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More green and less blue water in the Alps during warmer summers

Nature Climate Change volume 10, pages 155–161 (2020)Cite this article

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Abstract

Climate change can reduce surface-water supply by enhancing evapotranspiration in forested mountains, especially during heatwaves. We investigate this ‘drought paradox’ for the European Alps using a 1,212-station database and hyper-resolution ecohydrological simulations to quantify blue (runoff) and green (evapotranspiration) water fluxes. During the 2003 heatwave, evapotranspiration in large areas over the Alps was above average despite low precipitation, amplifying the runoff deficit by 32% in the most runoff-productive areas (1,300–3,000 m above sea level). A 3 °C air temperature increase could enhance annual evapotranspiration by up to 100 mm (45 mm on average), which would reduce annual runoff at a rate similar to a 3% precipitation decrease. This suggests that green-water feedbacks—which are often poorly represented in large-scale model simulations—pose an additional threat to water resources, especially in dry summers. Despite uncertainty in the validation of the hyper-resolution ecohydrological modelling with observations, this approach permits more realistic predictions of mountain region water availability.

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Fig. 1: Simulation results highlight the spatial heterogeneity in latent heat (ET in energy units).
Fig. 2: Relationship between elevation and blue- and green-water fluxes.
Fig. 3: Analysis of anomalies in blue- and green-water fluxes during the 2003 drought.
Fig. 4: Conceptual representation of the drought paradox in the Alps.

Data availability

Data sources are summarized in the Supplementary Table 6. The DEM, land-cover product and MODIS snow-cover product used for this study are publicly available (https://esdac.jrc.ec.europa.eu/projects/Alpsis/Ecalp_data.html, https://www.eea.europa.eu/data-and-maps/data/clc-2006-raster-4 and https://doi.org/10.5067/MODIS/MOD10A2.006, respectively). FLUXNET data for running and validating T&C plot-scale simulations were downloaded from https://fluxnet.fluxdata.org/. FLUXCOM GPP and ET estimates were downloaded from http://www.fluxcom.org/, ERA-Interim latent heat estimates were downloaded from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim and TRENDY estimates of GPP were downloaded from http://www-lscedods.cea.fr/invsat/RECCAP/. Hydrological and meteorological/snow-depth data from Switzerland are publicly available (https://www.hydrodaten.admin.ch/ and https://gate.meteoswiss.ch/idaweb/more.do, respectively). Daily data from the Austrian network are provided by the Austrian hydrological service (https://ehyd.gv.at/) and are publicly available. Hydrological and meteorological data for the French stations are publicly available (http://www.hydro.eaufrance.fr/ and https://donneespubliques.meteofrance.fr/, respectively). Hydrological data for the Slovenian stations are publicly available (http://www.arso.gov.si/vode/podatki/). Hydrological, meteorological and snow-depth data for the Piemonte region and the Friuli-Venezia-Giulia region (Italy) are publicly available (http://www.arpa.piemonte.it/ and http://www.meteo.fvg.it/archivio.php?ln=&p=dati, respectively). Hydrological, meteorological and snow-depth data for the Trentino-Alto Adige region (Italy) are provided by the University of Trento and are available on request.

Code availability

Tethys-Chloris source code used for these simulations (11/2015 version) is available at https://hyd.ifu.ethz.ch/forschung/models.html. Saved simulation results and the analysis scripts are available on request from the corresponding author. The software used to generate all the results is MATLAB 2016b.

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Acknowledgements

T.M. thanks D. Anghileri for her support regarding the computational part of this study. Numerical simulations were performed on the ETH Euler cluster. T.M. and S.F. thank the Stavros Niarchos Foundation and the ETH Zurich Foundation (grant ETH‐29 14‐2) for their support. C.P. acknowledges the support of the Swiss National Science Foundation (SNSF), the Stavros Niarchos Foundation and the ETH Zurich Foundation (grants P2EZP2_162293 and P300P2_174477). G.M. was supported by ‘The Branco Weiss Fellowship—Society in Science’ administered by ETH Zurich. We thank the participants of the TRENDY project, namely, P. Levy (Hyland), S. Sitch and C. Huntingford (JULES/ TRIFFID), B. Poulter (LPJ), A. Ahlström (LPJ-GUESS), S. Levis (NCAR-CLM4), N. Viovy, S. Zaehle (OCN), M. Lomas (SDGVM) and N. Zeng (VEGAS), who made their simulation results (TRENDY v.1, experiment S2) freely available. This work used eddy covariance data acquired and shared by the FLUXNET community, including CarboEuropeIP and CarboItaly. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Centre and the OzFlux, ChinaFlux and AsiaFlux offices.

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Authors and Affiliations

  1. ETH Zurich, Institute of Environmental Engineering, Zurich, Switzerland

    Theodoros Mastrotheodoros, Peter Molnar, Paolo Burlando, Gabriele Manoli & Simone Fatichi

  2. Département de géographie and Centre d’études nordiques, Université de Montréal, Montréal, Québec, Canada

    Christoforos Pappas

  3. Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK

    Gabriele Manoli

  4. Centre for Water Resource Systems, Vienna University of Technology, Vienna, Austria

    Juraj Parajka & Borbala Szeles

  5. Department of Civil, Environmental and Mechanical Engineering, DICAM, University of Trento, Trento, Italy

    Riccardo Rigon & Michele Bottazzi

  6. IBM, Zurich Research Laboratory, Zurich, Switzerland

    Panagiotis Hadjidoukas

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Contributions

S.F. and T.M. designed the study. J.P., R.R., B.S. and M.B. contributed with data. P.H. contributed to the optimization of the parallel simulations. T.M. performed the simulations and the analyses. T.M. designed the figures with contributions from G.M and S.F. The results were synthesized by T.M., S.F., C.P. and P.M. T.M. and S.F. wrote the manuscript with contributions from all other authors.

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Correspondence to Simone Fatichi.

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Peer review information Nature Climate Change thanks Rene Orth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model comparison with discharge and snow depth observations.

Model validation against measured daily runoff (a–c) and snow depth (d–f) during the entire three-year simulation period (2001–2003). The latter was conducted accounting for the period with observed snow cover only. The maps (a, d) show the location of each station, coloured according to the model performance, as explained in the subplots c and f for runoff and snow depth, respectively. Subplots b and e show the histograms of the mean annual bias in mm yr−1 and cm, respectively for all the stations (for discharge, stations with more than 50% gaps are excluded, and for snow depth the bias is computed for the period with snow presence). The parts of the histogram that correspond to stations with annual bias in simulated discharge less than 250 mm and bias in mean snow depth less than 5 cm are highlighted, and the percentage of the corresponding stations is shown. For runoff, we used the Spearman R and the percent bias (c) and for snow we used the bias in snow duration and the bias in average snow depth (d). Both runoff (b) and snow depth (e) biases have medians very close to zero (1.1 mm year−1 and −0.03 cm, respectively, shown with the black circles on the x-axes).

Extended Data Fig. 2 Simulated and observed mean annual runoff.

Comparison between simulated (Qsim) and observed (Qobs) mean annual runoff for the entire simulation period in 381 locations (in mm hr−1) color-coded according to their glacier fraction. Closed circles are used for natural catchments and open circles for the heavily regulated ones; the R2 is reported separately for those two groups. The inset shows the comparison of catchment-averaged annual P – ET against observed annual runoff (Qobs) in mm yr−1, using the same symbols. In both plots the one-one line is also included.

Extended Data Fig. 3 Spatial distribution of mean annual evapotranspiration.

Spatial distribution of mean annual ET (mm yr−1) for the period 2001–2003 over the entire Alpine domain as simulated with T&C and estimated with other distributed products. Green corresponds to the T&C output, black to ERA Interim estimates1 and purple to FLUXCOM estimates2. The symbols on the upper x-axis show the median of each distribution (green circle, black diamond and purple triangle, for T&C, ERA Interim, and FLUXCOM, respectively).

Extended Data Fig. 4 Water fluxes as a function of elevation.

Relationship between water fluxes (km3 yr−1) and elevation (a) annually and (b) for the growing season (May-September) computed for the 2001–2003 period, if not indicated otherwise in the legend. Negative values on the y-axis in subplot b denote supply of water from soil storage.

Extended Data Fig. 5 Spatial distribution of simulated mean soil water content and its anomaly.

Mean soil water content integrated over 1 m depth simulated by T&C over the entire simulation period (a) and the anomaly in soil water content during the 2003 growing season (May-September) compared to the mean of 2001–2003 growing seasons (b).The two insets on the left side zoom in Val d’Adige river to show that the areas directly next to the rivers have higher soil moisture and slightly lower deficits due to topographic convergence, despite the overall drier conditions in the valley.

Extended Data Fig. 6 Comparison of observed and simulated runoff anomalies in August 2003.

Observed and simulated monthly runoff anomalies (in mm) during August 2003 compared with the August mean of the three simulated years (2001–2003). Only unregulated catchments that had a negative anomaly are included (N = 187). The colour shows the magnitude of the anomalies expressed in units of standard deviations (standard deviations were computed using all available data for each station). The inset map shows the locations of the stations coloured according to the model total bias in reproducing monthly runoff during August 2003.

Extended Data Fig. 7 Alpine scale monthly evapotranspiration.

Total monthly evapotranspiration (ET) averaged for the period 2001–2003 (a), and monthly ET anomalies during the period November 2002-October 2003 in comparison to the 2001–2003 average (b). Different colors correspond to T&C results, ERA Interim and FLUXCOM estimates. Solid lines depict the averages over the entire domain and the color bands represent the interquartile range.

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Mastrotheodoros, T., Pappas, C., Molnar, P. et al. More green and less blue water in the Alps during warmer summers. Nat. Clim. Chang. 10, 155–161 (2020). https://doi.org/10.1038/s41558-019-0676-5

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