Seasonal snow cover decreases young water fractions in high Alpine catchments
Corresponding Author
Natalie Ceperley
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute of Geography, Faculty of Science, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Correspondence
Natalie Ceperley, Institute of Geography, University of Bern Hallerstrasse 12, 3012 Bern, Switzerland.
Email: [email protected]
Search for more papers by this authorGiulia Zuecco
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Search for more papers by this authorHarsh Beria
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Search for more papers by this authorLuca Carturan
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Department of Geosciences, University of Padova, Padova, Italy
Search for more papers by this authorAnthony Michelon
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Search for more papers by this authorDaniele Penna
Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy
Search for more papers by this authorJoshua Larsen
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
The Birmingham Institute of Forest Research (BIFoR), University of Birmingham, Birmingham, UK
Search for more papers by this authorBettina Schaefli
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute of Geography, Faculty of Science, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Search for more papers by this authorCorresponding Author
Natalie Ceperley
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute of Geography, Faculty of Science, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Correspondence
Natalie Ceperley, Institute of Geography, University of Bern Hallerstrasse 12, 3012 Bern, Switzerland.
Email: [email protected]
Search for more papers by this authorGiulia Zuecco
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Search for more papers by this authorHarsh Beria
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Search for more papers by this authorLuca Carturan
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Department of Geosciences, University of Padova, Padova, Italy
Search for more papers by this authorAnthony Michelon
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Search for more papers by this authorDaniele Penna
Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy
Search for more papers by this authorJoshua Larsen
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
The Birmingham Institute of Forest Research (BIFoR), University of Birmingham, Birmingham, UK
Search for more papers by this authorBettina Schaefli
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute of Geography, Faculty of Science, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Search for more papers by this authorFunding information: Fondazione Cassa di Risparmio di Padova e Rovigo, Grant/Award Number: Bando Starting Grants 2015; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grant/Award Number: PP00P2_157611; Università degli Studi di Padova, Grant/Award Number: DOR2019
Abstract
Estimation of young water fractions (Fyw), defined as the fraction of water in a stream younger than approximately 2–3 months, provides key information for water resource management in catchments where runoff is dominated by snowmelt. Knowing the average dependence of summer flow on winter precipitation is an essential context for comparing regional drought severity and provides the hydrological template for downstream water users and ecosystems. However, Fyw estimation based on seasonal signals of stable isotopes of oxygen and hydrogen has not yet explicitly addressed how to parsimoniously include the seasonal shift of water input from snow. Using experimental data from three high-elevation, Alpine catchments (one dominated by glacier and two by snow), we propose a framework to explicitly include the delays induced by snow storage into estimates of Fyw. Scrutinizing the key methodological choices when estimating Fyw from isotope data, we find that the methods used to construct precipitation input signals from sparse isotope samples can significantly impact Fyw. Given this sensitivity, our revised procedure estimates a distribution of Fyw values that incorporates a wide range of possible methodological choices and their uncertainties; it furthermore compares the commonly used amplitude ratio approach to a direct convolution approach, which circumvents the assumption that the isotopic signals have a sine curve shape, an assumption that is generally violated in snow-dominated environments. Our new estimates confirm that high-elevation Alpine catchments have low Fyw values, spanning from 8 to 11%. Such low values have previously been interpreted as the impact of seasonal snow storage alone, but our comparison of different Fyw estimation methods suggests that these low Fyw values result from a combination of both snow cover effects and longer storage in the subsurface. In contrast, in the highest elevation, glacier dominated catchment, Fyw is 3–4 times greater compared to the other two catchments, due to the lower storage and faster drainage processes. A future challenge, capturing spatio-temporal snowmelt isotope signals during winter baseflow and the snowmelt period, remains to improve constraints on the Fyw estimation technique.
Open Research
DATA AVAILABILITY STATEMENT
All data and accompanying Matlab files to support the analyses and findings presented in this work is included in Data S2.
Supporting Information
| Filename | Description |
|---|---|
| hyp13937-sup-0001-FigureS1.tifTIFF image, 2.1 MB | Figure S1. Flow duration curves for each study catchment. Isotope concentrations of individual samples are plotted according to the exceedance probability of the streamflow at the moment they were sampled. |
| hyp13937-sup-0002-FigureS2.TIFTIFF image, 4 MB | Figure S2. Snowpack validation in two sites (VdN and NBPV) based on snow-covered area (SCA) extracted from satellite data (solid black line with circles). The snowpack simulation in the third site (BCC) was done with snow depth measurements but only differentiated whether there was snow or not. |
| hyp13937-sup-0003-FigureS3.epsPS document, 1.1 MB | Figure S3. Subplots showing the effect of snowpack parameters on the young water fraction (Fyw, Conv). In all subplots, the x-axis shows the degree day factor (ζ). The first row shows the effect of varying the minimum critical temperature (Tcrit1), above which precipitation no longer falls exclusively as snow, the second row shows the effect of varying the maximum critical temperature (Tcrit2), above which precipitation always falls as rain. The colour axis shows the Fyw, Conv. The columns are the different sites. The black circles show the parameters selected with the snowpack validation. |
| hyp13937-sup-0004-FigureS4.epsPS document, 708.7 KB | Figure S4. Boxplots showing the effect of choices (colours) for construction of time-series of precipitation on final Fyw,Conv determined according to 4 methods (rows) at 3 sites (columns). The final row shows the adjusted R2. Numbers (along x-axes) and names (legend) correspond to Table 1. In each box, the central dot indicates the median, edges of the solid box indicate the range of the 25th to 75th percentiles, whiskers extend to the most extreme data points and outliers are shown as open circles. |
| hyp13937-sup-0005-FigureS5.epsPS document, 122.7 KB | Figure S5. For each case study, the left (blue) plot shows the distribution of the mean and the right (orange) plot, shows the distribution of the SD. The full range of values for all samples can be seen in Table S5. The SD shows the SD per option, not the SD across the options. |
| hyp13937-sup-0006-FigureS6.epsPS document, 50.8 KB | Figure S6. Distribution of SDs of Gamma parameters in the convolution approach (Fyw,Conv); shown is the distribution of tau (τ) and not β = τ/α since we inferred τ and not beta in the Matlab implementation. |
| hyp13937-sup-0007-Supinfo1.docxWord 2007 document , 38.4 KB | Data S1. Contains sections of the supporting information S1, Additional details regarding study catchments and data sets including Table S1, Sample count according to catchment, S2 Additional details regarding the Snowpack simulation, S3 Details on fitted sine curve parameters, including Tables S2–S4, Section S4 which contains further details regarding the uncertainty of Fyw calculation, and references cited in the supporting information |
| hyp13937-sup-0008-TableS5.xlsExcel spreadsheet, 14.4 MB | Table S5. Contains full uncertainty calculation per option in Table 1, including all mean and SDs of sine wave parameters, young water fraction, mean of determined gamma function parameters alpha and tau and their SDs, R2 values around fit, and whether each was retained in the figures for each site and option. |
| hyp13937-sup-0009-Supinfo2.zipapplication/x-zip-compressed, 5 MB | Data S2. This file contains a Matlab script to perform all calculations in this manuscript. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- Ala-Aho, P., Tetzlaff, D., McNamara, J. P., Laudon, H., Kormos, P., & Soulsby, C. (2017). Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach. Water Resources Research, 53(7), 5813–5830. https://doi.org/10.1002/2017WR020650
- Ala-aho, P., Tetzlaff, D., McNamara, J. P., Laudon, H., & Soulsby, C. (2017). Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) model. Hydrology and Earth System Sciences, 21(10), 5089–5110. https://doi.org/10.5194/hess-21-5089-2017
- Amin, I. E., & Campana, M. E. (1996). A general lumped parameter model for the interpretation of tracer data and transit time calculation in hydrologic systems. Journal of Hydrology, 179(1–4), 1–21. https://doi.org/10.1016/0022-1694(95)02880-3
- Benettin, P., Soulsby, C., Birkel, C., Tetzlaff, D., Botter, G., & Rinaldo, A. (2017). Using SAS functions and high-resolution isotope data to unravel travel time distributions in headwater catchments. Water Resources Research, 53(3), 1864–1878. https://doi.org/10.1002/2016wr020117
- Beria, H., Larsen, J. R., Ceperley, N. C., Michelon, A., & Schaefli, B. (2019, December). Increased occurrence of ephemeral snowpacks will augment groundwater recharge and worsen summer low flow conditions in the Alps. Paper presented at the AGU Fall Meeting, San Francisco, CA. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/580254
- Beria, H., Larsen, J. R., Ceperley, N. C., Michelon, A., Vennemann, T., & Schaefli, B. (2018). Understanding snow hydrological processes through the lens of stable water isotopes. Wiley Interdisciplinary Reviews-Water, 5(6), 1–23. https://doi.org/10.1002/wat2.1311
- Beria, H., Larsen, J. R., Michelon, A., Ceperley, N. C., & Schaefli, B. (2020). HydroMix v1.0: A new Bayesian mixing framework for attributing uncertain hydrological sources. Geoscientific Model Development, 13(5), 2433–2450. https://doi.org/10.5194/gmd-13-2433-2020
- Birkel, C., Soulsby, C., & Tetzlaff, D. (2015). Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates. Hydrological Processes, 29(13), 2956–2969. https://doi.org/10.1002/hyp.10414
- Campbell, É. M. S., Pavlovskii, I., & Ryan, M. C. (2020). Snowpack disrupts relationship between Fyw and isotope amplitude ratio; approximately 1/5 of mountain streamflow less than one year old. Hydrological Processes, 1–14. https://doi.org/10.1002/hyp.13914
- Carturan, L. (2016). Replacing monitored glaciers undergoing extinction: A new measurement series on La Mare Glacier (Ortles-Cevedale, Italy). Journal of Glaciology, 62(236), 1093–1103. https://doi.org/10.1017/jog.2016.107
- Carturan, L., Baroni, C., Becker, M., Bellin, A., Cainelli, O., Carton, A., … Seppi, R. (2013). Decay of a long-term monitored glacier: Careser Glacier (Ortles-Cevedale, European Alps). The Cryosphere, 7(6), 1819–1838. https://doi.org/10.5194/tc-7-1819-2013
- Carturan, L., Baroni, C., Carton, A., Cazorzi, F., Dalla Fontana, G., Delpero, C., … Zanoner, T. (2014). Reconstructing fluctuations of La Mare glacier (Eastern Italian Alps) in the Late Holocene: New evidence for a little ice age maximum around 1600 AD. Geografiska Annaler: Series A-Physical Geography, 96(3), 287–306. https://doi.org/10.1111/geoa.12048
- Carturan, L., De Blasi, F., Cazorzi, F., Zoccatelli, D., Bonato, P., Borga, M., & Dalla Fontana, G. (2019). Relevance and scale dependence of hydrological changes in Glacierized catchments: Insights from historical data series in the eastern Italian Alps. Water, 11(1), 25. https://doi.org/10.3390/w11010089
- Carturan, L., Zuecco, G., Seppi, R., Zanoner, T., Borga, M., Carton, A., & Dalla Fontana, G. (2016). Catchment-scale permafrost mapping using spring water characteristics. Permafrost and Periglacial Processes, 27(3), 253–270. https://doi.org/10.1002/ppp.1875
- Ceperley, N., Michelon, A., Escoffier, N., Mayoraz, G., Boix Canadell, M., Horgby, A., … Boss, S. (2018). Salt gauging and stage-discharge curve, Avançon de Nant, outlet Vallon de Nant catchment (Version V1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1154798
- Dahlke, H. E., Lyon, S. W., Jansson, P., Karlin, T., & Rosqvist, G. (2014). Isotopic investigation of runoff generation in a glacierized catchment in northern Sweden. Hydrological Processes, 28(3), 1383–1398. https://doi.org/10.1002/hyp.9668
- Douinot, A., Tetzlaff, D., Maneta, M., Kuppel, S., Schulte-Bisping, H., & Soulsby, C. (2019). Ecohydrological modelling with EcH2O-iso to quantify forest and grassland effects on water partitioning and flux ages. Hydrological Processes, 33(16), 2174–2191. https://doi.org/10.1002/hyp.13480
- Engel, M., Penna, D., Bertoldi, G., Dell'Agnese, A., Soulsby, C., & Comiti, F. (2016). Identifying run-off contributions during melt-induced run-off events in a glacierized alpine catchment. Hydrological Processes, 30(3), 343–364. https://doi.org/10.1002/hyp.10577
- Fang, Z. F., Carroll, R. W. H., Schumer, R., Harman, C., Wilusz, D., & Williams, K. H. (2019). Streamflow partitioning and transit time distribution in snow-dominated basins as a function of climate. Journal of Hydrology, 570, 726–738. https://doi.org/10.1016/j.jhydrol.2019.01.029
- Guastini, E., Zuecco, G., Errico, A., Castelli, G., Bresci, E., Preti, F., & Penna, D. (2019). How does streamflow response vary with spatial scale? Analysis of controls in three nested Alpine catchments. Journal of Hydrology, 570, 705–718. https://doi.org/10.1016/j.jhydrol.2019.01.022
- Harpold, A. A., Kaplan, M. L., Klos, P. Z., Link, T., McNamara, J. P., Rajagopal, S., … Steele, C. M. (2017). Rain or snow: Hydrologic processes, observations, prediction, and research needs. Hydrology and Earth System Sciences, 21(1), 1–22. https://doi.org/10.5194/hess-21-1-2017
- He, Z. H., Parajka, J., Tian, F. Q., & Bloschl, G. (2014). Estimating degree-day factors from MODIS for snowmelt runoff modeling. Hydrology and Earth System Sciences, 18(12), 4773–4789. https://doi.org/10.5194/hess-18-4773-2014
- Hock, R. (2003). Temperature index melt modelling in mountain areas. Journal of Hydrology, 282(1–4), 104–115. https://doi.org/10.1016/S0022-1694(03)00257-9
- Hrachowitz, M., Benettin, P., van Breukelen, B. M., Fovet, O., Howden, N. J. K., Ruiz, L., … Wade, A. J. (2016). Transit times-the link between hydrology and water quality at the catchment scale. Wiley Interdisciplinary Reviews: Water, 3(5), 629–657. https://doi.org/10.1002/wat2.1155
- Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R. S., Clague, J. J., … Winder, M. (2017). Toward mountains without permanent snow and ice. Earth's Future, 5(5), 418–435. https://doi.org/10.1002/2016ef000514
- Jansson, P., Hock, R., & Schneider, T. (2003). The concept of glacier storage: A review. Journal of Hydrology, 282(1-4), 116–129. https://doi.org/10.1016/S0022-1694(03)00258-0
- Jasechko, S., Kirchner, J. W., Welker, J. M., & McDonnell, J. J. (2016). Substantial proportion of global streamflow less than three months old. Nature Geoscience, 9(2), 126. https://doi.org/10.1038/Ngeo2636
- Jbabdi, S. (2020). Metropolis Hastings. Retrieved from https://www.mathworks.com/matlabcentral/fileexchange/41231-metropolis-hastings
- Kirchner, J. W. (2016a). Aggregation in environmental systems - Part 2: Catchment mean transit times and young water fractions under hydrologic nonstationarity. Hydrology and Earth System Sciences, 20(1), 299–328. https://doi.org/10.5194/hess-20-299-2016
- Kirchner, J. W. (2016b). Aggregation in environmental systems – Part 1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments. Hydrology and Earth System Sciences, 20(1), 279–297. https://doi.org/10.5194/hess-20-279-2016
- Kirchner, J. W. (2019). Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: Theory and benchmark tests. Hydrology and Earth System Sciences, 23(1), 303–349. https://doi.org/10.5194/hess-23-303-2019
- Kuczera, G., & Parent, E. (1998). Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm. Journal of Hydrology, 211(1–4), 69–85. https://doi.org/10.1016/S0022-1694(98)00198-X
- Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal on Optimization, 9(1), 112–147. https://doi.org/10.1137/S1052623496303470
- Małoszewski, P., & Zuber, A. (1982). Determining the turnover time of groundwater systems with the aid of environmental tracers. Journal of Hydrology, 57(3–4), 207–231. https://doi.org/10.1016/0022-1694(82)90147-0
- McGuire, K. J., & McDonnell, J. J. (2006). A review and evaluation of catchment transit time modeling. Journal of Hydrology, 330(3-4), 543–563. https://doi.org/10.1016/j.jhydrol.2006.04.020
- Michelon, A., Ceperley, N., Beria, H., Larsen, J., & Schaefli, B. (2018, April). Quantification of snowmelt processes in a high Alpine catchment from hydrographs, satellite images and stable water isotopes. Paper presented at the EGU General Assembly, Vienna, Austria. https://ui.adsabs.harvard.edu/abs/2018EGUGA..2013901M/abstract
- Ohlanders, N., Rodriguez, M., & McPhee, J. (2013). Stable water isotope variation in a Central Andean watershed dominated by glacier and snowmelt. Hydrology and Earth System Sciences, 17(3), 1035–1050. https://doi.org/10.5194/hess-17-1035-2013
- Ohmura, A. (2001). Physical basis for the temperature-based melt-index method. Journal of Applied Meteorology, 40(4), 753–761. https://doi.org/10.1175/1520-0450(2001)040<0753:Pbfttb>2.0.Co;2
- Penna, D., Engel, M., Bertoldi, G., & Comiti, F. (2017). Towards a tracer-based conceptualization of meltwater dynamics and streamflow response in a glacierized catchment. Hydrology and Earth System Sciences, 21(1), 23–41. https://doi.org/10.5194/hess-21-23-2017
- Penna, D., Engel, M., Mao, L., Dell'Agnese, A., Bertoldi, G., & Comiti, F. (2014). Tracer-based analysis of spatial and temporal variations of water sources in a glacierized catchment. Hydrology and Earth System Sciences, 18(12), 5271–5288. https://doi.org/10.5194/hess-18-5271-2014
- Penna, D., van Meerveld, H. J., Zuecco, G., Fontana, G. D., & Borga, M. (2016). Hydrological response of an Alpine catchment to rainfall and snowmelt events. Journal of Hydrology, 537, 382–397. https://doi.org/10.1016/j.jhydrol.2016.03.040
- Penna, D., Zuecco, G., Crema, S., Trevisani, S., Cavalli, M., Pianezzola, L., … Borga, M. (2017). Response time and water origin in a steep nested catchment in the Italian Dolomites. Hydrological Processes, 31(4), 768–782. https://doi.org/10.1002/hyp.11050
- Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe, N., … Yang, D. (2015). Elevation-dependent warming in mountain regions of the world. Nature Climate Change, 5(5), 424–430. https://doi.org/10.1038/Nclimate2563
- Piovano, T. I., Tetzlaff, D., Ala-aho, P., Buttle, J., Mitchell, C. P. J., & Soulsby, C. (2018). Testing a spatially distributed tracer-aided runoff model in a snow-influenced catchment: Effects of multicriteria calibration on streamwater ages. Hydrological Processes, 32(20), 3089–3107. https://doi.org/10.1002/hyp.13238
- Puntsag, T., Mitchell, M. J., Campbell, J. L., Klein, E. S., Likens, G. E., & Welker, J. M. (2016). Arctic Vortex changes alter the sources and isotopic values of precipitation in northeastern US. Scientific Reports, 6, 22647. https://doi.org/10.1038/srep22647
- Rango, A., & Martinec, J. (1995). Revisiting the degree-day method for snowmelt computations. Water Resources Bulletin, 31(4), 657–669. https://doi.org/10.1111/j.1752-1688.1995.tb03392.x
- Rinaldo, A., Benettin, P., Harman, C. J., Hrachowitz, M., McGuire, K. J., van der Velde, Y., … Botter, G. (2015). Storage selection functions: A coherent framework for quantifying how catchments store and release water and solutes. Water Resources Research, 51(6), 4840–4847. https://doi.org/10.1002/2015wr017273
- Rücker, A., Boss, S., Kirchner, J. W., & von Freyberg, J. (2019). Monitoring snowpack outflow volumes and their isotopic composition to better understand streamflow generation during rain-on-snow events. Hydrology and Earth System Sciences Discussions, 2019, 1–37. https://doi.org/10.5194/hess-2019-11
- Santos, A. C., Portela, M. M., Rinaldo, A., & Schaefli, B. (2018). Analytical flow duration curves for summer streamflow in Switzerland. Hydrology and Earth System Sciences, 22(4), 2377–2389. https://doi.org/10.5194/hess-22-2377-2018
- Schaefli, B., Hingray, B., Niggli, M., & Musy, A. (2005). A conceptual glacio-hydrological model for high mountainous catchments. Hydrology and Earth System Sciences, 9, 95–109. https://doi.org/10.5194/hessd-2-73-2005
- Schaefli, B., Nicotina, L., Imfeld, C., Da Ronco, P., Bertuzzo, E., & Rinaldo, A. (2014). SEHR-ECHO v1.0: A Spatially Explicit Hydrologic Response model for ecohydrologic applications. Geoscientific Model Development, 7(6), 2733–2746. https://doi.org/10.5194/gmd-7-2733-2014
- Schmieder, J., Garvelmann, J., Marke, T., & Strasser, U. (2018). Spatio-temporal tracer variability in the glacier melt end-member - How does it affect hydrograph separation results? Hydrological Processes, 32(12), 1828–1843. https://doi.org/10.1002/hyp.11628
- Smith, A., Tetzlaff, D., Laudon, H., Maneta, M., & Soulsby, C. (2019). Assessing the influence of soil freeze-thaw cycles on catchment water storage-flux-age interactions using a tracer-aided ecohydrological model. Hydrology and Earth System Sciences, 23(8), 3319–3334. https://doi.org/10.5194/hess-23-3319-2019
- Sprenger, M., Leistert, H., Gimbel, K., & Weiler, M. (2016). Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes. Reviews of Geophysics, 54(3), 674–704. https://doi.org/10.1002/2015rg000515
- Sprenger, M., Tetzlaff, D., Buttle, J., Laudon, H., & Soulsby, C. (2018). Water ages in the critical zone of long-term experimental sites in northern latitudes. Hydrology and Earth System Sciences, 22(7), 3965–3981. https://doi.org/10.5194/hess-22-3965-2018
- Staudinger, M., Stoelzle, M., Seeger, S., Seibert, J., Weiler, M., & Stahl, K. (2017). Catchment water storage variation with elevation. Hydrological Processes, 31(11), 2000–2015. https://doi.org/10.1002/hyp.11158
- Stockinger, M. P., Bogena, H. R., Lucke, A., Diekkruger, B., Cornelissen, T., & Vereecken, H. (2016). Tracer sampling frequency influences estimates of young water fraction and streamwater transit time distribution. Journal of Hydrology, 541, 952–964. https://doi.org/10.1016/j.jhydrol.2016.08.007
- Strasser, U., Bernhardt, M., Weber, M., Liston, G. E., & Mauser, W. (2008). Is snow sublimation important in the alpine water balance? The Cryosphere, 2(1), 53–66. https://doi.org/10.5194/tc-2-53-2008
- Viviroli, D., Durr, H. H., Messerli, B., Meybeck, M., & Weingartner, R. (2007). Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resources Research, 43(7), 1–23. https://doi.org/10.1029/2006wr005653
- von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., & Kirchner, J. W. (2018). Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments. Hydrology and Earth System Sciences, 22(7), 3841–3861. https://doi.org/10.5194/hess-22-3841-2018
- Walck, C. (2007). Handbook on statistical distributions for experimentalists. Particle Physics Group, University of Stockholm, Stockholm, Sweden, p. 202. https://s3.cern.ch/inspire-prod-files-1/1ab434101d8a444500856db124098f9c. Accessed 20, Oct 2020.
- Zuecco, G., Carturan, L., De Blasi, F., Seppi, R., Zanoner, T., Penna, D., … Dalla Fontana, G. (2019). Understanding hydrological processes in glacierized catchments: Evidence and implications of highly variable isotopic and electrical conductivity data. Hydrological Processes, 33(5), 816–832. https://doi.org/10.1002/hyp.13366
- Zuecco, G., Penna, D., & Borga, M. (2018). Runoff generation in mountain catchments: Long-term hydrological monitoring in the Rio Vauz Catchment, Italy. Cuadernos De Investigacion Geografica, 44(2), 397–428. https://doi.org/10.18172/cig.3327
- Zuecco, G., Rinderer, M., Penna, D., Borga, M., & van Meerveld, H. J. (2019). Quantification of subsurface hydrologic connectivity in four headwater catchments using graph theory. Science of the Total Environment, 646, 1265–1280. https://doi.org/10.1016/j.scitotenv.2018.07.269
Citing Literature
15 December 2020
Pages 4794-4813
