Variation in the Climate Change Signal of the Discharge for the River Mitano Part 2: Different Estimations of Potential Evapotranspiration (PET) as a Source of Uncertainty

The estimation of the PET climate change signal is an important contributor to uncertainty in prediction of freshwater resources, particularly in regions where precipitation (P) is closely in balance with PET (Kingston et al., 2009), and there is a seasonal switch between P-PET deficit and surplus (Kingstonand Taylor, 2010).

Over 50 different methods of estimating PET exist (Lu etal., 2005), which each encompass different rates of change in PET for water resources. The Food and Agricultural Organisation of the United Nations (FAO) recommends use of the Penman-Monteith equation (Allen et al., 1998), however, in many locations (with certain climatological environments and where empirical calculations are robust) insufficient data prevent its use and compel application of simplified, empirical methods requiring fewer input data (Kingston et al., 2009). Priestley-Taylor is a widely used simplification of Penman-Monteith, and Hargreaves is the method recommended by the FAO in absence of sufficient data to calculate Penman-Monteith (Kingston et al., 2009). Therefore the differences between these three methods is particularly important for examining differences in projections of freshwater in practice.

Kingston et al.’s (2009) study compares the global response of 6 commonly used PET methods within hydrological models to a 2 °C rise in global mean temperature (relative to a 1961-90 baseline) under different GCMs. The results, displayed in Figure 1, show that different methods of estimating PET can produce markedly different climate change signals, and are thus an important source of uncertainty in freshwater predictions.

Figure 1. Annual PET for (a) the 1961-1990 baseline and (b-f) the assigned 2 °C climate change signal (scenario minus baseline), grouped by GCM. Ham=Hamon; Har=Hargreaves; PM=Penman Monteith; PT=Priestley-Taylor; BC=Blaney-Criddle; JH=Jensen-Haise. (Source)

Although PET increases at all latitudes for all PET methods and GCMs for the 2 °C climate scenario, differences of over 100% (> 200 mm in the tropics) exist in the climate signal between PET methods, for all GCMs (Figure 4) (Kingston et al., 2009). Maximum absolute differences occur at the same latitudes as the peak PET climate change signal (Kingston et al., 2009) – often coinciding with tropical African regions. Notably, although Priestley-Monteith, Priestley-Taylor and Hargreaves are generally closely grouped, they can still exhibit differences of over 60 mm at certain latitudes for certain GCMs (Kingston et al., 2009).

Table 1 shows that the mean range (variation) between PET methods and GCMs is higher for East Africa, where the Mitano Basin is located, than the Mediterranean and Southeast Asia. The key point here is that in each region, the choice of PET methods adds substantial uncertainty to the existing uncertainty associated with climate change signal between GCMs, and is of a comparable magnitude. Furthermore, in the case of East Africa and Southeast Asia, the choice of PET method for certain GCMs can actually determine the direction of projected changes in water surplus entirely (Kingston and Taylor, 2010).

Table 1. Statistics regarding percentage change in water surplus for three selected regions. (Source)

At the basin-scale, Kingston and Taylor (2010) compared baseline-scenario PET ratios for the three commonly used methods of calculating PET. The results are displayed in Table 2, and show that substantial differences exist in the PET climate change signal between the three methods for the HadCM3 2 °C scenario, with this difference most apparent during May, where the increase in PET ranges from 9-17% between methods (Kingston and Taylor, 2010).

Table 2. Percentage Change in monthly PET from baseline to the assigned 2 °C HadCM3 climate scenario. (Source)

This method dependence of PET estimation can be attributed to two factors. The first is the inclusion of different meteorological variables in each method (Kingston et al., 2009). For example, whilst Penman-Monteith is based on net radiation, temperature, wind-speed and vapour pressure), Priestley-Taylor is based on solely on net radiation and temperature (Priestley and Taylor, 1972). Figure 1 shows that the two methods’ signals diverge strongly at latitudes encompassing large arid or semi-arid zones (e.g. 10-30 °N), which suggests the absence of humidity data in Priestley-Taylor is critical in their differences (Kingston et al., 2009). The second factor is the different empirical formulation of each method, demonstrated through comparison of the Hamon and Blaney-Criddle signals in Figure 1; both methods are based on mean monthly temperature and day-length, yet have markedly different climate change signals (Kingston et al., 2009). The reliability of PET methods is further dependent on the reliability of input climate data – for example, compared to the relatively high confidence that can be placed in data such as observed temperature, Penman-Monteith is subject to data collection uncertainties such as the reliability of specification of canopy conductance and cloud cover (Kingston et al., 2009).

Ultimately, differences in the representation of evaporation (and snowmelt) processes lead to hydrological model structural uncertainty on projected changes, which can be substantial (e.g. Hagemannet al., 2013; Schewe et al., 2013). In some regions (e.g., high latitudes; Hagemann et al., 2013) with reductions in precipitation (Schewe et al., 2013), hydrological model uncertainty can be greater than climate model uncertainty—although this is based on small numbers of climate models (Cisneros et al., 2014). Incorporating uncertainty in hydrological model structure would have increased the range in projected impacts (Figure 3) on the Mitano Basin catchment scale even further.