Sensitivity of future water availability projections to Global Climate Model , evapotranspiration estimation method , and greenhouse gas emission scenario

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Introduction
Climate change will result in significant impacts on hydrologic processes.The 2014 Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) reported that climate change will significantly affect future precipitation (P), temperature (T ) and evapotranspiration (ET) and these changes will affect the quantity and quality of water resources.The most recent report of the National Climate Assessment and Development Advisory Committee (NCADAC, 2013) indicated that the average annual temperature in the United States (US) has increased by 0.7 to 0.9 • C since record keeping began in 1895 and is expected to continue to rise (Georgakakos et al., 2014;Walsh et al., 2014).The NCADAC report also indicated that Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Model (GCM) precipitation projections show a consistent increase in Alaska and the far north of the continental US and a consistent decrease in the far Southwest US, but that GCM projections are inconsistent in the precipitation transition zone of the US continent.The uncertainty in climate change projections makes actionable water resources planning difficult in many regions.In order to predict changes in the hydrologic cycle, and future water supply and demand, estimates of changes in P, T and ET must be evaluated on a regional basis, and the uncertainty of these estimates must be quantified (Ishak et al., 2010).
Previous research has evaluated existing and potential future spatiotemporal changes in P, T and ET for various regions around the globe (e.g.Chaouche et al., 2010;Chong-Hai and Ying, 2012;Johnson and Sharma, 2009;Kharin et al., 2013;Maurer and Hidalgo, 2008;Quintana Seguí et al., 2010;Sung et al., 2012;Thomas, 2000;Wang et al., 2013;Xu et al., 2006).It is well known that future GCM projections of temperature and precipitation vary significantly due to both the different radiative forcing assumptions of carbon dioxide scenarios (e.g.CMIP3 Special Report on Emissions Scenarios (SRES) and CMIP5 Representative Concentration Pathways (RCP trajectories)) and different GCM model physics (Hawkins andSutton, 2009, 2010).Future ET projections have been shown to depend on ET estimation methods in ad-Introduction

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Full dition to GCMs.For example Kingston et al. (2009) used 5 GCMs from the CMIP3 climate projections and 6 different ET equations to estimate global ET and found that the choice of ET method contributes to different projections of the future state of water resources which varies by region.They found that the Hamon and Jensen-Haise ET estimates showed the greatest changes in both humid and arid regions while the Penman-Monteith and Priestley-Taylor estimates frequently showed smallest change.
Similarly McAfee (2013) used three ET equations with 17 CMIP3 GCMs to evaluate the uncertainty of future global ET projections and found that the Hamon equation showed more significant and consistently positive trends in ET compared to the Preistly-Taylor and Penman methods.
Although these studies indicate that the choice of GCM, ET method and RCP trajectory all contribute to different regional projections of P, T , ET and thus future water availability, most studies have primarily focused on the uncertainties caused by GCMs and/or RCP trajectories.However hydrologic models developed to estimate future water availability as a result of projected climate change use many different types of ET estimation methods (Zhao et al., 2013).Because the choice of ET estimation method may be as important as the choice of GCM or RCP trajectory, better understanding of the contribution of each of these factors to the overall prediction uncertainty of future water availability is necessary (Taylor et al., 2013).
The objective of this research is to comprehensively evaluate the relative sensitivity of future P, ET and water availability (defined here as P-ET) projections to choice of GCM, ET method and RCP trajectory over the continental US.Variance-based global sensitivity analysis (Saltelli et al., 2010) and Monte Carlo Filtering (Rose et al., 1991) are used to quantify the uncertainty and important input factors controlling these projections.Global sensitivity analysis (GSA) quantifies the relative importance of multiple uncertain factors over the entire range of factor values, and thus is preferred over local, one factor at a time, sensitivity analysis (Homma and Saltelli, 1996;Saltelli, 1999).Monte Carlo Filtering can identify sets of model simulations and input factors that meet a specified criteria or threshold.Thus global sensitivity analysis and Monte Carlo Fil-Introduction

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Full tering offer an opportunity to gain insight into the sources of uncertainty, and drivers of particular types of wet/dry behavior, when estimating future water availability under projected climate change.

Methods
All retrospective and future climate variables were obtained from the CMIP5 archive (accessible for download at http://pcmdi9.llnl.gov/).The "historical" runs of CMIP5 were used for the retrospective period  and the same ensemble member runs (r1i1p1 ensemble) of CMIP5 were used for two future periods: future period 1 (2030-2060), and future period 2 (2070-2100).Data for three RCP trajectories, RCP2.6,RCP4.5 and RCP8.5 were included in the analyses.Taylor et al. (2012) describes an overview of CMIP5 and RCP trajectories and compares the differences between CMIP5 and CMIP3 model projections.Data from the CMIP5 archive were used to calculate monthly mean P, ET, and P-ET for the retrospective and both future periods over each of the 9 US climate regions identified by the National Climatic Data Center (Karl and Koss, 1984, Fig. 1).Future changes in monthly mean P, ET, and P-ET were estimated by subtracting the monthly mean value for the retrospective period from the monthly mean value for future period 1 or future period 2, as appropriate (Baker and Huang, 2014).
Ten 1998; Hargreaves and Allen, 2003;Irmak et al., 2003;Tabari, 2010;Tabari et al., 2013;Xu and Singh, 2001) and are summarized in Table 1 (hereafter precipitation is referred to as P, and PET and RET are both referred to as ET for convenience).
Variables directly used from the CMIP5 monthly model output included precipitation (pr), maximum and minimum temperature (tasmax and tasmin), radiation (rlds, rlus, rsds, and rsus), air pressure (psl and ps), and wind speed (sfcWind) (http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf).Other variables needed in the 10 evapotranspiration equations were calculated using the variables from CMIP5 monthly model output (for details see Table 1).Monthly output that included all the variables needed for the Penman-Monteith reference evapotranspiration method (the most data intensive method) was available for both the retrospective period, and for the RCP2.6,RCP 4.5, and RCP8.5 trajectories for the future periods, for 9 CMIP5 models.Table 2 lists the 9 models from the CMIP5 archive that were used in this study.
The sensitivity of changes in future P, ET and (P-ET) to the choice of GCM, ET estimation method, and RCP trajectory was evaluated using the variance-based GSA method of Saltelli et al. (2010).Given a model of the form Y = f (X 1, X 2 , ...X k ), with Y a scalar, the variance-based first order effect for a generic factor X i can be written (Saltelli et al., 2010): where X i is the i th factor (in our case either GCM, ET method or RCP trajectory) and X ∼i is the vector of all factors except X i .The expectation operator E X ∼i (Y |X i ) indicates that the mean of Y is taken over all possible values of X except X i (i.e.X ∼i ) while keeping X i fixed.The variance, V X i , is then taken of this quantity over all possible values of X i .The first order sensitivity coefficient is expressed as: (2) Introduction

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Full Where V (Y ) the total variance of Y over all X i .S i is a normalized index varying between 0 and 1, because V X i (E X ∼i (Y |X i )) varies between 0 and V (Y ) according to the identity (Mood et al., 1974): is called the residual.The total effect index, including first order and higher order effects (i.e.interactions between factor X i and other factors) of the factor X i on the model output is calculated (Saltelli et al., 2010): The first order sensitivity of estimated future changes in mean monthly P, ET, and P-ET to choice of GCM, ET estimation method and RCP trajectory were calculated over the 9 US climate regions for each future period in order to evaluate the relative contributions of each of these factors on the uncertainty of future changes.A total of 270 simulations (9 GCMs × 10 evapotranspiration methods × 3 RCP trajectories) was used in the analysis.Sensitivity of projected changes in P were evaluated for both choice of GCM and choice of RCP trajectory.Sensitivity of projected changes in ET and P-ET were evaluated for choice of GCM, choice of ET estimation method, and choice of RCP trajectory.
For projected changes in water availability (P-ET) Monte Carlo filtering (Saltelli et al., 2008) was used to identify whether projected wetter or drier future conditions (i.e.larger or smaller water availability) could be attributed to specific GCMs, ET estimation methods, or RCP trajectories.For each future period the ensemble of 270 projections of change in water availability were categorized as either wet future condition (mean change in (P -ET) ≥ 0) or dry future condition (mean change in (P -ET) < 0).Next for Introduction

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Full each factor (X i = GCM, ET method, RCP trajectory) the histograms of wet (f wet |X i ) and dry (f dry |X i ) future conditions over the range of possible values of that factor were estimated.To identify the factors that are most responsible for driving the model into projected wet or dry future conditions for each factor, X i , the distributions (f wet |X i ) and (f dry |X i ) were tested for significant difference using the X 2 two sample test for categorical variables with α = 0.05 (Rao and Scott, 1981).If for a given factor X i the two distributions are significantly different, then X i is a key factor in driving into either a wet or dry condition (Saltelli et al., 2008).

Results and discussion
Future P, ET and water availability projections include large uncertainties stemming from different sources.Figures 2 and 3 present maps of the mean change (Fig. 2) and the standard deviation of change (Fig. 3) in annual P (top chart), ET (middle) and water availability (P -potential or reference ET; bottom) over the continental US calculated over all GCMs, ET estimation methods, and RCP trajectories for future period 2 (2070-2100).Major portions of the West, Southwest and South show a mean decrease in annual precipitation, while the rest of the continental US shows a mean increase (Fig. 2a).
Future annual ET shows a mean increase over retrospective annual ET over the entire US (Fig. 2b), with the largest increase in the South region.Following the patterns of P and ET, future annual water availability (P-ET) shows a significant mean decrease in the West, Southwest and South regions and a slight decline, or negligible change in most other regions (Fig. 2c).These mean changes in annual P, ET and P-ET are relatively small compared to the standard deviation of changes in annual P, ET, and P-ET (Fig. 3).Water availability in particular has a large standard deviation, resulting in coefficients of variation larger than one throughout the continental US. Figure 4 shows the seasonal changes in the monthly mean and standard deviation of water availability (P-ET) over the nine US regions.Blue and red lines represent the changes in monthly mean water availability for future period 1 and future period 2, respectively and the error bars represent one standard deviation around each mean value.All regions of the continental US show drier conditions (negative mean changes) in the summer season (June-August).Southern regions (Southeast, South, Southwest and West) show drier conditions throughout the year, however northern portions of the US (i.e. the Northeast, Ohio Valley, Upper Midwest, Northern Rockies and Plains and Northwest) show wetter conditions (positive mean changes) in the winter season.The results are consistent with those reported by McAfee (2013) who used 3 ET methods (Hamon, Priestly-Taylor and Penman-Monteith) to estimate global changes in ET over the entire globe.As found by Baker and Huang (2014) for both CMIP3 and CMIP5 projections, mean ET is projected to be higher in future period 2 than in future period 1, and mean precipitation projections are approximately equivalent in future period 1 and future period 2. Thus the projected mean changes in water availability for future period 2 (red lines in Fig. 4) are larger in magnitude than the projected changes for future period 1 (blue lines).In all regions, and for both future periods, the one standard deviation error bars bracket zero mean change indicating large uncertainty in the projections throughout the year.
Figure 5 shows the first order sensitivity of change in P to GCM and RCP trajectory over the nine US climate regions for future periods 1 and 2. For projected changes in P, the choice of GCM is generally more important than choice of RCP trajectory for all regions and both future periods.This is consistent with results found by Gaetani and Mohino ( 2013) and Knutti and Sedláček (2012) who showed significant differences in precipitation predictions among CMIP5 models.It should be noted that these results do not indicate that the choice of RCP trajectory does not affect the change in precipitation, only that the choice of RCP trajectory is less influential than the choice of GCM.There are no consistent seasonal patterns of the first-order sensitivity coefficients for either GCM or RCP trajectory in either future period.However, during the spring months, the Introduction

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Full ET increases in future period 2 over future period 1, with a concomitant decrease in the influence of both ET method and GCM.In future period 1 the GCM sensitivity coefficients are greater than the RCP trajectory sensitivity coefficients over most regions; however, in future period 2 the RCP sensitivity coefficients become more important.These results are consistent with those found by sKingston et al. ( 2009) who showed that projected increase in ET varied by more than 100 % between ET methods, and Schwalm et al. (2013) who found the choice of ET estimation method is sensitive and even more influential than the choice of model in predicting ET; however, neither of these studies looked at the influence of RCP trajectory on ET projections.Burke and Brown (2008) evaluated uncertainties in the projection of future drought using several drought indices.They found that there are large uncertainties in regional changes in drought and changes in drought are dependent on both index definition and GCM ensemble members.Similarly, Fig. 7 shows that projected change in water availability depend strongly on both the choice of GCM and ET estimation method.In all regions except the Southeast projected change in water availability is most sensitive to ET estimation method in the warm season (May through October) and most sensitive to GCM in the cool season (December through March).For the Southeast region the sensitivity coefficients for GCM and ET method are quite similar throughout the year.The sensitivity coefficients for RCP trajectory are very low in future 1, but increase in Introduction

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Full future 2, becoming approximately equal to the GCM sensitivity coefficients in the summer season in future 2. These results are similar to results reported by Orlowsky and Seneviratne (2013) who found that the greenhouse gas emission scenario uncertainty is not as important as differences among GCMs or internal climate variability when predicting Standardized Precipitation Index (SPI) and soil moisture (SMA).However, they also found that uncertainty due to greenhouse gas emission scenario increased in later future periods.Taylor et al. (2013) showed the patterns of changes in future drought were similar between the A1B scenario in CMIP3 and the RCP2.6 trajectory in CMIP5, reinforcing our finding that the choice of RCP trajectory is less important than the choice of GCM and ET estimation method when estimating future water availability.
The results of the GSA show that the choice of ET method has important implications when making future ET projections and future water availability projections.Many hydrologic models use a single evapotranspiration method for simulation, which may substantially increase the uncertainty, and reduce the reliability of future projections.Figure 8 shows the change in annual mean water availability over all 9 GCMs for the RCP 4.5 trajectory in future period 1 (2030-2060) predicted by the ten different ET methods used in this study (a: Hargreaves, b: Blaney-Criddle, c: Hamon, d: Kharrufa, e: Irmak-Rn, f: Irmak-Rs, g: Dalton, h: Meyer, i: Penman-Monteith, j: Priestley-Taylor).This figure clearly shows that the changes in water availability for future period 1 are diverse and depend strongly on the choice of ET method.Except for the Hargreaves method (Fig. 8a) the temperature based methods (e.g.Blaney-Criddle (Fig. 8b), Hamon (Fig. 8c) and Kharrufa (Fig. 8d)) predict drier conditions over the continental US than the other methods.The mass transfer based methods (e.g.Dalton (Fig. 8g) and Meyer (Fig. 8h)) predict generally wetter conditions over most of the continental US compared to other methods.The combination methods (Penman Monteith (Fig. 8i) and Introduction

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Full US (generally drier conditions in the West, Southwest and South and generally wetter elsewhere), the Hamon method predicts a different pattern of water availability over the Southwest, South and Northern Rockies and Plains regions.Monte Carlo filtering (Saltelli et al., 2008) was conducted to further investigate whether projected wetter or drier future conditions (i.e.larger or smaller annual mean water availability) could be attributed to specific GCMs, ET estimation methods, or RCP trajectories.Figure 9 shows the histograms for wet conditions and dry conditions in future 2 over the Southeast US by GCM, ET method and RCP trajectory for the example month of July. Figure 10 shows similar histograms for the Northern Rockies and Plains, a region with differing behavior from the Southeast US.Table 3 shows the P value results for the X 2 test for all months in both futures for the Southeast and Northern Rockies and Plains regions.P values greater than 0.05 (in bold in the tables) indicate the two histograms are not significantly different from each other.Tables 4-6 show the fraction of time that a particular GCM (Table 4), ET method (Table 5), or RCP trajectory (Table 6) projected drier future conditions in each of the 9 US climate regions for each month, with fractions higher than 0.5 (in bold in the tables).
Monte Carlo filtering results indicate that GCM and ET methods both produce statistically significant different wet condition and dry condition histograms in both the Southeast and Northern Rockies and Plains regions for almost all months in both future periods indicating particular GCMs and ET methods tend to systematically produce wet or dry conditions (Fig. 9 and 10, Table 3).Some GCMs (i.e.MIROC_ESM and BCC-CSM (Table 4)) and ET methods (i.e.Priestley-Taylor, Blaney-Criddle, and Kharrufa (Table 5)) predict dry conditions a majority of the time for all regions in both future periods.However the remaining GCMs and ET methods project both wetter or drier futures depending on the region and future period.Results in Tables 4 through 6 show that for the South, West and Southwest regions drier conditions are predicted a majority of the time in both future periods by all GCMs and RCP trajectories, and all ET methods except Hargreaves.For RCP trajectory, P values indicate the histograms are statistically significantly different in fewer cases than for either GCM or ET method Introduction

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Full for both future 1 and 2 (Table 3).These results are consistent with the first order sensitivity coefficients results that showed the RCP trajectory is not as important a factor as GCM or ET method in driving differences in future projections, but that the sensitivity to choice of RCP trajectory increases in future period 2.

Conclusions
Future changes in precipitation and evapotranspiration will lead to changes in the hydrologic cycle.Understanding and quantifying how these projected changes vary with choice of GCM, ET method and RCP trajectory is important for designing robust ensembles of scenarios to include in future water resources planning.This study assessed the future mean change in monthly precipitation, evapotranspiration and water availability (P-ET) projected by CMIP5 simulations over the continental US and analyzed the sensitivity of the projected changes to the choice of GCM, ET estimation method, and RCP trajectory.Nine GCMs, ten ET estimation methods, and three RCP trajectories were included in the analyses.Variance-based global sensitivity analysis (Saltelli et al., 2010) was conducted in order to determine the relative contributions of the choice of GCMs, ET estimation methods, and RCP trajectory to uncertainty in future prediction.Monte Carlo filtering was used to investigate whether particular GCMs, ET methods, and/or RCP scenarios consistently led to wet or dry future projections.The CMIP5 results, when averaged over nine GCMs, ten ET methods, and three RCP trajectories, indicate that the West, Southwest, and South US are projected to experience a decrease in annual precipitation, while all other regions of the continental US are projected experience an increase in annual mean precipitation for both future periods 1 and 2.An increase in annual mean ET is predicted over the entire continental US for both future periods, with the largest increases in West, South and Southeast.Future water availability is projected to significantly decrease in the West, Southwest, and South regions of the continental US.A slight decline or negligible change is projected in most other regions.The standard deviations of projected changes in P, ET and Introduction

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Full The global sensitivity analyses showed that projected changes in precipitation are more sensitive to the choice of GCM than the choice of RCP trajectory over the entire continental US for both future periods.However the choice of RCP trajectory becomes more important in future period 2. The most sensitive factor for the future ET projections is the choice of ET estimation method for all regions in both future periods.The first order sensitivity of projected change in future ET to choice of RCP trajectory increases in future period 2 compared to future 1, with a concomitant decrease in the first order sensitivity to the choice of GCM.For projected change in future water availability the choice of ET method constitutes the dominant source of uncertainty in warmer months (May through September) and the choice of GCM is the dominant source of uncertainty in the cooler months (November through March) over all regions except the Southeast where the sensitivity of GCM and ET method are roughly equal throughout the year.Sensitivity of change in future water availability to RCP trajectory is very small for future period 1, but increased in future period 2.
Monte Carlo filtering results indicated that both GCMs and ET methods produced statistically different histograms for wetter or drier future conditions (i.e.larger or smaller mean future water availability) for almost all months in both future periods.Two GCMs (MIROC_ESM and BCC-CSM) and three ET methods (Priestley-Taylor, Blaney-Criddle, and Kharrufa) predicted dry conditions a majority of the time for all regions in both future periods; however, the remaining GCMs and ET methods projected both wetter and drier futures depending on the region.
Results of this study indicate that when predicting the effects of future climate on water resources the choice of evapotranspiration method should be carefully evaluated.
Rather than the typical practice of using a single ET method to drive a hydrologic model with future climate projections, an ensemble of ET methods should be used in addition to an ensemble of GCMs and a variety of RCP trajectories.The GSA methodology adopted here assumed that all the GCMs, ET methods and RCP trajectories used in Introduction

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Full this study were equally appropriate for use in all US regions (i.e the sensitivity coefficients were evaluated by equally weighting each GCM, ET method and RCP trajectory) which is likely not to be the case.When making future projections of potential climate change on water resources Reliability Ensemble Averaging (REA) (Giorgi and Mearns, 2002) or Bayesian-based indicator-weighting (Asefa and Adams, 2013;Tebaldi et al., 2005) could be used to weight the results of an ensemble of GCMs and ET methods based on how close the retrospective GCM-ET method predictions agree with past observations (bias criterion) and how well the future GCM-ET-RCP projections agree with other future GCM-ET-RCP predictions (convergence criterion).This study assumed that ET methods that have been developed and parameterized based on vegetation response to current CO 2 levels and climatic conditions will be valid under future CO 2 levels and climatic conditions.Future research should explore the validity of this assumption by incorporating potential changes in plant transpiration (e.g.stomatal conductance) to changing CO 2 levels into the ET estimation methodologies.

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Full    Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | commonly used ET estimation methods (Hargreaves, Blaney-Criddle, Hamon, Kharrufa, Irmak-Rn, Irmak-Rs, Dalton, Meyer, Penman-Monteith and Priestley-Taylor) were used in this study.These ET estimation methods can be divided into potential ET (PET) estimated by the Priestley-Taylor and Kharrufa equations, and reference ET (RET) estimated by the other eight methods.The methods can be further classified into temperature-based ET equations (Hargreaves, Blaney-Criddle, Hamon, and Kharrufa), radiation-based ET equations (Irmak-Rn and Irmak-Rs), mass transfer-based ET equations (Dalton and Meyer), and combination ET equations (Penman-Monteith and Priestley-Taylor).These equations are well-described in many papers (e.g., Allen et al., Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | These figures clearly show that the uncertainty caused by different GCMs, ET methods, and RCP trajectories make actionable water resources planning based on climate change projections difficult.Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | sensitivity of change in P to choice of RCP trajectory increases substantially in future 2 compared to future 1 in the Northeast, Ohio Valley, Upper Midwest, South, Southwest and West regions.First order sensitivities of mean change in ET to GCM, ET method and RCP trajectory are shown in Fig. 6.This figure clearly shows that the choice of ET method is the most influential factor for projecting change in ET for both future periods, except for the month of March in the Northeast, Upper Midwest and Northern Rockies and Plains.High sensitivity of mean change in ET to GCM selection occurs in spring for several regions (Northeast, Upper Midwest and Northern Rockies and Plains), indicating a divergence of model predictions during this time.The influence of the RCP trajectory on Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | P-ET are large compared to the mean changes, making actionable water resources planning based on these climate change projections difficult.
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Table 4 .
The fraction of future dry conditions over all months by GCM (Future period 1 and 2).

Table 5 .
The fraction of future dry condition over all months by ET estimation method and region (Future period 1 and 2).

Table 6 .
The fraction of future dry condition over all months by RCP trajectory and region (Future period 1 and 2).