Modeling impacts of climate change on evapotranspiration and soil moisture spatial patterns in an alpine catchment.

1. Modeling impacts of climate change on evapotranspiration and soil
moisture spatial patterns in an alpine catchment.
Johannes Brenner1,2, Giacomo Bertoldi1, Stefano Della Chiesa1, Georg Niedrist1, Ulrike Tappeiner1,3, and
Axel Bronstert2
1Institute for Alpine Environment, EURAC research, Bolzano, Italy.
2Institute for Earth and Environmental Sciences, University of Potsdam, Germany.
3Institute of Ecology, University of Innsbruck, Austria
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2. Introduction
General Motivation
• Mountains Region are considered particularly vulnerable to CC 1, esp.
considering the alterations of the water cycle 2
• Complex topography  scale vs. computational effort
Aims
• temporal & spatial investigation of climate change impact on
evapotranspiration and soil moisture in a dry alpine valley
• Identify topographic/landcover characteristics of esp. vulnerable
regions
1 Brunetti et al. (2006). Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series.
International Journal of Climatology, 26(3), 345–381.
2 Bates et al. (2008). Climate Change and water. IPCC Technical Paper VI (p. 214). Geneva, Switzerland: IPCC Secretariat. Retrieved from http://www.ipcc.ch 2

3. Study area
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4. Study Area - Climate
Climate Diagrams for the period 1990-2010
• Dry inner-alpine valley
• Climate zones: Temperate – boreal - polar/alpine
• No precipitation station above 2100 m
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5. Methods
• RCM ensemble based on SRES A1B (ESEMBLES
project)1
• Ctrl: 1990-2010, Scen2100: 2080-2100
• ∆ approach (30 day moving average)
• ∆ change signals at daily scale for air
temperature and precipitation
Downscale
Technique
TopoSUB
Tool
GEOtop
Model
Simulation
set-up
1 Van der Linden, P., & Mitchell, J. (2009). ENSEMBLES: Climate change and its impacts at seasonal, decadal and centennial timescales (p. 160). Exeter, UK.
Retrieved from http://ensembles-eu.metoffice.com/docs/Ensembles_final_report_Nov09.pdf
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6. Methods
Downscale
Technique
TopoSUB1
Tool
GEOtop
Model
Simulation
set-up
1 Fiddes, J., & Gruber, S. (2012). TopoSUB: a tool for efficient large area numerical modelling in complex topography at sub-grid scales.
Geoscientific Model Development Discussions, 5(5), 1245–1257.
2 Hartigan, J. A., & Wong, M. A. (1979). A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.
Clustering
• sampling of most important aspects of land
surface heterogeneities and land cover
• K-Means clustering algorithm 2
• based on 20m grids
GEOtop
• 1-dimensional simulations for cluster centroids
Mapping
• Crisp memberships
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7. Methods
• Distrubuting meterological input
• Energy and mass conservation
• Soil Volumetric Water Content
• Actual Evapotranspiration
• Snow Accumulation & Snow melt
• Application in Mountain Areas
Downscale
Technique
TopoSUB
Tool
GEOtop1,2
Model
Simulation
set-up
1 Rigon et al. (2006). GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets. Journal of Hydrometeorology, 7(3), 371–388.
2 Endrizzi et al. (2013). GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing,
snow cover and terrain effects. Geoscientific Model Development Discussions, 6(4), 6279–6341.
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8. Methods
• Simulation calibration/performance
• 2010/2011 (Altitudinal Transect)
• Multiple Point Simulation (300 cluster centroids)
• baseline simulation 1990-2010
• 7 scenario simulation 2080-2100
Downscale
Technique
TopoSUB
Tool
GEOtop
Model
Simulation
set-up
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9. Altitudinal transect
Station B20 - 2000 m
Station B15 - 1500 m
Station B10 - 1000 m
Calibration Station
Validation Station
Validation Station
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10. Results
Calibration of soil parameters at B15
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Station RMSE Θ5cm
(vol %)
RMSE Θ20cm
(vol %)
RMSE ETA
(mm/month)
B20 9 7 --
B15 9 11 16
B10 9 7

11. Results
Climate Change Projections for the Venosta Valley
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scen2100 DJF MAM JJA SON
∆P (%) +14 +1.7 -13 +16
∆T (°C) +3.1 +3.3 +4.2 +3.2

12. Results
Climate Change Impact – Snow Cover Duration
Baseline Simulation ∆% (scen2100-ctrl)
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Mean abs. change: -40 days
Major impact in forest belt: -60 days (9 weeks)

13. Results
Climate Change Impact – Evapotranspiration
Baseline Simulation ∆abs (scen2100-ctrl)
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14. Results
Climate Change Impact – Evapotranspiration
∆abs (scen2100-ctrl)
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ChangeinMeanAnnualETA(mm)
Aspect
Forest: South-east
Major impact
Pasture: East
Bare Soil: South-east
Grassland & Agriculture:
No effect of aspect

15. Results
Climate Change Impact – Seasonal Evapotranspiration
Scen2100 ensemble meanBaseline mean
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16. Results
Climate Change Impact – Seasonal Evapotranspiration - Winter
4 14
+ 250%
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17. Results
Climate Change Impact – Seasonal Evapotranspiration - Spring
48 69
+ 43%
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18. Results
Climate Change Impact – Seasonal Evapotranspiration - Summer
131 149
+ 12%
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19. Results
Climate Change Impact – Seasonal Evapotranspiration - Fall
53 62
+ 17%
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20. Results
Climate Change Impact – Seasonal Evapotranspiration
4 14
+ 250%
48 69
+ 43%
131 149
+ 12%
53 62
+ 17%
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21. Results
Climate Change Impact – Soil Water Content – Severe Water Stress
1 Jasper et al. (2006). Changes in summertime soil water patterns in complex terrain due to climatic change. Journal of Hydrology, 327(3-4), 550–563.
doi:10.1016/j.jhydrol.2005.11.061
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Critical soil moisture level is refered to plant available water
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22. Change in Nr. of days with Severe Water Stress in 20cm soil
depth
ChangeinActualEvapotranspiration(mm)
Results
Climate Change Impact – Soil Water Content – Severe Water Stress
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1000 – 1400 m a.s.l
South - East

23. Conclusion & Outlook
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Conclusion
• General decrease in snow cover duration which drive major
increase in evapotraspiration in winter and spring
• Specific sites, which are already characterized by water
stress, show an increase in drought days
Future work
• Sensetivity of lateral water fluxes
• Dynamic vegetation
• Improve soil parameterization

24. Acknowledgment
GEOtop is an Open Source collaborative project
www.geotop.org
Main model developers:
Università di Trento; Zurich University; Mountain-eering S.r.l; EURAC research
This study is mainly founded by the projects “HiResAlp”
and “HydroAlp” from the South Tyrol research found.
We hereby would like to thank:
S. Endrizzi, University of Zurich, for the GEOtop model code development.
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25. Results
Climate Change Impact – Soil Water Content – Severe Water Stress
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1100 – 1500 m

26. Results
Climate Change Impact - Altitude Transect
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27. Results
Climate Change Impact - Altitude Transect
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28. Results
calibration at B15 – Evapotranspiration
RMSE = 16.4mm/month, BIAS = -29mm, PBIAS = -5.3%
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29. Results
Calibration (B15) and Validation (B10, B20) – Vol. Soil Water Content
RMSE = 0.09, BIAS = 0.04, inSD = 32% RMSE = 0.07, BIAS = -0.06, inSD = 37%
RMSE = 0.09, BIAS = 0.02, inSD = 40% RMSE = 0.11, BIAS = -0.06, inSD = 50%
RMSE = 0.09, BIAS = -0.02, inSD = 24% RMSE = 0.07, BIAS = -0.11, inSD = 19%
VALIDATIONB20VALIDATIONB10CALIBRATIONB15
5cm Soil Depth Observation (±SD) & Simulation 20cm Soil Depth
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