Pan-evaporation measurements and Morton-point potential evaporation estimates in Australia: are their trends the same?

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 29: (2009) Published online 19 June 2008 in Wiley InterScience ( Pan-evaporation measurements and Morton-point potential evaporation estimates in Australia: are their trends the same? Dewi G. C. Kirono,* Roger N. Jones and Helen A. Cleugh CSIRO Marine and Atmospheric Research, Centre for Australian Weather and Climate Research, A partnership between CSIRO and the Australian Bureau of Meteorology, Aspendale, Australia ABSTRACT: This paper compares Australian pan-evaporation (E pan ) observations with point potential evaporation (E p ) estimates derived using the Morton method. In particular, it focuses on trends in both E p and E pan. E p is defined as the potential evaporation from an area which is so small that the effects of the evaporation on the overpassing air would be negligible, while E pan is evaporation measured using a standard pan. The analyses are based on monthly data from 28 sites and refer to the period The results show that E p and E pan are strongly correlated on the monthly time scale. Furthermore, the sign of the monthly trends are in agreement, on average, at 60% of the sites. Where there is agreement, positive trends outweigh negative trends. This is consistent with the fact that although trends in E pan and E p varied from site to site and from month to month, the median trends were positive except for December. The correlation between the trends in both quantities across all sites is statistically significant (R = 0.51) which indicates that projected changes based on climate model outputs can be used to estimate changes in future potential evaporation. Copyright 2008 Royal Meteorological Society KEY WORDS Australia; potential evaporation; modelled potential evaporation; trend; climate change; pan evaporation; climate projections Received 25 February 2007; Revised 23 October 2007; Accepted 7 May Introduction The magnitude of total evaporation is often comparable to total rainfall and therefore it is critical to the water balance of a location - be it a paddock, catchment or region. Changes in evaporation rates will alter hydrological regimes and affect soil moisture availability, and a changing climate is anticipated to affect water resources partly via changes in evaporative demand. Therefore, it is important to understand how evaporation has behaved in the past, what the current trends are and what is expected in the future (Thomas, 2000; Cohen et al., 2002; Hidalgo et al., 2005). Unfortunately, it is rather difficult to directly measure evaporation (e.g. Brutsaert, 1982) and indirect approaches are used to estimate it from variables directly related to evaporation (Huntington, 2006). One of the common approaches for estimating actual evaporation is to firstly estimate potential evaporation, which refers to evaporation that would occur if water were unlimited (e.g. if the surface was saturated or over a water body such as a lake). Actual evaporation can be defined as the fraction of potential evaporation that depends on water availability. The most widespread method for measuring potential evaporation is by means * Correspondence to: Dewi G. C. Kirono, CSIRO Marine and Atmospheric Research, Private Bag No 1, Aspendale 3195, Australia. dewi.kirono@csiro.au of pan evaporation. Potential evaporation can also be estimated based on readily available climate variables using models such as the Penman Monteith reference evaporation model (Allen et al., 1998). This model estimates the maximum quantity of water evaporated from the soil and transpired from the vegetation of a specific surface (e.g. short grass). Negative trends in measured pan evaporation have been reported over the latter half of the 20th century in many parts of the world such as in India (Chattopadhyay and Hulme, 1997), northern Europe and USA (Brutsaert and Parlange, 1998, Lawrimore and Peterson, 2000; Roderick and Farquhar, 2002), Thailand (Tebakari et al., 2005) and China (Liu and Zeng, 2004; Liu et al., 2004; Xu et al., 2005). Although data from a few regions indicate increases [i.e. Northeast Brazil (da Silva, 2004) and Israel (Cohen et al., 2002) ], the decreases in many regions seem to be inconsistent with observed positive trends in temperature, resulting in a pan-evaporation paradox debate (Brutsaert and Parlange, 1998; Roderick and Farquhar, 2002). Negative trends in Penman Monteith estimates of potential evaporation have also been reported in some regions such as China (Chen et al., 2005) and the Tibetan Plateau (Shenbin et al., 2006; Zhang et al., 2007). However, there are also reports on significant increases in Penman Monteith estimates in the United Copyright 2008 Royal Meteorological Society

2 712 D. G. C. KIRONO ET AL. Kingdom (Yang et al., 2005) and in the Volta river basin in West Africa (Oguntunde et al., 2006). These suggest that, like the trends in pan evaporation, trends in potential evaporation are negative in some regions and positive in other regions. In Australia, negative trends in annual pan-evaporation measurement since 1970 have been identified (Roderick and Farquhar, 2004), although, on the average, the decrease is not statistically significant (Kirono and Jones, 2007; Jovanovic et al., 2008). A combined record of Melbourne sunken tank and western Victorian A Class pan-evaporation measurements for (Nathan et al., 1984; Bruce, 1990), however, show increases. This indicates contrasting trends in pan evaporation over the last 30 years (1970 to early 2000) compared to the earlier period ( ). Estimates of Mortonpoint potential evaporations based on the global climate model(gcm) output also indicate that the potential evaporation increases since the late 1800s and will continue in the future (CSIRO, 2001; CSIRO and BoM, 2007). Therefore, there is an apparent discrepancy which could affect confidence in the model projections (Farquhar and Roderick, 2005). Reconciling the differences between the different estimates of evaporation trends derived from instrumental records or from model has been identified as a key area for research particularly for Australia (Cai and Jones, 2005). The present study focuses on the relationship, for Australia, between pan-evaporation (E pan ) measurements and potential evaporation (E p ) estimates using the Morton method. 2. Data and method 2.1. Data Monthly climatological data ( ) used to calculate E p and E pan were selected from 28 stations across Australia. These stations were chosen on the basis of the records being available for as long as possible (1970 to present) and their being as complete as possible (with missing data less than 1%). The locations are depicted in Figure 1, while detailed information about each site is given in Roderick and Farquhar (2004) and in Kirono and Figure 1. Location of stations used in this study. Jones (2007). All data were obtained from the Australian Bureau of Meteorology archive (climate data: Australia, Version 2.2.). Where missing data were minimal (i.e. less than 1% of the total number of data points), we used linear regression to fill in the missing records by using data from the nearest neighbouring station. The climatological inputs to calculate E p are monthly relative humidity, air temperature and the ratio of observed to maximum possible sunshine duration. To make sure that we used a homogenous climate series, defined as one where variations are caused only by variations in climate (WMO, 2003), we checked for inhomogeneities in all data at each station using the Maronai and Yohani (1978) bivariate test, also known as the Potter method (as given by WMO, 2003). This test detects a single systematic change in the mean of an independent time series, based on a second correlated series that is assumed to be unchanged. It indicates whether a step change has occurred, gives the maximum likelihood estimates and the time and magnitude of step change, so that the data can be adjusted as needed. Kirono and Jones (2007) describe this procedure in more detail, especially as applied to pan-evaporation data Calculation of E p We used the Morton (1983) method to calculate E p since the method has been used to construct the Australian Bureau of Meteorology atlas for evaporation (BoM, 2001) and future projection for potential evaporation (CSIRO, 2001; CSIRO and BoM, 2007). Future potential evaporations were estimated indirectly using GCM output because some GCM do not provide evaporation data. Even if they do simulate actual and/or potential evaporation using physically explicit methods, the estimation is subject to the uncertainties and feedbacks of land surface schemes. Rind et al. (1997) review the impact of surface parameterizations within GCM, concluding that potential evaporation calculated within GCM is likely to be underestimated and recommend, with reservations, that potential evaporation instead be calculated offline using GCM-climate outputs. CSIRO climate models in the late 1990s were found to overestimate potential evaporation, so off-line methods have been used since that time. The Morton model, in particular, compares favourably with other methods for calculating potential evaporation for rainfall run-off modelling (Chiew et al., 1993) and is preferable since it does not need aerodynamic input to produce realistic results. This benefit reduces the margin of error associated with wind-speed data taken from simulated GCM. There are three different types of evaporation that can be generated using the Morton method, i.e. point potential evaporation, areal potential evaporation, and actual evaporation. In this study, the E p refers to the point potential evaporation that is a measure of potential evaporation from an area which is so small that the effects of the evaporation on the overpassing air would be negligible. Hence, estimates of E p are thought to be

3 AUSTRALIAN OBSERVED AND MODELLED POTENTIAL EVAPORATION, an equivalent of the evaporation pan, which measures the evaporation at a point and is too small to modify the temperature and humidity of the passing air when it becomes well mixed. Morton s (1983) method for E p is based on simultaneously solving the energy transfer and balance equations, using a constant-energy transfer coefficient. The following two equations represent the energy-balance and vapour-transfer equations, respectively (Morton, 1983): E p = R T [γpf T + 4εσ(T P + 273) 3 ](T P T) = R T λ P f T (T P T) (1) E p = f T (v P v D ) (2) in which E p is the potential evaporation (or point potential evapotranspiration as it is referred to by Morton, 1983) in units of latent heat; T P and T are the equilibrium and air temperatures, respectively, in C; R T is the net radiation for soil-plant surfaces at air temperature; f T is the vapour-transfer coefficient; γ is the psychrometric constant; p is the atmospheric pressure; σ is the Stefan Boltzmann constant; ε is the surface emissivity; v P is the saturation vapour pressure at T P ; v D is the saturation vapour pressure at the dew-point temperature and λ P = γp + 4εσ(T P + 273) 3 /f T. The form of the vapour-transfer coefficient is as follows: f T = (p s /p) 0.5 f Z /ζ (3) in which f Z is a constant; ζ is a dimensionless stability factor; and p and p s are the atmospheric pressure and the sea-level atmospheric pressure, respectively. Following the method of BoM (2001), we set f Z as 29.2 Wm 2 mbar 1 (Morton s original value for f Z is 28 Wm 2 mbar 1 ). If T P is the sum of a trial value (T P ) and a correction ([δt P ]), and if δv P = P [δt P ], the solution to Equations (1) and (2) is [δt P ] = [R T /f T + v D v P + λ P (T T P )]/ ( P + λ P ) (4) in which v P and P are the saturation vapour pressure and the slope of the saturation vapour pressure curve at T P, respectively. As described in Morton (1983), during the iterative process R T,f T,v D and T remain constant, and the effects of changes in λ P are so small that it is assumed constant at its initial value. For the initial trial, T P is set equal to the air temperature (T ), v P is set equal to the saturation vapour pressure at air temperature (v)and P is set equal to the slope of the saturation vapour pressure curve at air temperature ( ). With each application of Equation (4), these three quantities change, and the process is repeated until the absolute value of [δt P ] becomes less than 0.01 C. The use of Equation (4) ensures that this takes place within four trials, even in arid climates where the difference between air and equilibrium temperatures may exceed 10 C. The potential evaporation estimate is obtained by using the value of T P obtained by the iterative process in Equation (1). Morton (1983) noted that the value of T P obtained from the first iteration would give the same value of potential evaporation as Kohler and Parmele s (1967) modification of the Penman (1948) equation. The Morton model does not require observations of wind speed. As a substitute, it calculates potential evaporation using a vapour-transfer coefficient that is dependent on atmospheric pressure but not on wind speed. Some justifications for assuming the vapourtransfer coefficient to be independent of wind speed are (1) it increases with increases in both surface roughness and wind speed, and wind speeds tend to be lower in rough areas than in smooth areas; (2) it increases with increase in the instability of the atmosphere, and this effect is more pronounced at low wind speeds than at high wind speeds; and (3) the use of climatological observations of wind speed can lead to significant error because of local variations in exposure and instrument height (Morton, 1983). 3. Results 3.1. Magnitudes of E pan and E p The magnitudes of both E pan and E p varied with season and location. On average, E p tends to overestimate E pan (Table I). Time series of monthly E pan and E p for are plotted for selected locations representing different climate regimes (Figure 2). Townsville, Wagga Wagga and Giles, represent a hot humid wet season and mild dry season, a mild to warm summer and cold winter, and a hot dry summer and cool winter, respectively. Table I. Mean and standard deviation of E pan and E p, as averaged across the 28 sites, and correlation coefficient (R) between E pan and E p. Month Mean (mm) Standard deviation (mm) E pan E p E pan E p January February March April May June July August September October November December Annual R

4 714 D. G. C. KIRONO ET AL. Figure 2. Time series of monthly E pan and E p at Giles, Wagga Wagga and Townsville for January, April, July and October. The annual rainfall for these sites is 1127, 578, and 287 mm, respectively. At Townsville and Wagga Wagga, E pan tends to be lower than E p in magnitude, and at Giles, which is drier than the other two sites, E pan is higher than E p. Figure 2 also suggests that E p and E pan exhibit similar interannual variability even though the magnitude of E pan variability tends to be higher than that of E p. This is shown in Table I, where it appears that the standard deviation of E p is approximately half of that of E pan. Figure 3 shows plots of all monthly E pan against E p for all 28 sites. The two measures are closely related with a correlation coefficient of 0.9. Such a strong relationship is also evident from Table I, which shows that, for each month, the correlation between E pan and E p ranges from 0.83 to On annual basis, the correlation coefficient is Monthly E p (mm) y = 1.08x R 2 = Monthly E pan (mm) Figure 3. Relationship between monthly E pan and E p Trends in E pan and E p The results for the trends in monthly E pan and E p varied from site to site and from month to month. Less than five sites showed statistically significant trends (Table II). Overall, the number of sites showing significant positive trends in E p is larger than that for E pan, while the number

5 AUSTRALIAN OBSERVED AND MODELLED POTENTIAL EVAPORATION, Table II. Total number of sites showing significant trends in E pan and E p. Month Significant positive trend Significant negative trend E pan E p E pan E p January February March April May June July August September October November December Annual of sites with significant negative trends in E p is smaller than that for E pan. Figure 4 summarizes the range of trend magnitude in each month by showing the median and the 90th and 10th percentiles of the trends over all 28 sites. It appears that (1) the median trend in E pan and E p are similar in sign, i.e. mostly positive, except for December. The median trend in E p tends to be slightly higher than that of E pan ; and (2) the range of trends in E p is smaller than those of E pan, particularly during the non-summer months (March November). This implies that the spatial variation of trends in E p is smaller than that of E pan. Figure 4 also suggests that the median trend in annual E pan and E p is positive and that the range of trends in annual E p is smaller than those of E pan. On average, 60% of sites show an agreement in trend sign (Table III), with more stations exhibiting agreement in positive trends compared to negative trends. This is consistent with the results presented in Figure 4, where it is revealed that the median trends are mostly positive, except for December. In Figure 5, we plot the trends in monthly E pan and E p to see whether the trends in the two measures relate to each other. Apparently, the trends in monthly E pan and E p are positively and strongly related with a correlation coefficient of 0.51 (significant at 0.05 level). The trends in annual E pan and E p, however, are less strongly correlated (R = 0.28). Figure 4. Ranges of trends in E pan and E p for any given month as obtained from 28 stations. Black circles represent the median, while the upper and lower ends represent the 90th and 10th percentiles values. Ranges of trends in annual E pan and E p are also plotted. This figure is available in colour online at

6 716 D. G. C. KIRONO ET AL. Table III. Total number of sites showing an agreement with the sign of trends in E pan and E p. Month Positive Trend direction Negative Total (sites) Total (%) January February March April May June July August September October November December Average Annual Discussion and conclusion Previous studies have reported a negative trend in annual pan evaporation, averaged over a number of sites over Australia, in the last 30 years or so (Roderick and Farquhar, 2004; Kirono and Jones, 2007; Jovanovic et al., 2008) even though the trends were not statistically significant. A combined record of Melbourne sunken tank and western Victorian A Class pan-evaporation measurements for , however, suggests a positive trend (Nathan et al., 1984; Bruce, 1990). Similarly, modelled Morton-point potential evaporation, an equivalent measure of pan evaporation, exhibits increases since the late 1800s (CSIRO, 2001). The dissimilarities obtained in these different studies could be because of different time periods and different measures of evaporation. This paper examines similarities between magnitude and trends in observed pan evaporation (E pan ) and estimated Morton-point potential evaporation (E p ) within the same period (i.e ) in Australia. Unlike previous studies, which dealt with trends in annual E pan, analyses in this study were mostly conducted on a monthly basis. Additionally, this study did not attempt to obtain a general trend based on the all-stations average time series like the previous study had. This is owing to our consideration that an all-stations average time series from a network of 28 stations may smooth out the original spatial variability within the region; hence, it may not really represent the whole Australian region. Instead, we obtained a general picture of the trend by examining the distribution of the trends across the 28 sites. It is shown that, within the same period (i.e ), the agreement between estimated E p and E pan is excellent (R = 0.9). Similarly, trends in monthly E p are found to be highly correlated with trends in monthly E pan (R = 0.51, statistically significant at the 0.05 level). The degree of agreement between trends in E pan and in E p is very high with, on average, 60% of sites agreeing in the Trends in monthly E p (mm/year 2 ) Trends in annual E p (mm/year 2 ) y = x R = Trends in monthly E pan (mm/year 2 ) y = x R = Trends in annual E pan (mm/year 2 ) Figure 5. Relationship between trends in monthly E pan and trends in monthly E p. sign of the trend for each month. Almost half of the sites also show agreement in the sign of the trend in annual basis. As expected, trends in monthly E pan and E p varied from site to site and from month to month, but the median trends were mostly positive except for December. It is worth noting that most of these trends were not statistically significant at the 0.05 level. On an annual basis, the trends also varied spatially, but the median trends in annual E pan and E p were both positive. The spatial variation of trends in annual E p is smaller than that of E pan. The ranges (90th and 10th percentile) for the former are +5.9 to 6.1 mm/year 2 whereas those for the latter are +3.8to 1.3 mm/year 2. All of these results are consistent with the fact that E p and E pan exhibit similar interannual variability even though their magnitude may differ. E pan measures the quantity of water evaporated from a small open water surface, whereas E p measures the quantity of water evaporated from a small land surface (soil, vegetation, and water) when water is not limited. The roughness and the exchange of energy of the air with water and land surfaces are not the same. In addition, the evaporation pan is mounted on a wooden platform

7 AUSTRALIAN OBSERVED AND MODELLED POTENTIAL EVAPORATION, above the surrounding soil. This provides the pan with a heat source (sink) not available to the vegetated-land surface. Consequently, the behaviour of E pan and E p may not always be similar throughout time. Our results suggest that the temporal and spatial variability of E pan tends to be higher than that of E p. This implies that more caution is required when one constructs an areal average of E pan in comparison to an areal average of E p and that a given magnitude of trend may be statistically significant for E p but may be not significant for E pan and vice versa. With regard to E pan, on average, only 4% of the sites show significant positive trends and only 5% of the sites show significant negative trends in monthly E pan. In contrast, about 3% of the sites show significant increases and 11% of the sites show significant decreases in annual E pan. With regard to E p, on average, 11% of the sites show significant positive trends and 0.1% of the sites show significant negative trends in monthly E p.in contrast, about 40% of the sites show significant increases and none of the sites show significant decreases in annual E p. Such a contrast highlights the problem of obtaining different results when trend analyses are performed using monthly or annual data. This is because of the fact that annual values may smooth out the original interannual variability within the monthly values. In summary, our results suggest that (1) estimated monthly and annual E p are strongly related to the observed monthly and annual E pan ; and (2) within the same time period (i.e ), the trends in observed E pan are reproduced by the trends in calculated E p. Accordingly, we are confident that estimating E p based on GCM-climate outputs, can provide useful guide to future changes in potential evaporation. 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