than the undisturbed forest stand, the increase in moisture during the seasons was higher, which supports higher melt rates because of in­ creased energy flux. However, forest structure and stage of recovery following fire disturbance may alter soil moisture patterns. As this study was conducted 15-16 years after the 2003 bushfires had occurred, the fire-disturbed forest stand had characteristics that differed from those that existed immediately following the fire. The coinciding area energy balance would likely have changed from what it was post-fire in 2003, though the extent of the changes are unknown. The progression of tree stem characteristics from being highly charred to having almost no char during the time of the study would have impacted snowpack albedo through shedding and stem emission of Qh. Similarly, the limited regrowth of Snow Gum would affect the broader area albedo and small canopies of the ~2 m trees could have stored more heat during the study than in the immediate post-fire forest. During the study, most of the tree stems within the fire-disturbed forest had become brittle and many within the larger fire-disturbed forest fell. It is likely that the fire-disturbed stems will continue to deteriorate, fall in the near future, and replacement by the living canopy of the re­ growth will occur. As the fire-disturbed stems are expected to fall faster than a new canopy is developed, a reduction of snow surface woody debris and, therefore, albedo is likely in the near future. The future progression will move the energy balance characteristics towards those of the undisturbed forest, however, this will be a slow process as Snow Gums development is slow following fire (Pickering and Barry, 2005). Fire disturbance significantly increases energy available for snow­ melt through enhancing shortwave radiation absorption due to lower snow surface albedos from woody debris, increases turbulent fluxes,

and longwave emission from the canopy. In addition, higher evapora­ tion rates also exist in these areas as snowpack surface temperatures are warmer, which removes potential runoff from the catchments affected by fire. As such, increases in snowmelt and reduced snowpack longevity can be expected in forests that are disturbed by fire. Increases in forest fires due to climate change (Lucas et al., 2007; Stocker, 2013) will likely increase energy available for snowmelt. The associated hydro­ logical responses will require careful management as times of maximum streamflow may change under new snowpack energy balance char­ acterises. Future research should be undertaken to determine the exact extent of these changes and their impact on area hydrology. 6. Conclusions Snowpack energy balance data was collected in an unforested area, undisturbed forest stand, and fire-disturbed forest stand during the 2018 and 2019 snow seasons in Australia's Snowy Mountains. It showed that undisturbed E. pauciflora canopies moderated energy fluxes through reduction to radiative and turbulent fluxes to the snowpack. The canopy of the fire-disturbed forest stand, which consisted of pre­ viously charred trees that were rotting at the time of measurement, had strong influence on the snowpack albedo and contributed to Qh. Though large-scale impacts of E. pauciflora canopies on energy balance were quantified, further work is needed to more accurately determine canopy contributions to snowpack energy balance and identify small-scale in­ teractions with single tree stems and snowpack physics. The decline of the snowpack in the Snowy Mountains is expected to continue as climate change progresses (Hennessy et al., 2008). An in­ crease in bushfires in the Snowy Mountains (Lucas et al., 2007) will act to further reduce snowpack extent and seasonal longevity through the removal of protective canopy, which may increase the burden on Australia's already strained freshwater water resources. Research into marginal snowpacks such as the one located in the Snowy Mountains will be critical as climate change progresses, snowpacks at higher la­ titudes and elevations develop more marginal characteristics due to warming, and new burdens are placed on water resources. 12

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Funding

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