Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands

Journal of Hydrology (2008) 349, 257– 267

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Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands Bart Kruijt

a,*

, Jan-Philip M. Witte

b,c

, Cor M.J. Jacobs a, Timo Kroon

d

a

Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands Kiwa Water Research, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands c Vrije Universiteit, Institute of Ecological Science, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands d Rijkswaterstaat RIZA, Institute for Inland Water Management and Waste Water Treatment, P.O. Box 17, 8200 AA Lelystad, The Netherlands b

Received 6 May 2007; received in revised form 23 October 2007; accepted 23 October 2007

KEYWORDS Climate scenario; CO2-effect; Evapotranspiration; Stomatal conductance; The Netherlands; Soil moisture; Water management

Summary The extent to which climate change will affect evapotranspiration and water deficits is still uncertain. Temperature increase was recently shown to lead to enhanced drought in the Netherlands. In contrast, experimental evidence shows that elevated atmospheric CO2 concentrations tend to reduce stomatal opening in plants. This leads to lower transpiration rates, although models of atmospheric and soil water feedback show that reductions may be smaller than expected from stomatal closure. We combined the various effects and feedbacks. First, we inferred partial corrections on ‘crop factors’ used in simple evaporation equations such as Makkink’s, for a range of crops and vegetation types in the Netherlands. Second, we applied these corrected factors to infer the likely effects on water deficits in the Netherlands, using a coupled set of hydrological models and national climate scenarios. The combined effects of CO2 on evapotranspiration are generally modest, between a reduction of a few percent for short crops to about 15% for tall, rough vegetation. These reductions are, however, of comparable but opposite magnitude to predicted temperature-induced increases in evapotranspiration. We show that, if combined within the coupled hydrological model, the CO2-effect would lead to a muchreduced desiccating effect of climate change. In general, it is argued that, especially for sub-regional spatial scales and seasonal time-scales, CO2 is likely to be a significant factor in the water balance even of relatively wet regions. ª 2007 Elsevier B.V. All rights reserved.

* Corresponding author. Tel.: +31 317 486440; fax: +31 317 419000. E-mail addresses: [email protected] (B. Kruijt), [email protected] (J.-P.M. Witte), [email protected] (C.M.J. Jacobs), timo.kroon@ rws.nl (T. Kroon). 0022-1694/$ - see front matter ª 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2007.10.052

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Introduction Rising atmospheric carbon dioxide (CO2) contributes to global warming and thus to changes in both precipitation and evapotranspiration (ET). CO2, however, also directly affects the productivity and functioning of plants, stimulating biomass growth while reducing the associated water use through reduced stomatal conductance. For the water balance at regional scales, this raises the question whether CO2 increase will have a net effect of increased runoff or, instead, of increased water shortages. Gedney et al. (2006), in a global statistical analysis of continental runoff data combined with a land surface model, suggested that over the past century increasing CO2 has indeed, through reduced transpiration, on average led to a net increase in runoff and for Europe specifically led to less reduced runoff. In contrast, we can infer from data and a simple model presented by Zhang et al. (2001) that ET in Europe, and especially the Netherlands, with relatively high precipitation rates, would on an annual time scale be mainly determined by available radiation and temperature and hence would be only moderately sensitive to other factors such as atmospheric CO2. This inference does not automatically imply, however, that ET at shorter time scales (such as seasonal ET) is not sensitive to CO2 either, and it is often the seasonal variation in the water balance that is of importance to society or nature. Hydrological effects of CO2 are important, for instance for the Netherlands, where water management is one of the primary issues in regional planning (Kabat et al., 2005). The Royal Netherlands Meteorological Institute KNMI has produced tailor-made scenarios for this country, including projections of evapotranspiration (Ko ¨nnen, 2001; Van den Hurk et al., 2006; Beersma et al., 2004). These scenarios predict wetter winters and dryer summers for the year 2050. Projections of concurrent increases in evapotranspiration are based on the sensitivity of Makkink’s reference evapotranspiration, ETref, to temperature. This quantity may be defined as the potential evapotranspiration of a well-watered and well-fertilised grassland (Makkink, 1957) and is computed as: kET ref ¼ a

s K# sþc

ð1Þ

where k is the latent heat of vaporization of water (J kg1), ETref is the reference evapotranspiration rate (kg m2 s1), s is the slope of the saturation-vapour pressure versus temperature curve (Pa K1), c is the temperature-dependent psychometric constant (Pa K1) and K# the incoming shortwave (global) radiation (W m2). The factor a is an empirical constant (dimensionless), called ‘Makkink’s a’, close to but not equal to unity. In order to apply Eq. (1) to vegetation other than grass, ETref needs to be multiplied with an empirical, vegetation-dependent ‘crop factor’, and several of these have been calibrated for different crops in the past. Eq. (1) shows that under otherwise similar conditions Makkink’s ETref will increase as a result of temperature change. This is the basis of the earlier projections of evapotranspiration changes in the Netherlands. However, direct effects of CO2 – through increased water use efficiency and plant growth – were not taken into account in these

scenarios. Such effects are implicit in the coefficient a of Eq. (1) and in the crop factors, so the coefficients or crop factors need to be recalibrated because of the projected changes in the atmospheric CO2 concentration (Gitay et al., 2002). In this paper we will assess the impact of the direct effect of an increasing atmospheric CO2 concentration on ETref and consequently on ET. Henceforth we will call this the CO2-effect for simplicity. Implications of the CO2-effect for moisture deficit will also be assessed. Moisture deficit is defined as the difference between annual potential and actual evapotranspiration. It is a measure of significant economic and ecological importance in the Netherlands, since crop production and the functioning of many ecosystems are directly related to this measure (De Wit, 1958; Runhaar et al., 1997). The CO2-effect has been studied extensively before in experiments (e.g. Medlyn et al., 1999). Theoretical studies also exist in which the CO2-effect has been studied from a large-scale perspective (Jacobs and De Bruin, 1992, 1997). Here we combine both groups of studies in an attempt to quantify the reduction of ET due to the CO2-effect. This can be viewed as a recalibration of coefficient a in (1) for future atmospheric composition, from results presented in the scientific literature. In addition, we will analyse how both temperature rise and the CO2-effect will affect the amount of soil moisture, available for crop growth, in the Netherlands.

Processes involved in CO2-effect on ET At the leaf level, stomatal aperture tends to reduce in response to increased CO2 concentrations. This effectively reduces water loss associated with CO2 uptake through the same stomata. In this way, water use efficiency (i.e. the proportion of the number of CO2 molecules fixed by the plant to the number of H2O molecules lost by transpiration) is higher. As CO2 increase usually leads to enhanced biomass production, it might be expected that if this enhancement is combined with increased water use efficiency this would lead to a near-zero net direct CO2-effect. After all, a higher leaf area would transpire at a lower rate. On the basis of a review, however, Bunce (2004) concludes that in most studies enhanced CO2 seldomly led to increased leaf area indices (LAI), except where ventilation was artificial, as in many chamber and greenhouse studies. In addition, he argues that increases of LAI above 3–4 m2 m2 hardly affect ET as a result of mutual shading, soil shading and increased canopy humidity. His review was based mainly on studies of crops, which are generally not nutrient-limited. Natural ecosystems can be expected to respond even less, because nutrients often limit their productivity and hence their responsiveness to CO2 (Ainsworth and Long, 2005). Following Bunce (2004) the LAI-effect of CO2 on ET will therefore be ignored. This assumption also implies that any relative changes in response to atmospheric CO2 in total conductance of a vegetation (‘crop conductance’) can be assumed similar to changes in stomatal conductances in this vegetation or crop. A rather poorly understood effect is, that temperature also affects stomatal closure, in a non-linear way: either

Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands or not linked to similar dependence of photosynthesis on temperature, there is usually an optimum temperature around the mean growing season value, above and below which conductance decreases (Jarvis, 1976). Moreover, transpiration T itself affects leaf temperature: temperature increase above the optimum leads to reduced transpiration, which in turn increases temperature and hence leads to progressive stomatal closure. Below the optimum, T reductions are limited by the same mechanisms (Jacobs and De Bruin, 1997). Because of the uncertainties associated with these temperature effects, we will ignore them in this study. Over seasonal time scales, reduced ET leads to less depletion of soil water, hence less water stress on ET and growth and thus less reduced (or more sustained) ET. This is an important feedback effect, especially in drier climates and with natural vegetation, where harvesting does not limit the growth period. This feedback will not be considered in our quantification of the direct CO2-effect, since the first step in this study is confined to potential ET, which is the ET of a well-watered vegetation. When we compute the effect of CO2 level rise on crop production, however, this feedback will be explicitly taken into account by applying a model in which the availability of soil moisture in the root zone is coupled to plant growth. Both photosynthesis and evapotranspiration directly depend on CO2 and water vapour concentrations in surrounding air, through the gradient from leaf to air. Photosynthesis depletes, and evapotranspiration enriches the air with CO2 and water vapour, respectively. Hence if mixing of air is limited (such as in a short, smooth vegetation with a relatively shallow atmospheric boundary-layer) there is a negative feedback that acts to limit the effects of CO2 both on photosynthesis and on T, which we will consider in this study (Jacobs and De Bruin, 1997). In a more general sense, this feedback effect affects the degree to which the surface evapotranspiration is ‘coupled’ to the atmosphere, where with strong coupling ET is more sensitive to stomatal conductance than with weak coupling (Jarvis and Mcnaughton, 1986; Mcnaughton and Jarvis, 1991).

Estimation of the CO2-effect General approach In several applied hydrological models, ETref is multiplied by a vegetation specific factor f (‘crop factor’) to yield the potential evapotranspiration ETp of the vegetation (Feddes, 1987) ET p ¼ f  ET ref

ð2Þ

Variables such as leaf area, vegetation structure, and, in case of natural vegetation, nutrient limitation are accounted for by crop factors. These are essentially implicit functions of surface conductance (stomatal conductance at canopy scale) and air humidity (De Bruin, 1983). To account for the CO2-effect we multiply the right-hand term in Eq. (2) with a correction factor c: ET p ¼ c  f  ET ref ð3Þ We will compute CO2-dependent, vegetation-specific multipliers c from three factors, relating to stomatal conductance, boundary-layer properties and transpiration share in total evapotranspiration, as follows:

c ¼ Sgs  ST  F T  DCO2

259 ð4Þ

where DCO2 denotes the change in atmospheric CO2 concentration (ppm), and Sgs (ppm1) is the sensitivity of gs (stomatal or crop conductance, mol m2 s1) to CO2, defined as the relative change of gs with an increase of CO2 Sgs ¼ ðdgs =gs Þ=dCO2

ð5Þ

ST () is the relative sensitivity of transpiration T (kg m2 s1) to gs ST ¼ ðdT=TÞ=ðdgs =gs Þ

ð6Þ

and FT () is the transpiration share of evapotranspiration (FT = T/ET). We will quantify Sgs, ST and FT in the successive sub-sections. First, we will use a literature review of experimentally observed plant response to elevated CO2. This will give us estimates of Sgs. Then we will derive ST from results presented by Jacobs and De Bruin (1992), who used processbased models of transpiration coupled to a detailed model for the atmospheric boundary-layer to assess the feedbacks between vegetation and the atmosphere on large-scale transpiration. Finally, FT will be derived from simulations with the coupled soil–water–atmosphere-plant model SWAP (Kroes et al., 2000).

Sensitivity of gs to CO2 (Eq. (5)) The decrease in stomatal conductance with increased CO2 levels has been described in numerous experiments. Most of these make use of open-top chambers in which plants are exposed to higher CO2 concentrations for a long time while temperature, humidity and radiation are kept in line with the outside air (Medlyn et al., 1999). Such chambers must be well-ventilated, which means that there is no vertical humidity gradient such as occurs normally in crops and vegetation. Measurements of evapotranspiration in such chambers are made with either small cuvettes for gas exchange, measuring leaf transpiration and stomatal conductance, or by monitoring the water balance, or using a lysimeter. In all cases, an evapotranspiration measurement is obtained with high aerodynamic conductance relative to stomatal conductance. Therefore, while the observed stomatal responses are useful, the measured changes in evapotranspiration are not representative for a change in evapotranspiration in the field. A number of measurements were taken in FACE experiments, in which CO2 concentrations were raised in the open field or even in a forest, by releasing large amounts of CO2 into the area over a long period (Ainsworth and Long, 2005). Evapotranspiration readings from lysimeter under such conditions are scarce, but they do give a better direct measurement of the projected changes in evapotranspiration, even though there may still be an overestimation of the CO2-effect, due to artificial ventilation and advection from outside the FACE area. We investigated a number of studies in detail, including already existing reviews and excluding studies based on chamber evaporation measurements. We also excluded results associated with water stress. Published results for

260 Table 1

B. Kruijt et al. Observed effects of CO2 increases on conductance gs

Vegetation/species

[CO2] ppm

Dgs =gs (%)

(%)

References

Crop height (cm)

Photosynthesis type

Potato Potato Alfalfa Birch Birch Beech Beans Trees Brassica campestris Brassica carinata Brassica juncea Brassica nigra Douglas fir Oak Alder Alder Forb Barley Barley Grass Grass Grass Peat bog Young tree Legume Lolium perenne Maize Aspen poplar Soy bean Soy bean Shrub Wheat Adult trees Winter wheat Summer wheat Summer wheat

680 700 700 700 800 700 700 550 600 600 600 600 550 700 600 900 550 680 700 550 550 700 560 700 550 700 680 560 680 700 550 680 700 700 550 600

59 32 15 10 25* 12 38 15.9 41 8.3 20 28 40 30 29 43 18.7 52 33 22.2 24.9 33 25* 25 22.9 20* 37 30 23 25* 11.6 22 9 21* 30 17

6 30

Cure and Acock (1986) fide Bunce (2004) fide Bunce (2004) Beerling et al. (1996) Wayne et al. (1998) Beerling et al. (1996) fide Bunce (2004) Ainsworth and Long (2005) Mishra et al. (1999) Mishra et al. (1999) Mishra et al. (1999) Mishra et al. (1999) Apple et al. (2000) Beerling et al. (1996) Liang et al. (1995) Liang et al. (1996) Ainsworth and Long (2005) Cure and Acock (1986) fide Bunce (2004) Ainsworth and Long (2005) Ainsworth and Long (2005) fide Bunce (2004) Heijmans et al. (2001) Medlyn et al. (1999) Ainsworth and Long (2005) Schapendonk et al. (1997) Cure and Acock (1986) Noormets et al. (2001) Cure and Acock (1986) Serraj et al. (1999) Ainsworth and Long (2005) Cure and Acock (1986) Medlyn et al. (1999) Dijkstra et al. (1999) Hunsaker et al. (2000) Agrawal and Deepak (2003)

30 20 50 1000 100 1000 50 1000 20 20 20 20 150 1000 150 150 50 100 50 20 50 20 20 150 50 20 200 200 50 50 200 50 2000 50 50 50

C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C3 C4 C3 C3 C3 C3 C3 C4 C3 C3 C3 C3 C3 C3 C3 C3 C3

10 10 10 2.4

43 10

5.1 30 8 5 7.2

3 4.1 3.5 1.5 3.9 15 5

Numbers marked with an asterisk (*) are derived from evapotranspiration measurements (Witte et al., 2006). Where possible an estimate of standard error is given.

CO2-sensitivity in conductance are presented in Table 1 and in graphical form in Fig. 1, including an error estimate (standard error), where this was available. A regression line is fitted and approximate 95% confidence intervals are given. Note, that, as stated in Section ‘Processes involved in CO2-effect on ET’, sensitivity of stomatal and crop conductance are assumed equal here. According to these data, with every 1 ppm increase in atmospheric CO2 concentration, under well-watered conditions, stomatal conductance and crop conductance changes on average with Sgs = 9.3 · 102% ± 1.5 · 102% for grass and herbal crops; Sgs = 6.8 · 102% ± 2.5 · 102% for woody crops and trees; Sgs = 11.8 · 102% ± 1.0 · 102% for C4 crops (based on very few observations).

Sensitivity of T to gs (Eq. (6)) As stated before, transpiration does not respond linearly to changes in stomatal conductance. Rather, this sensitivity depends on the degree to which the evaporation increases the humidity in the immediate surroundings of the leaves. This in turn depends on the rate at which the atmospheric boundary-layer air is mixed (turbulence) and on the depth of this boundary-layer (typically between a few hundred and 2000 m). We have drawn up Fig. 2 from Jacobs and De Bruin (1992), who studied ST theoretically for a range of surfaces and boundary-layer conditions. It shows that in the first place the sensitivity of T to gs depends on the aerodynamic conductance, or the roughness of the surface. In the second place, the presence of a boundary-layer reduces the sensitivity to gs. Finally, the figure shows that this sensitivity depends on the value of gs itself: with higher conductance, the sensitivity is lower, because the coupling to the

Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands

261

Change in stomatal/crop conductance (%)

0

-20

-40

-60 grass and C3 crops trend 95% confidence interval -80 100

200

300

400

CO2 rise (ppm )

trees 500trend 600 95% confidence interval C4 crops trend 95% confidence interval

Figure 1 Observed effects of increased CO2 concentrations (ppm) on change in crop conductance gs ( change in stomatal conductance), and regression lines with 95% confidence intervals. Data points from Table 1.

From Jacobs and De Bruin (1992) it appears that, within these two ranges, the lower percentage represents cases in which the boundary-layer plays a bigger role, and the higher percentage a highly advective situation, where humidity is mainly affected by air transported from elsewhere.

The transpiration share FT

Figure 2 The sensitivity of the transpiration to crop conductance gs under conditions such as described by Jacobs and De Bruin (1992). Re-plotted to typical values using data from this publication.

atmosphere is relatively weaker. Well-irrigated and nonstressed vegetation has a relatively high stomatal conductance. For this study, we can therefore use sensitivity ranges from Fig. 2 that are on the high side of gs ST = [0.15  0.20] for a smooth surface, such as short grass; ST = [0.40  0.75] for a rough surface, such as forest.

The transpiration share FT is mainly determined by vegetation cover. It represents the fraction of evapotranspiration that is transpired. This share may vary with the seasons, depending on vegetation cover. In winter, fields usually lie fallow so that FT  0. In winter, potential evapotranspiration is also low so that ET is less sensitive to FT. On the other hand, grasslands have a high cover throughout the year and, with that, FT is permanently high too. With the integrated one-dimensional soil–water–atmosphere–plant model SWAP (Kroes et al., 2000; Van Dam, 2000) we calculated for 10 successive years (1980–1990) and on a soil without moisture stress, the FT of four crops: potato, maize, grassland and wheat. Table 2 gives the average resulting FT-values for the summer and winter

Table 2 Mean transpiration share FT of four crops, for the whole year, summer season (decade 9–27 = 1 April–30 September), and winter season (decade 1–8 and 28–36)

Year Summer Winter

Potatoes

Grass

Maize

Wheat

0.61 0.81 0.06

0.81 0.81 0.80

0.63 0.79 0.15

0.49 0.67 0.02

Computed with SWAP for representative meteorological conditions in the Netherlands (Witte et al., 2006).

262

B. Kruijt et al.

season. On the basis of this, we define the following two vegetation categories: (1) Grasslands and other short, permanent cover vegetation (such as heathland), with an FT = 0.8 throughout the year, (2) Agricultural fields and other deciduous vegetation (such as deciduous forests and gardens), with FT = 0.6 on average, FT = 0.8 in the summer season and FT = 0.1 in the winter season. Also evergreen conifers (such as spruce) are in this category because their high interception loss in winter causes a low FT.

Overall change in ET Substitution of sensitivities Ssg and ST (Sections ‘Sensitivity of gs to CO2’ and ‘Sensitivity of T to gs’) and the transpiration shares FT (Section ‘The transpiration share FT’) in Eq. (4), enables us to draw up Table 3. The results presented in Table 3 apply to projected rise of atmospheric CO2 concentrations in 2050 and 2100 of 150 and 385 ppm, respectively (Gitay et al., 2002). Using Eq. (4), the factors c presented in Table 3 can be used to correct the potential evapotranspiration for these years. The factors represent a mixture of experimental and theoretical results, and are limited to direct effects of CO2. In addition, other considerations were made to assign correction factors to the four vegetation categories of Table 3 (Witte et al., 2006).

Effects of climate change and CO2 concentration rise on soil moisture in the Netherlands Hydrological models used In this section we will estimate the effect of increased atmospheric CO2 on moisture availability. To this end we used hydrological models for the unsaturated zone that require precipitation P and reference evapotranspiration ETref, corrected for the CO2-effect according to the previous section. We used three nation-wide hydrological models to simulate the annual transpiration deficits in the Netherlands under various climate scenarios. These models are: (1) NAGROM, a distributed groundwater model, (2) MOZART, a one-dimensional soil moisture model and (3) DM, a surface water distribution model. The soil–water–atmosphere–plant model MOZART (Vermulst et al., 1998) is a one-dimensional model, which simulates vertical transport of water in the unsaturated zone. For application on a national scale, a pseudo-steady-state approach has been developed, solving Richard’s equation with a time step of 10 days for plots of 500 · 500 m. Soil moisture characteristics and soil water retention curves

Table 3 Proposed factor c for the correction of potential evapotranspiration ETp (according to Eq. (3)) for 2050 and 2100 (rise of CO2 concentration of 150 and 385 ppm, respectively) (Presented are the estimated minimum (min), mean and maximum (max) numbers for evapotranspiration reduction) b Vegetation

2050

2100

Min

Mean

Max

Min

Mean

Max

Year 1. Grasslands, dry & nutrient-poor reservesa 2. Deciduous, shrubs, C4 crops 3. Other fields, conifers 4. Other nature reserves

0.99 0.98 0.98 0.97

0.98 0.96 0.97 0.96

0.97 0.94 0.95 0.94

0.96 0.96 0.95 0.93

0.95 0.91 0.92 0.89

0.93 0.84 0.88 0.84

Summer 1. Grasslands, dry & nutrient-poor reservesa 2. Deciduous, shrubs, C4 crops 3. Other fields, conifers 4. Other nature reserves

0.99 0.98 0.97 0.97

0.98 0.95 0.96 0.96

0.97 0.92 0.94 0.94

0.96 0.95 0.93 0.93

0.95 0.88 0.89 0.89

0.93 0.78 0.84 0.84

Winter 1. Grasslands, dry & nutrient-poor reservesa 2. Deciduous, shrubs, C4 crops 3. Other fields, conifers 4. Other nature reserves

0.99 1.00 1.00 0.97

0.98 0.99 0.99 0.96

0.97 0.99 0.99 0.94

0.96 0.99 0.99 0.93

0.95 0.99 0.99 0.89

0.93 0.98 0.98 0.84

a

Dry dunes, dry heath. Further explanation of categories: The estimates for Categories 1 and 2 are directly based on Eq. (4). Although nutrient-poor and dry nature reserves have, on average, a higher aerodynamic roughness than grasslands, they are assigned to Category 1 because nutrient-poverty tempers evapotranspiration reduction. Maize is assigned to Category 2. This crop is moderately rough, aerodynamically, so less rough than deciduous forest, but, on the other hand, the C4 photosynthesis increases the sensitivity of maize to CO2. ‘Other fields’ have a season dependent transpiration share in the evapotranspiration. Aerodynamically speaking, they are ‘moderately rough’. The estimates are averages from the calculations for ‘smooth’ and ‘rough’ grasslands. Coniferous trees are assigned to Category 3 because of their low transpiration share, which is caused by a high interception rate. Category 4 received the same transpiration share as that of grassland (80%), but an aerodynamic roughness just between ‘smooth’ and ‘rough’. The role of advection can be accounted for by varying ST. b

Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands were taken from Wo ¨sten et al. (2001) and derived from the 1:50,000 soil map of the Netherlands. Fifteen crop types were defined to simulate crop development and transpiration. As groundwater levels in the Netherlands are generally shallow, capillary rise to the root zone is an important source of water for plants. Drainage of groundwater to local surface waters is a function of groundwater level relative to surface water level (Fig. 3). MOZART accounts for two feedbacks between soil moisture and vegetation that can temper effects of climate change on ET. Crop development can be hampered under conditions of moisture deficit, saline damage or water logging. Reduction of transpiration is calculated according to Feddes (1987) as a function of both pressure head and saline concentration in the root zone. The lower boundary condition of MOZART is an annual seepage flux, taken from the national steady-state groundwater model NAGROM (De Lange, 1991, 1996), based on the analytical element method (Strack, 1989). A Cauchy upper boundary condition is used to describe the relation between the regional aquifer and the drainage system. MOZART and NAGROM are coupled through an interface, based on similarities in the schematizations (Vermulst et al., 1998). In an iterative procedure, common model parameters are adjusted so that the average groundwater recharge of MOZART equals the groundwater recharge of NAGROM. MOZART is also coupled to the surface water distribution model DM, which simulates the major components of the surface water management system in the Netherlands, such as rivers and canals (Anon, 1993). For each time step, MOZART determines the water demand for each district, while DM simulates the distribution in the network and calculates the supply of surface water to each district iteratively.

Climate scenarios Three scenarios are considered to account for the bandwidth of future projections of climate change (Ko ¨nnen, 2001), see Table 4. Taken from these, the scenarios Controller and Environmentalist are based on projections by the International Panel for Climate Change Third Assessment Report (IPCC, Gitay et al., 2002). Compared to the regional projections made by the more recent IPCC Fourth Assessment Report (AR4 Working group 1, IPCC, 2007) these scenarios correspond roughly to the ‘minimum’ and 75% quartile (25% of models predict higher temperatures) of global models. The Dry scenario is based on simulations with regional climate models, and corresponds more to changes projected for the Mediterranean in IPCC AR4. Within each scenario, we distinguished two weather years: an average year and an extremely dry year (return period: 2.2 and 110 years, respectively). These weather years have been selected from historical time series of precipitation, ETref and discharges of the Rhine River (Beersma and Buishand, 2004; Beersma et al., 2004).

Results Fig. 4 shows a map of the proportional change in moisture deficit under the Environmentalist scenario in an average weather year (proportional to the present situation). In the left-hand panel of Fig. 4 only the effect of a higher temperature on ETref is taken into account while the righthand panel of Fig. 4 includes the CO2-effect. Results, averaged over the country, for all three scenarios and two contrasting weather years are summarized in Table 5. The CO2-effect appears to considerably counter-balance the

MOZART plot

Drainage systems

263

Drainage relation

Evapotranspiration Precipitation Primary

Drainage q

drainage system

Secondary

Root zone

drainage system

Tertiary drainage system

Capillary rise Groundwater table

Tertiary

Secondary

Subsoil

Primary

Seepage s

Groundwater depth

Figure 3 Plot-schematization of the subsoil and drainage system of MOZART. Drainage is a function of groundwater depth relative to surface water level. Surface water level is computed with the surface water distribution model DM, seepage is computed with the national groundwater model NAGROM.

264 Table 4

B. Kruijt et al. Characteristics of climate scenarios for 2050 (Beersma and Buishand, 2004) Scenario Controller

Environmentalist

Dry scenario

Source of information Temperature

IPCC central estimate +1 C

IPCC over-estimate +2 C

KNMI regional model +2.3 C

Precipitation P Year Winter Summer ETref (year) Sea level Rhine discharge (summer)

+3% +6% +1.4% +4% +25 cm 5%

+6% +12% +2.8% +8% +45 cm 11%

4% +13% 20% +19% +45 cm 27%

Predicted changes in ETref based on rising temperatures, not accounting for the CO2-effect. Winter = December–February; summer = June–August.

Figure 4 Proportional change in moisture deficits (mm/year), (positive values: increase of deficits) under the Environmentalist scenario in an average year (Table 4). In the left-hand figure, only the effect of temperature rise on ETref is taken into account; in the figure on the right, also the CO2-effect on ETref is accounted for. Changes relative to current deficits.

Table 5 Predicted effect for three scenarios (Table 4) and for two weather years (Beersma et al. (2004)) of climate change on average moisture deficits (mm/year) in the Netherlands, without (Dt, only temperature effect) and with (Dt + CO2) accounting for the CO2-effect on ET Weather year

Average Extremely dry

Present

19 () 137 ()

Controller

Environmentalist

Dry scenario

Dt

Dt + CO2

Dt

Dt + CO2

Dt

Dt + CO2

22 (+16%) 149 (+9%)

18 (8%) 134 (2%)

25 (+33%) 161 (+17%)

20 (+6%) 144 (+5%)

68 (+256%) 242 (+76%)

58 (+203%) 222 (+62%)

Numbers shown are absolute deficits (mm/year). Between brackets: change relative to the present situation (%).

Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: A practical approach for the Netherlands Proportion of the Netherlands (%)

265

Proportion of the Netherlands (%)

50

40

40 Δt Δt + ΔCO2

30

Δt Δt + ΔCO2

30 20 20 10

10

0 < -5

-5 - 0

0

0-5

5 - 10 10 - 25 > 25

Drop of higest groundwater level (cm)

0 < -5

-5 - 0

0

0-5

5 - 10 10 - 25 > 25

Drop of lowest groundwater level (cm)

Figure 5 Impact on the distribution over all model grid points of the average-year Environmentalist scenario (Table 4) on the highest (left) and on the lowest (right) groundwater levels in the Netherlands, without (Dt, only temperature effect) and with (Dt + DCO2) accounting for the CO2-effect on ET. Highest/lowest groundwater level is defined here as the average of three highest/ lowest levels in a year. In the Netherlands, high groundwater levels occur in winter and low groundwater levels in summer.

effect of higher temperatures, especially in the Controller and the Environmentalist scenarios. Changes in precipitation and evapotranspiration affect the amount of groundwater recharge and thus both groundwater levels and groundwater flow. Groundwater levels relative to the root zone determine the amount of capillary rise, which in the Netherlands is often an important source of water to the plants in dry periods. These effects of climate change are accounted for in our hydrological models. Climate change will, on average, lead to lower summer groundwater levels and, except for the Dry scenario, to higher winter groundwater levels. As shown in Fig. 5, the CO2-effect appears to considerably reduce the groundwater drop in summer (lowest groundwater level in Fig. 5); while the effect on groundwater levels in winter (when ET is near zero) is very small (highest groundwater level in Fig. 5).

Discussion and conclusions Direct effects on evapotranspiration Overall, the results of this study suggest that direct effects of CO2 reducing evapotranspiration can be expected to be moderate, up to 5% in the coming 50 years and up to 15% by 2100, with relatively stronger effects in summer and in rougher, natural vegetation such heath lands and (deciduous) forests. These effects are, however, of similar order and opposite to the previously projected effects of temperature on ET (Beersma et al., 2004), implying considerable tempering of net climate effects on the water balance of the Netherlands. To compare, the IPCC AR4 states that, for north-western Europe, the various models disagree on whether there will be decreases or increases in summer droughts (IPCC, 2007; Wang, 2005). In this study we ignored a number of feedbacks between soil, plant and atmosphere. The most obvious one is the direct effect of CO2 on biomass and leaf area, potentially enhancing transpiration. We argued in the introduction,

however, that present state of the art does not suggest this is an important effect. Other feedbacks have been described, and include complex air and leaf temperature and humidity interactions that have been poorly studied. These potential effects require more (experimental) studies. The use of crop factors (f, Eq. (2)) is disputable because they assume each crop to behave similarly everywhere and any time, independently of the weather and climate, as they are empirically determined average correction factors. Weather patterns in reality affect f, as well as the degree to which the atmospheric boundary-layer exerts an influence on feedbacks. For the constant a in Makkink’s equation (Eq. (1)) similar arguments apply as it depends on the meteorological conditions, such as depths of the atmospheric boundary-layer and relative importance of advective conditions during its calibration (De Bruin, 1983). Especially when projecting ET into a future climate this approach is likely to be biased, and a and f might need to be recalibrated. In this study we have effectively adjusted these constants, but only indirectly and only for the CO2-effect. These disadvantages of the empirical approach, however, are also strengths: the factors f and a provide a summary of all the atmospheric feedback effects that are usually ignored or poorly modelled when more mechanistic equations are used for surface–atmosphere exchange (such as using only the Penman–Monteith equation; Monteith, 1981). Furthermore, their definition allows efficient use of current hydrological models to assess effects at regional scales. New climate scenarios for the Netherlands (van den Hurk et al., 2006) account for the possibility of changes in weather regime, in which the relative effect of advective conditions and boundary-layer depths might change. To thoroughly understand the fully coupled system and the role of CO2 there is a need for studies using a meso-scale meteorology model, coupled to regional hydrology and seasonal vegetation and crop development. The need for such studies is also stressed by global modelling communities (Alpert et al., 2006). Such a model should then be applied under a range of climate scenarios. New ‘Makkink’ constants (or

266 advances on the correction factors presented here) could eventually be ‘calibrated’ from results of such studies. Despite the relationships shown by Zhang et al. (2001) implying that evapotranspiration should be only moderately sensitive to plant functioning and the consideration from basic relationships that in conditions of water surplus ET should only depend on the available radiative energy, we show that ET, in particular T, is sensitive to plant responses to CO2. First, it is implicit in the Makkink equation (Eq. (1)) that ET depends on stomata and leaf area (De Bruin, 1983), second, the evaluations of boundary-layer effects (Jacobs and De Bruin, 1992) do diminish the sensitivity, but do not completely eliminate it. Finally, it should be realised that it is the transpiration component that is sensitive, mainly during summer, when water deficits do exist. Rates at a particular time of the year, at a particular place, can be reduced but as this leads to lower water deficits, this can be compensated for by higher rates elsewhere and by higher evaporation from soils or open water. Thus, relationships such as shown by Zhang et al. (2001) should be considered with caution at smaller temporal and spatial scales than a year and a region, respectively. The review of experimental results regarding the CO2-effect (Fig. 1, Table 1) suggests that stomatal conductance of shorter vegetation (less coupled to atmosphere) responds stronger to CO2. This may be because, if it is true that plants develop strategies to optimize water use efficiency, the higher boundary-layer (or aerodynamic) resistances of short vegetation counteract the effects of stomatal closure. Increased water use efficiency may, for this reason, only be achieved with stronger CO2 responses. This may even be an evolutionary trait and not simply an acclimation phenomenon because the stronger stomatal responses are also being observed in controlled environments (where the boundarylayer feedback does not exist).

Likely effects of CO2 rise on soil moisture in the Netherlands The CO2-effect is largely based on international studies and generic coupled modelling of the surface and atmospheric boundary-layer for temperate zone vegetation. Therefore the effect may be assumed to apply generally. The projections for water deficits in the Netherlands, however, depend on the regional climate scenarios and hydrological conditions and may only apply for this country or lowland Western-Europe. Our results agree reasonably well with those of Gedney et al. (2006), and as already stated, models in the IPCC AR4 do not, on average, predict important changes in soil moisture (Wang, 2005) either. For other continents, the balance of CO2-effects and other climate effects is likely to be different. The projected effects of climate change on moisture deficits in the Netherlands show that these are strongly sensitive to the CO2-effect on transpiration of crops and natural vegetation. Thus, given the corrections to crop factors, implied in Table 3, the CO2-effect is likely to result in larger ground water storage than expected in current climate scenarios, including the updated scenarios of KNMI (Van den Hurk et al., 2006). In that case, in such scenarios the damage from deficits during dry periods will be overes-

B. Kruijt et al. timated, and damage from too shallow groundwater during wet periods may be (slightly) underestimated.

Acknowledgements We are indebted to helpful suggestions by Adrie Buishand and Bart van den Hurk of KNMI, Netherlands, and by Han Dolman, of Vrije Universiteit, Amsterdam. This study was supported by Kiwa Water Research and RIZA, Netherlands.

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