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

1

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

Study area

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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 m4

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

DownscaleTechnique

TopoSUBTool

GEOtopModel

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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Methods

DownscaleTechnique

TopoSUB1

Tool

GEOtopModel

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 landsurface 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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Methods

• Distrubuting meterological input

• Energy and mass conservation

• Soil Volumetric Water Content

• Actual Evapotranspiration

• Snow Accumulation & Snow melt

• Application in Mountain Areas

DownscaleTechnique

TopoSUBTool

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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Methods

• Simulation calibration/performance

• 2010/2011 (Altitudinal Transect)

• Multiple Point Simulation (300 cluster centroids)

• baseline simulation 1990-2010

• 7 scenario simulation 2080-2100

DownscaleTechnique

TopoSUBTool

GEOtopModel

Simulation set-up

8

Altitudinal transect

Station B20 - 2000 m

Station B15 - 1500 m

Station B10 - 1000 m

Calibration Station

Validation Station

Validation Station

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Results

Calibration of soil parameters at B15

10

Station RMSE Θ5cm

(vol %)

RMSE Θ20cm

(vol %)

RMSE ETA(mm/month)

B20 9 7 --

B15 9 11 16

B10 9 7

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

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)

Results

Climate Change Impact – Evapotranspiration

Baseline Simulation ∆abs (scen2100-ctrl)

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Results

Climate Change Impact – Evapotranspiration

∆abs (scen2100-ctrl)

14

Change in M

ean

Annual ETA (

mm

)

Aspect

Forest: South-east

Major impact

Pasture: East

Bare Soil: South-east

Grassland & Agriculture:

No effect of aspect

Results

Climate Change Impact – Seasonal Evapotranspiration

Scen2100 ensemble meanBaseline mean

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Results

Climate Change Impact – Seasonal Evapotranspiration - Winter

4 14

+ 250%

16

Results

Climate Change Impact – Seasonal Evapotranspiration - Spring

48 69

+ 43%

17

Results

Climate Change Impact – Seasonal Evapotranspiration - Summer

131 149

+ 12%

18

Results

Climate Change Impact – Seasonal Evapotranspiration - Fall

53 62

+ 17%

19

Results

Climate Change Impact – Seasonal Evapotranspiration

4 14

+ 250%

48 69

+ 43%

131 149

+ 12%

53 62

+ 17%

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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

1

Change in Nr. of days with Severe Water Stress in 20cm soil

depth

Change in A

ctu

alEvapotr

ansp

irati

on

(mm

)

Results

Climate Change Impact – Soil Water Content – Severe Water Stress

22

1000 – 1400 m a.s.l

South - East

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

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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Results

Climate Change Impact – Soil Water Content – Severe Water Stress

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1100 – 1500 m

Results

Climate Change Impact - Altitude Transect

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Results

Climate Change Impact - Altitude Transect

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Results

calibration at B15 – Evapotranspiration

RMSE = 16.4mm/month, BIAS = -29mm, PBIAS = -5.3%

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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%

VA

LID

AT

ION

B20

VA

LID

AT

ION

B10

CA

LIB

RAT

ION

B15

5cm Soil Depth Observation (±SD) & Simulation 20cm Soil Depth

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