Gruppe für Hydrologie - framework to assess the effects of · 2015. 11. 17. · A physically based...
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A physically based hydrological framework to assess the effects of climate change in a data sparse alpine environment Inauguraldissertation der Philosophisch‐naturwissenschaftlichen Fakultät der Universität Bern vorgelegt von Martina Kauzlaric von Locarno Leiter der Arbeit: Prof. Dr. R. Weingartner Co‐Leiter: Dr. B. Schädler Geographisches Institut der Universität Bern
Gruppe für Hydrologie - framework to assess the effects of · 2015. 11. 17. · A physically based hydrological framework to assess the effects of climate change in a data sparse
Universität Bern
Von der Philosophischnaturwissenschaftlichen Fakultät angenommen.
iii
Summary This study presents a
robust modelling framework developed
for assessing the available
water
resources in a mountainous environment today and in the future. The semidiscrete, physicallybased
Penn State
Integrated Hydrologic Model (PIHM) has been
identified as a suitable hydrological model
for this challenging task. Here we present the customization, the enhancement and the application of
this on a catchment with sparse
data in the past and in
the present, we evaluate extensively
and
thoroughly its performance, its potential and its limitations. The melt modules for snow and ice were
upgraded from a simple
temperatureindex model to a model
including the influence of
global
radiation. Stationary catchment attributes
such as soil and land
cover data were used
to distribute
parameters, and the latters were mainly estimated basing on literature or experiments on site. Besides
the examination of the internal
consistency with a multicriteria
validation, evaluating snowpack,
icemelt and different components of
the water balance, model
results have been
further validated
with discharge measurements. Results
show a very good performance of
the model over different
spatial and temporal scales.
Sound hydrological modeling frameworks are needed to serve and support decision making in local to
regional water management, in particular in mountainous regions, as main suppliers of natural water
resources. In view of climate
change, increasingly complex physically
based models are applied in
order to guarantee the predictability for new climate or environmental boundary conditions. However,
while
the hydrological predictability might
increase with complex models,
data demands of course
increases too.
In the present study we
investigate the challenges, the gains
and the problems of an
increased
complexity in describing snow and precipitation patterns in data sparse regions at higher altitudes and
in an environment with a highly
complex topography. Particular
efforts were put in improving
the
modelling of
snow and glacier dynamics and melt
including a
timevarying albedo and gravitational
redistribution of snow
in a hydrological model. Furthermore, an extensive meteorological measuring
network was set up and exploited generating high resolution input data.
We provide evidence of the limits of an enhanced description of the albedo of snow depending on the
surrounding conditions, when other
processes we cannot describe emerge.
We also show the
importance of the maintenance and
extension of shortterm monitoring
networks in mountain
regions, demonstrating that an
increased sampling of hydrometeorological
and cryospheric data –
even on the basis of one to three years – retrieves the best model performance, and therefore these
are crucial for the improvement of the hydrological knowledge of the local system.
With the developed modelling framework we estimated available water resources for the headwaters
of the study area, and made an overall plausibility assessment using all the available data, with very
good results. Overall
the headwaters of the
catchment provide 110 mio m3. Generally
the western
part of the catchment is richer in water resources than the eastern part, corresponding respectively to
80% and 20% of the total water resources. Tseuzier, the subbasin at the western boundary, represents
the “water tower” of the area, supplying 70% of the total estimated water amount
In this work we integrated many relevant aspects of hydrological modelling, especially pertinent to the
context of climate change in alpine regions, like transferability in time and space, as well as flexibility,
and proved our modelling framework
to be
robust over different scales. As such,
it provides a solid
Summary
iv
basis for hydrological analysis of
the system under changing forcing
conditions, like in the case
of
climate change.
We performed an exploratory climate
sensitivity analysis, basing on
the data of ten
climate model
chains driven by the single emission scenario A1B, postprocessed and interpolated to the MeteoSwiss
weather station Montana (as provided
by the CH2011 initiative). Two
scenario periods in the 21st
century were assessed relative to the short reference period 20072012: the near
future 20482053
and the far future 20972102.
The most pronounced changes are
expected in the snow cover
dynamics
in the headwaters of the study area, and evaporation rates
in the
lower parts of the study
area, with changes in temperature as the main drivers. Basing on the applied scenarios, for all models,
a reshaped annual cycle, with earlier rise of spring runoff, significantly reduced summer runoff, and a
tendency for increased winter runoff
in the first half of the
21st century, becoming increasingly
pronounced towards the end of
the century, and resulting in a
significant change of regime:
first
transforming from a bglacionival
regime in a nival alpin regime
as a transition before
becoming,
characterized by a nival de transition regime in the far future.
All
in all, the overall available water resources are going to be affected only to a minor extent, with
mean changes in
the order of magnitude of 510%, however
the major changes estimated for
the
summer runoff, reduced by about
20% in the near future, and
almost 50% in the far future
are
expected to have major consequences
for the water management strategies
of the region. In
particular in dry years conflicts might arise between different usages, which here are mainly irrigation,
hydropower production, tourism and artificial snow making.
Ackowledgements
“I am neither the first
commentator, nor the most luminous.
Therefore I will
make no particular claim of originality
for the remarks presented here, but can
only hope that they are framed in useful ways”
J.W. Kirchner
Ackowledgements
This study is embedded in the framework of the MontanAqua project. MontanAqua is financed by the
NRP 61 (National Research Project
61, Sustainable Water Management).
I would like to thank the
Swiss National Science Foundation for funding the MontanAqua research project (4061401259646 1)
within the National Research Program “Sustainable Water Management” (NRP 61).
I would like to
thank Gopal Bhatt and Xuan Yu at
the Penn State University for
their patience, their
help for the customization of
the model (developed by their
research group), and their
support
throughout the first simulations. I also thank Christopher Duffy for his support and openness, as well
as for evaluating this work.
I gratefully acknowledge M. Huss for providing the stake measurements from the Plaine Morte glacier,
as well as the detailed mass balance data. Furthermore, other data were thankfully provided by the
FOEN (discharge data), Maurice
Perraudin from Lienne SA (discharge
data), Meteoswiss
(meteorological data) and SLF
(meteorological data). All people
involved in the tracer
experiment
organization, sampling, etc… are also deeply acknowledged.
The CH2011 data were obtained from the Center for Climate Systems Modeling (C2SM).
I am particularly grateful
to my mentor Bruno Schädler, who always
followed me through the wide
meanders of the thesis, providing me the right
instruments for this long
journey, good advices and a
lot of optimism.
He and my supervisor Rolf Weingartner always supported and trusted me, even in the most uncertain
and in the darkest moments, and this is something I will never forget.
A big and fat thank you goes
to my colleagues and friends, for
their help in every field of
life, their
love, their patience and for
sharing their lifes and their
interests with me, you made my
days. In
particular I want to thank my
sister, Martìn, Giovanna, Nina,
Jan, Ole, Anne Catherine, Raffi,
Yuri,
Judith, Michl, my parents, Ioan and Alex, who each in its own way helped me to find my way.
Table of contents
I 2 MontanAqua ...................................................................................................................................................... 6
I 4 Structure of the thesis ....................................................................................................................................... 9
II CONTEXT AND DATA ....................................................................................................... 11
II 1 Study region .................................................................................................................................................... 12
II 1.1 Glacier, karst and tracer experiments .................................................................................................... 14
II 1.2 Case study: the Tseuzier catchment ....................................................................................................... 19
II 1.3 Data ......................................................................................................................................................... 22
II 1.3.2 Available meteorological and hydrological data .............................................................................. 24
II 2 Temporary measurement network ................................................................................................................ 28
II 2.2 Data ......................................................................................................................................................... 28
II 3 Climatic data – the basics ............................................................................................................................... 36
II 3.1 Projections for climate change – CH2011 .............................................................................................. 36
II 3.2 Signals for different time periods ........................................................................................................... 38
II 3.2.1 Main climatological variables: precipitation and temperature ....................................................... 38
II 3.2.2 Discharge .......................................................................................................................................... 44
III METHODS ......................................................................................................... 47
III 1 Physically based hydrological framework ..................................................................................................... 48
III 1.1 Model setup .......................................................................................................................................... 49
III 1.2 Input data preprocessing ....................................................................................................................... 54
III 1.2.1 Parameter setting ............................................................................................................................ 54
III 1.2.2 Incorporate topographic effects on the energy input .................................................................... 57
III 1.2.3 Precipitation: necessary adjustments ............................................................................................. 59
III 2 Cryosphere and high resolution precipitation .............................................................................................. 63
Table of contents
III 2.2 Increasing complexity of the modelling framework .............................................................................. 63
III 2.2.1 Cryosphere: varying albedo and snow redistribution ..................................................................... 64
III 2.2.2 Precipitation: data preprocessing and interpolation ...................................................................... 66
III 3 Sensitivity and climate change ...................................................................................................................... 69
III 3.1 Sensitivity ............................................................................................................................................... 70
III 3.2 Climate change ....................................................................................................................................... 71
IV RESULTS ............................................................................................................ 77
IV 1 Application of the developed physically based hydrological framework: the Tseuzier case study and
plausibility of the results ...................................................................................................................................... 78
IV 1.1 Plausibility: a multicriterial validation ................................................................................................... 78
IV 1.1.1 Snow and ice .................................................................................................................................... 78
IV 1.1.2 Evapotranspiration .......................................................................................................................... 82
IV 1.2 Results: Tseuzier case study .................................................................................................................. 84
IV 1.2.1 Simulation results ............................................................................................................................ 84
IV 1.2.1.1 External contribution of karstified area added separately .................................................... 84
IV 1.2.1.2 External contribution of karstified area added as a source at the Loquesse spring .............. 88
IV 1.2.2 Performance .................................................................................................................................... 89
IV 2 Adressing key elements of mountain hydrology in a data sparse alpine environment: the assets and
drawbacks of an increased complexity ................................................................................................................ 91
IV 2.1 Plausibility: a multicriterial validation ................................................................................................... 91
IV 2.1.1 Snow and ice .................................................................................................................................... 91
IV 2.1.2 Water balance ................................................................................................................................. 94
IV 2.2
Results ............................................................................................................................................ 94
IV 2.2.2 Performance .................................................................................................................................... 96
IV 3.1 Spatial distribution of available water resources .................................................................................. 98
IV 3.1.1 Tièche .............................................................................................................................................. 98
IV 3.1.2 Ertentse ......................................................................................................................................... 103
IV 3.1.3 Vatseret ......................................................................................................................................... 104
IV 3.2.4 Boverèche ...................................................................................................................................... 106
IV 3.1.3 Headwaters ................................................................................................................................... 108
IV 4 Sensitivity and climate change .................................................................................................................... 109
IV 4.1 Sensitivity analysis: effects of different settings and different time frames ...................................... 109
IV 4.2 Climate change impact assessment for the headwaters of the study region .................................... 113
IV 4.2.1 Effects of climate change on snow cover ..................................................................................... 113
IV 4.2.2 Effects of climate change on the hydrological cycle and on the water availability ..................... 117
IV 4.2.3 Effects of climate change on the water balance ........................................................................... 119
IV 5 Discussion .................................................................................................................................................... 121
A.1 Sources ..................................................................................................................................................... 146
A.2 Evaporation .............................................................................................................................................. 146
B.2 The effect of inclination and exposition .................................................................................................. 150
C Snow and icemelt parameters ....................................................................................................................... 152
D Land cover, soil and geology parameters ....................................................................................................... 153
E Performance: Indexes ...................................................................................................................................... 155
F Discharge measurements ................................................................................................................................ 157
Figures
ix
Figures
Fig. 1: Schema of Working package 1 (WP1): the availability of natural water resources. ....................................... 7
Fig. 2: Study area with catchments’ and subcatchments’ boundaries in black, main rivers and springs ................ 13
Fig. 3: Uranine colouring the water coming from the Loquesse spring on 4 September 2012 ............................... 18
Fig. 4: Study area with measuring stations, catchments’ and subcatchments’ boundaries. ................................... 20
Fig. 5: Mean monthly cycle of springs’ discharge (Lourantse, Loquesse and the sum of these two) for the period
between October 1976 and September 1981 compared with the discharge recorded at the Lie110 gauging
station .............................................................................................................................................................. 26
Fig. 6: Mean annual cycle of the relationship between discharge recorded at the Lie110 gauging station and the
discharge estimated by Lienne SA at the Tseuzier lake dam .......................................................................... 28
Fig. 7: Map of the study area showing the boundaries of the case study catchment Tseuzier, land use, the river
network and the position of the measuring stations ...................................................................................... 29
Fig. 8: Overview of
installed gauging stations,
together with the main
springs present in
the area, and when
available the corresponding mean specific discharge Qspec [mm/d], as estimated by Crestin (2001). ........ 33
Fig. 9: Annual cycle of Delta T, and Delta P for the scenario period 20212050 (left) and 20702099 (right) at the
station MVE, as provided by CH2011. ............................................................................................................. 38
Fig. 10: Monthly cycle of precipitation P at MVE
for the period 20072012,
represented as the median of
the
time series together with the interquartile range (between 25 and 75% quartile); envelope of the minimum
and maximum monthly quartiles as well as the interquartile range computed using all possible consecutive
6 years blocks between 1980 and 2009 .......................................................................................................... 39
Fig. 11: (above) Monthly cycle of precipitation P at MVE for the two periods 19802009 and 20072012, with the
medians of the time series, their
interquartile range
(between 25 and 75% quartile), and
their envelope
representing the minimum and maximum
monthly precipitation; (below) the
same as above but for
temperature T. ................................................................................................................................................. 41
Fig. 12: Seasonal anomalies of temperature against precipitation for winter – between November and April –and
for summer – between May and October – .................................................................................................... 42
Fig. 13: Annual (hydrological year between October of the previous year and September of the year of interest)
anomalies of temperature against precipitation. ............................................................................................ 44
Fig. 14:
(above) Monthly cycle of measured discharge Q of
the Tseuzier catchment for the
two periods 1980
2009 and 19742012, with the medians of the time series, their interquartile range (between 25 and 75%
quartile), and their envelope
representing the minimum
and maximum monthly discharge; (below)
the
same as above but the second time series covering the period 20072012. ................................................. 45
Fig. 15: Overview of input data required to run a simulation of PIHM. ................................................................... 55
Fig. 16: Mean daily
incoming clear sky solar radiation
in the main Tseuzier subcatchment for the four seasons:
winter (DJF), spring (MAM), summer (JJA) and autumn (SON). ...................................................................... 58
Fig. 17: Transverse profile in
the Tseu_Lie subcatchment of the
shading factor, defined as the
ratio between
potential incoming solar radiation on a flat surface at the same location and the effectively incoming solar
radiation, on 4 different dates during the year. .............................................................................................. 59
Fig. 18: Plot of annual precipitation against elevation for precipitation used in the case study between 1975 and
1982 as well as between 2007 and 2012 with 5 to 95% range of the Meteoswiss corrected data;
for the
longterm precipitation climatology by Kirchhofer and Sevruk (1992) 19511980 as well as for the longterm
precipitation climatology by Schwarb et al.(2011) 19711990; and data recorded at the totalizer WEH ..... 62
Fig. 19: Daily
interpolated precipitation on the 17th Ocober 2010 and on the 10th October 2011,as well as their
standardized values ......................................................................................................................................... 68
Fig. 20: (above) Monthly cycle of precipitation P at MVE for the two periods present 20072012 and near future
20482053, with the medians of the time series, their
interquartile range (between 25 and 75% quartile),
and their envelope representing the minimum and maximum monthly precipitation;
(below) the same as
above but for temperature T ........................................................................................................................... 73
Figures
x
Fig. 21: (above) Monthly cycle of precipitation P at MVE for the two periods present 20072012 and far future
20972102, with the medians of the time series, their
interquartile range (between 25 and 75% quartile),
and their envelope representing the minimum and maximum monthly precipitation; (below) the same as
above but for temperature T ........................................................................................................................... 74
Fig. 22: Tseuzier catchment, the extension of
it across the hydrographic boundaries due to the Karst drainage
system, Plaine Morte glacier in 3D and projected in 2D. ................................................................................ 78
Fig. 23: Seasonal snow cover evolution between 2007 and 2012: at the VDS2 snow station as measured with a
ultrasonic
sensor, as modelled by using precipitation
recorded at the station,
as modelled by using the
modified grid precipitation of Meteoswiss, and on element 452 ................................................................... 79
Fig. 24: Calculated melt water
runoff from Glacier de
la Plaine Morte for
the hydrological years 2009/2010–
2011/2012 and reconstruction by Huss et al.(2013) based on stake measurements .................................... 81
Fig. 25: Daily observed and simulated runoff between 1975 and 1982, modelled daily ablation with the fractions
of ice melt and snowmelt and precipitation .................................................................................................... 85
Fig. 26: Monthly observed and simulated runoff between 1975 and 1982, modelled monthly ablation with the
fractions of ice melt and snowmelt, monthly observed and simulated discharge from the Loquesse source
and precipitation .............................................................................................................................................. 86
Fig. 27: Measured daily discharge at Lie110, simulated in the river element at the location of Lie110, simulated in
the river element at the location of Lie110 and adding the simulated contribution from the external karstic
area until September, simulated in
the river element at the
location of Lie110 and adding
the simulated
contribution from the external karstic area until October in 2011 (first panel); the same but in 2012 (second
panel); the same but between 12
July 2012 and 25 October 2012 and with additionally daily discharge
simulated in the river element
downstream of the Loquesse
spring, measured just downstream of
the
Loquesse spring, simulated in the
river element at the location of
Lou, measured at Lou and
simulated
contribution from the external karstic area until September (third and fourth panel) .................................. 87
Fig. 28: Daily observed and
simulated runoff between 1975
and 1982 in the Tseuzier
catchment, with three
different simulations: once simply
adding the monthly measured
contribution from the two
springs
Lourantse and Loquesse as an
external source when simulating
Tseu_Lie, once adding the
simulated
external contribution from the
karstic area as an external
source when simulating Tseu_Lie and
once
summing separately – or subsequentely –
the simulated external contribution from the karstic area with
the streamflow simulated at the outlet of Tseu_Lie. ...................................................................................... 89
Fig. 29: Flow duration curves of observed and simulated streamflow for the period 19751982 and for the period
20072012. ....................................................................................................................................................... 90
Fig. 30: (above) Seasonal snow cover evolution between 2007 and 2012: at the VDS2 snow station as measured,
and as simulated with a fix
albedo, as well as with a
varying albedousing different precipitation
data;
(below) calculated melt water runoff from Glacier de la Plaine Morte for the hydrological years 2009/2010–
2011/2012 applying a fix albedo
as well as applying a varying
albedo, and reconstruction by Huss
et
al.(2013) based on stake measurements ......................................................................................................... 92
Fig. 31: Daily observed and simulated runoff, modelled daily ablation with the fractions of ice melt and snowmelt
and precipitation
in the Tseuzier catchment between 2010 and 2012, using different model settings and
different meteorological data.. ........................................................................................................................ 95
Fig. 32: Flow duration curves of observed and simulated streamflow for the period 1.10.200731.12.2012 using
different model settings and different meteorological data ........................................................................... 97
Fig. 33: Runoff simulated at the outlet of Tièche between 2007 and 2012, and discharge measured at Tie100 in
the same period ............................................................................................................................................... 99
Fig. 34: Runoff simulated at the outlet of Tièche
in 2008
and 2011, and discharge measured at Tie100
in the
same period ................................................................................................................................................... 100
Fig. 35: Runoff simulated at the outlet of Tièche in 2012 using set ups ; and discharge measured at Tie100 in the
same period ................................................................................................................................................... 101
Fig. 36: Daily simulated runoff
at the outlet of Ertentse
between 1975 and 1980,
and measured discharge
between 1956 and 1961. ............................................................................................................................... 103
Fig. 37: Monthly discharge between October 1975 and October 1981: measured on the Ertentse, simulated at
the outlet of the Ertentse subcatchment, measured at Lie110 and the sum of the measured outflow at the
Lourantse and Loquesse springs. ................................................................................................................... 104
Fig. 38: Monthly discharge in
the Vatseret subcatchment between
October 1975 and October 1979;
and
between January 2007 and December 2012. ................................................................................................ 105
Fig. 39: Mean monthly annual cycle between October and September
(hydrological year) of outflow
from: the
sum of
the sources MOL38 and MOL9,
the RAN 1 source and
the RAN 28 source, and with additionally
simulated streamflow at the outlet of the Boverèche subcatchment .......................................................... 107
Fig. 40: Hourly discharge of the Boverèche measured at Colombire Oct.2010Sept.2011. .................................. 108
Fig. 41: Daily simulated discharge
of the Tseu_Lie subcatchment between
2007 and 2012 using different
settings: applying a varying
soil depth, adding the gravitational
redistribution of snow and changing
the
description of albedo from a fix albedo to a varying albedo ......................................................................... 110
Fig. 42: (above) Annual cycle of
simulated discharge of the Tseu_Lie
subcatchment between 2007 and 2012
using different settings: applying a
varying soil depth, adding the
gravitational redistribution of snow,
changing the description of albedo
from a fix albedo to a
varying albedo, and additionally with
the
projections for the near future;
(below) annual cycle of simulated discharge
in the Tseuzier catchment in
different years and as the mean over different periods:
for the period 19751982,
for the corresponding
projection in the near future 20162023, for the corresponding projection in the far future 20652072 and
for the same period but assuming clogging on the glacier, in 1976, in 1980 and in 2011 ........................... 112
Fig. 43: Simulated snow cover in
the Tseu_Lie subcatchment for the
three periods present
(20072012), near
future (20482053) and far future (20972102) ............................................................................................ 115
Fig. 44: Spread of the
simulated snow cover in the
Tseu_Lie subcatchment for the two
periods near future
(20482053) and far future (20972102) ....................................................................................................... 116
Fig. 45: Monthly cycle of
discharge Q of the headwaters
of the study area, as well
as of each of its
subcatchments for the three periods present (20072012), near future (20482053) and far future (2097
2102), with the medians of the time series, their
interquartile range (between 25 and 75% quartile), and
their envelope representing the minimum and maximum monthly precipitation runoff ............................ 118
Fig. 46: Monthly cycle of the components of the water balance for the headwaters of the study area in the three
periods present (20072012), near future (20482053) and far future (20972102). .................................. 120
Fig. 47: Monthly coefficient of determination for different extrapolations of solar radiation. ............................ 157
Fig. 48: Conversion factor for direct radiation for a southwest exposed surface with 25° slope, and for a north
west exposed surface with 25° slope at the same location of the Montana station.................................... 157
Fig. 49: Global solar radiation measured at the Montana station: measured and computed with GRASS .......... 157
Fig. 50: View of the cross section of the Lie110 gauging station ........................................................................... 157
Fig. 51: Water level measured at Loquesse against water level measured at Lie110 ........................................... 159
Fig. 52: Stagedischarge relationship at the Lie110 gauging station ...................................................................... 160
Fig. 53: Picture of the Loquesse spring the day of the
installation of the gauging station (24 July 2012), as seen
from the station’s site; and picture of the installed water pressure sensor ................................................. 161
Fig. 54: Difference in discharge between Lie110 and Lou as a function of water level recorded at Loq .............. 162
Fig. 55: Stagedischarge relationship
at the Lourantse gauging station
for the period between 12.7.2012
and
25.10.2012. .................................................................................................................................................... 163
Fig. 56: Picture of the gauging station Erte_2011 on 11 October 2011 ................................................................ 164
Fig. 57: Stagedischarge relationship at
the Erte_2011 gauging station for
the period between 27.7.2011 and
25.9.2011. ...................................................................................................................................................... 164
Fig. 58: Picture of the
installed water pressure sensor for
Erte_2012 and overview of the
location of the
Erte_2012 gauging station. ............................................................................................................................ 165
Fig. 59: Stagedischarge relationship at
the Erte_2012 gauging station for
the period between 25.7.2012 and
31.12.2012. .................................................................................................................................................... 165
Fig. 60: View of the cross section of the Tie100 gauging stations ......................................................................... 166
Fig. 61: Picture of the Tie100 gauging station. ....................................................................................................... 166
Fig. 62: Stagedischarge relationships at the Tie100 gauging station .................................................................... 169
Fig. 63: (above) Monthly cycle of precipitation P at MVE for the two periods 19312012 and 19802009, with the
medians of the time series, their
interquartile range
(between 25 and 75% quartile), and
their envelope
representing the minimum and maximum
monthly precipitation; (below) the
same as above but for
temperature T. ............................................................................................................................................... 170
Tables
xiii
Tables
Tab. 1: Headwaters’subcatchments description ...................................................................................................... 14
Tab. 2: Tseuzier’s subcatchments description .......................................................................................................... 21
Tab. 3: List of meteorological and gauging stations already present in the study area .......................................... 25
Tab. 4: List of meteorological, snow and rain gauging stations in the study area available 20072012 ................. 30
Tab. 5: List of gauging stations installed in the study area in the period 20112012. ............................................. 34
Tab. 6: Parameters values defined for the simulation of snow and icemelt with a varying albedo ...................... 65
Tab. 7: Measured seasonal mass balance of Glacier de
la Plaine Morte for
the hydrological years 2009/2010–
2011/2012 [m w.e.] (Huss et al. 2013) compared with the simulated melt. .................................................. 82
Tab. 8: Mean annual
simulated water balance components in
the Tseuzier catchment for the
two simulation
periods 19751982 and 20072012 in [mm].................................................................................................... 83
Tab. 9: Performance indexes for the two simulation periods 19751982 and 20072012...................................... 90
Tab. 10: Measured seasonal mass balance of Glacier de
la Plaine Morte for the hydrological years 2009/2010–
2011/2012 (Huss et al. 2013)
compared to the seasonal mass
balance simulated applying a fix
albedo
(SIMalbfix) as well as a varying albedo (SIMalbvar) in [m w.e.]. ..................................................................... 93
Tab. 11: Mean annual simulated water balance components in the Tseuzier catchment between 2010 and 2012:
using different model settings and different meteorological data. ................................................................ 94
Tab. 12: Performance indexes for different model set ups and input data ............................................................. 97
Tab. 13: Mean annual simulated water balance components between 2007 and 2012 of the Tseuzier basin and
of the headwaters of the study area. ............................................................................................................ 109
Tab. 14: Overview of the water availability in the headwaters of the study area for different periods ............... 119
Tab. 15: Mean annual simulated water balance components of the headwaters of the study area for the three
periods present (20072012), near future (20482053) and far future (209720102). ................................ 121
Tab. 16: Monthly regression factors
for the period 1.1.1975 31.12.1980
for computing global radiation at the
MVE station .................................................................................................................................................... 148
Tab. 17: Monthly regression factors
for the gap period between
28.2.197731.5.1977 for computing
global
radiation at the MVE station using data at the SIO station ........................................................................... 149
Tab. 18: Parameters values defined for the simulation of snow and icemelt with a fix albedo .......................... 152
Tab. 19: Landcover and topsoil parameters. .......................................................................................................... 153
GHG Greenhouse gas
FOEN
Swiss Federal Office of the Environment (BAFU in german: Bundesamt für Umwelt)
Lie SA
Lienne SA hydropower production company
Loq Loquesse spring
Lou Lourantse spring
NRP61
National Research Programme "Sustainable Water Management"
PIHM
Penn State Integrated Hydrologic Model
RCM Regional climate model
2
I 1 Object of research
In the last decades with the awareness of a changing and evolving environment, the number of studies
on how
the climate and human activities affect
the natural systems and cycles has constantly risen,
with the spillover effect to stimulate the development of more sophisticated models able to describe
the system processes, and
improving predictions (Silberstein 2006; Liu and Gupta 2007).
In addition,
efforts have been made
in order to enhance the spatial and temporal scale at which predictions are
made. However the support of these developments by the increasing power of computers was neither
accompanied by the same significant
increase in the availability of
data, nor in the quality of
the
measured data (Drécourt 2004a; Silberstein 2006).
Particularly in conjuction with the changes expected to happen to the locally available water resources
resulting from climate change, an
increasing number of
regional climate change
impact assessment
studies have been launched the last decade, with the increasing awareness that global sustainability is
made of local/regional sustainability,
and that resources management and
natural variability are
tightly coupled and interact.
As matter of fact
stakeholders, managers and politicians
need to be
informed and included in such studies, as we need them to be able to undertake measures and make
decisions on adaptation and mitigation strategies for the future (Reynard et al. 2014; Schneider et al.
2014). For this purpose, the establishment of a robust and reliable modelling framework
is required.
Hydrological or watershed models are crucial, as they serve here as exploratory and predictive tools.
Usually to be able
to adequately address questions about
the past, present and
future status of an
environment it would be appropriate to focus efforts to monitor and anticipate changes and have the
means to provide a historical context for the measurements. Yet, climate and hydrological monitoring
in mountain areas are known to be difficult and challenging tasks, as besides the tough environment
conditions to which measuring instruments are exposed, these remote areas require major efforts to
visit, maintain and keep
the measurements ongoing
(Diaz 2005). An other
fundamental problem is
that often many of the equations
used to represent processes occurring
in the hydrological cycle
require calibration, thus the parameters involved cannot be directly measured, or they are
invariably
applied at a scale different to that at which they were derived (Grayson and Blöschl 2000), and this is
even more true in alpine areas.
Hence, in such regions since
usually available observations are
discontinuous in space and time,
and furthermore do not provide
sufficient information about the
detailed processes that are represented by the model, it is often of practical impossibility to calibrate
it properly for any time and spatial scales.
Generally it could be said that
the inclusion of more processes
and/or controlling variables in
the
system can only be justified on
the basis that the inclusion of
additional controlling mechanisms
should both improve predictive skill and facilitate the estimation of parameter values on the basis of
physiological characteristics or measurements
(Montaldo et al. 2007). On the other hand, especially
for impact studies it is quintessential to keep the physical basis in the description of the dynamics, i.e.
more complex and detailed, as
it assures a consistent reproduction of the behaviour of the system.
The higher the degree of conceptualization, the higher is the danger this would lead to a model that
mimics the system without understanding it.
At this point it
is clear that the choice of an appropriate model
is a demanding task, requiring good
diplomatic skills: the tradeoffs
between parsimony, complexity and
robustness should be tackled
identifying
the optimum between data availability, model complexity and predictive performance.
It
appears that in this sense an
implicit requirement is the model
to be flexible, i.e. extensible
and
I INTRODUCTION
I 1 Object of research
adaptable to the given circumstances.
For most of the countries around
the world basic digital geospatial data
such as a digital elevation
model (DEM), soil, geology and
landuse maps are actually
available, with varying resolution
and
precision. They allow a
topographic as well as a physiographic characterization of
the environment,
whose features can be described with attributes. If sufficiently accurate these attributes have a great
potential, and regardless of being
quantitative or qualitative, are
viewed as relevant and
discriminatory indicators for processes
(Pflaunder 2001). These data, as well as any other source of
information like studies carried out within or close to the study area or literature should be combined
and exploited in order to allow the implementation of a physically based model, despite the possible
scarcity of data and observations
on site. Maybe one or some
of the processes might need
some
degree of simplification, in which
case adjustments of the parameters
will be needed, allowing
tailoring the model to the specific behaviour of the studied system. Automatic methods for parameter
adjustment seek to take advantage of the speed and power of digital computers, while being objective
and relatively easy to implement. In contrast, the trial and error method (manual approach), which has
been developed and refined over the years to result in excellent model calibration, is complicated and
highly laborintensive, and the expertise acquired by the modeller is not easily transferred (Boyle et al.
2000). However here this limitation
is not considered decisive
in carrying out the modelling
task, as
this configuration is still considered representative of the best process understanding achievable from
available data and catchment knowledge (Konz et al. 2010).
All in all,
the use of a procedure
including manual calibration and commonly available data appears
particularly promising, as
it offers the possibility to rely almost entirely on the available data, exploit
the hydrological knowledge of experts and transfer the model settings established
in subbasins with
relatively good data to other
ungauged basins. Such applications
suggest that the model can
be
regarded as a very powerful
tool for monitoring water resources:
it serves as an interpolator
at
locations where it is practically impossible to observe the necessary information (Drécourt 2004a).
Of course, all of this envisages the availability of (at least) one subbasin where the model settings can
be verified either through direct measurements, or indirectly through some kind of plausibility checks.
In order to judge model’s
predictive performance meaningful criteria
need to be chosen. Spatio
temporal dynamics as well as
spatial fields of instantaneous and
timeintegrated hydrological
variables, such as evapotranspiration,
soil moisture, channel discharge, or
more typical and
characteristic for an alpine environment such as snowpack and snow melt, are adequate variables to
make such an evaluation. The
quality and confidence of these
different intermediate results,
respectively measurements, must be carefully appraised, because of course data can be corrupted by
different
types of error. Uncertainties might be present
in the forcing terms, in
the measurements
themselves as well as in the
spatial (or eventually temporal)
extrapolation of these, in
the model
structure and parametrization. Moreover
scaling uncertainties arise from
differences in the
discretization of the model, in
the description of the physics
behind this and finally from
the
observations, which are usually
carried out at a precise point
location (Melching et al. 1990).
Still,
usually the accuracy of at least some of these data is good enough to represent a precious source of
information, enabling to evaluate
reasonably well the outcomes of
the applied modelling chain.
Montanari and Di Baldassarre showed
that if measurements are made
following stateoftheart
techniques, observation uncertainty has
a limited impact, with respect
to model structural
uncertainty, on the results of hydrological models (Montanari and Di Baldassarre 2013). Further they
I INTRODUCTION
I 1 Object of research
4
showed that particular care should be taken in discarding measurements, as in hydrological modelling
any information is important and the presence of data errors does not necessarily limit the usefulness
of observed records, from what it follows that an appropriate selection of hydrological complexity and
calibration strategy can increase the
robustness of hydrological applications
against data errors
(Montanari and Di Baldassarre 2013).
The hydrological cycle
in alpine environments is to a
large extent controlled by snow accumulation,
storage, redistribution, and melting
(Parajka et al. 2012; Warscher
et al. 2013). High altitudinal
gradients, a strong variability of
meteorological variables in time and
space, usually only locally
quantified snow cover dynamics,
complex and often unknown
hydrogeological settings, and
heterogeneous land use and soil
formations result in high uncertainties
in the quantification of the
water balance and
the prediction of discharge rates
(Warscher et al. 2013). However, despite
these
difficulties, hydrological modeling systems
are needed and applied to serve
and support decision
making in water management. This is particularly the case in mountainous regions, which play a crucial
role as the “water towers”
feeding downstream areas
(Viviroli et al. 2007). The more
the processes
occurring at
these high elevations are
simplified and conceptualized within a model,
the more they
suffer from a lack of physical relevance and physical parameter interpretability (Drécourt 2004b; Clark
and Vrugt 2006). This implies
that their predictability for new
climate or environmental boundary
conditions might be restricted and
not representative. Therefore increasingly
complex physically
based models are applied. This may enable a more comprehensive and enhanced perspective of the
sensitivity and the effects of
climate change on the water
balance, including the consideration
of
feedback processes on the different components of the hydrological cycle (for example the effects of
snow albedo on snow cover pattern, and ultimately on runoff generation (Jost et al. 2012; Pellicciotti
et al. 2012)). Furthermore, internal inconsistencies, such as an underestimation of precipitation input
that can be compensated for by an overestimation of meltwater (Konz and Seibert 2010; Pellicciotti et
al. 2012), might be reduced or avoided. However, while the hydrological predictability might increase
with
complex models, data demands of
course increase
as well. A parallel evaluation of
these two
issues, increased complexity and increased data availability, should help us to evidence, wheter we are
getting the right answers for the right reasons. In a time of local and global change in the water cycle,
when practical hydrological applications are increasingly used for impact studies and risk analysis this
is crucial.
During the past decades the Alpine climate has been subject to pronounced decadalscale variability,
but also to distinctive longterm
trends consistent with the global
climate response to increasing
greenhouse gas (GHG) concentrations
(Gobiet et al. 2014). In the
last 100 years
the average annual
temperature in Switzerland has risen by more than 1.5° C (FOEN 2012). A trend analysis of 1959–2008
gridded Swiss temperatures showed that
the seasonal
trends are all positive and highly
significant,
with an average annual warming
rate of 0.35°C/decade (Ceppi et
al. 2012). Spatial and temporal
variability are pronounced on a seasonal scale, however they clearly identified an anomalouslystrong
warming at
low elevations in autumn and early winter and aboveaverage spring temperature trends
at elevations close to the
snowline (Ceppi et al. 2012).
Warming in Switzerland ,
particularly
pronounced
from 1980 onwards, appears to be about twice that of the global average
(FOEN 2012;
Gobiet et al. 2014), which may be explained
in part by the differences
in physical characteristics of
land and sea surfaces and
is mainly caused by water vapour enhanced greenhouse warming
(FOEN
2012; Philippona 2013). Furthermore,
large areas in
the northern hemisphere, and
in particular the
Alps, are permanently or during prolonged periods covered with ice and snow. These areas are getting
I INTRODUCTION
I 1 Object of research
5
smaller, meaning there is a larger dark surface area and