Natural Resources, 2012, 3, 145-155 Published Online September 2012 (
Demand and Supply of Water for Agriculture: Influence of
Topography and Climate in Pre-Alpine, Mesoscale
Jürg Fuhrer1, Karsten Jasper1,2
1Air Pollution/Climate Group, Agroscope Research Station Art, Zurich, Switzerland; 2Hydrology Department, Federal Office of the
Environment, Bern, Switzerland.
Received June 6th, 2012; revised July 19th, 2012; accepted July 28th, 2012
With climate change, water may become limited for intensive agriculture even in regions presently considered “wa-
ter-rich”. Information about the potential water requirement and its temporal and spatial variability can help to develop
future water management plans. A case study was carried out for Switzerland with its highly complex pre-alpine topog-
raphy and steep gradients in climate. The hydrological model WaSiM-ETH was used to simulate net irrigation require-
ment (NIR) for cropland, grassland and orchards using criteria to define irrigation periods based either on the water
stress level (expressed by the ratio of actual (aET) to potential evapotranspiration (pET) (Method 1) or on thresholds for
soil water potential (Method 2). Simulations for selected catchments were carried out with a daily time step for the pe-
riod 1981-2010 using a 500 × 500 m spatial resolution. Catchment-scale NIR ranged between 0 and 4.3 million m3 and
0 and 7.3 million m3 for the two methods, respectively, with no trend over the observation period in any catchment.
During the heat wave in 2003, NIR increased by a factor of 1.5 to 2.3 relative to the mean, and in catchments where
discharge is directly dependent on precipitation, NIR in the summer of 2003 reached the limits of river water availabil-
ity. In contrast, in a region with water supply from glacier melt water, highest NIR in 2003 still remained far below total
river discharge. The results show that NIR varies strongly between years and across the landscape, and even in a pres-
ently cool-temperate climate, irrigation may put pressure on regional water resources under extreme climatic conditions
that may become more frequent by the end of the 21st century.
Keywords: Agriculture; Irrigation; Climate; Discharge; WaSim-ETH
1. Introduction
Agriculture is among the sectors most directly affected
by climate variability and climate change, which is
mainly due to the strong dependency of crop and live-
stock production on water, and thus to the tight link to
the global hydrological cycle [1]. In many rain-fed pro-
duction systems water is limiting agricultural output be-
cause the climatic water balance, i.e. the ratio between
precipitation (P) and potential evapotranspiration (pET),
is low [2]. To cope with water limitation, a large fraction
of the total available surface and/or groundwater is ab-
stracted for irrigation in semi-arid and arid regions, for
instance around 60% in the Mediterranean region of
Europe where according to farm survey data the area that
is equipped for irrigation is 51% in Greece, 43% in Italy,
about 30% in Portugal, Malta and Cyprus, and 23% in
Spain [3]. In contrast, in most Central and Northern
European regions supplemental irrigation is only used to
optimize yields of selected high-value crops during epi-
sodes of high pETP ratios, and thus agricultural wa-
ter use is currently of minor importance [4]. However, in
recent decades trends were observed towards increasing
frequency and length of dry spells in Southern and Cen-
tral Europe [5], drier summers particularly in Central
Europe due to increasing temperatures [6], and a dou-
bling of the length of western European summer heat
waves since 1880 [7]. Moreover, outputs of global and
regional climate models project a further increase and a
spread of dry spells across Europe during this century [8].
As a consequence, water-related problems in agricultural
production could become more serious in the long-run,
even in today’s cool and humid regions [9].
The European heat wave in 2003 is often taken as an
illustration of the situation that could become common
by the end of this century [10]. It has caused substantial
crop losses due to insufficient rainfall [11]. Such an ex-
Copyright © 2012 SciRes. NR
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
treme situation increases the demand for irrigation, thus
putting pressure on natural water resources and causing
conflicts between different water users [4]. In response to
the threat of an increasing competition between different
sectors, national drought management plans have already
been developed in several EU member states [12] and
To develop sustainable water management plans for ag-
riculture, quantitative estimates of water requirements
under different climatic conditions are needed. Such es-
timates enable to localize geographical hotspots, to iden-
tify the most water-demanding production systems, and
to evaluate the efficiency of different irrigation strategies
and techniques [13,14]. In countries with ample water
supply, detailed information about current agricultural
water use is often not available because mandatory re-
porting is lacking. In Switzerland, the only available es-
timate is from a survey among Cantonal authorities. Ac-
cording to this survey a total of 140 million m3 of water
mostly taken from rivers and lakes is used annually to
irrigate approximately 50,000 ha of which the largest
fraction is located in the dry and warm Rhone valley
(Valais) [15]. But for planning purposes, a more detailed
analysis is necessary that accounts for spatial and tem-
poral variability in potential irrigation water requirement
in relation to the availability of surface water (river dis-
charge). A high level of detail is particularly necessary in
pre-alpine and alpine regions of Europe because of the
complex topography and hydrology, and pronounced
climatic gradients that are not sufficiently accounted for
in global [16] or regional [17] models.
Net irrigation requirement (NIR), i.e. the total amount
of water during the growing season necessary to reach
optimal crop yield without considering efficiency factors
for irrigation systems, is essentially determined by the
balance between all relevant water fluxes in and out of a
cropping system. Therefore, to assess NIR across larger
regions or catchments, spatially distributed hydrological
models are necessary that consider all relevant hydro-
logical processes and include crop-specific parameters
and phenological patterns. In the present study, the
model WaSiM-ETH [18,19] was applied which was pre-
viously used to estimate the long-term response to cli-
mate change scenarios of major river discharge [20], or
of soil moisture in different catchments in Switzerland
[21]. The model allows defining criteria to trigger irriga-
tion. These criteria can be based on upper and lower
thresholds of soil water potential, which is the default
method of the model. As an alternative, thresholds of an
index related to the level of water limitation, i.e. the ratio
between actual (aET) and potential evapotranspiration
(pET) can be used, which is directly related to the ratio
between actual and non-water-limited crop yield [22].
The aim of the study was 1) to estimate average NIR
during 1981-2010 for three main land use types (crop-
land, grassland and orchards) in selected catchments
based on the two criteria for irrigation triggering; 2) to
analyze inter-annual variability and trends; and 3) to re-
late NIR to observed discharge in the main rivers of these
catchments to identify possible limitations for surface
water availability.
2. Data and Methods
2.1. Catchments
Six catchments representing different climatic and soil
conditions, topography and degree of glaciations were
chosen (Figure 1): Thur, Emme, and Broye on the Cen-
tral Plateau, central Rhone (Rhone valley between Brigue
and Sion) in the Valais, Ticino located south of the Alps,
and Dischmabach in the eastern Alps. In Table 1, data
are summarized to characterize the catchments. Together,
the selected catchments covered a total area of 6159 km2.
Their agricultural land was 2252 km2 representing about
16% of the total agricultural area of the country.
2.2. Meteorological Data
Daily data for precipitation (428 stations), air tempera-
ture (84 stations), wind speed (100 stations), vapor pres-
sure (61 stations), relative humidity (86 stations), global
radiation (68 stations) and relative sunshine duration (68)
from different monitoring networks were extracted from
the CLIMAP-net databank of the Swiss Federal Office
for Meteorology (MeteoSuisse,
The stations covered an altitude rangeing from 203 to
3580 m a.s.l. with the majority of stations located below
1000 m a.s.l. Precipitation measurements were error-
corrected, separately for snow and rain using the methods
described in Schulla and Jasper [23], with separate cor-
rection factors for individual regions. In addition, data of
temperature and radiation were adjusted for the effects of
aspect and slope after their interpolation on the model
grid. All station-based data were spatially interpolated
using a weighted combination of inverse-distance inter-
polation and altitude-dependent regression (for details,
see [23]). Altitude-dependent regression was applied
separately to regions differing in the overall climate [24].
Gridded data of all parameters were extracted for each
2.3. Geospatial Data
Spatial information was derived from three gridded GIS
data layers: elevation (from Swisstopo at, land use (Swiss Land-Use
Statistics 2004/09 at, and soil char-
acteristics (Swiss Soil Suitability Map, [25]). For the
Copyright © 2012 SciRes. NR
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
Copyright © 2012 SciRes. NR
Figure 1. Location of selected catchments. Circles mark the location of the gauge.
Table 1. Characteristics of the selected catchments.
Thur Emme Broye Rhone Ticino Dischmabach
Size (km2) 1696 939 392 1574 1515 43
Mean altitude (m a.s.l.)
Altitude range
356 - 2504
458 - 2221
441 - 1504
484 - 4435
220 - 3402
1668 - 3146
Fraction of agricultural land
(cropland/grassland) (%)
Mean temperature (oC)1) 14.7 14.6 16.2 14.1 15.5 8.6
Precipitation sum (mm)1) 825 666 664 488 931 718
Fraction of glaciation (%) 0 0 0 8.2 0.7 2.1
1) Regional means for April-September, 1981-2010.
application of the hydrological model, the original land-
use statistics with 72 classes were reclassified into 16 clas-
ses from which three aggregated classes were extracted for
this study: cropland, grassland and orchards. The neces-
sary soil parameters, e.g. soil-water retention curves and
saturated hydraulic conductivities for major soil textural
groups, were not directly available. They were derived
from the Swiss Soil Suitability Map [25]. All spatial data
sets were adjusted to the 500 × 500 m model grid.
2.4. The Hydrological Model WaSiM-ETH
Simulations with a daily time-step were carried out with
the distributed catchment model WaSiM-ETH (Water
Flow and Balance Simulation Model) [19]. A detailed
description of the model can be found at
[18]. The flexibility of WaSiM-ETH allows both short-
term (flood runoff) and long-term (water balance) simu-
lations. Briefly, the model considers spatial interpola-
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
tions of meteorological data and their adjustment with
respect to slope and shadowing (temperature and radia-
tion), evapotranspiration, level of interception storage
and evaporation from this storage, snow accumulation,
snowmelt and evaporation from snow, glacier melt and
runoff, surface runoff generation, soil water dynamics in
pre-defined multi-layered soils, interflow generation, per-
colation, groundwater recharge and water level, ground-
water runoff (baseflow) and runoff concentration, dis-
charge routing within river channels. Soil water dynam-
ics in the unsaturated soil zone is calculated using a dis-
crete formulation of the Richards equation with parame-
terization after [26]. In this study, van Genuchten pa-
rameters were based on values provided in [27].
The model allows simulating macro-pore fluxes and
soil water extractions by multi-layered vegetation. Inter-
flow is generated as layer-specific rates, depending on
hydraulic potential, water content, hydraulic conductivity
and gradient, and flow density (see [18]). Base flow is
determined as exfiltration from the groundwater into the
surface river system. Simulation of discharge routing
within the river channels is based on hydraulic calcula-
tions of flow velocities (kinematic wave approach). Fi-
nally, aET is derived from pET [28] by accounting for
the effect of soil matrix potential (
). There are two
limits of
where aET starts to decrease through re-
ductions in plant transpiration: one limit defines the start
of stress due to water shortage and a second one marks
the onset of wetness stress.
 
 
wp ws
ws pwp
ws os
os sat
sat os
 
 
 
 
 
 
where θ is the volumetric water content, ψ is the matrix
potential (suction), θpwp is the water content at the per-
manent wilting point (|ψ| 150 m), θws is the water con-
tent at which aET starts to decrease due to water limita-
tion, θsat is the saturated water content, and θos is the wa-
ter content at which aET starts to decrease due to water
logging (oxygen stress) (see [21] for more details). Both
θws and θos vary with vegetation and soil type. However,
due to the lack of detailed information, θws was calcu-
lated with respect to a constant suction of |ψ| = 350 hPa
for all land use types, and θos was assumed for water
contents > 0.95 × θsat.
Vegetation parameters (e.g. LAI, cover, surface resis-
tance, rooting depth) were assigned to each land use type
according to the stage of vegetation development (phenol-
ogy). For cropland, phenological development was de-
termined dynamically using an approach that simulates
the starting day of active growth and the timing of the
subsequent key phenological stages based on growing-
degree-days (GDD) [18,29]. Parameterization of the
phenology model was based on data from Pöhler et al.
[30], as described in the model description [18]. The
representation of crop phenology by parameters for a
single “average” crop was chosen because different crops
(e.g. wheat, maize, barley, potato etc.) are usually grown
in multi-year rotations and gridded crop yield statistics
for each year were not available at the catchment scale.
For permanent grassland, canopy development was pa-
rameterized based on data from the experimental grass-
land site at Oensingen, Switzerland [31], with the inter-
mediate cutting dates depending on altitude. For orchards
(fruit trees), simple pre-defined seasonal patterns and
altitude dependency were used instead of uncertain dy-
namic phenology curves [19].
2.5. Net Irrigation Requirement, NIR
NIR was estimated for each grid cell based on two
methods. Method 1 was implemented as a new feature
into WaSiM-ETH. The method uses the ratio between
aET and pET assuming that the two terms are equal un-
der non-water-limited conditions when crop yield is at a
maximum. According to [22], aETpET is inversely
related to the ratio between non-water-limited and actual
yield of a crop with a nearly linear relationship between
1 and 0.5 for aETpET . For the present application, a
value of aETpET of 1 was set at an equivalent soil
matrix potential of 350 hPa for all land use types. A cut-
off value for aETpET of 0.8 was used as the criteria
to trigger irrigation and a value of 1 to stop irrigation.
Method 2 was based on the soil matrix potential. Irriga-
tion was triggered at a value of 50% of the usable field
capacity (equivalent to about 1000 hPa) and stopped at
80% (equivalent to about 200 hPa). For both approaches,
the time step of the simulation was one day, and daily
irrigation amounts were summed up over the dynami-
cally calculated growing season.
2.6. Model Calibration and Validation
Calibration and validation of the hydrological model
were carried out separately for each catchment. The skill
of the model was assessed with respect to runoff produc-
tion using daily observed data from a total of 26 runoff
gauges as the reference [21]. Model calibration was per-
formed with data of the period 1981-1990, and data of
the period 1991-2000 were used for model validation.
Besides checks of the calculated values of water balances
(e.g. runoff volumes, evapotranspiration rates) and a visual
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Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
Copyright © 2012 SciRes. NR
agement for hydropower generation, which strongly in-
fluences the natural runoff regimes (e.g. runoff abstrac-
tions from one catchment to another).
inspection of the agreement in the patterns of observed
and simulated runoff data, a statistical evaluation of the
simulation results was made by calculating the efficiency
criterion (NSC) proposed by Nash and Sutcliffe [32]
NSC is often used to assess the predictive power of hy-
drological models and indicates how well the plot of ob-
served versus simulated data fits the 1:1 line. NSC is
given by,
3.2. NIR in Selected Catchments
Catchment-scale NIR varied considerably due to differ-
ences in size, land use and climate. Table 3 provides a
summary of the total amounts calculated for the tem-
perature-dependent length of the growing season. Overall,
the results obtained with the two criteria for irrigation
were in good agreement, although Method 2 based on
soil water potential criteria yielded consistently higher
estimates than Method 1 based on the aET/pET criteria.
For the Thur catchment, i.e. the largest catchment with a
considerable fraction of both cropland and grassland, the
relative difference between the two methods was larger
than for the smaller catchments. With respect to land use
types, the difference between the two methods tended to
be larger for cropland than for grassland. Table 3 also
provides the estimated average fraction of potentially
irrigated land. It ranged from 20% to 55%, and the area-
weighted mean for all catchments amounted to 25%
(23% for cropland, 27% for grassland).
with x being, in this case, the observed discharge, y the
modeled discharge, the average of all x, and n the num-
ber of time steps. NSC ranges between and 1.0, with
NSC = 1 being the optimal value. In general, model
simulation can be judged as satisfactory if NSC > 0.50
2.7. Discharge Data
Monthly data for discharge volumes measured between
1981 and 2010 at the outlet of the main rivers (Table 1)
in each catchment were obtained from the data base of
the Federal Office of the Environment
( Locations are indicated in
Figure 1.
Based on regression analysis of the relation between
NIR and model input parameters, area-weighted mean r2
was the highest for “slope”’ (0.914), “soil hydraulic con-
ductivity” (0.86) and “aET/P” (0.88).
2.8. Trend Analysis 3.3. Irrigation Depth
Monotonic trends in the time series for NIR were carried
out using the non-parametric Mann-Kendall trend test.
Average irrigation depth (i.e. the amount of irrigation
ha–1 of irrigated land) during the growing season was
higher for cropland that for grassland (Figure 2). Differ-
ences between catchments were larger for cropland than
for grasslands. Means (in m3·ha-1) for all land use types
combined were 567 (Rhone), 329 (Broye), 303 (Emme),
254 (Thur), 182 (Ticino) and 109 Dischmabach. The
highest single annual value was around 1700 m3·ha–1 for
cropland in the Rhone catchment for the dry year of
3. Results
3.1. Model Calibration and Validation
For the investigated catchments, the efficiency criterion
NCS for the calibration data set was always higher than
0.50 (Table 2) and indicated sufficient accuracy of the
hydrological simulation. For the validation data set, NCS
ranged from 0.7 to 0.9, with the exception of the Rhone
catchment (gauge “Sion”) with a lower NCS. In the latter
region and also in the Ticino basin, limitation in the skill
was likely caused by the lack of data on reservoir man-
Excluding the alpine catchment (Dischmabach) with
only grassland and no cropland, the value for 2003 for all
types of land use combined was between 1.5 and 2.3
times larger than the mean.
Table 2. Catchment-specific values for NCS (Nash-Sutcliff efficiency criterion) for calibration and validation data sets.
Catchment (gauge)
Calibration, 1981-1990 0.83 0.87 0.85 0.50 0.71 0.88
Validation, 1991-2000 0.81 0.85 0.82 0.40 0.70 0.90
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
Table 3. Total net irrigation requirement (NIR in million m3) calculated with two methods for the selected catchments for
cropland, grassland and the total agricultural area (including orchards), total area of agricultural land (km2) and fraction (%) of
irrigated land obtained with Method 1. Data are means for the growing season during the period 1981-2010.
Land use type Method Thur Emme Broye Rhone a Ticino Dischmabach
Method 1 2.4 2.2 2.8 0.4 <0.1
Method 2 2.9 2.4 3.2 0.4 <0.1
Area (%) 338 (17) 178 (27)145 (41) 6 (69) 7 (9) 0 (0)
Method 1 1.7 0.8 1.1 2.3 0.8 <0.1
Method 2 4.2 2.3 2.5 2.8 1.0 <0.1 Grassland
Area (%) 599 (11) 305 (13)104 (31) 245 (19) 186 (21) 12 (5)
Method 1 4.3 3.0 4.0 3.6 0.9 <0.1
Method 2 7.3 4.8 5.8 4.2 1.1 <0.1 Total area
Area (%) 978 (13) 491 (18)254 (36) 283 (22) 195 (21) 12 (5)
aBetween Brigue and Sion.
3.4. Trend
Averaged for all catchments, but excluding the small
alpine Dischmabach catchment, irrigation depth during
the observation period averaged 282 m3·ha–1 for all land
use categories combined. Inter-annual variation was small
and ranged between approximately 200 and 350 m3·ha–1,
with the exception of the year 2003 with 550 m3·ha–1
(Figure 2). No significant temporal trend could be de-
tected over the period 1981-2010 (data not shown).
3.5. NIR vs. Discharge
The annual course of discharge differed between catch-
ments. While regimes in Rhone and Dischma catchments
showed a pronounced summertime maximum, the rest of
the catchments were characterized by low-flow during
summertime and highest discharge during spring (Figure
3(a)). These patterns were exacerbated in 2003. Relative
to discharge, average NIR amounted to <1 (Dischma) to
45% (Broye) (not shown). For 2003, NIR clearly exceeds
the available discharge in the Broye catchment (>200%
in July and August), and maximum monthly NIR frac-
tions were 40% in the catchments of Emme and Thur, but
below 2% in Rhone and Dischmabach (Figure 3(b)).
4. Discussion
Water limitation is central to the discussion of possible
effects of climate change on agricultural production [1],
and measures to improve agricultural water management
to cope with increasing drought risks and water scarcity
is a common element in national adaptation strategies
[34]. The results of the present study show that even in a
Figure 2. Box-plots for annual, growing season mean irr iga-
tion depth separately for cropland and grassland in the
selected catchments. Period: 1981 - 2010. Plots show median,
25th and 75th percentiles (box), 10th and 90th percentiles
(whiskers) and 5th and 95 th percentiles (points).
“water-rich” country of Europe, with high average an-
nual precipitation and discharge amounts of 1500 and
990 mm, respectively [35], substantial amounts of irri-
gation may be required in some sub-regions, or during
extremely warm and dry years that may become more
frequent in a future climate. Irrigation requirements are
the lowest in the areas with high amounts of precipitation
such as the Ticino catchment south of the Alps, and the
alpine catchment Dischmabach, and the highest in the
dry inner-alpine valley of the Rhone and in the Broye
catchments with a relatively low precipitation amount
(Table 1). The influence of topography is reflected in a
strong negative relationship between slope and NIR
which can be explained by the fact that sloped terrain is
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Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
generally located at higher altitudes with a cooler and
wetter climate, which overrides the effect of stronger
surface runoff and interflow which can negatively affect
the water balance [21].
Figure 3. a) Seasonal variation in mean (±STD) river discharge at the outlet of the catchments for 1981-2010 (bars) and for
2003 (points), and b) in mean net irrigation requirement (NIR) (+STD) (bars) and NIR in % of discharge in 2003 (points).
Copyright © 2012 SciRes. NR
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
The results reveal that irrigation depth in some regions
can reach values well above 1000 m3·ha–1. Such high
values are not evident from larger-scale studies with
resolutions of 10 × 10 km grid cells [13]. Thus, a high
degree of detail is necessary to identify small hotspots of
water demand in a highly structured pre-alpine landscape
with heterogeneous climatic conditions and pronounced
effects of topography.
Higher values resulted from the EPIC simulations for
neighboring countries such as Germany (1010 m3·ha–1),
France (4050 m3·ha–1) or Italy (9130 m3·ha–1) [17]. The
difference may reflect differences in soil texture and cli-
mate between regions in which irrigation is applied. Here,
for a warm/dry region such as the inner-alpine Rhone
Valley, the estimated mean is 948 m3·ha–1 for cropland,
and over 1700 m3·ha–1 in a dry year, as in 2003. However,
the comparison between results from different studies
may be biased because of differences in the methodology,
including model parameterization with respect to soil
(e.g. van Genuchten parameters) and vegetation; for in-
stance, Wriedt et al. [17] used parameters for a “repre-
sentative” crop in five main crop categories, and van der
Velde et al. [14] used specific parameters for maize in
France, while in the present study parameters for a single
“average crop” were used based on data from Germany
[30]. This simplification was necessary because spatially
explicit statistical data for crops are not available at the
scale of interest, and because crops are almost exclu-
sively grown in multi-crop rotations and thus over time,
different crops such as maize, winter wheat and root
crops may be present in each grid. Hence, differences in
crop-specific water requirements during the different
phenological stages, as described by specific crop coeffi-
cients provided in the literature [22], are ignored here,
but they could be included in more detailed modeling for
regions where spatially explicit crop statistics are avail-
able. The uncertainty may be less important for perennial
grasslands than for arable crops because water use is less
dependent on phenological changes during the growing
season. It should also be noted that the present study
covers a 30-year period, while other mean estimates are
based on different and often much shorter periods.
NIR estimates were obtained with two different meth-
ods for irrigation triggering. The results for the two
methods agree reasonably well, although estimates with
Method 2 are systematically higher than those obtained
with Method 1. Method 2 follows common practical
recommendations for irrigation control (e.g. [36]); it is
based on criteria for soil matrix potential with 200 hPa as
the value to stop irrigation. In contrast, Method 1 is
based on criteria for aETpET and uses a value of 1 as
the stop value, which was set here to a matrix potential of
350 hPa. Through a direct comparison for selected sites,
it could be observed that the different criteria for the start
of irrigation consistently leads to more frequent but
shorter irrigation periods with Method 1 than with
Method 2, which results in lower irrigation totals at the
end of the season (data not shown). Clearly, the outcome
of any calculation of NIR is sensitive to the choice of
these criteria and leads to different irrigation strategies
[17]. The criteria used here are set to obtain a maximum
theoretical water requirement for irrigation. In reality,
irrigation may be limited by economic considerations or
lack of access to water or suitable technologies.
The annual data for the observation period reveal no
apparent upward or downward trend with time, although
in all regions of Switzerland mean temperature during
the growing season has increased by about 2˚C over the
time period considered (according to data from Meteo-
Suisse at According to the Pen-
man-Monteith relationship, for each degree of warming
pET should increase by some 2% - 3% thus leading to a
corresponding increase in evaporative water use by crops
of less than 10% [37]. Unlike temperature, precipitation
amount did not change significantly between 1981 and
2010. Hence, the extent of climatic changes need to be
more substantial in order to cause significantly increased
pETP ratio and, consequently, detectable changes in
irrigation requirement. The effect of lower precipitation
in combination with a small positive difference in tem-
perature is illustrated by the difference in irrigation depth
for crops between the Rhone catchment (948 m3·ha–1)
with an average seasonal precipitation amount (April to
September) of below 488 mm and an average tempera-
ture of 14.1˚C (Table 1), and the Thur catchment on the
Central Plateau (307 m3·ha–1) with 825 mm precipitation
and a mean temperature of 14.7˚C (Table 1). Hence, any
trend in irrigation requirement during the next decades
may depend primarily on changes in precipitation rather
than temperature, but projections of future precipitation
changes at the relevant scales of resolution remain highly
uncertain [38]. For Switzerland, summer precipitation is
projected to decline between 21% and 27% by 2085
(2070-2099) relative to 1980-2009 when assuming the
SRES A2 emission scenario [39]. This decrease may be
much more relevant to NIR in the currently driest re-
The summer of 2003, which caused significant crop
losses in Switzerland [40] and elsewhere in Europe is
often considered indicative of prevailing conditions in
Switzerland towards the end of the 21st century [41]. This
trend would lead more frequently to limiting soil mois-
ture [1]. Calanca [42] estimated that the risk to reach
critical soil moisture conditions for crop production on
the Central Plateau could increase from 15% during the
next 100 years to about 50%. The simulated decrease in
Copyright © 2012 SciRes. NR
Demand and Supply of Water for Agriculture: Influence of Topography and Climate in Pre-Alpine,
Mesoscale Catchments
soil water content during the crop growing season in
2003 by about 40% relative to a reference period agrees
with what is projected using an extreme climate change
scenario, as shown for the Thur catchment [21]. The data
obtained here for NIR in 2003 provide an estimate of the
effect of these extreme conditions in 2003 with substan-
tially higher temperatures and lower than average pre-
cipitation in all parts of the country. Under these condi-
tions, NIR to avoid crop losses increases by a factor of 2
to 3. In a similar study for maize production in France,
the corresponding increase was from 1068 million m3 in
a “normal” year (2002) to 1761 million m3 in 2003 [14].
This leads to the question whether or not such a high
demand for irrigation would exceed the availability of
water that can be abstracted from surface waters, which
is the main source of irrigation water in Switzerland [15].
As in comparable regions, mountains and highlands
provide considerable quantities of water during the grow-
ing season due to high precipitation amounts and snow
and glacier melt water. This water can be abstracted for
irrigation downstream provided that the water in major
river systems and lakes is easily accessible. In the Rhone
catchment where 40% of the discharge is contributed
from alpine runoff [35], highest average NIR in July of
2003 still remains small when compared with the amount
of Rhone river. The discharge value for 2003 differs little
from the average because a higher contribution of melt
water compensates for the lower precipitation input.
Even when assuming a lower efficiency of 75% for
sprinkler irrigation systems [43,44], the supply exceeds
by large the potential demand. Nevertheless, it cannot be
ruled out that limitations in water availability may occur
episodically in smaller sub-catchments. In contrast, in the
absence of glaciers summertime discharge depends di-
rectly on precipitation and to some extent on snowmelt in
spring and thus water in smaller channels becomes lim-
ited during dry years, as recorded in 2003 [45]. An ex-
ample is the Broye catchment where NIR in July and
August of 2003 exceeded to the amount of discharge in
the main river by a factor of 2, even when assuming
100% efficiency thus indicating potential water shortage
during critical parts of the cropping season.
5. Conclusions
1) This study underlines that across areas of topog-
raphically complex pre-alpine landscapes, spatially ex-
plicit application of hydrological models with a high
resolution reveals the distribution of potential irrigation
needs, although absolute estimates of water demand may
be subject to uncertainties in model parameters or soil
input data. Moreover, simulations would benefit from
more detailed crop-specific data and higher resolution
land use statistics.
2) Even in a water-rich country, irrigation require-
ments in drier sub-regions may be substantially higher
than those estimated with larger-scale models, and in the
most extreme years, reflecting more frequent future cli-
matic conditions, amounts of water necessary to maintain
optimal yields are several times higher than in “average”
years. However, no trend towards increased water de-
mand could be detected over the 1981-2010 period.
3) In regions where glacier melt water is absent and
river discharge depends directly on rainfall and spring-
time snowmelt, water demand may exceed the limits of
surface water availability. In such hot-spot areas, im-
proved agricultural water management will be necessary
to cope with future climatic conditions to avoid overdraft
and associated negative impacts on water quality and
6. Acknowledgements
This study was part of the NCCR Climate project
AGRISK funded by the Swiss National Science Founda-
tion, and of the EU-funded project ACQWA. Additional
funding was obtained from the Federal Office of Agri-
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