Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2109-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2109-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2021
Research article |
| 20 Apr 2021
| 20 Apr 2021
Learning about precipitation lapse rates from snow course data improves water balance modeling
Francesco Avanzi
CORRESPONDING AUTHOR
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Giulia Ercolani
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Simone Gabellani
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Edoardo Cremonese
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Paolo Pogliotti
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Gianluca Filippa
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
Umberto Morra di Cella
Climate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy
CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
Sara Ratto
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Hervè Stevenin
Regione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
Marco Cauduro
Direzione Operativa, C.V.A. S.p.A., Via Stazione 31, 11024 Châtillon, Italy
Stefano Juglair
Direzione Operativa, C.V.A. S.p.A., Via Stazione 31, 11024 Châtillon, Italy
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Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
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Tessa Maurer, Francesco Avanzi, Steven D. Glaser, and Roger C. Bales
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Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
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Jan Pisek, Angela Erb, Lauri Korhonen, Tobias Biermann, Arnaud Carrara, Edoardo Cremonese, Matthias Cuntz, Silvano Fares, Giacomo Gerosa, Thomas Grünwald, Niklas Hase, Michal Heliasz, Andreas Ibrom, Alexander Knohl, Johannes Kobler, Bart Kruijt, Holger Lange, Leena Leppänen, Jean-Marc Limousin, Francisco Ramon Lopez Serrano, Denis Loustau, Petr Lukeš, Lars Lundin, Riccardo Marzuoli, Meelis Mölder, Leonardo Montagnani, Johan Neirynck, Matthias Peichl, Corinna Rebmann, Eva Rubio, Margarida Santos-Reis, Crystal Schaaf, Marius Schmidt, Guillaume Simioni, Kamel Soudani, and Caroline Vincke
Biogeosciences, 18, 621–635, https://doi.org/10.5194/bg-18-621-2021, https://doi.org/10.5194/bg-18-621-2021, 2021
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Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
Francesco Avanzi, Joseph Rungee, Tessa Maurer, Roger Bales, Qin Ma, Steven Glaser, and Martha Conklin
Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, https://doi.org/10.5194/hess-24-4317-2020, 2020
Short summary
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Cited articles
Allamano, P., Claps, P., Laio, F., and Thea, C.: A data-based assesment of the
dependence of short-duration precipitation on elevation, Phys. Chem. Earth, 34, 635–641, https://doi.org/10.1016/j.pce.2009.01.001, 2009. a
Allerup, P., Madsen, H., and Vejen, F.: A Comprehensive Model for Correcting
Point Precipitation, Hydrol. Res., 28, 1–20,
https://doi.org/10.2166/nh.1997.0001, 1997. a
Alpert, P.: Mesoscale indexing of the distribution of orographic precipitation
over high mountains, J. Clim. Appl. Meteorol., 25,
532–545,
https://doi.org/10.1175/1520-0450(1986)025<0532:MIOTDO>2.0.CO;2,
1986. a, b
Anders, A. M., Roe, G. H., Hallet, B., Montgomery, D. R., Finnegan, N. J., and
Putkonen, J.: Spatial patterns of precipitation and topography in the
Himalaya, in: Tectonics, Climate, and Landscape Evolution, Geol. Soc. Am., 398, https://doi.org/10.1130/2006.2398(03), 2006. a
Avanzi, F., De Michele, C., Ghezzi, A., Jommi, C., and Pepe, M.: A
processing-modeling routine to use SNOTEL hourly data in snowpack dynamic
models, Adv. Water Res., 73, 16–29,
https://doi.org/10.1016/j.advwatres.2014.06.011, 2014. a, b
Avanzi, F., De Michele, C., Gabriele, S., Ghezzi, A., and Rosso, R.:
Orographic Signature on Extreme Precipitation of Short Durations, J. Hydrometeorol., 16, 278–294, https://doi.org/10.1175/JHM-D-14-0063.1, 2015. a, b, c
Avanzi, F., Maurer, T., Glaser, S. D., Bales, R. C., and Conklin, M. H.:
Information content of spatially distributed ground-based measurements for
hydrologic-parameter calibration in mixed rain-snow mountain headwaters,
J. Hydrol., 582, 124478,
https://doi.org/10.1016/j.jhydrol.2019.124478, 2020a. a
Avanzi, F., Rungee, J., Maurer, T., Bales, R., Ma, Q., Glaser, S., and Conklin, M.: Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts, Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, 2020b. a, b, c
Bales, R., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and
Dozier, J.: Mountain hydrology of the western United States, Water
Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006. a, b
Barros, A. P. and Kuligowski, R. J.: Orographic effects during a severe
wintertime rainstorm in the Appalachian mountains, Mon. Weather Rev.,
126, 2648–2672,
https://doi.org/10.1175/1520-0493(1998)126<2648:OEDASW>2.0.CO;2,
1997. a
Bergström, S.: The HBV model – its structure and applications., Tech. Rep.,
SMHI Reports Hydrology, 1992. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29,
https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Blanchet, J., Marty, C., and Lehning, M.: Extreme value statistics of snowfall
in the Swiss Alpine region, Water Resour. Res., 45, W05424,
https://doi.org/10.1029/2009WR007916, 2009. a, b
Bonacina, L. C. W.: Orographic rainfall and its place in the hydrology of the
globe, Q. J. Roy. Meteor. Soc., 71, 41–55,
https://doi.org/10.1002/qj.49707130705, 1945. a
Buzzi, A., Tartaglione, N., and Malguzzi, P.: Numerical Simulations of the
1994 Piedmont Flood: Role of Orography and Moist Processes, Mon. Weather Rev., 126, 2369–2383,
https://doi.org/10.1175/1520-0493(1998)126<2369:NSOTPF>2.0.CO;2,
1998. a
Church, J. E.: Recent studies of snow in the United States, Q. J. Roy. Meteor. Soc., 40, 43–52,
https://doi.org/10.1002/qj.49704016905,
1914. a
Church, J. E.: Snow Surveying: Its Principles and Possibilities, Geogr.
Rev., 23, 529–563, 1933. a
Collados-Lara, A.-J., Pardo-Igúzquiza, E., Pulido-Velazquez, D., and
Jiménez-Sánchez, J.: Precipitation fields in an alpine Mediterranean
catchment: Inversion of precipitation gradient with elevation or undercatch
of snowfall?, Int. J. Clim., 38, 3565–3578,
https://doi.org/10.1002/joc.5517,
2018. a, b
Cox, L., Bartee, L., Crook, A., Farnes, P., and Smith, J.: The care and feeding
of snow pillows, in: Proceedings of the 46th Annual Western Snow Conference,
40–47, Otter Rock, Oregon, 1978. a
Cremonese, E., Filippa, G., Galvagno, M., Siniscalco, C., Oddi, L., Morra di
Cella, U., and Migliavacca, M.: Heat wave hinders green wave: The impact of
climate extreme on the phenology of a mountain grassland, Agr. Forest Meteorol., 247, 320–330,
https://doi.org/10.1016/j.agrformet.2017.08.016, 2017. a
Crespi, A., Brunetti, M., Lentini, G., and Maugeri, M.: 1961–1990
high-resolution monthly precipitation climatologies for Italy, Int.
J. Clim., 38, 878–895, https://doi.org/10.1002/joc.5217,
2018. a
Da Ronco, P., Avanzi, F., De Michele, C., Notarnicola, C., and Schaefli,
B.: Comparing MODIS snow products Collection 5 with Collection 6 over Italian
Central Apennines, Int. J. Remote Sens., 41, 4174–4205,
https://doi.org/10.1080/01431161.2020.1714778, 2020. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Clim., 28, 2031–2064,
https://doi.org/10.1002/joc.1688, 2008. a, b, c
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.: Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016. a
Elsen, P. R., Monahan, W. B., and Merenlender, A. M.: Topography and human
pressure in mountain ranges alter expected species responses to climate
change, Nat. Commun., 11, 1–10, 2020. a
Foehn, A., Hernández, J. G., Schaefli, B., and Cesare, G. D.: Spatial
interpolation of precipitation from multiple rain gauge networks and weather
radar data for operational applications in Alpine catchments, J. Hydrol., 563, 1092–1110,
https://doi.org/10.1016/j.jhydrol.2018.05.027, 2018. a
Frei, C. and Isotta, F. A.: Ensemble Spatial Precipitation Analysis From Rain
Gauge Data: Methodology and Application in the European Alps, J. Geophys. Res.-Atmos., 124, 5757–5778,
https://doi.org/10.1029/2018JD030004, 2019. a, b
Frei, C. and Schär, C.: A precipitation climatology of the Alps from
high-resolution rain-gauge observations, Int. J. Clim., 18, 873–900,
https://doi.org/10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9, 1998. a, b, c, d
Galewsky, J.: Rain shadow development during the growth of mountain ranges: An
atmospheric dynamics perspective, J. Geophys. Res.-Earth, 114, https://doi.org/10.1029/2008JF001085,
2009. a
Galewsky, J. and Sobel, A.: Moist Dynamics and Orographic Precipitation in
Northern and Central California during the New Year’s Flood of 1997,
Mon. Weather Rev., 133, 1594–1612, https://doi.org/10.1175/MWR2943.1,
2005. a
Garavaglia, F., Gailhard, J., Paquet, E., Lang, M., Garçon, R., and Bernardara, P.: Introducing a rainfall compound distribution model based on weather patterns sub-sampling, Hydrol. Earth Syst. Sci., 14, 951–964, https://doi.org/10.5194/hess-14-951-2010, 2010. a
Gerber, F., Lehning, M., Hoch, S. W., and Mott, R.: A close-ridge small-scale
atmospheric flow field and its influence on snow accumulation, J. Geophys. Res.-Atmos., 122, 7737–7754,
https://doi.org/10.1002/2016JD026258,
2017. a, b
Gerber, F., Mott, R., and Lehning, M.: The Importance of Near-Surface Winter
Precipitation Processes in Complex Alpine Terrain, J. Hydrometeorol., 20, 177–196, https://doi.org/10.1175/JHM-D-18-0055.1,
2019. a, b
Germann, U., Galli, G., Boscacci, M., and Bolliger, M.: Radar precipitation
measurement in a mountainous region, Q. J. Roy. Meteor. Soc., 132, 1669–1692, https://doi.org/10.1256/qj.05.190, 2006. a
Giorgi, F., Torma, C., Coppola, E., Ban, N., Schär, C., and Somot, S.:
Enhanced summer convective rainfall at Alpine high elevations in response to
climate warming, Nat. Geosci., 9, 584–589, 2016. a
Griessinger, N., Mohr, F., and Jonas, T.: Measuring snow ablation rates in
alpine terrain with a mobile multioffset ground-penetrating radar system,
Hydrol. Proc., 32, 3272–3282,
https://doi.org/10.1002/hyp.13259,
2018. a
Grünewald, T., Bühler, Y., and Lehning, M.: Elevation dependency of mountain snow depth, The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, 2014. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hantel, M., Maurer, C., and Mayer, D.: The snowline climate of the Alps
1961–2010, Theoretical and Applied Climatology, 110, 517–537, 2012. a
Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., Schumer, R., and Steele, C. M.: Rain or snow: hydrologic processes, observations, prediction, and research needs, Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, 2017. a
Harrison, B. and Bales, R.: Skill Assessment of Water Supply Forecasts for
Western Sierra Nevada Watersheds, J. Hydrol. Eng., 21,
04016002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001327, 2016. a, b
Houston, J. and Hartley, A. J.: The central Andean west-slope rainshadow and
its potential contribution to the origin of hyper-aridity in the Atacama
Desert, Int. J. Clim., 23, 1453–1464,
https://doi.org/10.1002/joc.938,
2003. a
Huning, L. S. and AghaKouchak, A.: Approaching 80 years of snow water
equivalent information by merging different data streams, Sci. Data, 7,
1–11, 2020. a
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F. P.: Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, 2015. a
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M., Lassègues, P.,
Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari,
M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E.,
Marigo, G., and Vertačnik, G.: The climate of daily precipitation in the
Alps: development and analysis of a high-resolution grid dataset from
pan-Alpine rain-gauge data, Int. J. Clim., 34,
1657–1675, https://doi.org/10.1002/joc.3794, 2014. a, b, c
Jiang, Q.: Moist dynamics and orographic precipitation, Tellus A, 55, 301–316,
https://doi.org/10.1034/j.1600-0870.2003.00025.x,
2003. a
Jost, G., Dan Moore, R., Weiler, M., Gluns, D. R., and Alila, Y.: Use of
distributed snow measurements to test and improve a snowmelt model for
predicting the effect of forest clear-cutting, J. Hydrol., 376, 94–106, https://doi.org/10.1016/j.jhydrol.2009.07.017, 2009. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology, Water
Resour. Res., 42, https://doi.org/10.1029/2005WR004362, 2006. a
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, 2014. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol.,
424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011,
2012. a, b
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a, b
Kochendorfer, J., Rasmussen, R., Wolff, M., Baker, B., Hall, M. E., Meyers, T., Landolt, S., Jachcik, A., Isaksen, K., Brækkan, R., and Leeper, R.: The quantification and correction of wind-induced precipitation measurement errors, Hydrol. Earth Syst. Sci., 21, 1973–1989, https://doi.org/10.5194/hess-21-1973-2017, 2017. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
et al.: Snow depth variability in the Northern Hemisphere mountains observed
from space, Nat. Commun,, 10, 1–12, 2019. a
López Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K. N.,
Revuelto, J., Latron, J., Móran-Tejeda, E., and Jonas, T.: Small scale
spatial variability of snow density and depth over complex alpine terrain:
Implications for estimating snow water equivalent, Adv. Water
Res., 55, 40–52, 2013. a
Lundberg, A. and Koivusalo, H.: Estimating winter evaporation in boreal forests
with operational snow course data, Hydrol. Proc., 17, 1479–1493,
https://doi.org/10.1002/hyp.1179, 2003. a
Lundquist, J. D., Dickerson-Lange, S. E., Lutz, J. A., and 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., 49, 6356–6370, https://doi.org/10.1002/wrcr.20504, 2013. a
Lundquist, J. D., Hughes, M., Henn, B., Gutmann, E. D., Livneh, B., Dozier, J.,
and Neiman, P.: High-Elevation Precipitation Patterns: Using Snow
Measurements to Assess Daily Gridded Datasets across the Sierra Nevada,
California*, J. Hydrometeorol., 16, 1773–1792,
https://doi.org/10.1175/JHM-D-15-0019.1, 2015. a, b, c, d
Malek, S. A., Avanzi, F., Brun-Laguna, K., Maurer, T., Oroza, C. A., Hartsough,
P. C., Watteyne, T., and Glaser, S. D.: Real-Time Alpine Measurement System
Using Wireless Sensor Networks, Sensors, 17, https://doi.org/10.3390/s17112583, 2017. a, b, c
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.: A Landsat-Era
Sierra Nevada Snow Reanalysis (1985-2015), J. Hydrometeorol., 17,
1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M., Payn,
R. A., and LaFontaine, J. H.: PRMS-IV, the Precipitation-Runoff Modeling
System, Version 4, Tech. Rep., U.S. Geological Survey Techniques and
Methods, https://doi.org/10.3133/tm6B7, 2015. a
Marra, F., Armon, M., Borga, M., and Morin, E.: Orographic effect on extreme
precipitation statistics peaks at hourly time scales, Geophys. Res.
Lett., 48, e2020GL091498, https://doi.org/10.1029/2020GL091498,
2021. a
Marty, C., Tilg, A.-M., and Jonas, T.: Recent Evidence of Large-Scale Receding
Snow Water Equivalents in the European Alps, J. Hydrometeorol.,
18, 1021–1031, https://doi.org/10.1175/JHM-D-16-0188.1,
2017. a
Metsämäki, S. J., Anttila, S. T., Markus, H. J., and Vepsäläinen, J. M.: A
feasible method for fractional snow cover mapping in boreal zone based on a
reflectance model, Remote Sens. Environ., 95, 77–95,
https://doi.org/10.1016/j.rse.2004.11.013,
2005. a, b
Michalet, R., Rolland, C., Joud, D., Gafta, D., and Callaway, R.: Associations
between canopy and understory species increase along a rainshadow gradient in
the Alps: habitat heterogeneity or facilitation?, Plant Ecol., 165,
145–160, 2003. a
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., and Lehning, M.:
Orographic effects on snow deposition patterns in mountainous terrain,
J. Geophys. Res., 119, 1419–1439, https://doi.org/10.1002/2013JD019880,
2014. a
Nitu, R., Roulet, Y.-A., Wolff, M., Earle, M., Reverdin, A., Smith, C.,
Kochendorfer, J., Morin, S., Rasmussen, R., Wong, K., Alastrué, J., Arnold,
L., Baker, B., Buisán, S., Collado, J., Colli, M., Collins, B., Gaydos, A.,
Hannula, H.-R., Hoover, J., Joe, P., Kontu, A., Laine, T., Lanza, L.,
Lanzinger, E., Lee, G., Lejeune, Y., Leppänen, L., Mekis, E., Panel, J.-M.,
Poikonen, A., Ryu, S., Sabatini, F., Theriault, J., Yang, D., Genthon, C.,
van den Heuvel, F., Hirasawa, N., Konishi, H., Motoyoshi, H., Nakai, S.,
Nishimura, K., Senese, A., and Yamashita, K.: WMO Solid Precipitation
Intercomparison Experiment (SPICE), Tech. Rep., World Meteorological
Organization, 2018. a
Pagano, T., Garen, D., and Sorooshian, S.: Evaluation of Official Western U.S.
Seasonal Water Supply Outlooks, 1922-2002, J. Hydrometeorol., 5,
896–909, https://doi.org/10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2, 2004. a, b, c, d
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems,
J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar,
imaging spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016. a
Panziera, L., James, C. N., and Germann, U.: Mesoscale organization and
structure of orographic precipitation producing flash floods in the Lago
Maggiore region, Q. J. Roy. Meteor. Soc., 141,
224–248, https://doi.org/10.1002/qj.2351,
2015. a
Patro, E. R., De Michele, C., and Avanzi, F.: Future perspectives of
run-of-the-river hydropower and the impact of glaciers’ shrinkage: The case
of Italian Alps, Appl. Energ., 231, 699–713,
https://doi.org/10.1016/j.apenergy.2018.09.063,
2018. a
Peck, E. L.: Snow measurement predicament, Water Resour. Res., 8, 244–248, 1972. a
Pignone, F., Rebora, N., Silvestro, F., , and Castelli, F.: GRISO (Generatore
Random di Interpolazioni Spaziali da Osservazioni incerte) – Piogge, Tech.
Rep., 2010. a
Poschlod, P.: The origin and development of the central European man-made
landscape, habitat and species diversity as affected by climate and its
changes – a review, Interdisciplinaria Archaeologica, Natural Sciences in
Archaeology, 6, 197–221, 2015. a
Puca, S., Porcu, F., Rinollo, A., Vulpiani, G., Baguis, P., Balabanova, S., Campione, E., Ertürk, A., Gabellani, S., Iwanski, R., Jurašek, M., Kaňák, J., Kerényi, J., Koshinchanov, G., Kozinarova, G., Krahe, P., Lapeta, B., Lábó, E., Milani, L., Okon, L'., Öztopal, A., Pagliara, P., Pignone, F., Rachimow, C., Rebora, N., Roulin, E., Sönmez, I., Toniazzo, A., Biron, D., Casella, D., Cattani, E., Dietrich, S., Di Paola, F., Laviola, S., Levizzani, V., Melfi, D., Mugnai, A., Panegrossi, G., Petracca, M., Sanò, P., Zauli, F., Rosci, P., De Leonibus, L., Agosta, E., and Gattari, F.: The validation service of the hydrological SAF geostationary and polar satellite precipitation products, Nat. Hazards Earth Syst. Sci., 14, 871–889, https://doi.org/10.5194/nhess-14-871-2014, 2014. a
Raleigh, M. S. and Small, E. E.: Snowpack density modeling is the primary
source of uncertainty when mapping basin-wide SWE with lidar, Geophys. Res. Lett., 44, 3700–3709, https://doi.org/10.1002/2016GL071999,
2017. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu,
R., Hall, M., Ikeda, K., and Gutmann, E.: How Well Are We Measuring Snow:
The NOAA/FAA/NCAR Winter Precipitation Test Bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1, 2012. a, b, c
Rebora, N., Ferraris, L., von Hardenberg, J., and Provenzale, A.: RainFARM:
Rainfall Downscaling by a Filtered Autoregressive Model, J. Hydrometeorol., 7, 724–738, https://doi.org/10.1175/JHM517.1, 2006. a
Rice, R. and Bales, R. C.: Embedded-sensor network design for snow cover
measurements around snow pillow and snow course sites in the Sierra Nevada of
California, Water Resour. Res., 46, W03537, https://doi.org/10.1029/2008WR007318, 2010. a
Rotunno, R. and Houze, R. A.: Lessons on orographic precipitation from the
Mesoscale Alpine Programme, Q. J. Roy. Meteor. Soc., 133, 811–830, https://doi.org/10.1002/qj.67, 2007. a, b
Ryan, W. A., Doesken, N. J., and Fassnacht, S. R.: Evaluation of Ultrasonic
Snow Depth Sensors for U.S. Snow Measurements, J. Atmos. Ocean. Tech., 25, 667–684, https://doi.org/10.1175/2007JTECHA947.1, 2008. a
Saft, M., Peel, M. C., Western, A. W., and Zhang, L.: Predicting shifts in
rainfall-runoff partitioning during multiyear drought: Roles of dry period
and catchment characteristics, Water Resour. Res., 52, 9290–9305,
https://doi.org/10.1002/2016WR019525, 2016. a
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, 2018. a
Sarker, R. P.: A dynamical model of orographic rainfall, Mon. Weather
Rev., 94, 555–572,
https://doi.org/10.1175/1520-0493(1966)094<0555:ADMOOR>2.3.CO;2,
1966. a
Sarmadi, F., Huang, Y., Thompson, G., Siems, S. T., and Manton, M. J.:
Simulations of orographic precipitation in the Snowy Mountains of
Southeastern Australia, Atmos. Res., 219, 183–199,
https://doi.org/10.1016/j.atmosres.2019.01.002,
2019. a
Schaefli, B. and Gupta, H.: Do Nash values have value?, Hydrol. Proc., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007. a
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty,
R. S.: Characteristics of the western United States snowpack from snowpack
telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999. a
Silvestro, F., Gabellani, S., Delogu, F., Rudari, R., and Boni, G.: Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model, Hydrol. Earth Syst. Sci., 17, 39–62, https://doi.org/10.5194/hess-17-39-2013, 2013. a, b
Skaugen, T., Stranden, H. B., and Saloranta, T.: Trends in snow water
equivalent in Norway (1931–2009), Hydrol. Res., 43, 489–499,
https://doi.org/10.2166/nh.2012.109,
2012. a
Smiraglia, C., AzzONI, R. S., D’Agata, C., Maragno, D., Fugazza, D.,
Diolaiuti, G. A., et al.: The evolution of the Italian glaciers from the
previous data base to the New Italian Inventory. Preliminary considerations
and results, Geografia Fisica e Dinamica Quaternaria, 38, 79–87, 2015. a
Smith, R. B.: Progress on the theory of orographic precipitation, Tech. Rep.
398, Geological Society of America, https://doi.org/10.1130/2006.2398(01), 2006. a
Smith, R. B. and Barstad, I.: A linear theory of orographic precipitation,
J. Atmos. Sci., 61, 1377–1391,
https://doi.org/10.1175/1520-0469(2004)061<1377:ALTOOP>2.0.CO;2,
2004. a
Spencer, M., Essery, R., Chambers, L., and Hogg, S.: The historical snow survey
of Great Britain: digitised data for Scotland, Scottish Geographical Journal,
130, 252–265, 2014. a
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover
classification system for local to global applications, J. Climate,
8, 1261–1283, 1995. a
Tang, Q. and Lettenmaier, D. P.: Use of satellite snow-cover data for
streamflow prediction in the Feather River Basin, California, International
J. Remote Sens., 31, 3745–3762,
https://doi.org/10.1080/01431161.2010.483493, 2010. a
Valery, A., Andréassian, V., Perrin, C., et al.: Inverting the hydrological
cycle: when streamflow measurements help assess altitudinal precipitation
gradients in mountain areas, Iahs Publ, 333, 281–286, 2009. a
Viale, M. and Nuñez, M. N.: Climatology of Winter Orographic Precipitation
over the Subtropical Central Andes and Associated Synoptic and Regional
Characteristics, J. Hydrometeorol., 12, 481–507,
https://doi.org/10.1175/2010JHM1284.1, 2011. a
Viviroli, D., Gurtz, J., and Zappa, M.: The Hydrological Modelling System
PREVAH, Tech. rep., Geographica Bernensia P40, Berne: Institute of
Geography, University of Berne, 2007a. a
Viviroli, D., Messerli, H. H. D. B., Meybeck, M., and Weingartner, R.:
Mountains of the world, water towers for humanity: Typology, mapping, and
global significance, Water Resour. Res., 43, W07447, https://doi.org/10.1029/2006WR005653
2007b. a, b
Vögeli, C., Lehning, M., Wever, N., and Bavay, M.: Scaling Precipitation Input
to Spatially Distributed Hydrological Models by Measured Snow Distribution,
Front. Earth Sci., 4, 108, https://doi.org/10.3389/feart.2016.00108, 2016. a
Winstral, A., Marks, D., and Gurney, R.: Simulating wind-affected snow
accumulations at catchment to basin scales, Adv. Water Res., 55,
64–79, https://doi.org/10.1016/j.advwatres.2012.08.011, 2013. a, b
Zhang, Z., Glaser, S., Bales, R., Conklin, M., Rice, R., and Marks, D.:
Insights into mountain precipitation and snowpack from a basin-scale
wireless‐sensor network, Water Resour. Res., 53, 6626–6641,
https://doi.org/10.1002/2016WR018825, 2017. a, b, c, d
Zheng, Z., Molotch, N. P., Oroza, C. A., Conklin, M. H., and Bales, R. C.:
Spatial snow water equivalent estimation for mountainous areas using
wireless-sensor networks and remote-sensing products, Remote Sens.
Environ., 215, 44–56, https://doi.org/10.1016/j.rse.2018.05.029,
2018. a
Short summary
Precipitation tends to increase with elevation, but the magnitude and distribution of this enhancement remain poorly understood. By leveraging over 11 000 spatially distributed, manual measurements of snow depth (snow courses) upstream of two reservoirs in the western European Alps, we show that these courses bear a characteristic signature of orographic precipitation. This opens a window of opportunity for improved modeling accuracy and, ultimately, our understanding of the water budget.
Precipitation tends to increase with elevation, but the magnitude and distribution of this...
