HYDROLOGICAL PROCESSES Hydrol. Process. 28, 4110–4118 (2014) Published online 19 July 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.9949
Exploring hydroclimatic change disparity via the Budyko framework Ype van der Velde,1,2,3* Nikki Vercauteren,2,3 Fernando Jaramillo,2,3 Stefan C. Dekker,1 Georgia Destouni2,3 and Steve W. Lyon2,3 1
Department of Environmental Sciences, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands 2 Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, Sweden 3 Bert Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Abstract: The Budyko framework characterizes landscape water cycles as a function of climate. We used this framework to identify regions with contrasting hydroclimatic change during the past 50 years in Sweden. This analysis revealed three distinct regions: the mountains, the forests, and the areas with agriculture. Each region responded markedly different to recent climate and anthropogenic changes, and within each region, we identified the most sensitive subregions. These results highlight the need for regional differentiation in climate change adaptation strategies to protect vulnerable ecosystems and freshwater resources. Further, the Budyko curve moved systematically towards its water and energy limits, indicating augmentation of the water cycle driven by changing vegetation, climate and human interactions. This finding challenges the steady state assumption of the Budyko curve and therefore its ability to predict future water cycles. Copyright © 2013 John Wiley & Sons, Ltd. KEY WORDS
hydroclimatic change; Budyko; water and energy balance; Sweden
Received 21 December 2012; Accepted 19 June 2013
INTRODUCTION As humans alter landscape, vegetation, climate and atmospheric composition, changes in the terrestrial water balance and fresh water resources are likely to occur. However, the complexity and multitude of interactions between vegetation, atmosphere and soil (Brutseart, 1982) prevent an unambiguous mechanistic quantification of human-induced effects on the hydrological cycle (Wagener et al., 2010; Sivapalan et al., 2011). This has led to the search for empirical organizing principles (Huxman et al., 2004; Troch et al., 2009; Schaefli et al., 2011) that summarize the effects of these complex interactions into emerging general relationships. As our available records of observational data mature, it becomes feasible to map out historic trajectories of change (e.g. Brutsaert, 2008; Destouni et al., 2013) and to evaluate empirical organizing principles under changing climatic an anthropogenic conditions (Renner and Bernhofer, 2012; Williams et al., 2012). In this paper, we focus our analyses on the Budyko framework (Budyko, 1958, 1974). The Budyko framework describes an empirical global relationship between the *Correspondence to: Ype van der Velde, Department of Environmental Sciences, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands. E-mail:
[email protected]
Copyright © 2013 John Wiley & Sons, Ltd.
evaporative index and climatic dryness (or aridity) index, in which the evaporative index is defined as the ratio between actual evapotranspiration (AET) and precipitation (P), and the dryness (or aridity) index is defined as the ratio of potential evapotranspiration (PET) to P. This relationship implies that for long timescales (centuries and longer), the equilibrium water balance is primarily constrained by water availability and atmospheric demand (Zhang et al., 2008). Although several studies have proposed expressions for this equilibrium (Zhang et al., 2004 and references therein), we refer to it as the ‘Budyko curve’ for simplicity and on the basis of the seminal work of M. I. Budyko (i.e. Budyko, 1958, 1974). Furthermore, we refer to the plot of the evaporative index versus the climatic dryness index as the ‘Budyko framework’. All interactions and feedbacks through the hydrologic cycle between vegetation, soil and atmosphere together create the Budyko curve, but the underlying mechanisms that cause this empirical equilibrium remain debated (Milly, 1994; Zhang et al., 2004; Donohue et al., 2007, 2010; Gerrits et al., 2009). Therefore, Schaefli et al. (2011) refer to the Budyko curve as an empirical organizing principle. Because of recent increased focus on the effects of global change on water resources (Sivapalan et al., 2011), the Budyko curve has seen a renaissance in hydrological research. For example, recent work by Donohue et al. (2011),
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Roderick and Farquhar (2011) and Renner et al. (2012) used the Budyko framework to assess the sensitivity of river discharge to climatic change. Donohue et al. (2011) identified potential spatial variability of runoff sensitivity in Australia's Murray–Darling Basin that could directly help water managers and policymakers to target planning activities that seek to mitigate potential effects of a changing climate on water resources. Other studies have used the Budyko framework to quantify the relation between land cover and evapotranspiration (Zhang et al., 2001; Oudin et al., 2008) or to disentangle effects of climate and human-induced change on catchment water balances (Wang and Hejazi, 2011; and Renner and Bernhofer, 2012). On the basis of these studies, we expect that under natural conditions (i.e. without climate change or human influence) and in the relatively short timescales considered in this study ( 1) is likely to increase AET (large ecosystem response), whereas discharge is likely to increase in an energy-limited environment (PET/P < 1; small ecosystem response). The ecosystem response to a change in energy availability, PET, is exactly opposite. If we thus regard the trajectory in Budyko space as the ecosystem response to changes in the water cycle, as described by Jones et al. (2012), the nonlinearity of the Budyko space reflects the nonlinear response of ecosystems to a change in water and/or energy availability. The trajectories in Budyko space are characterized by a direction and magnitude of change over the period considered. The direction of change is calculated by the equation: " # Δ AET Δy P (1) ¼ arctan PET α ¼ arctan Δx Δ P ΔAETP AETΔP ¼ arctan ΔPETP PETΔP where AET [mmY1] is the average AET over the period, and ΔAET [mmY2] is the trend in AET over the period. Similarly, PET [mmY1], ΔPET[mmY2], P [mmY1] and ΔP[mmY2] are the average PET and trend in PET over the period, the average P and trend in average P over the period, respectively. Correspondingly, the magnitude of change, β [Y1], in Budyko space is calculated by the equation: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 ΔAETP AETΔP ΔPETP PETΔP β¼ þ (2) 2 2 P P
Both the direction and the magnitude of observed change are plotted in Budyko space (Figure 3) and projected into ‘real’ space across Sweden (Figure 4). RESULTS AND DISCUSSION Change trajectories across Sweden
The majority of locations in Sweden move closer to the theoretical water and energy limits except for the yellow points in the lower left corner in Budyko space, which seem to diverge from the centre of the point cloud (Figures 3 and 4). These yellow points belong to the mountain regions, and their behaviour will be discussed in the next section. Following from our expectation that ‘natural’ catchments move in all directions through Budyko space, the discharge regionalization approach was expected to yield no systematic Sweden-wide changes in Budyko space. Hence, the emerging systematic movement of Sweden in Budyko space towards the theoretical water and energy limits (Figure 3) indicates that Sweden is neither necessarily natural in its change trajectory nor necessarily stationary with regards to water cycling. The emergence of oriented trajectories across Sweden signifies that all regions in Sweden are affected by a single overarching and long-term change. Sweden moving closer to the theoretical limits in the Budyko framework also indicates that the Swedish landscapes are becoming more efficient at using available water (i.e. the fraction of energy turned into heat and water turned into river runoff is decreasing). Because this behaviour is accompanied by an increase in precipitation in most regions (Figure 2), it implies that the cycling of water through the Swedish landscape has accelerated. In addition to Sweden becoming warmer and wetter in the past 50 years (Figure 2), we find that the water use of Swedish ecosystems diverged. Energy-limited environments have
Figure 3. The direction of change of Sweden (A) and the magnitude of change (B) in Budyko space during period 1961–2010. The red lines represent the energy limit (no more energy can be consumed than is available) and the water limit (no more water can be consumed than is available)
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A
B
Figure 4. The direction (A) and magnitude (B) of the trajectories in Budyko space plotted across Sweden. The direction of the arrows in Figure A corresponds to the direction of colours and the length of the arrows corresponds to the magnitude of change (Figure B)
tended to become more energy limited (the yellow and orange points), which resulted in higher river discharges. In contrast, regions with sufficient energy have increased their ability to use the water available (the blue points, Destouni et al., 2013), mainly through improved agricultural productivity (Jordbruksverket, 2011). When we compare our results with the trajectories in Budyko space reported by Destouni et al. (2013) for several large catchments in Sweden, we see marked differences for the catchments in northern Sweden with relatively low evaporative indices. These differences are a result from the downscaling approach. The large northern catchments include many different types of landscapes ranging from high mountains to wetlands and coastal zones. Therefore, given the nonlinearity of the Budyko space, the overall catchment trajectory is quite different from the trajectories of the individual landscape types on which we focus in this study. The few points that are just above and on top off the energy limit line in Figure 3 correspond to open water surfaces. When we consider that independent datasets are used for each of the axes (i.e. discharge on the y-axis and PET estimates from temperature and incoming radiation on the x-axis), the positioning of open water surfaces on top of the energy limit, where they theoretically should Copyright © 2013 John Wiley & Sons, Ltd.
be, indicates that the different datasets correspond well to each other. We used the Priestley–Taylor approach to calculate trends in PET, which ignores trends in wind speed and vapour pressure. Donohue et al. (2010) showed that including wind speed in PET calculations significantly reduces trends in PET compared with that of the Priestley–Taylor derived PET, caused by a significant decline in global wind speed above land during the past 50 years. Accounting for this decrease in wind speed in our study would result in even larger differences between AET and PET, reinforcing the observed Budyko trajectories. Our use of the Priestley–Taylor approach also explains why the points in the lower left corner of Figure 3 do not coincide with the energy limit. These points belong to strongly energy-limited environments. Under these conditions, melting of snow, heating of the soil and a short growing season of vegetation cause AET to be smaller than PET. The Priestley–Taylor approach does not account for these processes, which thus explains why the point cloud deviates from the energy limit line under severe energy-limited conditions. This, however, does not affect the validity of calculating change trajectories. Hydrol. Process. 28, 4110–4118 (2014)
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Regional differentiation
The hydroclimatic trajectories group across the map of Sweden such that we can clearly distinguish three regions related to three types of change adaptations, represented in Figure 3A by mostly yellow, mostly red and mostly blue points. (1) The mountain ecosystem has experienced the largest increase in precipitation that appears to outpace the ability of this ecosystem to increase its water use efficiency (Figure 4, AET/P decreases and Q/P increases). Increasing precipitation in the mountains has, thus, directly led to higher river discharges. Given these higher discharges, wetter soil conditions and increased erosion are also likely to have occurred. Complementary to Loarie et al. (2009), who showed that the temperature change in mountain ecosystems is relatively slow, we show that increasing precipitation instead of increasing temperature may drive ecosystem change in the mountains. Also, the large lakes in the south show downward trajectories (orange) parallel to the energy limit line. Similar to the mountains, an increase in precipitation over the lakes directly increases discharge with the lake evaporation remaining relatively constant. (2) Forests have reacted to increased precipitation and temperature by increasing their evapotranspiration in such a way that the evaporative index (AET/P) remained relatively constant. This is shown by the mostly red and horizontal arrows in the forested areas of Figure 4A. This result signals that the recent climatic and land use-induced changes do not necessarily outpace the ability of the forests to adapt their water-use and energy-use strategies to the prevailing conditions. This ability to keep pace with climatic changes (so far) indicates the adaptable nature of forests. This increase in forest evapotranspiration corresponds to reported large increases in forest biomass (up to 60% increase for south Sweden) during the past 50 years (Hellström and Lindström, 2008). Given the long and steady increase in forest biomass since the start of biomass measurements in the 1920, the hypothesis that the observed increase in forest evapotranspiration is a result of forest recovery to past disturbances, as discussed by Jones (2011) and Andersson (1987), seems unlikely for Sweden. The observed water cycle change, thus, mainly reflects a combination of climate change and the ability of vegetation to adapt to these changes. A similar result was reported by Hember et al. (2012), who found that forests in Canada respond to environmental change by accelerating their biomass production and thus transpiration. However, on longer timescales, a shift in vegetation species may occur, for example when Copyright © 2013 John Wiley & Sons, Ltd.
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the wetter conditions create longer periods of flooding that locally cause a shift from pine forests to wetlands. (3) Agricultural areas (Figure 1) all have trajectories that move in upward direction (Figures 3A and 4A) indicating that observed increases in water use over agricultural areas is larger than the increase in precipitation. This can be attributed to improved farming practices with drainage, irrigation, fertilization and more efficient crops (e.g. Jordbruksverket, 2011; Jaramillo et al., 2013). The observed changes in the water cycle for these regions are, thus, mainly human induced. As such, these regions have become increasingly vulnerable to water shortages during the growing season over the past 50 years. The magnitude of change in Budyko space (Figure 4B) over the 1961–2010 period is an indicator for ecosystem sensitivity to future climate and land use change. A large change magnitude signals that no negative feedback mechanisms were in place to counter climate or other human-induced changes or that climate and humaninduced changes were particularly large for that region. Because of the previously described nonlinearity of Budyko space, the magnitude of change should not be compared throughout the country and as such Figure 3B gives only little information. However, when plotting the magnitude of change back into real space (Figure 4B), we can use the change magnitude to identify regions with more change than their surroundings. Furthermore, the magnitude of change helps to interpret the significance of the direction of change (a particular direction with a very small magnitude is of little relevance). We then see that sensitive ecosystems (with larger relative change magnitudes than their surroundings) occur over the entire range of Swedish climates (i.e. of dryness index values in Figure 3B) and within each of the previously defined adaptation types. In the mountain area, we see large magnitudes for the regions with the highest mountains, driven by an increase in P and a decrease in AET. Note, however, that the uncertainties in the fluxes for these regions are very large, and these results need further study. In the forested areas, we find large magnitudes for the landscapes bordering the northern mountain range to the east. In particular, these regions have large intact wetland and mire systems that are likely to be relatively sensitive to changes in the water and energy balance. Furthermore, large change magnitudes are found for Southern Sweden greater than 200 m.a.s.l (Figure 1). From Figure 2, we infer that these large magnitudes are mainly driven by a relatively large increase in P. Within the agricultural areas, we see large differences in magnitudes. Especially the agricultural areas north of the capital Stockholm and the agricultural areas surrounding Hydrol. Process. 28, 4110–4118 (2014)
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the two largest lakes in the south show large change magnitudes. Here, the change magnitudes are far larger than the change magnitudes for the traditionally agricultural southern tip (Scania) of Sweden. Possible explanations for this difference are that the Scania region receives more precipitation and thus can more easily accommodate the increase in agricultural evapotranspiration. Secondly, Jaramillo et al. (2013) showed that the increase in biomass production in the Scania region starts in the early 1950s (outside our investigation period) and peaks round 1970, whereas the biomass increase for the Stockholm region starts after 1960 (within our period) and peaks around 1990. Hence, for the period of our study (1960–2010), the relative increase in biomass production (and thus in AET) is larger for the Stockholm than for the Scania region. Will the global empirical Budyko curve change under climate change?
The Budyko curve, by definition, represents a steady state, in which vegetation has co-evolved with the landscape to optimize water and energy use for given climatic conditions. This means that for every possible climatological change, there should be a subsequent change in vegetation (following a vegetation succession series slowly changing the ambient soil and atmosphere) to bring a given catchment back to the Budyko curve. However, for Sweden during the past 50 years, it can be seen that the Budyko curve itself has changed and moved towards its theoretical limits. We can put forward four potential explanations for this behaviour: • The observed change is temporary because of water use adaptation of the current vegetation to a rapidly changing climate. Over longer timescales (centuries), the vegetation types are expected to adapt, and regions migrate back again to the original, ‘old’ Budyko curve. • The observed change may signal an ecosystem recovery to past disturbances (Jones, 2011), for example the recovery following acidic deposition on forests leading to needle loss (Andersson, 1986). However, the continuously increasing forest biomass since 1920 in Sweden, reported by Hellström and Lindström (2008), implies that this disturbance should have occurred before 1920. • There has been an acceleration of the hydrological cycle caused by human ecosystem management, such as intensified agriculture, water efficient crops and drainage of forests and wetlands. This human management prevents a natural adaptation of vegetation species, as expected under the co-evolution perspective of the Budyko curve, and thus changes the Budyko curve. Copyright © 2013 John Wiley & Sons, Ltd.
• The globally increasing CO2 and nutrient concentrations during the recent decades have reduced the water and energy amounts required for the same biomass production (De Boer et al., 2012). This implies that vegetation can do ‘more’ with ‘less’, which changes the interactions between vegetation, atmosphere and hydrological cycle such that the Budyko curve is shifting towards its limits. All four explanations are likely to contribute in various degrees to the observed change of the Budyko curve across Sweden, and we do not presume to provide an answer to the relative role of each. Our goal is rather to highlight the inherent interconnection between vegetation adaptation, human management of resources and climatic changes that can lead to systematic shifts in Budyko curves under various timescale perspectives. More research is needed to quantify these contributions and to understand how climate change is affecting the Budyko curve. Our results clearly challenge the view of the Budyko curve as a constant empirical organizing principle emerging from soil, vegetation and atmosphere feedbacks on the hydrological cycle and thus questions its ability to predict future water cycles.
CONCLUSIONS We conclude that change trajectories in Budyko space have revealed important insights both to the systems studied and to the general utility of the Budyko framework. Sweden, for example, can be subdivided into three regions with unique hydroclimatic change adaptation: the mountains, the forests and the agricultural areas. Next to this regional differentiation, based on the direction of change in Budyko space, the magnitude of change revealed the most sensitive areas within each of these regions. National and regional water management, to be effective, should acknowledge these regional differences and adapt policies to optimize water system resilience to land use and climate change, accordingly. Lastly, depending on how long lived the observed changes in Budyko space are, we may have to reconsider the idea of a steady-state Budyko curve emerging from co-evolution of landscape, vegetation and climate, when humans manage the landscape and alter the atmospheric composition.
ACKNOWLEDGEMENTS
The authors acknowledge support from the Baltic Nest Institute and the BEAM and Ekoklim projects at Stockholm University. G. D. and F. J. also acknowledge funding from the Swedish Research Council (VR, project number 2009-3221). Hydrol. Process. 28, 4110–4118 (2014)
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APPENDIX A The Priestley–Taylor (Priestley and Taylor, 1972) approach calculates PET as function of incoming radiation and T. The Swedish Meteorological and Hydrological Institute provides nationwide seasonal estimates for incoming radiation for the period 1983–2010 on their website on the basis of 15 stations (www.smhi.se). We fitted a linear model with average T, average daily maximum T difference and precipitation P to the ratio of incoming radiation at the earth's surface over extraterrestrial radiation at the atmosphere boundary (Figure A1A). This empirical model for incoming radiation is subsequently used in a Priestley– Hydrol. Process. 28, 4110–4118 (2014)
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Taylor equation. Figure A1B compares the results of the Priestly–Taylor approach with PET values derived from the Moderate-Resolution Imaging Spectroradiometer (Mu et al.,
A
2011). Using our empirical model for incoming radiation, we were able to create time series of PET for the entire 1961–2010 period.
B
Figure A1. Relations between measured and predicted radiation (A) and the Moderate-Resolution Imaging Spectroradiometer derived and predicted PET via Priestley–Taylor (B).
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