PIAHSProceedings of the International Association of Hydrological SciencesPIAHSProc. IAHS2199-899XCopernicus PublicationsGöttingen, Germany10.5194/piahs-374-143-2016A snow and ice melt seasonal prediction modelling system for Alpine reservoirsFörsterKristiankristian.foerster@uibk.ac.athttps://orcid.org/0000-0001-7542-2820OesterleFelixHanzerFlorianhttps://orcid.org/0000-0001-7143-4226SchöberJohannesHuttenlauMatthiasStrasserUlrichalpS – Centre for Climate Change Adaptation Innsbruck, Innsbruck, AustriaInstitute of Geography, University of Innsbruck, Innsbruck, AustriaTIWAG, Tiroler Wasserkraft AG, Innsbruck, AustriaKristian Förster (kristian.foerster@uibk.ac.at)17October2016374143150This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://piahs.copernicus.org/articles/374/143/2016/piahs-374-143-2016.htmlThe full text article is available as a PDF file from https://piahs.copernicus.org/articles/374/143/2016/piahs-374-143-2016.pdf
The timing and the volume of snow and ice melt in Alpine catchments are
crucial for management operations of reservoirs and hydropower generation.
Moreover, a sustainable reservoir operation through reservoir storage and
flow control as part of flood risk management is important for downstream
communities. Forecast systems typically provide predictions for a few days in
advance. Reservoir operators would benefit if lead times could be extended in
order to optimise the reservoir management. Current seasonal prediction
products such as the NCEP (National Centers for Environmental Prediction)
Climate Forecast System version 2 (CFSv2) enable seasonal forecasts up to
nine months in advance, with of course decreasing accuracy as lead-time
increases.
We present a coupled seasonal prediction modelling system that runs at
monthly time steps for a small catchment in the Austrian Alps (Gepatschalm).
Meteorological forecasts are obtained from the CFSv2 model. Subsequently,
these data are downscaled to the Alpine Water balance And Runoff Estimation
model AWARE running at monthly time step. Initial conditions are obtained
using the physically based, hydro-climatological snow model AMUNDSEN that
predicts hourly fields of snow water equivalent and snowmelt at a regular
grid with 50 m spacing. Reservoir inflow is calculated taking into account
various runs of the CFSv2 model. These simulations are compared with observed
inflow volumes for the melting and accumulation period 2015.
Introduction
Hydropower is a major contributor to carbon-free energy production in the
European Alps where ideal conditions for this type of energy production
prevail . In general, mountainous regions are subjected
to strong altitudinal gradients, which is important for efficient hydropower
production. Moreover, mountain ridges induce orographic lifting of air masses
causing higher precipitation depths than in surrounding lowland regions
. Snow and ice are accumulated and seasonally
released as melt water in spring and summer making mountainous headwaters
very important for hydrological regimes of various European rivers and thus
for hydropower as well .
Reservoirs are not only relevant for hydropower generation but also for flood
control and water supply. Thus, hydropower management depends on various
factors that need to be addressed in day-by-day operation. Typically,
long-term observations and short-term meteorological and hydrological
predictions serve as a basis for this task. However, predicting several
components of the water balance components at various spatial and temporal
scales is challenging in this environment as meteorological fields such as
temperature or precipitation greatly vary in space and time, even at short
time scales .
In general, two types of seasonal prediction strategies are available for
hydrologic applications : On one hand, common methods
in hydrology are based on the Ensemble Seasonal Prediction, building upon
procedures which resample historical meteorological data. This method is well
suited where initial conditions can be seen as a “land surface memory” that
last over a certain time period . On the other hand, climate
models based on (dynamical) seasonal methodology rely on physical modelling
of the coupled atmosphere-ocean system. This is important as atmosphere-ocean
systems interact at a broad range of scales, influenced by sea surface
temperature anomalies as well as associated surface air pressure fields such
as El Niño Southern Oscillation (ENSO) or the North Atlantic Oscillation
(NAO), and their atmospheric teleconnections strongly affect weather patterns
at smaller scales . The pioneering work of
showed the applicability of dynamical seasonal prediction
systems in principle. The recently released Climate Forecast System version 2
, fulfilling all requirements for seasonal initial value
based predictions as claimed by , provides seasonal
forecasts every 6 h operationally. Data is available online and its
capabilities for hydrological applications are considered as promising
. The physical basis as well as the availability of data
makes CFSv2 an interesting source of information about anomalies of
meteorological variables for the next few months even if the spatial
resolution is comparatively coarse and the predictive accuracy decreases with
increasing lead times. In the framework of this paper, first results of CFSv2
forecasts for a small catchment in Tyrol/Austria (Gepatschalm) are presented.
A simple statistical downscaling approach is applied in order to scale
gridded monthly averages of temperature and precipitation to monthly station
recordings. Then, a water balance model with snow and ice melt capabilities
that has been designed for this task is calibrated and validated for
different periods of time using re-analysis data of the climate model.
Finally, first results of the melting season 2015 and the following
accumulation season are presented.
Study area
In order to test the applicability of seasonal predictions for small
meso-scale catchments relevant for hydropower generation, the Gepatschalm
catchment (55 km2) has been selected. It represents a typical high
altitude Alpine catchment with an altitudinal range between 1895 and
3504 m a.s.l. The latter value is close to the altitude of the highest
summit of the Ötztal Alps, the Wildspitze (3770 m a.s.l.). Approximately
37 % of the area is covered by glaciers, according to the 3rd Austrian
Glacier Inventory updated in 2006 . Moreover, downstream
of the Gepatschalm gauge a reservoir was built and completed in 1964. The
reservoir capacity is 139 million m3; it has been designed for
additional inflow of several catchments in the vicinity that are diverted to
the reservoir. Here, the natural catchment area is investigated
(Fig. ).
Map showing the study area located in Tyrol/Austria (hatched area in the small map)
including the Gepatschalm catchment upstream of the Gepatsch reservoir. The Gepatschferner
is the largest glacier in Western Austria (glaciers in light grey). The Inn river catchment
is highlighted in green in the small map.
The runoff as recorded by the Gepatschalm gauge since 1983 is
strongly influenced by seasonal effects as snow and ice melt runoff dominates
in late spring and summer resulting in mean monthly runoff depths greater
500 mm whereas the winter runoff is very low (less than 10 mm per month).
According to the Austrian Glacier Inventory, the Gepatschferner – the
largest glacier of the study region – covered 16.6 km2 in 2006. At
present, glaciers undergo a rapid decline both in the study area and
worldwide . For long-term investigation, the
areal glacier extent has to be continuously updated in order to incorporate
the variable surface ice melt areas .
Scatter plots of modelled vs. observed temperature (a) and
precipitation (b). Each point represents the mean temperature and
the precipitation depth of one month in the period 1986–2009, respectively.
Besides the diagonal line (dashed), the regression line is also added to each
plot (solid).
Set-up of seasonal forecasts and water balance simulationsThe CFSv2 model and linear regression downscaling
The NCEP Climate Forecast System version 2 (CFSv2) has been developed at the
Environmental Modeling Center at the National Centers for Environmental
Prediction (NCEP). Since 2011, the model has been made operational. The model
software, all relevant data and the seasonal forecasts are available to the
public (see, e.g., http://cfs.ncep.noaa.gov/). Computations are
performed on a horizontal grid with a resolution of roughly 100 km and 64
vertical levels of the atmosphere . Besides the atmospheric
model, CFSv2 also includes a land surface, cryosphere and ocean model. These
model components are coupled in order to adequately describe interactions
among the systems described by each of these sub-models. A detailed
description of the model dynamics and physics is provided by
. For practical purposes in the framework of this
study, we focus on some features that are relevant for hydrological
modelling.
CFSv2 is run 4 times a day performing simulations for the next 9 months.
Additionally, a re-analysis dataset ranging from 1979–2014 is available. In
order to assess the applicability of CFSv2 data for the study area, monthly
time series of temperature and precipitation extracted from the re-analysis
dataset have been compared to observations recorded by a meteorological
station. For investigating the Gepatschalm catchment in this context, the
nearest meteorological station that provides long-term recording is located
in the vicinity of the dam of the Gepatsch Reservoir (approx. 7 km north of
the gauge). Long-term time series are needed as a sound database for model
evaluations, also with respect to sub-sequent bias corrections.
Figure shows scatter plots for temperature and precipitation
with modelled values plotted against observations. Each point represents one
month within the period 1986–2009. Mean monthly temperature coincides well
with observation. A cold bias yielding approximately -3.4 K can be
observed from the plot in Fig. a. For the subsequent
simulations, the respective slope and intercept parameter derived from
regression analyses are applied in order to linearly adjust the
underestimation of temperature for the study area. This procedure can be seen
as a simple bias correction method (R2=0.97). The modelled monthly
precipitation depth is on average twice the value obtained through long-term
records. Instead of using a linear regression, a pure multiplicative
correction is used instead without assuming an intercept
parameter that might yield negative values in the transformation procedure
(R2=0.47, Fig. b). Time series of shortwave radiation are
also derived from the CFSv2 data. Other than temperature and precipitation,
shortwave radiation recordings are only available as of 2011. Since
re-analysis data are available until 2014, at least three years of both
observed and modelled values are directly comparable (see
Fig. ). CFSv2 tracks the seasonal characteristics very
well. However, shortwave radiation is overestimated during the summer months.
Due to the short overlapping period that covers both observed and modelled
values, monthly mean shortwave radiation time series are used “as is” in
this study as the number of values is too small for statistical corrections.
Instead, overestimation of shortwave radiation is compensated through
calibration of the water balance model as described in the next section. This
approach is seen as feasible method to compensate for model biases since the
melt computations include an adjustable radiation dependent melt factor.
Thus, the radiation biases are corrected through model calibration in this
study.
Modelled and observed shortwave radiation. Observations have been
recorded at the Weisssee climate station (2480 m a.s.l., see
Fig. ), whereas modelled values are derived from CFSv2 without
any modification.
The water balance model AWARE and its application
Nowadays, hydrological models typically operate at one hour or one day time
steps. For flood forecasting, hourly time steps are typical, whereas in urban
hydrology, even smaller time steps are employed. For monthly time series of
input data, these models cannot be applied without utilising further temporal
disaggregation techniques, which scale monthly values to smaller time steps.
For instance, propose a scaling of historical daily time
series so that these time series match the respective monthly means of the
forecast. Another possibility is to revisit water balance models that operate
at one month time steps , which might be seen as feasible
approach given that seasonal forecasts address anomalies rather than
predicting the weather of specific days. In this study, the latter
methodology is pursued for practical reasons.
Here, a simple water balance model (“AWARE”: Alpine Water balance And
Runoff Estimation model) has been developed including all relevant processes
for the study area. The model design also addresses initialisation of system
states through other models operating operationally. The physically-based
hydro-climatological model AMUNDSEN ,
running at hourly time step, is used to derive snow water equivalent (SWE)
maps using 50 m grid spacing in operational mode for the Inn River catchment
in Tyrol and adjacent regions (green area in the small map of
Fig. ). In order to employ areal SWE maps as initial conditions
for the water balance model with meteorological forcing using CFSv2, the
water balance model is a distributed model and has been designed to be
capable of running on the same grids as AMUNDSEN.
The water balance model includes 5 different process modules:
(i) meteorological pre-processing, (ii) snow and ice melt,
(iii) evapotranspiration, (iv) soil water balance, and (v) groundwater. In
order to account for lower temperatures and higher precipitation totals at
higher altitudes, empirical gradients for temperature and precipitation are
applied to the meteorological input downscaled for the meteorological station
situated close to the Gepatsch reservoir. The methodology and parameters are
similar to the ones in AMUNDSEN. Snow and ice melt are calculated separately
assuming different parameterisations of the enhanced degree day method which
also incorporates radiation , which is crucial for snow and
ice melt. In contrast to the cited study that considers hourly melt rates,
the proposed methodology is applied to monthly averages of temperature and
radiation. This is viewed as an adequate adaptation since the original degree
day method can be also reliably applied using average monthly degree day
values . Evapotranspiration is computed according to the
approach. Water fluxes associated with the soil water
balance are approximated according to . A linear storage is
implemented to account for the groundwater storage and low flow recession.
Time series of observed (blue line) and modelled runoff (red line)
calculated using the water balance model AWARE forced by CFSv2 data.
A split sample test was applied in order to calibrate and validate the model
using different periods of time as shown in
Fig. . The period 1995–2009 was used for calibration, while
the period 1985–1992 was used for validation (due to some missing values in
the CFSv2 re-analyses data, the years 1993–1994 were not considered).
Table summarises the calibration and validation period
for the water balance model driven by CFSv2 re-analyses. To quantify the
deviations between model and observations, a set of performance measures were
calculated : Nash–Sutcliffe model efficiency (NSE) values
between 0.65 and 0.75 are viewed as “good” model performance rating for
monthly time steps. Hence, the NSE value achieved for the calibration period
is within the range of “good” values whereas the respective value of the
validation period is even “very good” according to this categorisation.
These findings also hold true for the RMSE observations standard deviation
ratio (RSR). For the percent bias the achieved results fall into the category
“satisfactory” (calibration period) and “very good” (validation period)
indicating smaller differences between modelled and observed runoff volume in
the validation period. However, the modelled peak melt runoff is
underestimated in some cases even though modelled runoff is on average
slightly higher compared to the observations. In contrast, the model
overestimates low flow during the winter months. Notwithstanding these
uncertainties in model skill, the modelled time series tracks the
observations well. Thus, it is assumed that the model is capable of
transforming seasonal predictions of meteorological fields to corresponding
runoff and SWE predictions for the study area.
Model performance measures for the water balance model AWARE forced
by CFSv2 re-analysis data. NSE = Nash–Sutcliffe model efficiency,
PBIAS = Percent Bias, RMSE = Root Mean Square Error, RSR = RMSE
observations standard deviation ratio, R= Pearson's correlation
coefficient.
Performance measureCalibrationValidation(1995–2009)(1985–1992)Number of months [–]18096NSE [–]0.780.76PBIAS [%]-23.67-5.83RMSE [mm month-1]79.4390.36RSR [–]0.470.49R [–]0.910.87Results and discussion
Test operation of the water balance model for seasonal prediction using CFSv2
data has been started in early summer 2015. As initial conditions are crucial
for seasonal predictions, appropriate initial states for the most important
hydrological storages need to be prepared prior to the model runs. For
mountainous and sub-arctic catchments, SWE is the most important variable
that needs to be initialised in order to accurately predict snowmelt.
Operational snow maps obtained by AMUNDSEN are used as initial conditions in
each seasonal forecast run of the water balance model (for an example see
Fig. ). SWE reaches up to several hundred millimetres at the
beginning of June 2015. Snow free conditions can be observed in the lower
elevation bands, whereas SWE is highest on the glaciers.
Areal initial values of snow water equivalent (SWE) provided by
AMUNDSEN for 1 June 2015.
Figure shows the first results of the water balance model
in forecast mode. Seasonal forecasts performed by CFSv2 were prepared for one
day each in June, July, and August 2015. The correction methods derived by
evaluating the re-analysis data were applied in order to account for
systematic errors in the forecasts likewise. The range of catchment-scale SWE
values (average of all grid cells) derived by different ensemble members is
subjected to small variations in autumn. Beyond mid-winter, the results
indicate a large variety of possible SWE evolutions. A peak accumulation in
May 2016 yielding a similar areal SWE is just as possible as a total melt.
The first case indicates average conditions whereas the latter case can be
viewed as a winter with little snowfall.
Seasonal forecasts of SWE (top) and runoff (bottom) using the water
balance model AWARE, initialised by AMUNDSEN and forced by CFSv2. In addition
to the ensemble mean, the range of results achieved by four CFSv2 runs in
August 2015 is shown as shaded area. The dashed green line indicates average
runoff conditions computed using re-analysis data (1985–2009).
The runoff depth as modelled by the water balance model is of the same order
of magnitude as the values recently observed in July, August and September
2015. For instance, the runoff time series of the run initialised in June
distinctively overestimate observations in the first month of the simulation
(precipitation input 240 mm month-1) whereas the value predicted for
July 2015 is smaller than the corresponding observation. From August 2015,
simulated runoff depths are overestimated compared to observations. This
finding also holds true for the runs initialised in July and August. However,
the range of values derived by four independent initialisations of the CFSv2
is large as it covers values from approx. 200 to 1000 mm month-1. In
contrast, the ensemble mean represents typical values observed for August.
The bandwidth of values derived by ensemble runs decreases in autumn and
winter as melt diminishes and precipitation falls mainly as snow merely
contributing to an increase in SWE. In June and August 2015, the
overestimation of runoff depth in some runs can be addressed to exceptionally
high precipitation depths computed by CFSv2.
When considering the entire simulation period, differences in
total precipitation depth among the ensemble members are relevant for
seasonal snow accumulations as well. For instance, a total precipitation
depth of 710 mm is predicted for the period August 2015–May 2016 if the run
initialised on 10 August 2015 at 06:00 z (06:00 UTC) is considered. In
contrast, the same evaluation applied to the run initialised at 18:00 z on
the same day yields 980 mm (see Fig. ).
Areal distribution of precipitation depth in mm for the period
August 2015–May 2016 as predicted on 10 August 2015 by two CFSv2 runs
initialised at (a) 06:00 z and (b) 18:00 z, respectively.
Apart from the run initialised in June, runs that are initialised in
consecutive months coincide reasonably with recent observations, at least for
one or two months. The differences between two consecutive runs are small for
up to three months in advance. However, if single ensemble runs are compared
with each other, a large range of forecasted runoff depths in summer and SWE
in winter and spring is obvious.
Conclusions and outlook
The present study analyses available CFSv2 forecasts for runoff and SWE in a
meso-scale Alpine catchment and hence, represents a first assessment of
feasibility. The results obtained by the water balance model, which is forced
using CFSv2 data, are promising with respect to future applications of
seasonal forecasts in the study area. The presented results indicate that the
model system as yet applied to a glaciated catchment – provides useful
results for lead times up to three months. In this snow accumulation and melt
dominated catchment, increased uncertainties regarding SWE and runoff appear
as the superposition of the uncertain precipitation and temperature inputs
for lead times larger than three months. However, if the positive trend of
results for lead times up to approximately 100 days could be verified in
further model tests, such a model system could support the day-by-day
operation as water management tool for hydropower companies. Therefore, an
operational use of the model chain CFSv2 → AWARE is pursued to
provide estimates of runoff for the upcoming one to three months. In this
way, seasonal predictions complement the already existing flood forecasting
system for the Inn River called HoPI – “Hochwasserprognose für den
Tiroler Inn” .
Further evaluations of the forecast data and subsequent water balance
simulations for the study area are still needed to understand possible
uncertainties in seasonal forecasts. For instance, the relatively high
precipitation depth values achieved by runs in June and August 2015 resulted
in model overestimation with respect to runoff. Moreover, SWE evolution in
the subsequent winter season strongly depends on seasonal precipitation
depth. To address these issues, a systematic evaluation of forecast data is
foreseen in order to achieve quantitative information about the accuracy of
the forecasts in the study area, i.e. by gathering forecasts (ensembles)
throughout several months (years) in order to relate uncertainties in
predictions to observations. Then, Model Output Statistics (MOS) (Warner,
2011) might be also beneficial to improve forecasts by means of correction
approaches that rely on previous forecasts and observations. Therefore, the
application of ensemble runs need to be performed more rigorously to gain
insight into uncertainties and to provide probability frameworks. Besides
methodological enhancements, extensions to the study area are also foreseen
to incorporate catchment areas that are artificially drained to the Gepatsch
Reservoir.
Data availability
Both re-analysis data as well as operational forecasts of the CFSv2 model are available for free.
CFSv2 re-analysis data (Saha et al., 2010):
http://cfs.ncep.noaa.gov/pub/raid0/cfsv2/climo_cfsr_month/flxf06/
Operational CFSv2 7 Day Rotating Archive (Saha et al., 2014):
http://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/cfs/
Acknowledgements
This work was carried out as part of the project “W01 MUSICALS II –
Multiscale Snow/Ice Melt Discharge Simulation for Alpine Reservoirs” at alpS
– Centre for Climate Change Adaptation in Innsbruck, Austria. The K1-Centre
alpS is funded through the Federal Ministry of Transport, Innovation and
Technology (BMVIT), the Federal Ministry of Science, Research and Economy
(BMWFW), as well as the Austrian Provinces of the Tyrol and Vorarlberg within
the scope of COMET – Competence Centers for Excellent Technologies. The
Programme COMET is managed by the Austrian Research Promotion Agency (FFG).
We want to thank TIWAG – Tiroler Wasserkraft AG for the collaboration and
co-funding the project, as well as the Editor W. Grabs for his valuable
comments.
ReferencesAchleitner, S., Schöber, J., Rinderer, M., Leonhardt, G., Schöberl,
F.,
Kirnbauer, R., and Schönlaub, H.: Analyzing the operational performance
of the hydrological models in an alpine flood forecasting system, J. Hydrol.,
412–413, 90–100, 10.1016/j.jhydrol.2011.07.047, 2012.Doblas-Reyes, F. J., García-Serrano, J., Lienert, F., Biescas, A. P., and
Rodrigues, L. R. L.: Seasonal climate predictability and forecasting:
Status and prospects, WIREs Clim Change, 4, 245–268,
10.1002/wcc.217, 2013.Fischer, A., Seiser, B., Stocker Waldhuber, M., Mitterer, C., and Abermann,
J.: Tracing glacier changes in Austria from the Little Ice Age to the present
using a lidar-based high-resolution glacier inventory in Austria, The
Cryosphere, 9, 753–766, 10.5194/tc-9-753-2015, 2015.
Hock, R.: A distributed temperature-index ice- and snowmelt model including
potential direct solar radiation, J. Glaciol., 45, 101–111, 1999.
Huttenlau, M., Bellinger, J., Schattan, P., Förster, K., Oesterle, F.,
Schneider, K., Achleitner, S., Schöber, J., Raffeiner, G., and Kirnbauer,
R.: Flood forecasting system for the Tyrolean Inn River (Austria):
current state and further enhancements of a modular forecasting system for
alpine catchments, in: Living with natural risks, 13th Congress INTERPRAEVENT
2016, Lucerne, 909–916, International Research Society INTERPRAEVENT,
Klagenfurt, Austria, 2016.Kanamitsu, M., Kumar, A., Juang, H.-M. H., Schemm, J.-K., Wang, W., Yang, F.,
Hong, S.-Y., Peng, P., Chen, W., Moorthi, S., and Ji, M.: NCEP Dynamical
Seasonal Forecast System 2000, B. Am. Meteorol. Soc., 83,
1019–1037, 10.1175/1520-0477(2002)083<1019:NDSFS>2.3.CO;2, 2002.Klemeš, V.: Operational testing of hydrological simulation models,
Hydrolog. Sci. J., 31, 13–24, 10.1080/02626668609491024, 1986.
Klemeš, V.: The modelling of mountain hydrology: the ultimate
challenge,
in: Hydrology of mountainous areas, edited by: Molnár, L., 29–43,
International Association of Hydrological Sciences, Wallingford, 1990.Koboltschnig, G. R. and Schöner, W.: The relevance of glacier melt in the
water cycle of the Alps: the example of Austria, Hydrol. Earth Syst. Sci.,
15, 2039–2048, 10.5194/hess-15-2039-2011, 2011.Kuentz, A., Mathevet, T., Gailhard, J., and Hingray, B.: Building long-term
and high spatio-temporal resolution precipitation and air temperature
reanalyses by mixing local observations and global atmospheric reanalyses:
the ANATEM model, Hydrol. Earth Syst. Sci., 19, 2717–2736,
10.5194/hess-19-2717-2015, 2015.Marzeion, B., Cogley, J. G., Richter, K., and Parkes, D.: Glaciers.
Attribution of global glacier mass loss to anthropogenic and natural
causes, Science, 345, 919–921, 10.1126/science.1254702, 2014.
McCabe, G. J. and Markstrom, S. L.: A Monthly Water-Balance Model
Driven By a Graphical User Interface, U.S. Geological Survey
Open-File report, 2007.
Moriasi, D. N., Arnold, J. G., van Liew, M. W., Bingner, R. L., Harmel,
R. D.,
and Veith, T. L.: Model evaluation guidelines for systematic quantification
of accuracy in watershed simulations, Trans. ASABE, 50, 885–900, 2007.Rango, A. and Martinec, J.: Revisting the degree-day method for snowmelt
computations, J. Am. Water Resour. Assoc., 31, 657–669,
10.1111/j.1752-1688.1995.tb03392.x, 1995.Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol.
Soc., 91, 1015–1057, 10.1175/2010BAMS3001.1, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D.,
Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The
NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
10.1175/JCLI-D-12-00823.1, 2014.Schaefli, B.: Projecting hydropower production under future climates: a
guide
for decision-makers and modelers to interpret and design climate change
impact assessments, WIREs Water, 2, 271–289, 10.1002/wat2.1083, 2015.
Schueller, F., Förster, K., Hanzer, F., Huttenlau, M., Marzeion, B.,
Strasser, U., Achleitner, S., and Kirnbauer, R.: Multiscale
Snow/Icemelt Discharge Simulations into Alpine Reservoirs: adding
Glacier Dynamics to a Hydrological Model, Geophysical Research
Abstracts, p. 3244, 2015.
Strasser, U.: Modelling of the mountain snow cover in the Berchtesgaden
National Park, vol. 55 of Berchtesgaden National Park research
report, Nationalparkverwaltung Berchtesgaden, Berchtesgaden, 2008.Strasser, U., Warscher, M., and Liston, G. E.: Modeling
Snow–Canopy Processes on an Idealized Mountain, J.
Hydrometeor., 12, 663–677, 10.1175/2011JHM1344.1, 2011.Thornthwaite, C. W.: An Approach toward a Rational Classification of
Climate, Geogr. Rev., 38, 55–94, 10.2307/210739, 1948.Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner,
R.:
Mountains of the world, water towers for humanity, Water Resour. Res., 43,
1–13, W07447, 10.1029/2006WR005653, 2007.Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P.: Long-range
experimental hydrologic forecasting for the eastern United States, J.
Geophys. Res., 107, 4429 (1–16), 10.1029/2001JD000659, 2002.Yuan, X., Wood, E. F., Luo, L., and Pan, M.: A first look at Climate
Forecast System version 2 (CFSv2) for hydrological seasonal prediction,
Geophys. Res. Lett., 38, 1–7, L13402, 10.1029/2011GL047792, 2011.Yuan, X., Wood, E. F., and Ma, Z.: A review on climate-model-based seasonal
hydrologic forecasting: Physical understanding and system development,
WIREs Water, 2, 523–536, 10.1002/wat2.1088, 2015.