Modeling Forest Snow Using Relative Canopy Structure Metrics
Abstract
:1. Introduction
2. Materials and Methods
- (1)
- Created high-resolution 1 m lidar-based representations of canopy structure and density.
- (2)
- Integrated this information relative to lidar-derived average local tree height and aspect to define relative forest structure metrics.
- (3)
- Analyzed and correlated relative forest structure metrics with local solar radiation data and manual snow measurements.
- (4)
- Generated empirical equations from these data to be used as offsets for modeled SWE from an open area for four generalized canopy groupings (that vary in size): forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest. Taken individually, these canopy groupings represent unique accumulation and ablation zones. Taken as a whole, these canopy groups represent the total forest area captured with the lidar data.
- (5)
- Integrated these empirical equations with a tiled 100 m snow model output to create a multi-component ensemble SWE output range.
2.1. Field Area
2.2. Snow Measurements
2.3. Canopy Estimates
2.4. Solar Radiation Data
2.5. Data Analysis
2.6. SnowModel and Data Analysis Integration
3. Results
3.1. Field Data
3.1.1. Snow Measurements
3.1.2. Canopy Measurements
3.1.3. Solar Radiation Measurements
3.2. Data Analysis
3.2.1. SWE in Forest Gaps
3.2.2. SWE on North-Facing Edges
3.2.3. SWE on South-Facing Edges
3.2.4. SWE in the Interior Forest
3.3. SnowModel Calibration
3.4. SnowModel Data Analysis Integration
4. Discussion
4.1. Relative Gap Area
4.2. Canopy Edges
4.3. Interior Forest
4.4. Model Parameters and Transferability
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bates, B.C.; Kundzewicz, Z.W.; Wu, S.; Palutikof, J.P. (Eds.) Climate Change and Water; Technical Paper of the Intergovernmental Panel on Climate Change; IPCC Secretariat: Geneva, Switzerland, 2008; 210p. [Google Scholar]
- Furniss, M.; Staab, B.; Hazelhurst, S.; Clifton, C.; Roby, K.; Ilhadrt, B.; Edwards, P. Water, Climate Change, and Forests: Watershed Stewardship for a Changing Climate; General Technical Report PNW-GTR812; Pacific Northwest Research Station: Portland, OR, USA, 2010; 75p. [CrossRef]
- Li, D.; Wrzesien, M.L.; Durand, M.; Adam, J.; Lettenmaier, D.P. How much runoff originates as snow in the Western United States, and how will that change in the future? Geophys. Res. Lett. 2017, 44, 6163–6172. [Google Scholar] [CrossRef]
- Christensen, N.S.; Lettenmaier, D.P. A multi model ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol. Earth Syst. Sci. 2007, 11, 1417–1434. [Google Scholar] [CrossRef]
- Guido, B.Z. Mountain Snowpack in the West and Southwest. 2008. Available online: https://www.southwestclimatechange.org/impacts/water/snowpack (accessed on 5 September 2016).
- Rango, A. Snow: The real water supply for the Rio Grande basin. N. Mex. J. Sci. 2006, 44, 99–118. [Google Scholar]
- Talsma, C.J.; Bennett, K.E.; Vesselinov, V.V. Characterizing drought behavior in the Colorado River Basin using unsupervised machine learning. Earth Space Sci. 2022, 9, e2021EA002086. [Google Scholar] [CrossRef]
- Goeking, S.; Tarboton, D. Forests and water yield: A synthesis of recent disturbance effects on streamflow and snowpack in western coniferous forests. J. For. 2020, 118, 172–192. [Google Scholar] [CrossRef]
- Fleck, S.R.S.; Cater, M.; Schleppi, P.; Ukonmaanaho, L.; Greve, M.; Hertel, C.; Weis, W.; Rumpf, S. Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests: Part XVII, Leaf Area Measurements; United Nations Economic Commission for Europe International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests): Hamburg, Germany, 2012. [Google Scholar]
- Giles-Hansen, K.; Li, Q.; Wei, X. The cumulative effects of forest disturbance and climate variability on streamflow in the Deadman River watershed. Forests 2019, 10, 196. [Google Scholar] [CrossRef]
- Zhang, M.; Liu, N.; Harper, R.; Li, Q.; Liu, K.; Wei, X. A global review on hydrological responses to forest change across multiple spatial scales: Importance of scale, climate, forest type and hydrological regime. J. Hydrol. 2017, 546, 44–59. [Google Scholar] [CrossRef]
- United States Forest Service. Rio Grande National Forest. 2019. Available online: https://www.fs.usda.gov/riogrande (accessed on 27 July 2019).
- United States Forest Service. CP District-Wide Salvage Project Final Environmental Impact Statement Conejos and Rio Grande Counties, Colorado. Rio Grande National Forest. La Jara, Colorado. 2018. Available online: https://www.fs.usda.gov/nfs/11558/www/nepa/103623_FSPLT3_4292373.pdf (accessed on 28 July 2019).
- Gleason, K.E.; McConnell, J.R.; Arienzo, M.M.; Chellman, N.; Calvin, W.M. Four-fold increase in solar forcing on snow in western U.S. burned forests since 1999. Nat. Commun. 2019, 10, 2026. [Google Scholar] [CrossRef] [PubMed]
- National Research Council. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia; The National Academies Press: Washington, DC, USA, 2011. [Google Scholar]
- Sexstone, G.A.; Clow, D.W.; Fassnacht, S.R.; Liston, G.E.; Hiemstra, C.A.; Knowles, J.F.; Penn, C.A. Snow Sublimation in Mountain Environments and its Sensitivity to Forest Disturbance and Climate Warming. Water Resour. Res. 2018, 54, 1191–1211. [Google Scholar] [CrossRef]
- Mote, P.W.; Li, S.; Lettenmaier, D.P.; Xiao, M.; Engel, R. Dramatic declines in snowpack in the western US. npj Clim. Atmos. Sci. 2018, 1, 2. [Google Scholar] [CrossRef]
- Stevens, J.T. Scale-dependent effects of post-fire canopy cover on snowpack depth in montane coniferous forests. Ecol. Appl. 2017, 27, 1888–1900. [Google Scholar] [CrossRef] [PubMed]
- Moeser, D.; Stähli, M.; Jonas, T. Improved snow interception modeling using canopy parameters derived from airborne LIDAR data. Water Resour. Res. 2015, 51, 5041–5051. [Google Scholar] [CrossRef]
- Moeser, D.; Mazzotti, G.; Helbig, N.; Jonas, T. Representing spatial variability of forest snow: Implementation of a new interception model. Water Resour. Res. 2016, 52, 1208–1226. [Google Scholar] [CrossRef]
- Roth, T.R.; Nolin, A.W. Characterizing maritime snow canopy interception in forested mountains. Water Resour. Res. 2019, 55, 4564–4581. [Google Scholar] [CrossRef]
- Essery, R.; Pomeroy, J. Soil-Vegetation-Atmosphere transfer schemes and large-scale hydrological models. In Proceedings of the International Association of Hydrological Science Conference, Maastricht, The Netherlands, 18–27 July 2001; pp. 343–347. [Google Scholar]
- Lundberg, A.; Halldin, S. Snow interception evaporation—Rates, processes, and measurement techniques. Theor. Appl. Climatol. 2001, 70, 117–133. [Google Scholar] [CrossRef]
- Montesi, J.; Elder, K.; Schmidt, R.A.; Davis, R.E. Sublimation of intercepted snow within a subalpine forest canopy at two elevations. J. Hydrometeorol. 2003, 5, 763–773. [Google Scholar] [CrossRef]
- Pomeroy, J.; Gray, D.M.; Shook, K.; Toth, B.; Essery, R.; Pietroniro, A.; Hedstrom, N. An evaluation of snow accumulation and ablation processes for land surface modeling. Hydrol. Process. 1998, 12, 2339–2367. [Google Scholar] [CrossRef]
- Pomeroy, J.; Parviainen, J.; Hedstrom, N.R.; Gray, D.M. Coupled modelling of forest snow interception and sublimation. Hydrol. Process. 1998, 12, 2317–2337. [Google Scholar] [CrossRef]
- Suzuki, K.N.; Yuichiro, O.; Takeshi, N. Effect of snow interception on the energy balance above deciduous and coniferous forests during a snowy winter. In Proceedings of the IUGG—Water Resource Systems, Sapporo, Japan, 30 June–11 July 2003. [Google Scholar]
- Broxton, P.D.; Harpold, A.A.; Biederman, J.A.; Troch, P.A.; Molotch, N.P.; Brooks, P.D. Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed-conifer forests. Ecohydrology 2015, 8, 1073–1094. [Google Scholar] [CrossRef]
- Broxton, P.D.; van Leeuwen WJ, D.; Biederman, J.A. Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resour. Res. 2019, 55, 3739–3757. [Google Scholar] [CrossRef]
- Currier, W.R.; Lundquist, J.D. Snow depth variability at the Forest edge in multiple climates in the Western United States. Water Resour. Res. 2018, 54, 8756–8773. [Google Scholar] [CrossRef]
- Essery, R.; Rutter, N.; Pomeroy, J.; Baxter, R.; Stähli, M.; Gustafsson, D.; Barr, A.; Bartlett, P.; Elder, K. SNOWMIP2 an evaluation of forest snow process simulations. Bull. Am. Meteorol. Soc. 2009, 90, 1120–1135. [Google Scholar] [CrossRef]
- Wei, X.; Liu, W.; Zhou, P. Quantifying the relative contributions of forest change and climatic variability to hydrology in large watersheds: A critical review of research methods. Water 2013, 5, 728–746. [Google Scholar] [CrossRef]
- Biederman, J.A.; Somor, A.J.; Harpold, A.A.; Gutmann, E.D.; Breshears, D.D.; Troch, P.A.; Gochis, D.; Scott, R.; Meddens, A.; Brooks, P. Recent tree die-off has little effect on streamflow in contrast to expected increases from historical studies. Water Resour. Res. 2015, 51, 9775–9789. [Google Scholar] [CrossRef]
- Hallema, D.W.; Sun, G.; Caldwell, P.V.; Norman, S.P.; Cohen, E.C.; Liu, Y.; Bladon, K.D.; McNulty, S.G. Burned forests impact water supplies. Nat. Commun. 2018, 9, 1307. [Google Scholar] [CrossRef] [PubMed]
- Harpold, A.A.; Biederman, J.A.; Condon, K.; Merino, M.; Korgaonkar, Y.; Nan, T.; Sloat, L.; Ross, M.; Brooks, P. Changes in snow accumulation and ablation following the Las Conchas Forest Fire, New Mexico, USA. Ecohydrology 2014, 7, 440–452. [Google Scholar] [CrossRef]
- Varhola, A.; Coops, N.C.; Weiler, M.; Moore, R.D. Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results. J. Hydrol. 2010, 392, 219–233. [Google Scholar] [CrossRef]
- Essery, R.; Pomeroy, J.; Ellis, C.; Link, T. Modelling longwave radiation to snow beneath forest canopies using hemispherical photography or linear regression. Hydrol. Process. 2008, 22, 2788–2800. [Google Scholar] [CrossRef]
- Lundquist, J.D.; Dickerson-Lange, S.E.; Lutz, J.A.; Cristea, N.C. Lower forest density enhances snow retention in regions with warmer winters: A global framework developed from plot-scale observations and modeling. Water Resour. Res. 2013, 49, 6356–6370. [Google Scholar] [CrossRef]
- Webster, C.; Rutter, N.; Jonas, T. Improving representation of canopy temperatures for modeling subcanopy incoming longwave radiation to the snow surface. J. Geophys. Res. Atmos. 2017, 122, 9154–9172. [Google Scholar] [CrossRef]
- Lawler, R.R.; Link, T.E. Quantification of incoming all-wave radiation in discontinuous forest canopies with application to snowmelt prediction. Hydrol. Process. 2011, 25, 3322–3331. [Google Scholar] [CrossRef]
- Musselman, K.N.; Pomeroy, J.W.; Link, T.E. Variability in shortwave irradiance caused by forest gaps: Measurements, modelling, and implications for snow energetics. Agric. For. Meteorol. 2015, 207, 69–82. [Google Scholar] [CrossRef]
- Moeser, D.; Broxton, P.; Harpold, A. Estimating the effects of forest structure changes from wildfire on snow water resources under varying meteorological conditions. Water Resour. Res. 2020, 56, e2020WR027071. [Google Scholar] [CrossRef]
- Mazzotti, G.; Currier, W.R.; Deems, J.S.; Pflug, J.M.; Lundquist, J.D.; Jonas, T. Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data. Water Resour. Res. 2019, 55, 6198–6216. [Google Scholar] [CrossRef]
- Kurzweil, J.R.; Metlen, K.; Abdi, R.; Strahan, R.; Hogue, T.S. Surface water runoff response to forest management: Low-intensity forest restoration does not increase surface water yields. For. Ecol. Manag. 2021, 496, 119387. [Google Scholar] [CrossRef]
- Smith, K.A.; Schneider, K.E.; Kinoshita, A.; Kurzweil, J.; Prucha, B.; Hogue, T.S. Water yield response to forest treatment patterns in a sierra nevada watershed. J. Hydrol. Reg. Stud. 2024, 53, 101762. [Google Scholar] [CrossRef]
- Varhola, A.; Coops, N.C. Estimation of watershed-level distributed forest structure metrics relevant to hydrologic modeling using lidar and Landsat. J. Hydrol. 2013, 487, 70–86. [Google Scholar] [CrossRef]
- Mazzotti, G.; Essery, R.; Moeser, D.; Jonas, T. Resolving small-scale forest snow patterns using an energy balance snow model with a 1-layer canopy. Water Resour. Res. 2020, 56, e2019WR026129. [Google Scholar] [CrossRef]
- Luce, C.H.; Tarboton, D.G.; Cooley, R.R. The influence of the spatial distribution of snow on basin-averaged snowmelt. Hydrol. Process. 1998, 12, 1671–1683. [Google Scholar] [CrossRef]
- Sexstone, G.A.; Driscoll, J.M.; Hay, L.E.; Hammond, J.C.; Barnhart, T.B. Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model. Hydrol. Process. 2020, 34, 2365–2380. [Google Scholar] [CrossRef]
- Sun, N.; Wigmosta, M.; Zhou, T.; Lundquist, J.; Dickerson-Lange, S.; Cristea, N. Evaluating the functionality and streamflow impacts of explicitly modelling forest–snow interactions and canopy gaps in a distributed hydrologic model. Hydrol. Process. 2018, 32, 2128–2140. [Google Scholar] [CrossRef]
- Currier, W.R.; Sun, N.; Wigmosta, M.; Cristea, N.; Lundquist, J.D. The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure. Hydrol. Process. 2022, 36, e14614. [Google Scholar] [CrossRef]
- Liston, G.E.; Elder, K. A distributed snow-evolution modeling system (SnowModel). J. Hydrometeorol. 2006, 7, 1259–1276. [Google Scholar] [CrossRef]
- Moeser, D.; Kurzweil, J.; Sexstone, G.A. Snow Measurements in Specific Canopy Structure Regimes for the 2022–2023 Water Years, North of Coal Creek, San Juan Mountains, Colorado, USA; U.S. Geological Survey Data Release; U.S. Geological Survey: Reston, VA, USA, 2023. [CrossRef]
- Moeser, C.D.; Sexstone, G.A. High Resolution Canopy Structure and Density Metrics for Southwest Colorado Derived from 2019 Aerial Lidar; U.S. Geological Survey Data Release; U.S. Geological Survey: Reston, VA, USA, 2023. [CrossRef]
- Solberg, S.; Brunner, A.; Hanssen, K.H.; Lange, H.; Næsset, E.; Rautiainen, M.; Stenberg, P. Mapping LAI in a Norway spruce forest using airborne laser scanning. Remote Sens. Environ. 2009, 113, 2317–2327. [Google Scholar] [CrossRef]
- Moeser, D.; Morsdorf, F.; Jonas, T. Novel forest structure metrics from airborne LiDAR data for improved snow interception estimation. Agric. For. Meteorol. 2015, 208, 40–49. [Google Scholar] [CrossRef]
- Moeser, D.; Morsdorf, F.; Jonas, T. Lidar2CanopyMetrics, Version 1.1 [Lidar2CanopyMetrics]; Novel Forest Structure Metrics from Airborne LiDAR Data for Improved Snow Interception Estimation; Zenodo. 2020. Available online: https://zenodo.org/records/4088667 (accessed on 13 May 2023).
- Landry, C.C.; Buck, K.A.; Raleigh, M.S.; Clark, M.P. Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: Anew integrative data source to develop and evaluate models of snow and hydrologic processes. Water Resour. Res. 2014, 50, 1773–1788. [Google Scholar] [CrossRef]
- Hammond, J.C.; Sexstone, G.A.; Putman, A.L.; Barnhart, T.B.; Rey, D.M.; Driscoll, J.M.; Liston, G.E.; Rasmussen, K.L.; McGrath, D.; Fassnacht, S.R.; et al. High Resolution SnowModel Simulations Reveal Future Elevation-Dependent Snow Loss and Earlier, Flashier Surface Water Input for the Upper Colorado River Basin. Earth’s Future 2023, 11, e2022EF003092. [Google Scholar] [CrossRef]
- Xia, Y.; Mitchell, K.; Ek, M.; Sheffield, J.; Cosgrove, B.; Wood, E.; Luo, L.; Alonge, C.; Wei, H.; Meng, J.; et al. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res. Atmos. 2012, 117, D03109. [Google Scholar] [CrossRef]
- Liston, G.E. SnowModel (Version 2020_10_02). 2020. Available online: https://gliston.cira.colostate.edu/SnowModel/code/snowmodel_2020_10_02.zip (accessed on 13 May 2021).
- Sexstone, G.A.; Moeser, C.D. SnowModel Simulations for the 2022–2023 Water Years, near Coal Creek, San Juan Mountains, Colorado, USA; U.S. Geological Survey Data Release; U.S. Geological Survey: Reston, VA, USA, 2024. [CrossRef]
- Saksa, P.C.; Conklin, M.H.; Battles, J.J.; Tague, C.L.; Bales, R.C. Forest thinning impacts on the water balance of Sierra Nevada mixed-conifer headwater basins. Water Resour. Res. 2017, 53, 5364–5381. [Google Scholar] [CrossRef]
- Tague, C.L.; Moritz, M.; Hanan, E. The changing water cycle: The eco-hydrologic impacts of forest density reduction in Mediterranean (seasonally dry) regions. Wiley Interdiscip. Rev. Water 2019, 6, e1350. [Google Scholar] [CrossRef]
- Dwivedi, R.; Biederman, J.A.; Broxton, P.D.; Lee, K.; van Leeuwen, W.J.D. Snowtography quantifies effects of forest cover on net water input to soil at sites with ephemeral or stable seasonal snowpack in Arizona, USA. Ecohydrology 2023, 16, e2494. [Google Scholar] [CrossRef]
- Harpold, A.A.; Guo, Q.; Molotch, N.; Brooks, P.; Bales, R.; Fernandez-Diaz, J.C.; Musselman, K.; Swetnam, T.; Kirchner, P.; Meadows, M.; et al. LiDAR-derived snowpack data sets from mixed conifer forests across the Western United States. Water Resour. Res. 2014, 50, 2749–2755. [Google Scholar] [CrossRef]
- Moeser, D.; Roubinek, J.; Schleppi, P.; Morsdorf, F.; Jonas, T. Canopy closure, LAI, and radiation transfer from airborne LiDAR synthetic images. Agric. For. Meteorol. 2014, 197, 158–168. [Google Scholar] [CrossRef]
- Mower, R.; Gutmann, E.D.; Lundquist, J.; Liston, G.E.; Rasmussen, S. Parallel SnowModel (v1.0): A parallel implementation of a Distributed Snow-Evolution Modeling System (SnowModel). EGUsphere 2023. preprint. [Google Scholar] [CrossRef]
- Dickerson-Lange, S.E.; Howe, E.R.; Patrick, K.; Gersonde, R.; Lundquist, J.D. Forest gap effects on snow storage in the transitional climate of the Eastern Cascade Range, Washington, United States. Front. Water 2023, 5, 1115264. [Google Scholar] [CrossRef]
- Helbig, N.; Moeser, D.; Teich, M.; Vincent, L.; Lejeune, Y.; Sicart, J.-E.; Monnet, J.-M. Snow processes in mountain forests: Interception modeling for coarse-scale applications. Hydrol. Earth Syst. Sci. Discuss. 2020, 24, 2545–2560. [Google Scholar] [CrossRef]
- Sun, N.; Yan, H.; Wigmosta, M.S.; Leung, L.R.; Skaggs, R.; Hou, Z. Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States. J. Geophys. Res. Atmos. 2019, 124, 5296–5313. [Google Scholar] [CrossRef]
- Andreadis, K.M.; Storck, P.; Lettenmaier, D.P. Modeling snow accumulation and ablation processes in forested environments. Water Resour. Res. 2009, 45, W05429. [Google Scholar] [CrossRef]
- Gelfan, A.N.; Pomeroy, J.W.; Kuchment, L.S. Modeling forest cover influences on snow accumulation, sublimation, and melt. J. Hydrometeorol. 2004, 5, 785–803. [Google Scholar] [CrossRef]
- Strasser, U.; Warscher, M.; Liston, G.E. Modeling snow-canopy processes on an idealized mountain. J. Hydrometeorol. 2011, 12, 663–677. [Google Scholar] [CrossRef]
- Burke, M.; Driscoll, A.; Heft-Neal, S.; Xue, J.; Burney, J.; Wara, M. The changing risk and burden of wildfire in the United States. Proc. Natl. Acad. Sci. USA 2021, 118, e2011048118. [Google Scholar] [CrossRef] [PubMed]
- Essen, M.; McCaffrey, S.; Abrams, J.; Paveglio, T. Improving wildfire management outcomes: Shifting the paradigm of wildfire from simple to complex risk. J. Environ. Plan. Manag. 2023, 66, 909–927. [Google Scholar] [CrossRef]
- Knapp, E.E.; Bernal, A.A.; Kane, J.M.; Fettig, C.J.; North, M.P. Variable thinning and prescribed fire influence tree mortality and growth during and after a severe drought Eric. For. Ecol. Manag. 2020, 479, 118595. [Google Scholar] [CrossRef]
- Venturas, M.D.; Todd, H.N.; Trugman, A.T.; Anderegg, W.R.L. Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage. New Phytol. 2021, 230, 1896–1910. [Google Scholar] [CrossRef] [PubMed]
Coarse Data Trends | ||
---|---|---|
Peak SWE | Melt-out Date | |
Small Gaps | ↓ | ↓ |
Large Gaps | ↓ | ↑ |
Large North-Facing Edges | ↑ | ↑↑ |
Small North-Facing Edges | ↓ | ↑↑ |
Large South-Facing Edges | ↓↓ | ↓↓ |
Small South-Facing Edges | ↓ | ↓ |
Dense Interior Forest | ↓↓ | ↑↑ |
Sparse Interior Forest | ↓ | ↑↑ |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Moeser, C.D.; Sexstone, G.; Kurzweil, J. Modeling Forest Snow Using Relative Canopy Structure Metrics. Water 2024, 16, 1398. https://doi.org/10.3390/w16101398
Moeser CD, Sexstone G, Kurzweil J. Modeling Forest Snow Using Relative Canopy Structure Metrics. Water. 2024; 16(10):1398. https://doi.org/10.3390/w16101398
Chicago/Turabian StyleMoeser, C. David, Graham Sexstone, and Jake Kurzweil. 2024. "Modeling Forest Snow Using Relative Canopy Structure Metrics" Water 16, no. 10: 1398. https://doi.org/10.3390/w16101398
APA StyleMoeser, C. D., Sexstone, G., & Kurzweil, J. (2024). Modeling Forest Snow Using Relative Canopy Structure Metrics. Water, 16(10), 1398. https://doi.org/10.3390/w16101398